Fixed merge conflicts
This commit is contained in:
charlesbvll
commit
23f5c9771d
1
.gitattributes
vendored
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1
.gitattributes
vendored
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@ -0,0 +1 @@
|
||||
monoloco/_version.py export-subst
|
||||
81
.github/workflows/tests.yml
vendored
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81
.github/workflows/tests.yml
vendored
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@ -0,0 +1,81 @@
|
||||
|
||||
# Adapted from https://github.com/openpifpaf/openpifpaf/blob/main/.github/workflows/test.yml,
|
||||
#which is: 'Copyright 2019-2021 by Sven Kreiss and contributors. All rights reserved.'
|
||||
# and licensed under GNU AGPLv3
|
||||
|
||||
name: Tests
|
||||
|
||||
on: [push, pull_request]
|
||||
|
||||
jobs:
|
||||
build:
|
||||
|
||||
runs-on: ${{ matrix.os }}
|
||||
strategy:
|
||||
matrix:
|
||||
include:
|
||||
- os: ubuntu-latest
|
||||
python: 3.7
|
||||
torch: 1.7.1+cpu
|
||||
torchvision: 0.8.2+cpu
|
||||
torch-source: https://download.pytorch.org/whl/torch_stable.html
|
||||
- os: ubuntu-latest
|
||||
python: 3.8
|
||||
torch: 1.7.1+cpu
|
||||
torchvision: 0.8.2+cpu
|
||||
torch-source: https://download.pytorch.org/whl/cpu/torch_stable.html
|
||||
- os: macos-latest
|
||||
python: 3.7
|
||||
torch: 1.7.1
|
||||
torchvision: 0.8.2
|
||||
torch-source: https://download.pytorch.org/whl/torch_stable.html
|
||||
- os: macos-latest
|
||||
python: 3.8
|
||||
torch: 1.7.1
|
||||
torchvision: 0.8.2
|
||||
torch-source: https://download.pytorch.org/whl/torch_stable.html
|
||||
- os: windows-latest
|
||||
python: 3.7
|
||||
torch: 1.7.1+cpu
|
||||
torchvision: 0.8.2+cpu
|
||||
torch-source: https://download.pytorch.org/whl/torch_stable.html
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v2
|
||||
- name: Set up Python ${{ matrix.python }}
|
||||
if: ${{ !matrix.conda }}
|
||||
uses: actions/setup-python@v2
|
||||
with:
|
||||
python-version: ${{ matrix.python }}
|
||||
- name: Set up Conda
|
||||
if: matrix.conda
|
||||
uses: s-weigand/setup-conda@v1
|
||||
with:
|
||||
update-conda: true
|
||||
python-version: ${{ matrix.python }}
|
||||
conda-channels: anaconda, conda-forge
|
||||
- run: conda --version
|
||||
if: matrix.conda
|
||||
- run: which python
|
||||
if: matrix.conda
|
||||
- run: python --version
|
||||
- name: Install
|
||||
run: |
|
||||
python -m pip install --upgrade pip setuptools
|
||||
python -m pip install -e ".[test]"
|
||||
- name: Print environment
|
||||
run: |
|
||||
python -m pip freeze
|
||||
python --version
|
||||
python -c "import monoloco; print(monoloco.__version__)"
|
||||
- name: Lint monoloco
|
||||
run: |
|
||||
pylint monoloco --disable=fixme
|
||||
- name: Lint tests
|
||||
if: matrix.os != 'windows-latest' # because of path separator
|
||||
run: |
|
||||
pylint tests/*.py --disable=fixme
|
||||
|
||||
- name: Test
|
||||
run: |
|
||||
pytest -vv
|
||||
2
.gitignore
vendored
2
.gitignore
vendored
@ -1,5 +1,5 @@
|
||||
.idea/
|
||||
data/
|
||||
data
|
||||
.DS_store
|
||||
__pycache__
|
||||
monoloco/*.pyc
|
||||
|
||||
26
.pylintrc
26
.pylintrc
@ -1,26 +0,0 @@
|
||||
|
||||
|
||||
[BASIC]
|
||||
variable-rgx=[a-z0-9_]{1,30}$ # to accept 2 (dfferent) letters variables
|
||||
|
||||
|
||||
Good-names=xx,dd,zz,hh,ww,pp,kk,lr,w1,w2,w3,mm,im,uv,ax,COV_MIN,CONF_MIN
|
||||
|
||||
|
||||
[TYPECHECK]
|
||||
|
||||
disable=import-error,invalid-name,unused-variable,E1102,missing-docstring,useless-object-inheritance,duplicate-code,too-many-arguments,too-many-instance-attributes,too-many-locals,too-few-public-methods,arguments-differ,logging-format-interpolation,import-outside-toplevel
|
||||
|
||||
|
||||
# List of members which are set dynamically and missed by pylint inference
|
||||
# system, and so shouldn't trigger E1101 when accessed. Python regular
|
||||
# expressions are accepted.
|
||||
|
||||
generated-members=numpy.*,torch.*,cv2.*
|
||||
|
||||
ignored-modules=nuscenes, tabulate, cv2
|
||||
|
||||
|
||||
|
||||
[FORMAT]
|
||||
max-line-length=120
|
||||
11
.travis.yml
11
.travis.yml
@ -1,11 +0,0 @@
|
||||
dist: xenial
|
||||
language: python
|
||||
python:
|
||||
- "3.6"
|
||||
- "3.7"
|
||||
install:
|
||||
- pip install --upgrade pip setuptools
|
||||
- pip install ".[test]"
|
||||
script:
|
||||
- pylint monoloco --disable=unused-variable,fixme
|
||||
- pytest -v
|
||||
13
LICENSE
13
LICENSE
@ -1,4 +1,4 @@
|
||||
Copyright 2018-2021 by EPFL/VITA. All rights reserved.
|
||||
Copyright 2018-2021 by Lorenzo Bertoni and contributors. All rights reserved.
|
||||
|
||||
This project and all its files are licensed under
|
||||
GNU AGPLv3 or later version.
|
||||
@ -7,3 +7,14 @@ If this license is not suitable for your business or project
|
||||
please contact EPFL-TTO (https://tto.epfl.ch/) for a full commercial license.
|
||||
|
||||
This software may not be used to harm any person deliberately or for any military application.
|
||||
|
||||
|
||||
The following files are based on the OpenPifPaf project which is
|
||||
"Copyright 2019-2021 by Sven Kreiss and contributors. All rights reserved." and licensed under GNU AGPLv3.
|
||||
|
||||
- tests/test_train_mono.py
|
||||
- tests/test_train_stereo.py
|
||||
- monoloco/visuals/pifpaf_show.py
|
||||
- monoloco/train/losses.py
|
||||
- monoloco/predict.py
|
||||
-.github/workflows/tests.yml
|
||||
|
||||
661
LICENSE.AGPL
661
LICENSE.AGPL
@ -1,661 +0,0 @@
|
||||
GNU AFFERO GENERAL PUBLIC LICENSE
|
||||
Version 3, 19 November 2007
|
||||
|
||||
Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
|
||||
Everyone is permitted to copy and distribute verbatim copies
|
||||
of this license document, but changing it is not allowed.
|
||||
|
||||
Preamble
|
||||
|
||||
The GNU Affero General Public License is a free, copyleft license for
|
||||
software and other kinds of works, specifically designed to ensure
|
||||
cooperation with the community in the case of network server software.
|
||||
|
||||
The licenses for most software and other practical works are designed
|
||||
to take away your freedom to share and change the works. By contrast,
|
||||
our General Public Licenses are intended to guarantee your freedom to
|
||||
share and change all versions of a program--to make sure it remains free
|
||||
software for all its users.
|
||||
|
||||
When we speak of free software, we are referring to freedom, not
|
||||
price. Our General Public Licenses are designed to make sure that you
|
||||
have the freedom to distribute copies of free software (and charge for
|
||||
them if you wish), that you receive source code or can get it if you
|
||||
want it, that you can change the software or use pieces of it in new
|
||||
free programs, and that you know you can do these things.
|
||||
|
||||
Developers that use our General Public Licenses protect your rights
|
||||
with two steps: (1) assert copyright on the software, and (2) offer
|
||||
you this License which gives you legal permission to copy, distribute
|
||||
and/or modify the software.
|
||||
|
||||
A secondary benefit of defending all users' freedom is that
|
||||
improvements made in alternate versions of the program, if they
|
||||
receive widespread use, become available for other developers to
|
||||
incorporate. Many developers of free software are heartened and
|
||||
encouraged by the resulting cooperation. However, in the case of
|
||||
software used on network servers, this result may fail to come about.
|
||||
The GNU General Public License permits making a modified version and
|
||||
letting the public access it on a server without ever releasing its
|
||||
source code to the public.
|
||||
|
||||
The GNU Affero General Public License is designed specifically to
|
||||
ensure that, in such cases, the modified source code becomes available
|
||||
to the community. It requires the operator of a network server to
|
||||
provide the source code of the modified version running there to the
|
||||
users of that server. Therefore, public use of a modified version, on
|
||||
a publicly accessible server, gives the public access to the source
|
||||
code of the modified version.
|
||||
|
||||
An older license, called the Affero General Public License and
|
||||
published by Affero, was designed to accomplish similar goals. This is
|
||||
a different license, not a version of the Affero GPL, but Affero has
|
||||
released a new version of the Affero GPL which permits relicensing under
|
||||
this license.
|
||||
|
||||
The precise terms and conditions for copying, distribution and
|
||||
modification follow.
|
||||
|
||||
TERMS AND CONDITIONS
|
||||
|
||||
0. Definitions.
|
||||
|
||||
"This License" refers to version 3 of the GNU Affero General Public License.
|
||||
|
||||
"Copyright" also means copyright-like laws that apply to other kinds of
|
||||
works, such as semiconductor masks.
|
||||
|
||||
"The Program" refers to any copyrightable work licensed under this
|
||||
License. Each licensee is addressed as "you". "Licensees" and
|
||||
"recipients" may be individuals or organizations.
|
||||
|
||||
To "modify" a work means to copy from or adapt all or part of the work
|
||||
in a fashion requiring copyright permission, other than the making of an
|
||||
exact copy. The resulting work is called a "modified version" of the
|
||||
earlier work or a work "based on" the earlier work.
|
||||
|
||||
A "covered work" means either the unmodified Program or a work based
|
||||
on the Program.
|
||||
|
||||
To "propagate" a work means to do anything with it that, without
|
||||
permission, would make you directly or secondarily liable for
|
||||
infringement under applicable copyright law, except executing it on a
|
||||
computer or modifying a private copy. Propagation includes copying,
|
||||
distribution (with or without modification), making available to the
|
||||
public, and in some countries other activities as well.
|
||||
|
||||
To "convey" a work means any kind of propagation that enables other
|
||||
parties to make or receive copies. Mere interaction with a user through
|
||||
a computer network, with no transfer of a copy, is not conveying.
|
||||
|
||||
An interactive user interface displays "Appropriate Legal Notices"
|
||||
to the extent that it includes a convenient and prominently visible
|
||||
feature that (1) displays an appropriate copyright notice, and (2)
|
||||
tells the user that there is no warranty for the work (except to the
|
||||
extent that warranties are provided), that licensees may convey the
|
||||
work under this License, and how to view a copy of this License. If
|
||||
the interface presents a list of user commands or options, such as a
|
||||
menu, a prominent item in the list meets this criterion.
|
||||
|
||||
1. Source Code.
|
||||
|
||||
The "source code" for a work means the preferred form of the work
|
||||
for making modifications to it. "Object code" means any non-source
|
||||
form of a work.
|
||||
|
||||
A "Standard Interface" means an interface that either is an official
|
||||
standard defined by a recognized standards body, or, in the case of
|
||||
interfaces specified for a particular programming language, one that
|
||||
is widely used among developers working in that language.
|
||||
|
||||
The "System Libraries" of an executable work include anything, other
|
||||
than the work as a whole, that (a) is included in the normal form of
|
||||
packaging a Major Component, but which is not part of that Major
|
||||
Component, and (b) serves only to enable use of the work with that
|
||||
Major Component, or to implement a Standard Interface for which an
|
||||
implementation is available to the public in source code form. A
|
||||
"Major Component", in this context, means a major essential component
|
||||
(kernel, window system, and so on) of the specific operating system
|
||||
(if any) on which the executable work runs, or a compiler used to
|
||||
produce the work, or an object code interpreter used to run it.
|
||||
|
||||
The "Corresponding Source" for a work in object code form means all
|
||||
the source code needed to generate, install, and (for an executable
|
||||
work) run the object code and to modify the work, including scripts to
|
||||
control those activities. However, it does not include the work's
|
||||
System Libraries, or general-purpose tools or generally available free
|
||||
programs which are used unmodified in performing those activities but
|
||||
which are not part of the work. For example, Corresponding Source
|
||||
includes interface definition files associated with source files for
|
||||
the work, and the source code for shared libraries and dynamically
|
||||
linked subprograms that the work is specifically designed to require,
|
||||
such as by intimate data communication or control flow between those
|
||||
subprograms and other parts of the work.
|
||||
|
||||
The Corresponding Source need not include anything that users
|
||||
can regenerate automatically from other parts of the Corresponding
|
||||
Source.
|
||||
|
||||
The Corresponding Source for a work in source code form is that
|
||||
same work.
|
||||
|
||||
2. Basic Permissions.
|
||||
|
||||
All rights granted under this License are granted for the term of
|
||||
copyright on the Program, and are irrevocable provided the stated
|
||||
conditions are met. This License explicitly affirms your unlimited
|
||||
permission to run the unmodified Program. The output from running a
|
||||
covered work is covered by this License only if the output, given its
|
||||
content, constitutes a covered work. This License acknowledges your
|
||||
rights of fair use or other equivalent, as provided by copyright law.
|
||||
|
||||
You may make, run and propagate covered works that you do not
|
||||
convey, without conditions so long as your license otherwise remains
|
||||
in force. You may convey covered works to others for the sole purpose
|
||||
of having them make modifications exclusively for you, or provide you
|
||||
with facilities for running those works, provided that you comply with
|
||||
the terms of this License in conveying all material for which you do
|
||||
not control copyright. Those thus making or running the covered works
|
||||
for you must do so exclusively on your behalf, under your direction
|
||||
and control, on terms that prohibit them from making any copies of
|
||||
your copyrighted material outside their relationship with you.
|
||||
|
||||
Conveying under any other circumstances is permitted solely under
|
||||
the conditions stated below. Sublicensing is not allowed; section 10
|
||||
makes it unnecessary.
|
||||
|
||||
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
|
||||
|
||||
No covered work shall be deemed part of an effective technological
|
||||
measure under any applicable law fulfilling obligations under article
|
||||
11 of the WIPO copyright treaty adopted on 20 December 1996, or
|
||||
similar laws prohibiting or restricting circumvention of such
|
||||
measures.
|
||||
|
||||
When you convey a covered work, you waive any legal power to forbid
|
||||
circumvention of technological measures to the extent such circumvention
|
||||
is effected by exercising rights under this License with respect to
|
||||
the covered work, and you disclaim any intention to limit operation or
|
||||
modification of the work as a means of enforcing, against the work's
|
||||
users, your or third parties' legal rights to forbid circumvention of
|
||||
technological measures.
|
||||
|
||||
4. Conveying Verbatim Copies.
|
||||
|
||||
You may convey verbatim copies of the Program's source code as you
|
||||
receive it, in any medium, provided that you conspicuously and
|
||||
appropriately publish on each copy an appropriate copyright notice;
|
||||
keep intact all notices stating that this License and any
|
||||
non-permissive terms added in accord with section 7 apply to the code;
|
||||
keep intact all notices of the absence of any warranty; and give all
|
||||
recipients a copy of this License along with the Program.
|
||||
|
||||
You may charge any price or no price for each copy that you convey,
|
||||
and you may offer support or warranty protection for a fee.
|
||||
|
||||
5. Conveying Modified Source Versions.
|
||||
|
||||
You may convey a work based on the Program, or the modifications to
|
||||
produce it from the Program, in the form of source code under the
|
||||
terms of section 4, provided that you also meet all of these conditions:
|
||||
|
||||
a) The work must carry prominent notices stating that you modified
|
||||
it, and giving a relevant date.
|
||||
|
||||
b) The work must carry prominent notices stating that it is
|
||||
released under this License and any conditions added under section
|
||||
7. This requirement modifies the requirement in section 4 to
|
||||
"keep intact all notices".
|
||||
|
||||
c) You must license the entire work, as a whole, under this
|
||||
License to anyone who comes into possession of a copy. This
|
||||
License will therefore apply, along with any applicable section 7
|
||||
additional terms, to the whole of the work, and all its parts,
|
||||
regardless of how they are packaged. This License gives no
|
||||
permission to license the work in any other way, but it does not
|
||||
invalidate such permission if you have separately received it.
|
||||
|
||||
d) If the work has interactive user interfaces, each must display
|
||||
Appropriate Legal Notices; however, if the Program has interactive
|
||||
interfaces that do not display Appropriate Legal Notices, your
|
||||
work need not make them do so.
|
||||
|
||||
A compilation of a covered work with other separate and independent
|
||||
works, which are not by their nature extensions of the covered work,
|
||||
and which are not combined with it such as to form a larger program,
|
||||
in or on a volume of a storage or distribution medium, is called an
|
||||
"aggregate" if the compilation and its resulting copyright are not
|
||||
used to limit the access or legal rights of the compilation's users
|
||||
beyond what the individual works permit. Inclusion of a covered work
|
||||
in an aggregate does not cause this License to apply to the other
|
||||
parts of the aggregate.
|
||||
|
||||
6. Conveying Non-Source Forms.
|
||||
|
||||
You may convey a covered work in object code form under the terms
|
||||
of sections 4 and 5, provided that you also convey the
|
||||
machine-readable Corresponding Source under the terms of this License,
|
||||
in one of these ways:
|
||||
|
||||
a) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by the
|
||||
Corresponding Source fixed on a durable physical medium
|
||||
customarily used for software interchange.
|
||||
|
||||
b) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by a
|
||||
written offer, valid for at least three years and valid for as
|
||||
long as you offer spare parts or customer support for that product
|
||||
model, to give anyone who possesses the object code either (1) a
|
||||
copy of the Corresponding Source for all the software in the
|
||||
product that is covered by this License, on a durable physical
|
||||
medium customarily used for software interchange, for a price no
|
||||
more than your reasonable cost of physically performing this
|
||||
conveying of source, or (2) access to copy the
|
||||
Corresponding Source from a network server at no charge.
|
||||
|
||||
c) Convey individual copies of the object code with a copy of the
|
||||
written offer to provide the Corresponding Source. This
|
||||
alternative is allowed only occasionally and noncommercially, and
|
||||
only if you received the object code with such an offer, in accord
|
||||
with subsection 6b.
|
||||
|
||||
d) Convey the object code by offering access from a designated
|
||||
place (gratis or for a charge), and offer equivalent access to the
|
||||
Corresponding Source in the same way through the same place at no
|
||||
further charge. You need not require recipients to copy the
|
||||
Corresponding Source along with the object code. If the place to
|
||||
copy the object code is a network server, the Corresponding Source
|
||||
may be on a different server (operated by you or a third party)
|
||||
that supports equivalent copying facilities, provided you maintain
|
||||
clear directions next to the object code saying where to find the
|
||||
Corresponding Source. Regardless of what server hosts the
|
||||
Corresponding Source, you remain obligated to ensure that it is
|
||||
available for as long as needed to satisfy these requirements.
|
||||
|
||||
e) Convey the object code using peer-to-peer transmission, provided
|
||||
you inform other peers where the object code and Corresponding
|
||||
Source of the work are being offered to the general public at no
|
||||
charge under subsection 6d.
|
||||
|
||||
A separable portion of the object code, whose source code is excluded
|
||||
from the Corresponding Source as a System Library, need not be
|
||||
included in conveying the object code work.
|
||||
|
||||
A "User Product" is either (1) a "consumer product", which means any
|
||||
tangible personal property which is normally used for personal, family,
|
||||
or household purposes, or (2) anything designed or sold for incorporation
|
||||
into a dwelling. In determining whether a product is a consumer product,
|
||||
doubtful cases shall be resolved in favor of coverage. For a particular
|
||||
product received by a particular user, "normally used" refers to a
|
||||
typical or common use of that class of product, regardless of the status
|
||||
of the particular user or of the way in which the particular user
|
||||
actually uses, or expects or is expected to use, the product. A product
|
||||
is a consumer product regardless of whether the product has substantial
|
||||
commercial, industrial or non-consumer uses, unless such uses represent
|
||||
the only significant mode of use of the product.
|
||||
|
||||
"Installation Information" for a User Product means any methods,
|
||||
procedures, authorization keys, or other information required to install
|
||||
and execute modified versions of a covered work in that User Product from
|
||||
a modified version of its Corresponding Source. The information must
|
||||
suffice to ensure that the continued functioning of the modified object
|
||||
code is in no case prevented or interfered with solely because
|
||||
modification has been made.
|
||||
|
||||
If you convey an object code work under this section in, or with, or
|
||||
specifically for use in, a User Product, and the conveying occurs as
|
||||
part of a transaction in which the right of possession and use of the
|
||||
User Product is transferred to the recipient in perpetuity or for a
|
||||
fixed term (regardless of how the transaction is characterized), the
|
||||
Corresponding Source conveyed under this section must be accompanied
|
||||
by the Installation Information. But this requirement does not apply
|
||||
if neither you nor any third party retains the ability to install
|
||||
modified object code on the User Product (for example, the work has
|
||||
been installed in ROM).
|
||||
|
||||
The requirement to provide Installation Information does not include a
|
||||
requirement to continue to provide support service, warranty, or updates
|
||||
for a work that has been modified or installed by the recipient, or for
|
||||
the User Product in which it has been modified or installed. Access to a
|
||||
network may be denied when the modification itself materially and
|
||||
adversely affects the operation of the network or violates the rules and
|
||||
protocols for communication across the network.
|
||||
|
||||
Corresponding Source conveyed, and Installation Information provided,
|
||||
in accord with this section must be in a format that is publicly
|
||||
documented (and with an implementation available to the public in
|
||||
source code form), and must require no special password or key for
|
||||
unpacking, reading or copying.
|
||||
|
||||
7. Additional Terms.
|
||||
|
||||
"Additional permissions" are terms that supplement the terms of this
|
||||
License by making exceptions from one or more of its conditions.
|
||||
Additional permissions that are applicable to the entire Program shall
|
||||
be treated as though they were included in this License, to the extent
|
||||
that they are valid under applicable law. If additional permissions
|
||||
apply only to part of the Program, that part may be used separately
|
||||
under those permissions, but the entire Program remains governed by
|
||||
this License without regard to the additional permissions.
|
||||
|
||||
When you convey a copy of a covered work, you may at your option
|
||||
remove any additional permissions from that copy, or from any part of
|
||||
it. (Additional permissions may be written to require their own
|
||||
removal in certain cases when you modify the work.) You may place
|
||||
additional permissions on material, added by you to a covered work,
|
||||
for which you have or can give appropriate copyright permission.
|
||||
|
||||
Notwithstanding any other provision of this License, for material you
|
||||
add to a covered work, you may (if authorized by the copyright holders of
|
||||
that material) supplement the terms of this License with terms:
|
||||
|
||||
a) Disclaiming warranty or limiting liability differently from the
|
||||
terms of sections 15 and 16 of this License; or
|
||||
|
||||
b) Requiring preservation of specified reasonable legal notices or
|
||||
author attributions in that material or in the Appropriate Legal
|
||||
Notices displayed by works containing it; or
|
||||
|
||||
c) Prohibiting misrepresentation of the origin of that material, or
|
||||
requiring that modified versions of such material be marked in
|
||||
reasonable ways as different from the original version; or
|
||||
|
||||
d) Limiting the use for publicity purposes of names of licensors or
|
||||
authors of the material; or
|
||||
|
||||
e) Declining to grant rights under trademark law for use of some
|
||||
trade names, trademarks, or service marks; or
|
||||
|
||||
f) Requiring indemnification of licensors and authors of that
|
||||
material by anyone who conveys the material (or modified versions of
|
||||
it) with contractual assumptions of liability to the recipient, for
|
||||
any liability that these contractual assumptions directly impose on
|
||||
those licensors and authors.
|
||||
|
||||
All other non-permissive additional terms are considered "further
|
||||
restrictions" within the meaning of section 10. If the Program as you
|
||||
received it, or any part of it, contains a notice stating that it is
|
||||
governed by this License along with a term that is a further
|
||||
restriction, you may remove that term. If a license document contains
|
||||
a further restriction but permits relicensing or conveying under this
|
||||
License, you may add to a covered work material governed by the terms
|
||||
of that license document, provided that the further restriction does
|
||||
not survive such relicensing or conveying.
|
||||
|
||||
If you add terms to a covered work in accord with this section, you
|
||||
must place, in the relevant source files, a statement of the
|
||||
additional terms that apply to those files, or a notice indicating
|
||||
where to find the applicable terms.
|
||||
|
||||
Additional terms, permissive or non-permissive, may be stated in the
|
||||
form of a separately written license, or stated as exceptions;
|
||||
the above requirements apply either way.
|
||||
|
||||
8. Termination.
|
||||
|
||||
You may not propagate or modify a covered work except as expressly
|
||||
provided under this License. Any attempt otherwise to propagate or
|
||||
modify it is void, and will automatically terminate your rights under
|
||||
this License (including any patent licenses granted under the third
|
||||
paragraph of section 11).
|
||||
|
||||
However, if you cease all violation of this License, then your
|
||||
license from a particular copyright holder is reinstated (a)
|
||||
provisionally, unless and until the copyright holder explicitly and
|
||||
finally terminates your license, and (b) permanently, if the copyright
|
||||
holder fails to notify you of the violation by some reasonable means
|
||||
prior to 60 days after the cessation.
|
||||
|
||||
Moreover, your license from a particular copyright holder is
|
||||
reinstated permanently if the copyright holder notifies you of the
|
||||
violation by some reasonable means, this is the first time you have
|
||||
received notice of violation of this License (for any work) from that
|
||||
copyright holder, and you cure the violation prior to 30 days after
|
||||
your receipt of the notice.
|
||||
|
||||
Termination of your rights under this section does not terminate the
|
||||
licenses of parties who have received copies or rights from you under
|
||||
this License. If your rights have been terminated and not permanently
|
||||
reinstated, you do not qualify to receive new licenses for the same
|
||||
material under section 10.
|
||||
|
||||
9. Acceptance Not Required for Having Copies.
|
||||
|
||||
You are not required to accept this License in order to receive or
|
||||
run a copy of the Program. Ancillary propagation of a covered work
|
||||
occurring solely as a consequence of using peer-to-peer transmission
|
||||
to receive a copy likewise does not require acceptance. However,
|
||||
nothing other than this License grants you permission to propagate or
|
||||
modify any covered work. These actions infringe copyright if you do
|
||||
not accept this License. Therefore, by modifying or propagating a
|
||||
covered work, you indicate your acceptance of this License to do so.
|
||||
|
||||
10. Automatic Licensing of Downstream Recipients.
|
||||
|
||||
Each time you convey a covered work, the recipient automatically
|
||||
receives a license from the original licensors, to run, modify and
|
||||
propagate that work, subject to this License. You are not responsible
|
||||
for enforcing compliance by third parties with this License.
|
||||
|
||||
An "entity transaction" is a transaction transferring control of an
|
||||
organization, or substantially all assets of one, or subdividing an
|
||||
organization, or merging organizations. If propagation of a covered
|
||||
work results from an entity transaction, each party to that
|
||||
transaction who receives a copy of the work also receives whatever
|
||||
licenses to the work the party's predecessor in interest had or could
|
||||
give under the previous paragraph, plus a right to possession of the
|
||||
Corresponding Source of the work from the predecessor in interest, if
|
||||
the predecessor has it or can get it with reasonable efforts.
|
||||
|
||||
You may not impose any further restrictions on the exercise of the
|
||||
rights granted or affirmed under this License. For example, you may
|
||||
not impose a license fee, royalty, or other charge for exercise of
|
||||
rights granted under this License, and you may not initiate litigation
|
||||
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
||||
any patent claim is infringed by making, using, selling, offering for
|
||||
sale, or importing the Program or any portion of it.
|
||||
|
||||
11. Patents.
|
||||
|
||||
A "contributor" is a copyright holder who authorizes use under this
|
||||
License of the Program or a work on which the Program is based. The
|
||||
work thus licensed is called the contributor's "contributor version".
|
||||
|
||||
A contributor's "essential patent claims" are all patent claims
|
||||
owned or controlled by the contributor, whether already acquired or
|
||||
hereafter acquired, that would be infringed by some manner, permitted
|
||||
by this License, of making, using, or selling its contributor version,
|
||||
but do not include claims that would be infringed only as a
|
||||
consequence of further modification of the contributor version. For
|
||||
purposes of this definition, "control" includes the right to grant
|
||||
patent sublicenses in a manner consistent with the requirements of
|
||||
this License.
|
||||
|
||||
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
||||
patent license under the contributor's essential patent claims, to
|
||||
make, use, sell, offer for sale, import and otherwise run, modify and
|
||||
propagate the contents of its contributor version.
|
||||
|
||||
In the following three paragraphs, a "patent license" is any express
|
||||
agreement or commitment, however denominated, not to enforce a patent
|
||||
(such as an express permission to practice a patent or covenant not to
|
||||
sue for patent infringement). To "grant" such a patent license to a
|
||||
party means to make such an agreement or commitment not to enforce a
|
||||
patent against the party.
|
||||
|
||||
If you convey a covered work, knowingly relying on a patent license,
|
||||
and the Corresponding Source of the work is not available for anyone
|
||||
to copy, free of charge and under the terms of this License, through a
|
||||
publicly available network server or other readily accessible means,
|
||||
then you must either (1) cause the Corresponding Source to be so
|
||||
available, or (2) arrange to deprive yourself of the benefit of the
|
||||
patent license for this particular work, or (3) arrange, in a manner
|
||||
consistent with the requirements of this License, to extend the patent
|
||||
license to downstream recipients. "Knowingly relying" means you have
|
||||
actual knowledge that, but for the patent license, your conveying the
|
||||
covered work in a country, or your recipient's use of the covered work
|
||||
in a country, would infringe one or more identifiable patents in that
|
||||
country that you have reason to believe are valid.
|
||||
|
||||
If, pursuant to or in connection with a single transaction or
|
||||
arrangement, you convey, or propagate by procuring conveyance of, a
|
||||
covered work, and grant a patent license to some of the parties
|
||||
receiving the covered work authorizing them to use, propagate, modify
|
||||
or convey a specific copy of the covered work, then the patent license
|
||||
you grant is automatically extended to all recipients of the covered
|
||||
work and works based on it.
|
||||
|
||||
A patent license is "discriminatory" if it does not include within
|
||||
the scope of its coverage, prohibits the exercise of, or is
|
||||
conditioned on the non-exercise of one or more of the rights that are
|
||||
specifically granted under this License. You may not convey a covered
|
||||
work if you are a party to an arrangement with a third party that is
|
||||
in the business of distributing software, under which you make payment
|
||||
to the third party based on the extent of your activity of conveying
|
||||
the work, and under which the third party grants, to any of the
|
||||
parties who would receive the covered work from you, a discriminatory
|
||||
patent license (a) in connection with copies of the covered work
|
||||
conveyed by you (or copies made from those copies), or (b) primarily
|
||||
for and in connection with specific products or compilations that
|
||||
contain the covered work, unless you entered into that arrangement,
|
||||
or that patent license was granted, prior to 28 March 2007.
|
||||
|
||||
Nothing in this License shall be construed as excluding or limiting
|
||||
any implied license or other defenses to infringement that may
|
||||
otherwise be available to you under applicable patent law.
|
||||
|
||||
12. No Surrender of Others' Freedom.
|
||||
|
||||
If conditions are imposed on you (whether by court order, agreement or
|
||||
otherwise) that contradict the conditions of this License, they do not
|
||||
excuse you from the conditions of this License. If you cannot convey a
|
||||
covered work so as to satisfy simultaneously your obligations under this
|
||||
License and any other pertinent obligations, then as a consequence you may
|
||||
not convey it at all. For example, if you agree to terms that obligate you
|
||||
to collect a royalty for further conveying from those to whom you convey
|
||||
the Program, the only way you could satisfy both those terms and this
|
||||
License would be to refrain entirely from conveying the Program.
|
||||
|
||||
13. Remote Network Interaction; Use with the GNU General Public License.
|
||||
|
||||
Notwithstanding any other provision of this License, if you modify the
|
||||
Program, your modified version must prominently offer all users
|
||||
interacting with it remotely through a computer network (if your version
|
||||
supports such interaction) an opportunity to receive the Corresponding
|
||||
Source of your version by providing access to the Corresponding Source
|
||||
from a network server at no charge, through some standard or customary
|
||||
means of facilitating copying of software. This Corresponding Source
|
||||
shall include the Corresponding Source for any work covered by version 3
|
||||
of the GNU General Public License that is incorporated pursuant to the
|
||||
following paragraph.
|
||||
|
||||
Notwithstanding any other provision of this License, you have
|
||||
permission to link or combine any covered work with a work licensed
|
||||
under version 3 of the GNU General Public License into a single
|
||||
combined work, and to convey the resulting work. The terms of this
|
||||
License will continue to apply to the part which is the covered work,
|
||||
but the work with which it is combined will remain governed by version
|
||||
3 of the GNU General Public License.
|
||||
|
||||
14. Revised Versions of this License.
|
||||
|
||||
The Free Software Foundation may publish revised and/or new versions of
|
||||
the GNU Affero General Public License from time to time. Such new versions
|
||||
will be similar in spirit to the present version, but may differ in detail to
|
||||
address new problems or concerns.
|
||||
|
||||
Each version is given a distinguishing version number. If the
|
||||
Program specifies that a certain numbered version of the GNU Affero General
|
||||
Public License "or any later version" applies to it, you have the
|
||||
option of following the terms and conditions either of that numbered
|
||||
version or of any later version published by the Free Software
|
||||
Foundation. If the Program does not specify a version number of the
|
||||
GNU Affero General Public License, you may choose any version ever published
|
||||
by the Free Software Foundation.
|
||||
|
||||
If the Program specifies that a proxy can decide which future
|
||||
versions of the GNU Affero General Public License can be used, that proxy's
|
||||
public statement of acceptance of a version permanently authorizes you
|
||||
to choose that version for the Program.
|
||||
|
||||
Later license versions may give you additional or different
|
||||
permissions. However, no additional obligations are imposed on any
|
||||
author or copyright holder as a result of your choosing to follow a
|
||||
later version.
|
||||
|
||||
15. Disclaimer of Warranty.
|
||||
|
||||
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
||||
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
||||
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
||||
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
||||
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||||
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
||||
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
||||
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
||||
|
||||
16. Limitation of Liability.
|
||||
|
||||
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
||||
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
||||
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
||||
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
||||
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
||||
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
||||
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
||||
SUCH DAMAGES.
|
||||
|
||||
17. Interpretation of Sections 15 and 16.
|
||||
|
||||
If the disclaimer of warranty and limitation of liability provided
|
||||
above cannot be given local legal effect according to their terms,
|
||||
reviewing courts shall apply local law that most closely approximates
|
||||
an absolute waiver of all civil liability in connection with the
|
||||
Program, unless a warranty or assumption of liability accompanies a
|
||||
copy of the Program in return for a fee.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
How to Apply These Terms to Your New Programs
|
||||
|
||||
If you develop a new program, and you want it to be of the greatest
|
||||
possible use to the public, the best way to achieve this is to make it
|
||||
free software which everyone can redistribute and change under these terms.
|
||||
|
||||
To do so, attach the following notices to the program. It is safest
|
||||
to attach them to the start of each source file to most effectively
|
||||
state the exclusion of warranty; and each file should have at least
|
||||
the "copyright" line and a pointer to where the full notice is found.
|
||||
|
||||
<one line to give the program's name and a brief idea of what it does.>
|
||||
Copyright (C) <year> <name of author>
|
||||
|
||||
This program is free software: you can redistribute it and/or modify
|
||||
it under the terms of the GNU Affero General Public License as published by
|
||||
the Free Software Foundation, either version 3 of the License, or
|
||||
(at your option) any later version.
|
||||
|
||||
This program is distributed in the hope that it will be useful,
|
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
GNU Affero General Public License for more details.
|
||||
|
||||
You should have received a copy of the GNU Affero General Public License
|
||||
along with this program. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
Also add information on how to contact you by electronic and paper mail.
|
||||
|
||||
If your software can interact with users remotely through a computer
|
||||
network, you should also make sure that it provides a way for users to
|
||||
get its source. For example, if your program is a web application, its
|
||||
interface could display a "Source" link that leads users to an archive
|
||||
of the code. There are many ways you could offer source, and different
|
||||
solutions will be better for different programs; see section 13 for the
|
||||
specific requirements.
|
||||
|
||||
You should also get your employer (if you work as a programmer) or school,
|
||||
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
||||
For more information on this, and how to apply and follow the GNU AGPL, see
|
||||
<http://www.gnu.org/licenses/>.
|
||||
2
MANIFEST.in
Normal file
2
MANIFEST.in
Normal file
@ -0,0 +1,2 @@
|
||||
include versioneer.py
|
||||
include monoloco/_version.py
|
||||
141
README.md
141
README.md
@ -1,9 +1,14 @@
|
||||
# Monoloco library [](https://pepy.tech/project/monoloco)
|
||||
# Monoloco library [](https://pepy.tech/project/monoloco)
|
||||
Continuously tested on Linux, MacOS and Windows: [](https://github.com/vita-epfl/monoloco/actions?query=workflow%3ATests)
|
||||
|
||||
|
||||
|
||||
<img src="docs/monoloco.gif" alt="gif" />
|
||||
|
||||
|
||||
This library is based on three research projects for monocular/stereo 3D human localization (detection), body orientation, and social distancing. Check the [demo video](https://www.youtube.com/watch?v=O5zhzi8mwJ4)!
|
||||
This library is based on three research projects for monocular/stereo 3D human localization (detection), body orientation, and social distancing. Check the __video teaser__ of the library on [__YouTube__](https://www.youtube.com/watch?v=O5zhzi8mwJ4).
|
||||
|
||||
---
|
||||
|
||||
> __MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization__<br />
|
||||
> _[L. Bertoni](https://scholar.google.com/citations?user=f-4YHeMAAAAJ&hl=en), [S. Kreiss](https://www.svenkreiss.com),
|
||||
@ -26,7 +31,7 @@ __[Article](https://arxiv.org/abs/2009.00984)__ &nbs
|
||||
|
||||
> __MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation__<br />
|
||||
> _[L. Bertoni](https://scholar.google.com/citations?user=f-4YHeMAAAAJ&hl=en), [S. Kreiss](https://www.svenkreiss.com), [A.Alahi](https://scholar.google.com/citations?user=UIhXQ64AAAAJ&hl=en)_, ICCV 2019 <br />
|
||||
__[Article](https://arxiv.org/abs/1906.06059)__ __[Citation](#Todo)__ __[Video](https://www.youtube.com/watch?v=ii0fqerQrec)__
|
||||
__[Article](https://arxiv.org/abs/1906.06059)__ __[Citation](#Citation)__ __[Video](https://www.youtube.com/watch?v=ii0fqerQrec)__
|
||||
|
||||
<img src="docs/surf.jpg" width="700"/>
|
||||
|
||||
@ -45,8 +50,6 @@ Packages have been installed with pip and virtual environments.
|
||||
|
||||
For quick installation, do not clone this repository, make sure there is no folder named monoloco in your current directory, and run:
|
||||
|
||||
|
||||
|
||||
```
|
||||
pip3 install monoloco
|
||||
```
|
||||
@ -136,9 +139,9 @@ If you provide a ground-truth json file to compare the predictions of the networ
|
||||
|
||||
For an example image, run the following command:
|
||||
|
||||
```
|
||||
```sh
|
||||
python -m monoloco.run predict docs/002282.png \
|
||||
--path_gt <to match results with ground-truths> \
|
||||
--path_gt names-kitti-200615-1022.json \
|
||||
-o <output directory> \
|
||||
--long-edge <rescale the image by providing dimension of long side>
|
||||
--n_dropout <50 to include epistemic uncertainty, 0 otherwise>
|
||||
@ -146,12 +149,12 @@ python -m monoloco.run predict docs/002282.png \
|
||||
|
||||
|
||||
|
||||
To show all the instances estimated by MonoLoco add the argument `show_all` to the above command.
|
||||
To show all the instances estimated by MonoLoco add the argument `--show_all` to the above command.
|
||||
|
||||
|
||||
|
||||
It is also possible to run [openpifpaf](https://github.com/vita-epfl/openpifpaf) directly
|
||||
by usingt `--mode keypoints`. All the other pifpaf arguments are also supported
|
||||
by using `--mode keypoints`. All the other pifpaf arguments are also supported
|
||||
and can be checked with `python -m monoloco.run predict --help`.
|
||||
|
||||
|
||||
@ -166,7 +169,7 @@ To run MonStereo on stereo images, make sure the stereo pairs have the following
|
||||
|
||||
You can load one or more image pairs using glob expressions. For example:
|
||||
|
||||
```
|
||||
```sh
|
||||
python3 -m monoloco.run predict --mode stereo \
|
||||
--glob docs/000840*.png
|
||||
--path_gt <to match results with ground-truths> \
|
||||
@ -175,9 +178,9 @@ python3 -m monoloco.run predict --mode stereo \
|
||||
|
||||
|
||||
|
||||
```
|
||||
```sh
|
||||
python3 -m monoloco.run predict --glob docs/005523*.png \ --output_types multi \
|
||||
--model data/models/ms-200710-1511.pkl \
|
||||
--mode stereo \
|
||||
--path_gt <to match results with ground-truths> \
|
||||
-o data/output --long_edge 2500 \
|
||||
--instance-threshold 0.05 --seed-threshold 0.05
|
||||
@ -198,10 +201,12 @@ An example from the Collective Activity Dataset is provided below.
|
||||
<img src="docs/frame0032.jpg" width="500"/>
|
||||
|
||||
To visualize social distancing run the below, command:
|
||||
```
|
||||
|
||||
```sh
|
||||
python -m monoloco.run predict docs/frame0032.jpg \
|
||||
--activities social_distance --output_types front bird
|
||||
```
|
||||
|
||||
<img src="docs/out_frame0032_front_bird.jpg" width="700"/>
|
||||
|
||||
## C) Raise hand detection
|
||||
@ -226,21 +231,21 @@ python -m monoloco.run predict \
|
||||
## D) Orientation and Bounding Box dimensions
|
||||
The network estimates orientation and box dimensions as well. Results are saved in a json file when using the command
|
||||
`--output_types json`. At the moment, the only visualization including orientation is the social distancing one.
|
||||
|
||||
<br />
|
||||
|
||||
## Training
|
||||
We train on the KITTI dataset (MonoLoco/Monoloco++/MonStereo) or the nuScenes dataset (MonoLoco) specifying the path of the json file containing the input joints. Please download them [here](https://drive.google.com/file/d/1bJPyA1HuX9uyJYf1IhiDqzhkvSokd4l0/view?usp=sharing) or follow [preprocessing instructions](#Preprocessing).
|
||||
We train on the KITTI dataset (MonoLoco/Monoloco++/MonStereo) or the nuScenes dataset (MonoLoco) specifying the path of the json file containing the input joints. Please download them [here](https://drive.google.com/drive/folders/1j0riwbS9zuEKQ_3oIs_dWlYBnfuN2WVN?usp=sharing) or follow [preprocessing instructions](#Preprocessing).
|
||||
|
||||
Results for MonoLoco++ are obtained with:
|
||||
Results for [MonoLoco++](###Tables) are obtained with:
|
||||
|
||||
```
|
||||
python -m monoloco.run train --joints data/arrays/joints-kitti-201202-1743.json --save --monocular
|
||||
python -m monoloco.run train --joints data/arrays/joints-kitti-mono-210422-1600.json
|
||||
```
|
||||
|
||||
While for the MonStereo ones just change the input joints and remove the monocular flag:
|
||||
```
|
||||
python3 -m monoloco.run train --joints <json file path> --save`
|
||||
While for the [MonStereo](###Tables) results run:
|
||||
|
||||
```sh
|
||||
python -m monoloco.run train --joints data/arrays/joints-kitti-stereo-210422-1601.json --lr 0.003 --mode stereo
|
||||
```
|
||||
|
||||
If you are interested in the original results of the MonoLoco ICCV article (now improved with MonoLoco++), please refer to the tag v0.4.9 in this repository.
|
||||
@ -255,41 +260,66 @@ Finally, for a more extensive list of available parameters, run:
|
||||
Preprocessing and training step are already fully supported by the code provided,
|
||||
but require first to run a pose detector over
|
||||
all the training images and collect the annotations.
|
||||
The code supports this option (by running the predict script and using `--mode pifpaf`).
|
||||
The code supports this option (by running the predict script and using `--mode keypoints`).
|
||||
|
||||
### Data structure
|
||||
|
||||
data
|
||||
├── arrays
|
||||
├── models
|
||||
├── outputs
|
||||
├── arrays
|
||||
├── kitti
|
||||
├── logs
|
||||
├── output
|
||||
|
||||
Run the following inside monoloco repository:
|
||||
```
|
||||
mkdir data
|
||||
cd data
|
||||
mkdir arrays models kitti logs output
|
||||
mkdir outputs arrays kitti
|
||||
```
|
||||
|
||||
|
||||
### Kitti Dataset
|
||||
Annotations from a pose detector needs to be stored in a folder. With PifPaf:
|
||||
Download kitti images (from left and right cameras), ground-truth files (labels), and calibration files from their [website](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) and save them inside the `data` folder as shown below.
|
||||
|
||||
```
|
||||
data
|
||||
├── kitti
|
||||
├── gt
|
||||
├── calib
|
||||
├── images
|
||||
├── images_right
|
||||
|
||||
|
||||
The network takes as inputs 2D keypoints annotations. To create them run PifPaf over the saved images:
|
||||
|
||||
```sh
|
||||
python -m openpifpaf.predict \
|
||||
--glob "<kitti images directory>/*.png" \
|
||||
--glob "data/kitti/images/*.png" \
|
||||
--json-output <directory to contain predictions> \
|
||||
--checkpoint=shufflenetv2k30 \
|
||||
--instance-threshold=0.05 --seed-threshold 0.05 --force-complete-pose
|
||||
```
|
||||
Once the step is complete, the below commands transform all the annotations into a single json file that will used for training
|
||||
|
||||
```
|
||||
**Horizontal flipping**
|
||||
|
||||
To augment the dataset, we apply horizontal flipping on the detected poses. To include small variations in the pose, we use the poses from the right-camera (the dataset uses a stereo camera). As there are no labels for the right camera, the code automatically correct the ground truth depth by taking into account the camera baseline.
|
||||
To obtain these poses, run pifpaf also on the folder of right images. Make sure to save annotations into a different folder, and call the right folder: `<NameOfTheLeftFolder>_right`
|
||||
|
||||
**Recall**
|
||||
|
||||
To maximize the recall (at the cost of the computational time), it's possible to upscale the images with the command `--long_edge 2500` (\~scale 2).
|
||||
|
||||
Once this step is complete, the below commands transform all the annotations into a single json file that will used for training.
|
||||
|
||||
|
||||
|
||||
For MonoLoco++:
|
||||
```sh
|
||||
python -m monoloco.run prep --dir_ann <directory that contains annotations>
|
||||
```
|
||||
!Add the flag `--monocular` for MonoLoco(++)!
|
||||
|
||||
For MonStereo:
|
||||
```sh
|
||||
python -m monoloco.run prep --mode stereo --dir_ann <directory that contains left annotations>
|
||||
```
|
||||
|
||||
### Collective Activity Dataset
|
||||
To evaluate on of the [collective activity dataset](http://vhosts.eecs.umich.edu/vision//activity-dataset.html)
|
||||
@ -317,7 +347,7 @@ which for example change the name of all the jpg images in that folder adding th
|
||||
|
||||
Pifpaf annotations should also be saved in a single folder and can be created with:
|
||||
|
||||
```
|
||||
```sh
|
||||
python -m openpifpaf.predict \
|
||||
--glob "data/collective_activity/images/*.jpg" \
|
||||
--checkpoint=shufflenetv2k30 \
|
||||
@ -325,20 +355,18 @@ python -m openpifpaf.predict \
|
||||
--json-output <output folder>
|
||||
```
|
||||
|
||||
Finally, to evaluate activity using a MonoLoco++ pre-trained model trained either on nuSCENES or KITTI:
|
||||
```
|
||||
python -m monstereo.run eval --activity \
|
||||
--dataset collective \
|
||||
--model <MonoLoco++ model path> --dir_ann <pifpaf annotations directory>
|
||||
```
|
||||
|
||||
## Evaluation
|
||||
|
||||
### 3D Localization
|
||||
We provide evaluation on KITTI for models trained on nuScenes or KITTI. We compare them with other monocular
|
||||
and stereo baselines, depending whether you are evaluating stereo or monocular settings. For some of the baselines, we have obtained the annotations directly from the authors and we don't have yet the permission to publish them.
|
||||
We provide evaluation on KITTI for models trained on nuScenes or KITTI. Download the ground-truths of KITTI dataset and the calibration files from their [website](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d). Save the training labels (one .txt file for each image) into the folder `data/kitti/gt` and the camera calibration matrices (one .txt file for each image) into `data/kitti/calib`.
|
||||
To evaluate a pre-trained model, download the latest models from [here](https://drive.google.com/drive/u/0/folders/1kQpaTcDsiNyY6eh1kUurcpptfAXkBjAJ) and save them into `data/outputs.
|
||||
|
||||
__Baselines__
|
||||
|
||||
We compare our results with other monocular
|
||||
and stereo baselines, depending whether you are evaluating stereo or monocular settings. For some of the baselines, we have obtained the annotations directly from the authors and we don't have yet the permission to publish them.
|
||||
|
||||
[MonoLoco](https://github.com/vita-epfl/monoloco),
|
||||
[Mono3D](https://www.cs.toronto.edu/~urtasun/publications/chen_etal_cvpr16.pdf),
|
||||
[3DOP](https://xiaozhichen.github.io/papers/nips15chen.pdf),
|
||||
[MonoDepth](https://arxiv.org/abs/1609.03677)
|
||||
@ -354,17 +382,26 @@ and save them into `data/kitti/3dop`
|
||||
[here](https://github.com/Parrotlife/pedestrianDepth-baseline/tree/master/MonoDepth-PyTorch)
|
||||
and save them into `data/kitti/monodepth`
|
||||
* **Geometrical Baseline and MonoLoco**:
|
||||
To include also geometric baselines and MonoLoco, add the flag ``--baselines`` to the evaluation command
|
||||
```
|
||||
To include also geometric baselines and MonoLoco, download a monoloco model, save it in `data/models`, and add the flag ``--baselines`` to the evaluation command
|
||||
|
||||
|
||||
The evaluation file will run the model over all the annotations and compare the results with KITTI ground-truth and the downloaded baselines. For this run:
|
||||
|
||||
```sh
|
||||
python -m monoloco.run eval \
|
||||
--dir_ann <annotation directory> \
|
||||
--model <model path> \
|
||||
--model data/outputs/monoloco_pp-210422-1601.pkl \
|
||||
--generate \
|
||||
--save \
|
||||
````
|
||||
```
|
||||
|
||||
For stereo results add `--mode stereo` and select `--model=monstereo-210422-1620.pkl`. Below, the resulting table of results and an example of the saved figures.
|
||||
|
||||
<img src="docs/results_stereo.jpg" width="550"/>
|
||||
### Tables
|
||||
|
||||
<img src="docs/quantitative.jpg" width="700"/>
|
||||
|
||||
<img src="docs/results_monstereo.jpg" width="700"/>
|
||||
|
||||
|
||||
### Relative Average Precision Localization: RALP-5% (MonStereo)
|
||||
@ -378,16 +415,16 @@ The modified file is called *evaluate_object.cpp* and runs exactly as the origin
|
||||
### Activity Estimation (Talking)
|
||||
Please follow preprocessing steps for Collective activity dataset and run pifpaf over the dataset images.
|
||||
Evaluation on this dataset is done with models trained on either KITTI or nuScenes.
|
||||
For optimal performances, we suggest the model trained on nuScenes teaser (#TODO add link)
|
||||
```
|
||||
python -m monstereo.run eval
|
||||
For optimal performances, we suggest the model trained on nuScenes teaser.
|
||||
|
||||
```sh
|
||||
python -m monstereo.run eval \
|
||||
--activity \
|
||||
--dataset collective \
|
||||
--model <path to the model> \
|
||||
--model <path to the model> \
|
||||
--dir_ann <annotation directory>
|
||||
```
|
||||
|
||||
|
||||
## Citation
|
||||
When using this library in your research, we will be happy if you cite us!
|
||||
|
||||
|
||||
BIN
docs/quantitative.jpg
Normal file
BIN
docs/quantitative.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 809 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 295 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 385 KiB |
BIN
docs/results_monstereo.jpg
Normal file
BIN
docs/results_monstereo.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 348 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 368 KiB |
@ -3,4 +3,6 @@
|
||||
Open implementation of MonoLoco / MonoLoco++ / MonStereo
|
||||
"""
|
||||
|
||||
__version__ = '0.5.0'
|
||||
from ._version import get_versions
|
||||
__version__ = get_versions()['version']
|
||||
del get_versions
|
||||
|
||||
527
monoloco/_version.py
Normal file
527
monoloco/_version.py
Normal file
@ -0,0 +1,527 @@
|
||||
|
||||
# This file helps to compute a version number in source trees obtained from
|
||||
# git-archive tarball (such as those provided by githubs download-from-tag
|
||||
# feature). Distribution tarballs (built by setup.py sdist) and build
|
||||
# directories (produced by setup.py build) will contain a much shorter file
|
||||
# that just contains the computed version number.
|
||||
|
||||
# This file is released into the public domain. Generated by
|
||||
# versioneer-0.19 (https://github.com/python-versioneer/python-versioneer)
|
||||
|
||||
# pylint: skip-file
|
||||
|
||||
"""Git implementation of _version.py."""
|
||||
|
||||
import errno
|
||||
import os
|
||||
import re
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
|
||||
def get_keywords():
|
||||
"""Get the keywords needed to look up the version information."""
|
||||
# these strings will be replaced by git during git-archive.
|
||||
# setup.py/versioneer.py will grep for the variable names, so they must
|
||||
# each be defined on a line of their own. _version.py will just call
|
||||
# get_keywords().
|
||||
git_refnames = "$Format:%d$"
|
||||
git_full = "$Format:%H$"
|
||||
git_date = "$Format:%ci$"
|
||||
keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
|
||||
return keywords
|
||||
|
||||
|
||||
class VersioneerConfig:
|
||||
"""Container for Versioneer configuration parameters."""
|
||||
|
||||
|
||||
def get_config():
|
||||
"""Create, populate and return the VersioneerConfig() object."""
|
||||
# these strings are filled in when 'setup.py versioneer' creates
|
||||
# _version.py
|
||||
cfg = VersioneerConfig()
|
||||
cfg.VCS = "git"
|
||||
cfg.style = "pep440"
|
||||
cfg.tag_prefix = "v"
|
||||
cfg.parentdir_prefix = "None"
|
||||
cfg.versionfile_source = "monoloco/_version.py"
|
||||
cfg.verbose = False
|
||||
return cfg
|
||||
|
||||
|
||||
class NotThisMethod(Exception):
|
||||
"""Exception raised if a method is not valid for the current scenario."""
|
||||
|
||||
|
||||
LONG_VERSION_PY = {}
|
||||
HANDLERS = {}
|
||||
|
||||
|
||||
def register_vcs_handler(vcs, method): # decorator
|
||||
"""Create decorator to mark a method as the handler of a VCS."""
|
||||
def decorate(f):
|
||||
"""Store f in HANDLERS[vcs][method]."""
|
||||
if vcs not in HANDLERS:
|
||||
HANDLERS[vcs] = {}
|
||||
HANDLERS[vcs][method] = f
|
||||
return f
|
||||
return decorate
|
||||
|
||||
|
||||
def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
|
||||
env=None):
|
||||
"""Call the given command(s)."""
|
||||
assert isinstance(commands, list)
|
||||
p = None
|
||||
for c in commands:
|
||||
try:
|
||||
dispcmd = str([c] + args)
|
||||
# remember shell=False, so use git.cmd on windows, not just git
|
||||
p = subprocess.Popen([c] + args, cwd=cwd, env=env,
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=(subprocess.PIPE if hide_stderr
|
||||
else None))
|
||||
break
|
||||
except EnvironmentError:
|
||||
e = sys.exc_info()[1]
|
||||
if e.errno == errno.ENOENT:
|
||||
continue
|
||||
if verbose:
|
||||
print("unable to run %s" % dispcmd)
|
||||
print(e)
|
||||
return None, None
|
||||
else:
|
||||
if verbose:
|
||||
print("unable to find command, tried %s" % (commands,))
|
||||
return None, None
|
||||
stdout = p.communicate()[0].strip().decode()
|
||||
if p.returncode != 0:
|
||||
if verbose:
|
||||
print("unable to run %s (error)" % dispcmd)
|
||||
print("stdout was %s" % stdout)
|
||||
return None, p.returncode
|
||||
return stdout, p.returncode
|
||||
|
||||
|
||||
def versions_from_parentdir(parentdir_prefix, root, verbose):
|
||||
"""Try to determine the version from the parent directory name.
|
||||
|
||||
Source tarballs conventionally unpack into a directory that includes both
|
||||
the project name and a version string. We will also support searching up
|
||||
two directory levels for an appropriately named parent directory
|
||||
"""
|
||||
rootdirs = []
|
||||
|
||||
for i in range(3):
|
||||
dirname = os.path.basename(root)
|
||||
if dirname.startswith(parentdir_prefix):
|
||||
return {"version": dirname[len(parentdir_prefix):],
|
||||
"full-revisionid": None,
|
||||
"dirty": False, "error": None, "date": None}
|
||||
else:
|
||||
rootdirs.append(root)
|
||||
root = os.path.dirname(root) # up a level
|
||||
|
||||
if verbose:
|
||||
print("Tried directories %s but none started with prefix %s" %
|
||||
(str(rootdirs), parentdir_prefix))
|
||||
raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
|
||||
|
||||
|
||||
@register_vcs_handler("git", "get_keywords")
|
||||
def git_get_keywords(versionfile_abs):
|
||||
"""Extract version information from the given file."""
|
||||
# the code embedded in _version.py can just fetch the value of these
|
||||
# keywords. When used from setup.py, we don't want to import _version.py,
|
||||
# so we do it with a regexp instead. This function is not used from
|
||||
# _version.py.
|
||||
keywords = {}
|
||||
try:
|
||||
f = open(versionfile_abs, "r")
|
||||
for line in f.readlines():
|
||||
if line.strip().startswith("git_refnames ="):
|
||||
mo = re.search(r'=\s*"(.*)"', line)
|
||||
if mo:
|
||||
keywords["refnames"] = mo.group(1)
|
||||
if line.strip().startswith("git_full ="):
|
||||
mo = re.search(r'=\s*"(.*)"', line)
|
||||
if mo:
|
||||
keywords["full"] = mo.group(1)
|
||||
if line.strip().startswith("git_date ="):
|
||||
mo = re.search(r'=\s*"(.*)"', line)
|
||||
if mo:
|
||||
keywords["date"] = mo.group(1)
|
||||
f.close()
|
||||
except EnvironmentError:
|
||||
pass
|
||||
return keywords
|
||||
|
||||
|
||||
@register_vcs_handler("git", "keywords")
|
||||
def git_versions_from_keywords(keywords, tag_prefix, verbose):
|
||||
"""Get version information from git keywords."""
|
||||
if not keywords:
|
||||
raise NotThisMethod("no keywords at all, weird")
|
||||
date = keywords.get("date")
|
||||
if date is not None:
|
||||
# Use only the last line. Previous lines may contain GPG signature
|
||||
# information.
|
||||
date = date.splitlines()[-1]
|
||||
|
||||
# git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
|
||||
# datestamp. However we prefer "%ci" (which expands to an "ISO-8601
|
||||
# -like" string, which we must then edit to make compliant), because
|
||||
# it's been around since git-1.5.3, and it's too difficult to
|
||||
# discover which version we're using, or to work around using an
|
||||
# older one.
|
||||
date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
|
||||
refnames = keywords["refnames"].strip()
|
||||
if refnames.startswith("$Format"):
|
||||
if verbose:
|
||||
print("keywords are unexpanded, not using")
|
||||
raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
|
||||
refs = set([r.strip() for r in refnames.strip("()").split(",")])
|
||||
# starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
|
||||
# just "foo-1.0". If we see a "tag: " prefix, prefer those.
|
||||
TAG = "tag: "
|
||||
tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)])
|
||||
if not tags:
|
||||
# Either we're using git < 1.8.3, or there really are no tags. We use
|
||||
# a heuristic: assume all version tags have a digit. The old git %d
|
||||
# expansion behaves like git log --decorate=short and strips out the
|
||||
# refs/heads/ and refs/tags/ prefixes that would let us distinguish
|
||||
# between branches and tags. By ignoring refnames without digits, we
|
||||
# filter out many common branch names like "release" and
|
||||
# "stabilization", as well as "HEAD" and "master".
|
||||
tags = set([r for r in refs if re.search(r'\d', r)])
|
||||
if verbose:
|
||||
print("discarding '%s', no digits" % ",".join(refs - tags))
|
||||
if verbose:
|
||||
print("likely tags: %s" % ",".join(sorted(tags)))
|
||||
for ref in sorted(tags):
|
||||
# sorting will prefer e.g. "2.0" over "2.0rc1"
|
||||
if ref.startswith(tag_prefix):
|
||||
r = ref[len(tag_prefix):]
|
||||
if verbose:
|
||||
print("picking %s" % r)
|
||||
return {"version": r,
|
||||
"full-revisionid": keywords["full"].strip(),
|
||||
"dirty": False, "error": None,
|
||||
"date": date}
|
||||
# no suitable tags, so version is "0+unknown", but full hex is still there
|
||||
if verbose:
|
||||
print("no suitable tags, using unknown + full revision id")
|
||||
return {"version": "0+unknown",
|
||||
"full-revisionid": keywords["full"].strip(),
|
||||
"dirty": False, "error": "no suitable tags", "date": None}
|
||||
|
||||
|
||||
@register_vcs_handler("git", "pieces_from_vcs")
|
||||
def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
|
||||
"""Get version from 'git describe' in the root of the source tree.
|
||||
|
||||
This only gets called if the git-archive 'subst' keywords were *not*
|
||||
expanded, and _version.py hasn't already been rewritten with a short
|
||||
version string, meaning we're inside a checked out source tree.
|
||||
"""
|
||||
GITS = ["git"]
|
||||
if sys.platform == "win32":
|
||||
GITS = ["git.cmd", "git.exe"]
|
||||
|
||||
out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root,
|
||||
hide_stderr=True)
|
||||
if rc != 0:
|
||||
if verbose:
|
||||
print("Directory %s not under git control" % root)
|
||||
raise NotThisMethod("'git rev-parse --git-dir' returned error")
|
||||
|
||||
# if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
|
||||
# if there isn't one, this yields HEX[-dirty] (no NUM)
|
||||
describe_out, rc = run_command(GITS, ["describe", "--tags", "--dirty",
|
||||
"--always", "--long",
|
||||
"--match", "%s*" % tag_prefix],
|
||||
cwd=root)
|
||||
# --long was added in git-1.5.5
|
||||
if describe_out is None:
|
||||
raise NotThisMethod("'git describe' failed")
|
||||
describe_out = describe_out.strip()
|
||||
full_out, rc = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
|
||||
if full_out is None:
|
||||
raise NotThisMethod("'git rev-parse' failed")
|
||||
full_out = full_out.strip()
|
||||
|
||||
pieces = {}
|
||||
pieces["long"] = full_out
|
||||
pieces["short"] = full_out[:7] # maybe improved later
|
||||
pieces["error"] = None
|
||||
|
||||
# parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
|
||||
# TAG might have hyphens.
|
||||
git_describe = describe_out
|
||||
|
||||
# look for -dirty suffix
|
||||
dirty = git_describe.endswith("-dirty")
|
||||
pieces["dirty"] = dirty
|
||||
if dirty:
|
||||
git_describe = git_describe[:git_describe.rindex("-dirty")]
|
||||
|
||||
# now we have TAG-NUM-gHEX or HEX
|
||||
|
||||
if "-" in git_describe:
|
||||
# TAG-NUM-gHEX
|
||||
mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
|
||||
if not mo:
|
||||
# unparseable. Maybe git-describe is misbehaving?
|
||||
pieces["error"] = ("unable to parse git-describe output: '%s'"
|
||||
% describe_out)
|
||||
return pieces
|
||||
|
||||
# tag
|
||||
full_tag = mo.group(1)
|
||||
if not full_tag.startswith(tag_prefix):
|
||||
if verbose:
|
||||
fmt = "tag '%s' doesn't start with prefix '%s'"
|
||||
print(fmt % (full_tag, tag_prefix))
|
||||
pieces["error"] = ("tag '%s' doesn't start with prefix '%s'"
|
||||
% (full_tag, tag_prefix))
|
||||
return pieces
|
||||
pieces["closest-tag"] = full_tag[len(tag_prefix):]
|
||||
|
||||
# distance: number of commits since tag
|
||||
pieces["distance"] = int(mo.group(2))
|
||||
|
||||
# commit: short hex revision ID
|
||||
pieces["short"] = mo.group(3)
|
||||
|
||||
else:
|
||||
# HEX: no tags
|
||||
pieces["closest-tag"] = None
|
||||
count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"],
|
||||
cwd=root)
|
||||
pieces["distance"] = int(count_out) # total number of commits
|
||||
|
||||
# commit date: see ISO-8601 comment in git_versions_from_keywords()
|
||||
date = run_command(GITS, ["show", "-s", "--format=%ci", "HEAD"],
|
||||
cwd=root)[0].strip()
|
||||
# Use only the last line. Previous lines may contain GPG signature
|
||||
# information.
|
||||
date = date.splitlines()[-1]
|
||||
pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
|
||||
|
||||
return pieces
|
||||
|
||||
|
||||
def plus_or_dot(pieces):
|
||||
"""Return a + if we don't already have one, else return a ."""
|
||||
if "+" in pieces.get("closest-tag", ""):
|
||||
return "."
|
||||
return "+"
|
||||
|
||||
|
||||
def render_pep440(pieces):
|
||||
"""Build up version string, with post-release "local version identifier".
|
||||
|
||||
Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
|
||||
get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
|
||||
|
||||
Exceptions:
|
||||
1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
|
||||
"""
|
||||
if pieces["closest-tag"]:
|
||||
rendered = pieces["closest-tag"]
|
||||
if pieces["distance"] or pieces["dirty"]:
|
||||
rendered += plus_or_dot(pieces)
|
||||
rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
|
||||
if pieces["dirty"]:
|
||||
rendered += ".dirty"
|
||||
else:
|
||||
# exception #1
|
||||
rendered = "0+untagged.%d.g%s" % (pieces["distance"],
|
||||
pieces["short"])
|
||||
if pieces["dirty"]:
|
||||
rendered += ".dirty"
|
||||
return rendered
|
||||
|
||||
|
||||
def render_pep440_pre(pieces):
|
||||
"""TAG[.post0.devDISTANCE] -- No -dirty.
|
||||
|
||||
Exceptions:
|
||||
1: no tags. 0.post0.devDISTANCE
|
||||
"""
|
||||
if pieces["closest-tag"]:
|
||||
rendered = pieces["closest-tag"]
|
||||
if pieces["distance"]:
|
||||
rendered += ".post0.dev%d" % pieces["distance"]
|
||||
else:
|
||||
# exception #1
|
||||
rendered = "0.post0.dev%d" % pieces["distance"]
|
||||
return rendered
|
||||
|
||||
|
||||
def render_pep440_post(pieces):
|
||||
"""TAG[.postDISTANCE[.dev0]+gHEX] .
|
||||
|
||||
The ".dev0" means dirty. Note that .dev0 sorts backwards
|
||||
(a dirty tree will appear "older" than the corresponding clean one),
|
||||
but you shouldn't be releasing software with -dirty anyways.
|
||||
|
||||
Exceptions:
|
||||
1: no tags. 0.postDISTANCE[.dev0]
|
||||
"""
|
||||
if pieces["closest-tag"]:
|
||||
rendered = pieces["closest-tag"]
|
||||
if pieces["distance"] or pieces["dirty"]:
|
||||
rendered += ".post%d" % pieces["distance"]
|
||||
if pieces["dirty"]:
|
||||
rendered += ".dev0"
|
||||
rendered += plus_or_dot(pieces)
|
||||
rendered += "g%s" % pieces["short"]
|
||||
else:
|
||||
# exception #1
|
||||
rendered = "0.post%d" % pieces["distance"]
|
||||
if pieces["dirty"]:
|
||||
rendered += ".dev0"
|
||||
rendered += "+g%s" % pieces["short"]
|
||||
return rendered
|
||||
|
||||
|
||||
def render_pep440_old(pieces):
|
||||
"""TAG[.postDISTANCE[.dev0]] .
|
||||
|
||||
The ".dev0" means dirty.
|
||||
|
||||
Exceptions:
|
||||
1: no tags. 0.postDISTANCE[.dev0]
|
||||
"""
|
||||
if pieces["closest-tag"]:
|
||||
rendered = pieces["closest-tag"]
|
||||
if pieces["distance"] or pieces["dirty"]:
|
||||
rendered += ".post%d" % pieces["distance"]
|
||||
if pieces["dirty"]:
|
||||
rendered += ".dev0"
|
||||
else:
|
||||
# exception #1
|
||||
rendered = "0.post%d" % pieces["distance"]
|
||||
if pieces["dirty"]:
|
||||
rendered += ".dev0"
|
||||
return rendered
|
||||
|
||||
|
||||
def render_git_describe(pieces):
|
||||
"""TAG[-DISTANCE-gHEX][-dirty].
|
||||
|
||||
Like 'git describe --tags --dirty --always'.
|
||||
|
||||
Exceptions:
|
||||
1: no tags. HEX[-dirty] (note: no 'g' prefix)
|
||||
"""
|
||||
if pieces["closest-tag"]:
|
||||
rendered = pieces["closest-tag"]
|
||||
if pieces["distance"]:
|
||||
rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
|
||||
else:
|
||||
# exception #1
|
||||
rendered = pieces["short"]
|
||||
if pieces["dirty"]:
|
||||
rendered += "-dirty"
|
||||
return rendered
|
||||
|
||||
|
||||
def render_git_describe_long(pieces):
|
||||
"""TAG-DISTANCE-gHEX[-dirty].
|
||||
|
||||
Like 'git describe --tags --dirty --always -long'.
|
||||
The distance/hash is unconditional.
|
||||
|
||||
Exceptions:
|
||||
1: no tags. HEX[-dirty] (note: no 'g' prefix)
|
||||
"""
|
||||
if pieces["closest-tag"]:
|
||||
rendered = pieces["closest-tag"]
|
||||
rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
|
||||
else:
|
||||
# exception #1
|
||||
rendered = pieces["short"]
|
||||
if pieces["dirty"]:
|
||||
rendered += "-dirty"
|
||||
return rendered
|
||||
|
||||
|
||||
def render(pieces, style):
|
||||
"""Render the given version pieces into the requested style."""
|
||||
if pieces["error"]:
|
||||
return {"version": "unknown",
|
||||
"full-revisionid": pieces.get("long"),
|
||||
"dirty": None,
|
||||
"error": pieces["error"],
|
||||
"date": None}
|
||||
|
||||
if not style or style == "default":
|
||||
style = "pep440" # the default
|
||||
|
||||
if style == "pep440":
|
||||
rendered = render_pep440(pieces)
|
||||
elif style == "pep440-pre":
|
||||
rendered = render_pep440_pre(pieces)
|
||||
elif style == "pep440-post":
|
||||
rendered = render_pep440_post(pieces)
|
||||
elif style == "pep440-old":
|
||||
rendered = render_pep440_old(pieces)
|
||||
elif style == "git-describe":
|
||||
rendered = render_git_describe(pieces)
|
||||
elif style == "git-describe-long":
|
||||
rendered = render_git_describe_long(pieces)
|
||||
else:
|
||||
raise ValueError("unknown style '%s'" % style)
|
||||
|
||||
return {"version": rendered, "full-revisionid": pieces["long"],
|
||||
"dirty": pieces["dirty"], "error": None,
|
||||
"date": pieces.get("date")}
|
||||
|
||||
|
||||
def get_versions():
|
||||
"""Get version information or return default if unable to do so."""
|
||||
# I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
|
||||
# __file__, we can work backwards from there to the root. Some
|
||||
# py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
|
||||
# case we can only use expanded keywords.
|
||||
|
||||
cfg = get_config()
|
||||
verbose = cfg.verbose
|
||||
|
||||
try:
|
||||
return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,
|
||||
verbose)
|
||||
except NotThisMethod:
|
||||
pass
|
||||
|
||||
try:
|
||||
root = os.path.realpath(__file__)
|
||||
# versionfile_source is the relative path from the top of the source
|
||||
# tree (where the .git directory might live) to this file. Invert
|
||||
# this to find the root from __file__.
|
||||
for i in cfg.versionfile_source.split('/'):
|
||||
root = os.path.dirname(root)
|
||||
except NameError:
|
||||
return {"version": "0+unknown", "full-revisionid": None,
|
||||
"dirty": None,
|
||||
"error": "unable to find root of source tree",
|
||||
"date": None}
|
||||
|
||||
try:
|
||||
pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
|
||||
return render(pieces, cfg.style)
|
||||
except NotThisMethod:
|
||||
pass
|
||||
|
||||
try:
|
||||
if cfg.parentdir_prefix:
|
||||
return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
|
||||
except NotThisMethod:
|
||||
pass
|
||||
|
||||
return {"version": "0+unknown", "full-revisionid": None,
|
||||
"dirty": None,
|
||||
"error": "unable to compute version", "date": None}
|
||||
@ -2,17 +2,23 @@
|
||||
import os
|
||||
import glob
|
||||
import csv
|
||||
import copy
|
||||
from collections import defaultdict
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image
|
||||
from sklearn.metrics import accuracy_score
|
||||
try:
|
||||
from sklearn.metrics import accuracy_score
|
||||
ACCURACY_SCORE = copy.copy(accuracy_score)
|
||||
except ImportError:
|
||||
ACCURACY_SCORE = None
|
||||
|
||||
from monoloco.network import Loco
|
||||
from monoloco.network.process import factory_for_gt, preprocess_pifpaf
|
||||
from monoloco.activity import social_interactions
|
||||
from monoloco.utils import open_annotations, get_iou_matches, get_difficulty
|
||||
from ..prep import factory_file
|
||||
from ..network import Loco
|
||||
from ..network.process import factory_for_gt, preprocess_pifpaf
|
||||
from ..activity import social_interactions
|
||||
from ..utils import open_annotations, get_iou_matches, get_difficulty
|
||||
|
||||
|
||||
class ActivityEvaluator:
|
||||
@ -79,7 +85,7 @@ class ActivityEvaluator:
|
||||
im_size = image.size
|
||||
assert len(im_size) > 1, "image with frame0001 not available"
|
||||
|
||||
for idx, im_path in enumerate(images):
|
||||
for im_path in images:
|
||||
|
||||
# Collect PifPaf files and calibration
|
||||
basename = os.path.basename(im_path)
|
||||
@ -101,14 +107,12 @@ class ActivityEvaluator:
|
||||
self.estimate_activity(dic_out, matches, ys_gt, categories=categories)
|
||||
|
||||
# Print Results
|
||||
acc = accuracy_score(self.all_gt[seq], self.all_pred[seq])
|
||||
acc = ACCURACY_SCORE(self.all_gt[seq], self.all_pred[seq])
|
||||
print(f"Accuracy of category {seq}: {100*acc:.2f}%")
|
||||
cout_results(self.cnt, self.all_gt, self.all_pred, categories=self.sequences)
|
||||
|
||||
def eval_kitti(self):
|
||||
"""Parse KITTI Dataset and predict if people are talking or not"""
|
||||
|
||||
from ..utils import factory_file
|
||||
files = glob.glob(self.dir_data + '/*.txt')
|
||||
# files = [self.dir_gt_kitti + '/001782.txt']
|
||||
assert files, "Empty directory"
|
||||
@ -118,7 +122,7 @@ class ActivityEvaluator:
|
||||
# Collect PifPaf files and calibration
|
||||
basename, _ = os.path.splitext(os.path.basename(file))
|
||||
path_calib = os.path.join(self.dir_kk, basename + '.txt')
|
||||
annotations, kk, tt = factory_file(path_calib, self.dir_ann, basename)
|
||||
annotations, kk, _ = factory_file(path_calib, self.dir_ann, basename)
|
||||
|
||||
# Collect corresponding gt files (ys_gt: 1 or 0)
|
||||
path_gt = os.path.join(self.dir_data, basename + '.txt')
|
||||
@ -149,7 +153,7 @@ class ActivityEvaluator:
|
||||
self.cnt['gt'][key] += 1
|
||||
self.cnt['gt']['all'] += 1
|
||||
|
||||
for i_m, (idx, idx_gt) in enumerate(matches):
|
||||
for (idx, idx_gt) in matches:
|
||||
|
||||
# Select keys to update results for Collective or KITTI
|
||||
keys = ('all', categories[idx_gt])
|
||||
@ -184,7 +188,7 @@ def parse_gt_collective(dir_data, seq, path_pif):
|
||||
with open(path, "r") as ff:
|
||||
reader = csv.reader(ff, delimiter='\t')
|
||||
dic_frames = defaultdict(lambda: defaultdict(list))
|
||||
for idx, line in enumerate(reader):
|
||||
for line in reader:
|
||||
box = convert_box(line[1:5])
|
||||
cat = convert_category(line[5])
|
||||
dic_frames[line[0]]['boxes'].append(box)
|
||||
|
||||
@ -7,13 +7,20 @@ Evaluate MonStereo code on KITTI dataset using ALE metric
|
||||
import os
|
||||
import math
|
||||
import logging
|
||||
import copy
|
||||
import datetime
|
||||
from collections import defaultdict
|
||||
|
||||
from tabulate import tabulate
|
||||
import numpy as np
|
||||
try:
|
||||
import tabulate
|
||||
TABULATE = copy.copy(tabulate.tabulate)
|
||||
except ImportError:
|
||||
TABULATE = None
|
||||
|
||||
from ..utils import get_iou_matches, get_task_error, get_pixel_error, check_conditions, \
|
||||
get_difficulty, split_training, parse_ground_truth, get_iou_matches_matrix, average, find_cluster
|
||||
get_difficulty, split_training, get_iou_matches_matrix, average, find_cluster
|
||||
from ..prep import parse_ground_truth
|
||||
from ..visuals import show_results, show_spread, show_task_error, show_box_plot
|
||||
|
||||
|
||||
@ -29,7 +36,7 @@ class EvalKitti:
|
||||
METHODS_STEREO = ['3dop', 'psf', 'pseudo-lidar', 'e2e', 'oc-stereo']
|
||||
BASELINES = ['task_error', 'pixel_error']
|
||||
HEADERS = ('method', '<0.5', '<1m', '<2m', 'easy', 'moderate', 'hard', 'all')
|
||||
CATEGORIES = ('pedestrian',)
|
||||
CATEGORIES = ('pedestrian',) # extendable with person_sitting and/or cyclists
|
||||
methods = OUR_METHODS + METHODS_MONO + METHODS_STEREO
|
||||
|
||||
# Set directories
|
||||
@ -39,8 +46,7 @@ class EvalKitti:
|
||||
path_val = os.path.join('splits', 'kitti_val.txt')
|
||||
dir_logs = os.path.join('data', 'logs')
|
||||
assert os.path.exists(dir_logs), "No directory to save final statistics"
|
||||
dir_fig = os.path.join('data', 'figures')
|
||||
assert os.path.exists(dir_logs), "No directory to save figures"
|
||||
dir_fig = os.path.join('figures', 'results')
|
||||
|
||||
# Set thresholds to obtain comparable recalls
|
||||
thresh_iou_monoloco = 0.3
|
||||
@ -49,9 +55,10 @@ class EvalKitti:
|
||||
thresh_conf_base = 0.5
|
||||
|
||||
def __init__(self, args):
|
||||
|
||||
self.mode = args.mode
|
||||
assert self.mode in ('mono', 'stereo'), "mode not recognized"
|
||||
self.net = 'monstereo' if self.mode == 'stereo' else 'monoloco_pp'
|
||||
self.verbose = args.verbose
|
||||
self.net = args.net
|
||||
self.save = args.save
|
||||
self.show = args.show
|
||||
|
||||
@ -110,7 +117,7 @@ class EvalKitti:
|
||||
methods_out = defaultdict(tuple) # Save all methods for comparison
|
||||
|
||||
# Count ground_truth:
|
||||
boxes_gt, ys, truncs_gt, occs_gt = out_gt # pylint: disable=unbalanced-tuple-unpacking
|
||||
boxes_gt, _, truncs_gt, occs_gt, _ = out_gt # pylint: disable=unbalanced-tuple-unpacking
|
||||
for idx, box in enumerate(boxes_gt):
|
||||
mode = get_difficulty(box, truncs_gt[idx], occs_gt[idx])
|
||||
self.cnt_gt[mode] += 1
|
||||
@ -144,10 +151,13 @@ class EvalKitti:
|
||||
self.show_statistics()
|
||||
|
||||
def printer(self):
|
||||
if self.save:
|
||||
os.makedirs(self.dir_fig, exist_ok=True)
|
||||
if self.save or self.show:
|
||||
print('-' * 100)
|
||||
show_results(self.dic_stats, self.CLUSTERS, self.net, self.dir_fig, show=self.show, save=self.save)
|
||||
show_spread(self.dic_stats, self.CLUSTERS, self.net, self.dir_fig, show=self.show, save=self.save)
|
||||
if self.net == 'monstero':
|
||||
if self.net == 'monstereo':
|
||||
show_box_plot(self.errors, self.CLUSTERS, self.dir_fig, show=self.show, save=self.save)
|
||||
else:
|
||||
show_task_error(self.dir_fig, show=self.show, save=self.save)
|
||||
@ -201,7 +211,7 @@ class EvalKitti:
|
||||
def _estimate_error(self, out_gt, out, method):
|
||||
"""Estimate localization error"""
|
||||
|
||||
boxes_gt, ys, truncs_gt, occs_gt = out_gt
|
||||
boxes_gt, ys, truncs_gt, occs_gt, _ = out_gt
|
||||
|
||||
if method in self.OUR_METHODS:
|
||||
boxes, dds, cat, bis, epis = out
|
||||
@ -363,7 +373,7 @@ class EvalKitti:
|
||||
for key in all_methods]
|
||||
|
||||
results = [[key] + alp[idx] + ale[idx] for idx, key in enumerate(all_methods)]
|
||||
print(tabulate(results, headers=self.HEADERS))
|
||||
print(TABULATE(results, headers=self.HEADERS))
|
||||
print('-' * 90 + '\n')
|
||||
|
||||
def stats_height(self):
|
||||
@ -373,10 +383,8 @@ class EvalKitti:
|
||||
self.name = name
|
||||
# Iterate over each line of the gt file and save box location and distances
|
||||
out_gt = parse_ground_truth(path_gt, 'pedestrian')
|
||||
boxes_gt, ys, truncs_gt, occs_gt = out_gt # pylint: disable=unbalanced-tuple-unpacking
|
||||
for label in ys:
|
||||
for label in out_gt[1]:
|
||||
heights.append(label[4])
|
||||
import numpy as np
|
||||
tail1, tail2 = np.nanpercentile(np.array(heights), [5, 95])
|
||||
print(average(heights))
|
||||
print(len(heights))
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# pylint: disable=too-many-statements,too-many-branches
|
||||
# pylint: disable=too-many-statements
|
||||
|
||||
"""Joints Analysis: Supplementary material of MonStereo"""
|
||||
|
||||
@ -11,26 +11,7 @@ import matplotlib.pyplot as plt
|
||||
|
||||
from ..utils import find_cluster, average
|
||||
from ..visuals.figures import get_distances
|
||||
|
||||
COCO_KEYPOINTS = [
|
||||
'nose', # 0
|
||||
'left_eye', # 1
|
||||
'right_eye', # 2
|
||||
'left_ear', # 3
|
||||
'right_ear', # 4
|
||||
'left_shoulder', # 5
|
||||
'right_shoulder', # 6
|
||||
'left_elbow', # 7
|
||||
'right_elbow', # 8
|
||||
'left_wrist', # 9
|
||||
'right_wrist', # 10
|
||||
'left_hip', # 11
|
||||
'right_hip', # 12
|
||||
'left_knee', # 13
|
||||
'right_knee', # 14
|
||||
'left_ankle', # 15
|
||||
'right_ankle', # 16
|
||||
]
|
||||
from ..prep.transforms import COCO_KEYPOINTS
|
||||
|
||||
|
||||
def joints_variance(joints, clusters, dic_ms):
|
||||
@ -184,8 +165,8 @@ def variance_figures(dic_fin, clusters):
|
||||
plt.title("Standard deviation of joints disparity")
|
||||
yys_p = [el for _, el in dic_fin['pifpaf']['std_d'].items()]
|
||||
yys_m = [el for _, el in dic_fin['mask']['std_d'].items()]
|
||||
yys_p_z = [el for _, el in dic_fin['pifpaf']['std_z'].items()]
|
||||
yys_m_z = [el for _, el in dic_fin['mask']['std_z'].items()]
|
||||
# yys_p_z = [el for _, el in dic_fin['pifpaf']['std_z'].items()]
|
||||
# yys_m_z = [el for _, el in dic_fin['mask']['std_z'].items()]
|
||||
plt.plot(xxs, yys_p, marker='s', label="PifPaf")
|
||||
plt.plot(xxs, yys_m, marker='o', label="Mask R-CNN")
|
||||
# plt.plot(xxs, yys_p_z, marker='s', color='b', label="PifPaf (meters)")
|
||||
|
||||
@ -13,10 +13,11 @@ import torch
|
||||
|
||||
from ..network import Loco
|
||||
from ..network.process import preprocess_pifpaf
|
||||
from ..network.geom_baseline import geometric_coordinates
|
||||
from ..utils import get_keypoints, pixel_to_camera, factory_file, factory_basename, make_new_directory, get_category, \
|
||||
from .geom_baseline import geometric_coordinates
|
||||
from ..utils import get_keypoints, pixel_to_camera, factory_basename, make_new_directory, get_category, \
|
||||
xyz_from_distance, read_and_rewrite
|
||||
from .stereo_baselines import baselines_association
|
||||
from ..prep import factory_file
|
||||
from .reid_baseline import get_reid_features, ReID
|
||||
|
||||
|
||||
@ -33,7 +34,8 @@ class GenerateKitti:
|
||||
|
||||
# Load Network
|
||||
assert args.mode in ('mono', 'stereo'), "mode not recognized"
|
||||
self.net = 'monstereo' if args.mode == 'mono' else 'monoloco_pp'
|
||||
self.mode = args.mode
|
||||
self.net = 'monstereo' if args.mode == 'stereo' else 'monoloco_pp'
|
||||
use_cuda = torch.cuda.is_available()
|
||||
device = torch.device("cuda" if use_cuda else "cpu")
|
||||
self.model = Loco(
|
||||
@ -92,7 +94,7 @@ class GenerateKitti:
|
||||
make_new_directory(di)
|
||||
dir_out = {self.net: di}
|
||||
|
||||
for mode, names in self.baselines.items():
|
||||
for _, names in self.baselines.items():
|
||||
for name in names:
|
||||
di = os.path.join('data', 'kitti', name)
|
||||
make_new_directory(di)
|
||||
@ -105,7 +107,7 @@ class GenerateKitti:
|
||||
boxes, keypoints = preprocess_pifpaf(annotations, im_size=(1242, 374))
|
||||
cat = get_category(keypoints, os.path.join(self.dir_byc, basename + '.json'))
|
||||
if keypoints:
|
||||
annotations_r, _, _ = factory_file(path_calib, self.dir_ann, basename, mode='right')
|
||||
annotations_r, _, _ = factory_file(path_calib, self.dir_ann, basename, ann_type='right')
|
||||
_, keypoints_r = preprocess_pifpaf(annotations_r, im_size=(1242, 374))
|
||||
|
||||
if self.net == 'monstereo':
|
||||
@ -120,7 +122,7 @@ class GenerateKitti:
|
||||
# Save txt files
|
||||
params = [kk, tt]
|
||||
path_txt = os.path.join(dir_out[self.net], basename + '.txt')
|
||||
save_txts(path_txt, boxes, all_outputs[self.net], params, mode=self.net, cat=cat)
|
||||
save_txts(path_txt, boxes, all_outputs[self.net], params, net=self.net, cat=cat)
|
||||
cnt_ann += len(boxes)
|
||||
cnt_file += 1
|
||||
|
||||
@ -135,7 +137,7 @@ class GenerateKitti:
|
||||
# monocular baselines
|
||||
for key in self.baselines['mono']:
|
||||
path_txt = {key: os.path.join(dir_out[key], basename + '.txt')}
|
||||
save_txts(path_txt[key], boxes, all_outputs[key], params, mode=key, cat=cat)
|
||||
save_txts(path_txt[key], boxes, all_outputs[key], params, net=key, cat=cat)
|
||||
|
||||
# stereo baselines
|
||||
if self.baselines['stereo']:
|
||||
@ -148,12 +150,12 @@ class GenerateKitti:
|
||||
|
||||
path_txt[key] = os.path.join(dir_out[key], basename + '.txt')
|
||||
save_txts(path_txt[key], all_inputs[key], all_outputs[key], params,
|
||||
mode='baseline',
|
||||
net='baseline',
|
||||
cat=cat)
|
||||
|
||||
print("\nSaved in {} txt {} annotations. Not found {} images".format(cnt_file, cnt_ann, cnt_no_file))
|
||||
|
||||
if self.net == 'monstereo':
|
||||
if self.baselines[self.mode] and self.net == 'monstereo':
|
||||
print("STEREO:")
|
||||
for key in self.baselines['stereo']:
|
||||
print("Annotations corrected using {} baseline: {:.1f}%".format(
|
||||
@ -165,9 +167,9 @@ class GenerateKitti:
|
||||
|
||||
def _run_stereo_baselines(self, basename, boxes, keypoints, zzs, path_calib):
|
||||
|
||||
annotations_r, _, _ = factory_file(path_calib, self.dir_ann, basename, mode='right')
|
||||
annotations_r, _, _ = factory_file(path_calib, self.dir_ann, basename, ann_type='right')
|
||||
boxes_r, keypoints_r = preprocess_pifpaf(annotations_r, im_size=(1242, 374))
|
||||
_, kk, tt = factory_file(path_calib, self.dir_ann, basename)
|
||||
_, kk, _ = factory_file(path_calib, self.dir_ann, basename)
|
||||
|
||||
uv_centers = get_keypoints(keypoints, mode='bottom') # Kitti uses the bottom center to calculate depth
|
||||
xy_centers = pixel_to_camera(uv_centers, kk, 1)
|
||||
@ -197,15 +199,15 @@ class GenerateKitti:
|
||||
return dic_xyz
|
||||
|
||||
|
||||
def save_txts(path_txt, all_inputs, all_outputs, all_params, mode='monoloco', cat=None):
|
||||
def save_txts(path_txt, all_inputs, all_outputs, all_params, net='monoloco', cat=None):
|
||||
|
||||
assert mode in ('monoloco', 'monstereo', 'geometric', 'baseline', 'monoloco_pp')
|
||||
assert net in ('monoloco', 'monstereo', 'geometric', 'baseline', 'monoloco_pp')
|
||||
|
||||
if mode in ('monstereo', 'monoloco_pp'):
|
||||
if net in ('monstereo', 'monoloco_pp'):
|
||||
xyzd, bis, epis, yaws, hs, ws, ls = all_outputs[:]
|
||||
xyz = xyzd[:, 0:3]
|
||||
tt = [0, 0, 0]
|
||||
elif mode in ('monoloco', 'geometric'):
|
||||
elif net in ('monoloco', 'geometric'):
|
||||
tt = [0, 0, 0]
|
||||
dds, bis, epis, zzs_geom, xy_centers = all_outputs[:]
|
||||
xyz = xyz_from_distance(dds, xy_centers)
|
||||
@ -222,25 +224,19 @@ def save_txts(path_txt, all_inputs, all_outputs, all_params, mode='monoloco', ca
|
||||
yy = float(xyz[idx][1]) - tt[1]
|
||||
zz = float(xyz[idx][2]) - tt[2]
|
||||
|
||||
if mode == 'geometric':
|
||||
if net == 'geometric':
|
||||
zz = zzs_geom[idx]
|
||||
|
||||
cam_0 = [xx, yy, zz]
|
||||
bi = float(bis[idx])
|
||||
epi = float(epis[idx])
|
||||
if mode in ('monstereo', 'monoloco_pp'):
|
||||
if net in ('monstereo', 'monoloco_pp'):
|
||||
alpha, ry = float(yaws[0][idx]), float(yaws[1][idx])
|
||||
hwl = [float(hs[idx]), float(ws[idx]), float(ls[idx])]
|
||||
# scale to obtain (approximately) same recall at evaluation
|
||||
conf_scale = 0.035 if net == 'monoloco_pp' else 0.033
|
||||
else:
|
||||
alpha, ry, hwl = -10., -10., [0, 0, 0]
|
||||
|
||||
# Set the scale to obtain (approximately) same recall at evaluation
|
||||
if mode == 'monstereo':
|
||||
conf_scale = 0.03
|
||||
elif mode == 'monoloco_pp':
|
||||
conf_scale = 0.033
|
||||
# conf_scale = 0.035 # nuScenes for having same recall
|
||||
else:
|
||||
conf_scale = 0.05
|
||||
conf = conf_scale * (uv_box[-1]) / (bi / math.sqrt(xx ** 2 + yy ** 2 + zz ** 2))
|
||||
|
||||
|
||||
@ -1,17 +1,34 @@
|
||||
|
||||
import json
|
||||
import logging
|
||||
import math
|
||||
from collections import defaultdict
|
||||
|
||||
import numpy as np
|
||||
|
||||
from ..utils import pixel_to_camera, get_keypoints
|
||||
from monoloco.utils import pixel_to_camera, get_keypoints
|
||||
|
||||
AVERAGE_Y = 0.48
|
||||
CLUSTERS = ['10', '20', '30', 'all']
|
||||
|
||||
|
||||
def geometric_coordinates(keypoints, kk, average_y=0.48):
|
||||
""" Evaluate geometric depths for a set of keypoints"""
|
||||
|
||||
zzs_geom = []
|
||||
uv_shoulders = get_keypoints(keypoints, mode='shoulder')
|
||||
uv_hips = get_keypoints(keypoints, mode='hip')
|
||||
uv_centers = get_keypoints(keypoints, mode='center')
|
||||
|
||||
xy_shoulders = pixel_to_camera(uv_shoulders, kk, 1)
|
||||
xy_hips = pixel_to_camera(uv_hips, kk, 1)
|
||||
xy_centers = pixel_to_camera(uv_centers, kk, 1)
|
||||
|
||||
for idx, xy_shoulder in enumerate(xy_shoulders):
|
||||
zz = compute_depth(xy_shoulder, xy_hips[idx], average_y)
|
||||
zzs_geom.append(zz)
|
||||
return zzs_geom, xy_centers
|
||||
|
||||
|
||||
def geometric_baseline(joints):
|
||||
"""
|
||||
List of json files --> 2 lists with mean and std for each segment and the total count of instances
|
||||
@ -28,8 +45,6 @@ def geometric_baseline(joints):
|
||||
'right_ankle']
|
||||
|
||||
"""
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
cnt_tot = 0
|
||||
dic_dist = defaultdict(lambda: defaultdict(list))
|
||||
|
||||
@ -48,13 +63,13 @@ def geometric_baseline(joints):
|
||||
errors = calculate_error(dic_dist['error'])
|
||||
|
||||
# Show results
|
||||
logger.info("Computed distance of {} annotations".format(cnt_tot))
|
||||
print("Computed distance of {} annotations".format(cnt_tot))
|
||||
for key in dic_h_means:
|
||||
logger.info("Average height of segment {} is {:.2f} with a std of {:.2f}".
|
||||
format(key, dic_h_means[key], dic_h_stds[key]))
|
||||
print("Average height of segment {} is {:.2f} with a std of {:.2f}".
|
||||
format(key, dic_h_means[key], dic_h_stds[key]))
|
||||
for clst in CLUSTERS:
|
||||
logger.info("Average error over the val set for clst {}: {:.2f}".format(clst, errors[clst]))
|
||||
logger.info("Joints used: {}".format(joints))
|
||||
print("Average error over the val set for clst {}: {:.2f}".format(clst, errors[clst]))
|
||||
print("Joints used: {}".format(joints))
|
||||
|
||||
|
||||
def update_distances(dic_fin, dic_dist, phase, average_y):
|
||||
@ -78,9 +93,9 @@ def update_distances(dic_fin, dic_dist, phase, average_y):
|
||||
dy_met = abs(float((dic_xyz['hip'][0][1] - dic_xyz['shoulder'][0][1])))
|
||||
|
||||
# Estimate distance for a single annotation
|
||||
z_met_real = compute_distance(dic_xyz_norm['shoulder'][0], dic_xyz_norm['hip'][0], average_y,
|
||||
mode='real', dy_met=dy_met)
|
||||
z_met_approx = compute_distance(dic_xyz_norm['shoulder'][0], dic_xyz_norm['hip'][0], average_y, mode='average')
|
||||
z_met_real = compute_depth(dic_xyz_norm['shoulder'][0], dic_xyz_norm['hip'][0], average_y,
|
||||
mode='real', dy_met=dy_met)
|
||||
z_met_approx = compute_depth(dic_xyz_norm['shoulder'][0], dic_xyz_norm['hip'][0], average_y, mode='average')
|
||||
|
||||
# Compute distance with respect to the center of the 3D bounding box
|
||||
d_real = math.sqrt(z_met_real ** 2 + dic_fin['boxes_3d'][idx][0] ** 2 + dic_fin['boxes_3d'][idx][1] ** 2)
|
||||
@ -94,9 +109,9 @@ def update_distances(dic_fin, dic_dist, phase, average_y):
|
||||
return cnt
|
||||
|
||||
|
||||
def compute_distance(xyz_norm_1, xyz_norm_2, average_y, mode='average', dy_met=0):
|
||||
def compute_depth(xyz_norm_1, xyz_norm_2, average_y, mode='average', dy_met=0):
|
||||
"""
|
||||
Compute distance Z of a mask annotation (solving a linear system) for 2 possible cases:
|
||||
Compute depth Z of a mask annotation (solving a linear system) for 2 possible cases:
|
||||
1. knowing specific height of the annotation (head-ankle) dy_met
|
||||
2. using mean height of people (average_y)
|
||||
"""
|
||||
|
||||
@ -27,7 +27,7 @@ def get_reid_features(reid_net, boxes, boxes_r, path_image, path_image_r):
|
||||
return features.cpu(), features_r.cpu()
|
||||
|
||||
|
||||
class ReID(object):
|
||||
class ReID:
|
||||
def __init__(self, weights_path, device, num_classes=751, height=256, width=128):
|
||||
super().__init__()
|
||||
torch.manual_seed(1)
|
||||
|
||||
@ -22,7 +22,7 @@ def baselines_association(baselines, zzs, keypoints, keypoints_right, reid_featu
|
||||
cnt_stereo['max'] = min(keypoints.shape[0], keypoints_r.shape[0]) # pylint: disable=E1136
|
||||
|
||||
# Filter joints disparity and calculate avg disparity
|
||||
avg_disparities, disparities_x, disparities_y = mask_joint_disparity(keypoints, keypoints_r)
|
||||
avg_disparities, _, _ = mask_joint_disparity(keypoints, keypoints_r)
|
||||
|
||||
# Iterate over each left pose
|
||||
for key in baselines:
|
||||
|
||||
@ -3,7 +3,7 @@ import torch
|
||||
import torch.nn as nn
|
||||
|
||||
|
||||
class MonStereoModel(nn.Module):
|
||||
class LocoModel(nn.Module):
|
||||
|
||||
def __init__(self, input_size, output_size=2, linear_size=512, p_dropout=0.2, num_stage=3, device='cuda'):
|
||||
super().__init__()
|
||||
|
||||
@ -1,213 +0,0 @@
|
||||
|
||||
import json
|
||||
import logging
|
||||
import math
|
||||
from collections import defaultdict
|
||||
|
||||
import numpy as np
|
||||
|
||||
from monoloco.utils import pixel_to_camera, get_keypoints
|
||||
|
||||
AVERAGE_Y = 0.48
|
||||
CLUSTERS = ['10', '20', '30', 'all']
|
||||
|
||||
|
||||
def geometric_coordinates(keypoints, kk, average_y=0.48):
|
||||
""" Evaluate geometric depths for a set of keypoints"""
|
||||
|
||||
zzs_geom = []
|
||||
uv_shoulders = get_keypoints(keypoints, mode='shoulder')
|
||||
uv_hips = get_keypoints(keypoints, mode='hip')
|
||||
uv_centers = get_keypoints(keypoints, mode='center')
|
||||
|
||||
xy_shoulders = pixel_to_camera(uv_shoulders, kk, 1)
|
||||
xy_hips = pixel_to_camera(uv_hips, kk, 1)
|
||||
xy_centers = pixel_to_camera(uv_centers, kk, 1)
|
||||
|
||||
for idx, xy_shoulder in enumerate(xy_shoulders):
|
||||
zz = compute_depth(xy_shoulders[idx], xy_hips[idx], average_y)
|
||||
zzs_geom.append(zz)
|
||||
return zzs_geom, xy_centers
|
||||
|
||||
|
||||
def geometric_baseline(joints):
|
||||
"""
|
||||
List of json files --> 2 lists with mean and std for each segment and the total count of instances
|
||||
|
||||
For each annotation:
|
||||
1. From gt boxes calculate the height (deltaY) for the segments head, shoulder, hip, ankle
|
||||
2. From mask boxes calculate distance of people using average height of people and real pixel height
|
||||
|
||||
For left-right ambiguities we chose always the average of the joints
|
||||
|
||||
The joints are mapped from 0 to 16 in the following order:
|
||||
['nose', 'left_eye', 'right_eye', 'left_ear', 'right_ear', 'left_shoulder', 'right_shoulder', 'left_elbow',
|
||||
'right_elbow', 'left_wrist', 'right_wrist', 'left_hip', 'right_hip', 'left_knee', 'right_knee', 'left_ankle',
|
||||
'right_ankle']
|
||||
|
||||
"""
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
cnt_tot = 0
|
||||
dic_dist = defaultdict(lambda: defaultdict(list))
|
||||
|
||||
# Access the joints file
|
||||
with open(joints, 'r') as ff:
|
||||
dic_joints = json.load(ff)
|
||||
|
||||
# Calculate distances for all the instances in the joints dictionary
|
||||
for phase in ['train', 'val']:
|
||||
cnt = update_distances(dic_joints[phase], dic_dist, phase, AVERAGE_Y)
|
||||
cnt_tot += cnt
|
||||
|
||||
# Calculate mean and std of each segment
|
||||
dic_h_means = calculate_heights(dic_dist['heights'], mode='mean')
|
||||
dic_h_stds = calculate_heights(dic_dist['heights'], mode='std')
|
||||
errors = calculate_error(dic_dist['error'])
|
||||
|
||||
# Show results
|
||||
logger.info("Computed distance of {} annotations".format(cnt_tot))
|
||||
for key in dic_h_means:
|
||||
logger.info("Average height of segment {} is {:.2f} with a std of {:.2f}".
|
||||
format(key, dic_h_means[key], dic_h_stds[key]))
|
||||
for clst in CLUSTERS:
|
||||
logger.info("Average error over the val set for clst {}: {:.2f}".format(clst, errors[clst]))
|
||||
logger.info("Joints used: {}".format(joints))
|
||||
|
||||
|
||||
def update_distances(dic_fin, dic_dist, phase, average_y):
|
||||
|
||||
# Loop over each annotation in the json file corresponding to the image
|
||||
cnt = 0
|
||||
for idx, kps in enumerate(dic_fin['kps']):
|
||||
|
||||
# Extract pixel coordinates of head, shoulder, hip, ankle and and save them
|
||||
dic_uv = {mode: get_keypoints(kps, mode) for mode in ['head', 'shoulder', 'hip', 'ankle']}
|
||||
|
||||
# Convert segments from pixel coordinate to camera coordinate
|
||||
kk = dic_fin['K'][idx]
|
||||
z_met = dic_fin['boxes_3d'][idx][2]
|
||||
|
||||
# Create a dict with all annotations in meters
|
||||
dic_xyz = {key: pixel_to_camera(dic_uv[key], kk, z_met) for key in dic_uv}
|
||||
dic_xyz_norm = {key: pixel_to_camera(dic_uv[key], kk, 1) for key in dic_uv}
|
||||
|
||||
# Compute real height
|
||||
dy_met = abs(float((dic_xyz['hip'][0][1] - dic_xyz['shoulder'][0][1])))
|
||||
|
||||
# Estimate distance for a single annotation
|
||||
z_met_real = compute_depth(dic_xyz_norm['shoulder'][0], dic_xyz_norm['hip'][0], average_y,
|
||||
mode='real', dy_met=dy_met)
|
||||
z_met_approx = compute_depth(dic_xyz_norm['shoulder'][0], dic_xyz_norm['hip'][0], average_y, mode='average')
|
||||
|
||||
# Compute distance with respect to the center of the 3D bounding box
|
||||
d_real = math.sqrt(z_met_real ** 2 + dic_fin['boxes_3d'][idx][0] ** 2 + dic_fin['boxes_3d'][idx][1] ** 2)
|
||||
d_approx = math.sqrt(z_met_approx ** 2 +
|
||||
dic_fin['boxes_3d'][idx][0] ** 2 + dic_fin['boxes_3d'][idx][1] ** 2)
|
||||
|
||||
# Update the dictionary with distance and heights metrics
|
||||
dic_dist = update_dic_dist(dic_dist, dic_xyz, d_real, d_approx, phase)
|
||||
cnt += 1
|
||||
|
||||
return cnt
|
||||
|
||||
|
||||
def compute_depth(xyz_norm_1, xyz_norm_2, average_y, mode='average', dy_met=0):
|
||||
"""
|
||||
Compute depth Z of a mask annotation (solving a linear system) for 2 possible cases:
|
||||
1. knowing specific height of the annotation (head-ankle) dy_met
|
||||
2. using mean height of people (average_y)
|
||||
"""
|
||||
assert mode in ('average', 'real')
|
||||
|
||||
x1 = float(xyz_norm_1[0])
|
||||
y1 = float(xyz_norm_1[1])
|
||||
x2 = float(xyz_norm_2[0])
|
||||
y2 = float(xyz_norm_2[1])
|
||||
xx = (x1 + x2) / 2
|
||||
|
||||
# Choose if solving for provided height or average one.
|
||||
if mode == 'average':
|
||||
cc = - average_y # Y axis goes down
|
||||
else:
|
||||
cc = -dy_met
|
||||
|
||||
# Solving the linear system Ax = b
|
||||
matrix = np.array([[y1, 0, -xx],
|
||||
[0, -y1, 1],
|
||||
[y2, 0, -xx],
|
||||
[0, -y2, 1]])
|
||||
|
||||
bb = np.array([cc * xx, -cc, 0, 0]).reshape(4, 1)
|
||||
xx = np.linalg.lstsq(matrix, bb, rcond=None)
|
||||
z_met = abs(np.float(xx[0][1])) # Abs take into account specularity behind the observer
|
||||
|
||||
return z_met
|
||||
|
||||
|
||||
def update_dic_dist(dic_dist, dic_xyz, d_real, d_approx, phase):
|
||||
""" For every annotation in a single image, update the final dictionary"""
|
||||
|
||||
# Update the dict with heights metric
|
||||
if phase == 'train':
|
||||
dic_dist['heights']['head'].append(float(dic_xyz['head'][0][1]))
|
||||
dic_dist['heights']['shoulder'].append(float(dic_xyz['shoulder'][0][1]))
|
||||
dic_dist['heights']['hip'].append(float(dic_xyz['hip'][0][1]))
|
||||
dic_dist['heights']['ankle'].append(float(dic_xyz['ankle'][0][1]))
|
||||
|
||||
# Update the dict with distance metrics for the test phase
|
||||
if phase == 'val':
|
||||
error = abs(d_real - d_approx)
|
||||
|
||||
if d_real <= 10:
|
||||
dic_dist['error']['10'].append(error)
|
||||
elif d_real <= 20:
|
||||
dic_dist['error']['20'].append(error)
|
||||
elif d_real <= 30:
|
||||
dic_dist['error']['30'].append(error)
|
||||
else:
|
||||
dic_dist['error']['>30'].append(error)
|
||||
|
||||
dic_dist['error']['all'].append(error)
|
||||
|
||||
return dic_dist
|
||||
|
||||
|
||||
def calculate_heights(heights, mode):
|
||||
"""
|
||||
Compute statistics of heights based on the distance
|
||||
"""
|
||||
|
||||
assert mode in ('mean', 'std', 'max')
|
||||
heights_fin = {}
|
||||
|
||||
head_shoulder = np.array(heights['shoulder']) - np.array(heights['head'])
|
||||
shoulder_hip = np.array(heights['hip']) - np.array(heights['shoulder'])
|
||||
hip_ankle = np.array(heights['ankle']) - np.array(heights['hip'])
|
||||
|
||||
if mode == 'mean':
|
||||
heights_fin['head_shoulder'] = np.float(np.mean(head_shoulder)) * 100
|
||||
heights_fin['shoulder_hip'] = np.float(np.mean(shoulder_hip)) * 100
|
||||
heights_fin['hip_ankle'] = np.float(np.mean(hip_ankle)) * 100
|
||||
|
||||
elif mode == 'std':
|
||||
heights_fin['head_shoulder'] = np.float(np.std(head_shoulder)) * 100
|
||||
heights_fin['shoulder_hip'] = np.float(np.std(shoulder_hip)) * 100
|
||||
heights_fin['hip_ankle'] = np.float(np.std(hip_ankle)) * 100
|
||||
|
||||
elif mode == 'max':
|
||||
heights_fin['head_shoulder'] = np.float(np.max(head_shoulder)) * 100
|
||||
heights_fin['shoulder_hip'] = np.float(np.max(shoulder_hip)) * 100
|
||||
heights_fin['hip_ankle'] = np.float(np.max(hip_ankle)) * 100
|
||||
|
||||
return heights_fin
|
||||
|
||||
|
||||
def calculate_error(dic_errors):
|
||||
"""
|
||||
Compute statistics of distances based on the distance
|
||||
"""
|
||||
errors = {}
|
||||
for clst in dic_errors:
|
||||
errors[clst] = np.float(np.mean(np.array(dic_errors[clst])))
|
||||
return errors
|
||||
@ -1,141 +0,0 @@
|
||||
|
||||
import math
|
||||
import torch
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
|
||||
class CustomL1Loss(torch.nn.Module):
|
||||
"""
|
||||
L1 loss with more weight to errors at a shorter distance
|
||||
It inherits from nn.module so it supports backward
|
||||
"""
|
||||
|
||||
def __init__(self, dic_norm, device, beta=1):
|
||||
super().__init__()
|
||||
|
||||
self.dic_norm = dic_norm
|
||||
self.device = device
|
||||
self.beta = beta
|
||||
|
||||
@staticmethod
|
||||
def compute_weights(xx, beta=1):
|
||||
"""
|
||||
Return the appropriate weight depending on the distance and the hyperparameter chosen
|
||||
alpha = 1 refers to the curve of A Photogrammetric Approach for Real-time...
|
||||
It is made for unnormalized outputs (to be more understandable)
|
||||
From 70 meters on every value is weighted the same (0.1**beta)
|
||||
Alpha is optional value from Focal loss. Yet to be analyzed
|
||||
"""
|
||||
# alpha = np.maximum(1, 10 ** (beta - 1))
|
||||
alpha = 1
|
||||
ww = np.maximum(0.1, 1 - xx / 78)**beta
|
||||
|
||||
return alpha * ww
|
||||
|
||||
def print_loss(self):
|
||||
xx = np.linspace(0, 80, 100)
|
||||
y1 = self.compute_weights(xx, beta=1)
|
||||
y2 = self.compute_weights(xx, beta=2)
|
||||
y3 = self.compute_weights(xx, beta=3)
|
||||
plt.plot(xx, y1)
|
||||
plt.plot(xx, y2)
|
||||
plt.plot(xx, y3)
|
||||
plt.xlabel("Distance [m]")
|
||||
plt.ylabel("Loss function Weight")
|
||||
plt.legend(("Beta = 1", "Beta = 2", "Beta = 3"))
|
||||
plt.show()
|
||||
|
||||
def forward(self, output, target):
|
||||
|
||||
unnormalized_output = output.cpu().detach().numpy() * self.dic_norm['std']['Y'] + self.dic_norm['mean']['Y']
|
||||
weights_np = self.compute_weights(unnormalized_output, self.beta)
|
||||
weights = torch.from_numpy(weights_np).float().to(self.device) # To make weights in the same cuda device
|
||||
losses = torch.abs(output - target) * weights
|
||||
loss = losses.mean() # Mean over the batch
|
||||
return loss
|
||||
|
||||
|
||||
class LaplacianLoss(torch.nn.Module):
|
||||
"""1D Gaussian with std depending on the absolute distance
|
||||
"""
|
||||
def __init__(self, size_average=True, reduce=True, evaluate=False):
|
||||
super().__init__()
|
||||
self.size_average = size_average
|
||||
self.reduce = reduce
|
||||
self.evaluate = evaluate
|
||||
|
||||
def laplacian_1d(self, mu_si, xx):
|
||||
"""
|
||||
1D Gaussian Loss. f(x | mu, sigma). The network outputs mu and sigma. X is the ground truth distance.
|
||||
This supports backward().
|
||||
Inspired by
|
||||
https://github.com/naba89/RNN-Handwriting-Generation-Pytorch/blob/master/loss_functions.py
|
||||
|
||||
"""
|
||||
mu, si = mu_si[:, 0:1], mu_si[:, 1:2]
|
||||
# norm = xx - mu
|
||||
norm = 1 - mu / xx # Relative
|
||||
|
||||
term_a = torch.abs(norm) * torch.exp(-si)
|
||||
term_b = si
|
||||
norm_bi = (np.mean(np.abs(norm.cpu().detach().numpy())), np.mean(torch.exp(si).cpu().detach().numpy()))
|
||||
|
||||
if self.evaluate:
|
||||
return norm_bi
|
||||
return term_a + term_b
|
||||
|
||||
def forward(self, outputs, targets):
|
||||
|
||||
values = self.laplacian_1d(outputs, targets)
|
||||
|
||||
if not self.reduce or self.evaluate:
|
||||
return values
|
||||
if self.size_average:
|
||||
mean_values = torch.mean(values)
|
||||
return mean_values
|
||||
return torch.sum(values)
|
||||
|
||||
|
||||
class GaussianLoss(torch.nn.Module):
|
||||
"""1D Gaussian with std depending on the absolute distance
|
||||
"""
|
||||
def __init__(self, device, size_average=True, reduce=True, evaluate=False):
|
||||
super().__init__()
|
||||
self.size_average = size_average
|
||||
self.reduce = reduce
|
||||
self.evaluate = evaluate
|
||||
self.device = device
|
||||
|
||||
def gaussian_1d(self, mu_si, xx):
|
||||
"""
|
||||
1D Gaussian Loss. f(x | mu, sigma). The network outputs mu and sigma. X is the ground truth distance.
|
||||
This supports backward().
|
||||
Inspired by
|
||||
https://github.com/naba89/RNN-Handwriting-Generation-Pytorch/blob/master/loss_functions.py
|
||||
"""
|
||||
mu, si = mu_si[:, 0:1], mu_si[:, 1:2]
|
||||
|
||||
min_si = torch.ones(si.size()).cuda(self.device) * 0.1
|
||||
si = torch.max(min_si, si)
|
||||
norm = xx - mu
|
||||
term_a = (norm / si)**2 / 2
|
||||
term_b = torch.log(si * math.sqrt(2 * math.pi))
|
||||
|
||||
norm_si = (np.mean(np.abs(norm.cpu().detach().numpy())), np.mean(si.cpu().detach().numpy()))
|
||||
|
||||
if self.evaluate:
|
||||
return norm_si
|
||||
|
||||
return term_a + term_b
|
||||
|
||||
def forward(self, outputs, targets):
|
||||
|
||||
values = self.gaussian_1d(outputs, targets)
|
||||
|
||||
if not self.reduce or self.evaluate:
|
||||
return values
|
||||
if self.size_average:
|
||||
mean_values = torch.mean(values)
|
||||
return mean_values
|
||||
return torch.sum(values)
|
||||
@ -9,13 +9,15 @@ import math
|
||||
import logging
|
||||
from collections import defaultdict
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from ..utils import get_iou_matches, reorder_matches, get_keypoints, pixel_to_camera, xyz_from_distance
|
||||
from ..utils import get_iou_matches, reorder_matches, get_keypoints, pixel_to_camera, xyz_from_distance, \
|
||||
mask_joint_disparity
|
||||
from .process import preprocess_monstereo, preprocess_monoloco, extract_outputs, extract_outputs_mono,\
|
||||
filter_outputs, cluster_outputs, unnormalize_bi
|
||||
from ..activity import social_interactions, is_raising_hand
|
||||
from .architectures import MonolocoModel, MonStereoModel
|
||||
filter_outputs, cluster_outputs, unnormalize_bi, laplace_sampling
|
||||
from ..activity import social_interactions
|
||||
from .architectures import MonolocoModel, LocoModel
|
||||
|
||||
|
||||
class Loco:
|
||||
@ -69,7 +71,7 @@ class Loco:
|
||||
self.model = MonolocoModel(p_dropout=p_dropout, input_size=input_size, linear_size=linear_size,
|
||||
output_size=output_size)
|
||||
else:
|
||||
self.model = MonStereoModel(p_dropout=p_dropout, input_size=input_size, output_size=output_size,
|
||||
self.model = LocoModel(p_dropout=p_dropout, input_size=input_size, output_size=output_size,
|
||||
linear_size=linear_size, device=self.device)
|
||||
|
||||
self.model.load_state_dict(torch.load(model_path, map_location=lambda storage, loc: storage))
|
||||
@ -116,7 +118,7 @@ class Loco:
|
||||
outputs = self.model(inputs)
|
||||
|
||||
outputs = cluster_outputs(outputs, keypoints_r.shape[0])
|
||||
outputs_fin, mask = filter_outputs(outputs)
|
||||
outputs_fin, _ = filter_outputs(outputs)
|
||||
dic_out = extract_outputs(outputs_fin)
|
||||
|
||||
# For Median baseline
|
||||
@ -136,7 +138,6 @@ class Loco:
|
||||
Apply dropout at test time to obtain combined aleatoric + epistemic uncertainty
|
||||
"""
|
||||
assert self.net in ('monoloco', 'monoloco_p', 'monoloco_pp'), "Not supported for MonStereo"
|
||||
from .process import laplace_sampling
|
||||
|
||||
self.model.dropout.training = True # Manually reactivate dropout in eval
|
||||
total_outputs = torch.empty((0, inputs.size()[0])).to(self.device)
|
||||
@ -277,8 +278,6 @@ def median_disparity(dic_out, keypoints, keypoints_r, mask):
|
||||
Filters are applied to masks nan joints and remove outlier disparities with iqr
|
||||
The mask input is used to filter the all-vs-all approach
|
||||
"""
|
||||
import numpy as np
|
||||
from ..utils import mask_joint_disparity
|
||||
|
||||
keypoints = keypoints.cpu().numpy()
|
||||
keypoints_r = keypoints_r.cpu().numpy()
|
||||
|
||||
@ -7,7 +7,7 @@ import numpy as np
|
||||
import torch
|
||||
import torchvision
|
||||
|
||||
from ..utils import get_keypoints, pixel_to_camera, to_cartesian, back_correct_angles
|
||||
from ..utils import get_keypoints, pixel_to_camera, to_cartesian, back_correct_angles, open_annotations
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
@ -30,7 +30,7 @@ def preprocess_monstereo(keypoints, keypoints_r, kk):
|
||||
inputs_r = preprocess_monoloco(keypoints_r, kk)
|
||||
|
||||
inputs = torch.empty((0, 68)).to(inputs_l.device)
|
||||
for idx, inp_l in enumerate(inputs_l.split(1)):
|
||||
for inp_l in inputs_l.split(1):
|
||||
clst = 0
|
||||
# inp_l = torch.cat((inp_l, cat[:, idx:idx+1]), dim=1)
|
||||
for idx_r, inp_r in enumerate(inputs_r.split(1)):
|
||||
@ -135,7 +135,6 @@ def preprocess_mask(dir_ann, basename, mode='left'):
|
||||
elif mode == 'right':
|
||||
path_ann = os.path.join(dir_ann + '_right', basename + '.json')
|
||||
|
||||
from ..utils import open_annotations
|
||||
dic = open_annotations(path_ann)
|
||||
if isinstance(dic, list):
|
||||
return [], []
|
||||
|
||||
@ -1,12 +1,15 @@
|
||||
# pylint: disable=too-many-statements, too-many-branches, undefined-loop-variable
|
||||
|
||||
"""
|
||||
Adapted from https://github.com/vita-epfl/openpifpaf/blob/master/openpifpaf/predict.py
|
||||
Adapted from https://github.com/openpifpaf/openpifpaf/blob/main/openpifpaf/predict.py,
|
||||
which is: 'Copyright 2019-2021 by Sven Kreiss and contributors. All rights reserved.'
|
||||
and licensed under GNU AGPLv3
|
||||
"""
|
||||
|
||||
import os
|
||||
import glob
|
||||
import json
|
||||
import copy
|
||||
import logging
|
||||
from collections import defaultdict
|
||||
|
||||
@ -17,7 +20,11 @@ import openpifpaf
|
||||
import openpifpaf.datasets as datasets
|
||||
from openpifpaf.predict import processor_factory, preprocess_factory
|
||||
from openpifpaf import decoder, network, visualizer, show, logger
|
||||
|
||||
try:
|
||||
import gdown
|
||||
DOWNLOAD = copy.copy(gdown.download)
|
||||
except ImportError:
|
||||
DOWNLOAD = None
|
||||
from .visuals.printer import Printer
|
||||
from .network import Loco
|
||||
from .network.process import factory_for_gt, preprocess_pifpaf
|
||||
@ -46,13 +53,15 @@ def get_torch_checkpoints_dir():
|
||||
|
||||
def download_checkpoints(args):
|
||||
torch_dir = get_torch_checkpoints_dir()
|
||||
pifpaf_model = os.path.join(
|
||||
torch_dir, 'shufflenetv2k30-201104-224654-cocokp-d75ed641.pkl')
|
||||
if args.checkpoint is None:
|
||||
pifpaf_model = os.path.join(torch_dir, 'shufflenetv2k30-201104-224654-cocokp-d75ed641.pkl')
|
||||
else:
|
||||
pifpaf_model = args.checkpoint
|
||||
dic_models = {'keypoints': pifpaf_model}
|
||||
if not os.path.exists(pifpaf_model):
|
||||
import gdown
|
||||
assert DOWNLOAD is not None, "pip install gdown to download pifpaf model, or pass it as --checkpoint"
|
||||
LOG.info('Downloading OpenPifPaf model in %s', torch_dir)
|
||||
gdown.download(OPENPIFPAF_MODEL, pifpaf_model, quiet=False)
|
||||
DOWNLOAD(OPENPIFPAF_MODEL, pifpaf_model, quiet=False)
|
||||
|
||||
if args.mode == 'keypoints':
|
||||
return dic_models
|
||||
@ -74,9 +83,9 @@ def download_checkpoints(args):
|
||||
model = os.path.join(torch_dir, name)
|
||||
dic_models[args.mode] = model
|
||||
if not os.path.exists(model):
|
||||
import gdown
|
||||
assert DOWNLOAD is not None, "pip install gdown to download monoloco model, or pass it as --model"
|
||||
LOG.info('Downloading model in %s', torch_dir)
|
||||
gdown.download(path, model, quiet=False)
|
||||
DOWNLOAD(path, model, quiet=False)
|
||||
return dic_models
|
||||
|
||||
|
||||
@ -218,8 +227,7 @@ def predict(args):
|
||||
|
||||
else:
|
||||
LOG.info("Prediction with MonStereo")
|
||||
boxes_r, keypoints_r = preprocess_pifpaf(
|
||||
pifpaf_outs['right'], im_size)
|
||||
_, keypoints_r = preprocess_pifpaf(pifpaf_outs['right'], im_size)
|
||||
dic_out = net.forward(keypoints, kk, keypoints_r=keypoints_r)
|
||||
dic_out = net.post_process(
|
||||
dic_out, boxes, keypoints, kk, dic_gt)
|
||||
@ -240,15 +248,19 @@ def factory_outputs(args, pifpaf_outs, dic_out, output_path, kk=None):
|
||||
# Verify conflicting options
|
||||
if any((xx in args.output_types for xx in ['front', 'bird', 'multi'])):
|
||||
assert args.mode != 'keypoints', "for keypoints please use pifpaf original arguments"
|
||||
if args.social_distance:
|
||||
assert args.mode == 'mono', "Social distancing only works with monocular network"
|
||||
else:
|
||||
assert 'json' in args.output_types or args.mode == 'keypoints', \
|
||||
"No output saved, please select one among front, bird, multi, json, or pifpaf arguments"
|
||||
if args.social_distance:
|
||||
assert args.mode == 'mono', "Social distancing only works with monocular network"
|
||||
|
||||
if args.mode == 'keypoints':
|
||||
annotation_painter = openpifpaf.show.AnnotationPainter()
|
||||
with openpifpaf.show.image_canvas(pifpaf_outs['image'], output_path) as ax:
|
||||
annotation_painter.annotations(ax, pifpaf_outs['pred'])
|
||||
return
|
||||
|
||||
elif any((xx in args.output_types for xx in ['front', 'bird', 'multi'])):
|
||||
if any((xx in args.output_types for xx in ['front', 'bird', 'multi'])):
|
||||
LOG.info(output_path)
|
||||
if args.activities:
|
||||
show_activities(
|
||||
@ -258,10 +270,6 @@ def factory_outputs(args, pifpaf_outs, dic_out, output_path, kk=None):
|
||||
figures, axes = printer.factory_axes(dic_out)
|
||||
printer.draw(figures, axes, pifpaf_outs['image'])
|
||||
|
||||
elif 'json' in args.output_types:
|
||||
if 'json' in args.output_types:
|
||||
with open(os.path.join(output_path + '.monoloco.json'), 'w') as ff:
|
||||
json.dump(dic_out, ff)
|
||||
|
||||
else:
|
||||
LOG.info(
|
||||
"No output saved, please select one among front, bird, multi, or pifpaf options")
|
||||
|
||||
@ -0,0 +1,2 @@
|
||||
|
||||
from .preprocess_kitti import parse_ground_truth, factory_file
|
||||
@ -1,350 +0,0 @@
|
||||
|
||||
# pylint: disable=too-many-statements, too-many-branches, too-many-nested-blocks
|
||||
|
||||
"""Preprocess annotations with KITTI ground-truth"""
|
||||
|
||||
import os
|
||||
import glob
|
||||
import copy
|
||||
import logging
|
||||
from collections import defaultdict
|
||||
import json
|
||||
import datetime
|
||||
from PIL import Image
|
||||
|
||||
import torch
|
||||
import cv2
|
||||
|
||||
from ..utils import split_training, parse_ground_truth, get_iou_matches, append_cluster, factory_file, \
|
||||
extract_stereo_matches, get_category, normalize_hwl, make_new_directory
|
||||
from ..network.process import preprocess_pifpaf, preprocess_monoloco
|
||||
from .transforms import flip_inputs, flip_labels, height_augmentation
|
||||
|
||||
|
||||
class PreprocessKitti:
|
||||
"""Prepare arrays with same format as nuScenes preprocessing but using ground truth txt files"""
|
||||
|
||||
dir_gt = os.path.join('data', 'kitti', 'gt')
|
||||
dir_images = '/data/lorenzo-data/kitti/original_images/training/image_2'
|
||||
dir_byc_l = '/data/lorenzo-data/kitti/object_detection/left'
|
||||
|
||||
# SOCIAL DISTANCING PARAMETERS
|
||||
THRESHOLD_DIST = 2 # Threshold to check distance of people
|
||||
RADII = (0.3, 0.5, 1) # expected radii of the o-space
|
||||
SOCIAL_DISTANCE = True
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
dic_jo = {'train': dict(X=[], Y=[], names=[], kps=[], K=[],
|
||||
clst=defaultdict(lambda: defaultdict(list))),
|
||||
'val': dict(X=[], Y=[], names=[], kps=[], K=[],
|
||||
clst=defaultdict(lambda: defaultdict(list))),
|
||||
'test': dict(X=[], Y=[], names=[], kps=[], K=[],
|
||||
clst=defaultdict(lambda: defaultdict(list)))}
|
||||
dic_names = defaultdict(lambda: defaultdict(list))
|
||||
dic_std = defaultdict(lambda: defaultdict(list))
|
||||
|
||||
def __init__(self, dir_ann, mode='mono', iou_min=0.3):
|
||||
|
||||
self.dir_ann = dir_ann
|
||||
self.iou_min = iou_min
|
||||
self.mode = mode
|
||||
assert self.mode in ('mono', 'stereo'), "modality not recognized"
|
||||
self.names_gt = tuple(os.listdir(self.dir_gt))
|
||||
self.dir_kk = os.path.join('data', 'kitti', 'calib')
|
||||
self.list_gt = glob.glob(self.dir_gt + '/*.txt')
|
||||
assert os.path.exists(self.dir_gt), "Ground truth dir does not exist"
|
||||
assert os.path.exists(self.dir_ann), "Annotation dir does not exist"
|
||||
|
||||
now = datetime.datetime.now()
|
||||
now_time = now.strftime("%Y%m%d-%H%M")[2:]
|
||||
dir_out = os.path.join('data', 'arrays')
|
||||
self.path_joints = os.path.join(dir_out, 'joints-kitti-' + now_time + '.json')
|
||||
self.path_names = os.path.join(dir_out, 'names-kitti-' + now_time + '.json')
|
||||
path_train = os.path.join('splits', 'kitti_train.txt')
|
||||
path_val = os.path.join('splits', 'kitti_val.txt')
|
||||
self.set_train, self.set_val = split_training(self.names_gt, path_train, path_val)
|
||||
|
||||
def run(self):
|
||||
|
||||
cnt_match_l, cnt_match_r, cnt_pair, cnt_pair_tot, cnt_extra_pair, cnt_files, cnt_files_ped, cnt_fnf, \
|
||||
cnt_tot, cnt_ambiguous, cnt_cyclist = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
cnt_mono = {'train': 0, 'val': 0, 'test': 0}
|
||||
cnt_gt = cnt_mono.copy()
|
||||
cnt_stereo = cnt_mono.copy()
|
||||
correct_ped, correct_byc, wrong_ped, wrong_byc = 0, 0, 0, 0
|
||||
cnt_30, cnt_less_30 = 0, 0
|
||||
|
||||
# self.names_gt = ('002282.txt',)
|
||||
for name in self.names_gt:
|
||||
path_gt = os.path.join(self.dir_gt, name)
|
||||
basename, _ = os.path.splitext(name)
|
||||
path_im = os.path.join(self.dir_images, basename + '.png')
|
||||
phase, flag = self._factory_phase(name)
|
||||
if flag:
|
||||
cnt_fnf += 1
|
||||
continue
|
||||
|
||||
if phase == 'train':
|
||||
min_conf = 0
|
||||
category = 'all'
|
||||
else: # Remove for original results
|
||||
min_conf = 0.1
|
||||
category = 'pedestrian'
|
||||
|
||||
# Extract ground truth
|
||||
boxes_gt, ys, _, _ = parse_ground_truth(path_gt, # pylint: disable=unbalanced-tuple-unpacking
|
||||
category=category,
|
||||
spherical=True)
|
||||
cnt_gt[phase] += len(boxes_gt)
|
||||
cnt_files += 1
|
||||
cnt_files_ped += min(len(boxes_gt), 1) # if no boxes 0 else 1
|
||||
|
||||
# Extract keypoints
|
||||
path_calib = os.path.join(self.dir_kk, basename + '.txt')
|
||||
annotations, kk, tt = factory_file(path_calib, self.dir_ann, basename)
|
||||
|
||||
self.dic_names[basename + '.png']['boxes'] = copy.deepcopy(boxes_gt)
|
||||
self.dic_names[basename + '.png']['ys'] = copy.deepcopy(ys)
|
||||
self.dic_names[basename + '.png']['K'] = copy.deepcopy(kk)
|
||||
|
||||
# Check image size
|
||||
with Image.open(path_im) as im:
|
||||
width, height = im.size
|
||||
|
||||
boxes, keypoints = preprocess_pifpaf(annotations, im_size=(width, height), min_conf=min_conf)
|
||||
|
||||
if keypoints:
|
||||
annotations_r, kk_r, tt_r = factory_file(path_calib, self.dir_ann, basename, mode='right')
|
||||
boxes_r, keypoints_r = preprocess_pifpaf(annotations_r, im_size=(width, height), min_conf=min_conf)
|
||||
cat = get_category(keypoints, os.path.join(self.dir_byc_l, basename + '.json'))
|
||||
|
||||
if not keypoints_r: # Case of no detection
|
||||
all_boxes_gt, all_ys = [boxes_gt], [ys]
|
||||
boxes_r, keypoints_r = boxes[0:1].copy(), keypoints[0:1].copy()
|
||||
all_boxes, all_keypoints = [boxes], [keypoints]
|
||||
all_keypoints_r = [keypoints_r]
|
||||
else:
|
||||
|
||||
# Horizontal Flipping for training
|
||||
if phase == 'train':
|
||||
# GT)
|
||||
boxes_gt_flip, ys_flip = flip_labels(boxes_gt, ys, im_w=width)
|
||||
# New left
|
||||
boxes_flip = flip_inputs(boxes_r, im_w=width, mode='box')
|
||||
keypoints_flip = flip_inputs(keypoints_r, im_w=width)
|
||||
|
||||
# New right
|
||||
keypoints_r_flip = flip_inputs(keypoints, im_w=width)
|
||||
|
||||
# combine the 2 modes
|
||||
all_boxes_gt = [boxes_gt, boxes_gt_flip]
|
||||
all_ys = [ys, ys_flip]
|
||||
all_boxes = [boxes, boxes_flip]
|
||||
all_keypoints = [keypoints, keypoints_flip]
|
||||
all_keypoints_r = [keypoints_r, keypoints_r_flip]
|
||||
|
||||
else:
|
||||
all_boxes_gt, all_ys = [boxes_gt], [ys]
|
||||
all_boxes, all_keypoints = [boxes], [keypoints]
|
||||
all_keypoints_r = [keypoints_r]
|
||||
|
||||
# Match each set of keypoint with a ground truth
|
||||
self.dic_jo[phase]['K'].append(kk)
|
||||
for ii, boxes_gt in enumerate(all_boxes_gt):
|
||||
keypoints, keypoints_r = torch.tensor(all_keypoints[ii]), torch.tensor(all_keypoints_r[ii])
|
||||
ys = all_ys[ii]
|
||||
matches = get_iou_matches(all_boxes[ii], boxes_gt, self.iou_min)
|
||||
for (idx, idx_gt) in matches:
|
||||
keypoint = keypoints[idx:idx + 1]
|
||||
lab = ys[idx_gt][:-1]
|
||||
|
||||
# Preprocess MonoLoco++
|
||||
if self.mode == 'mono':
|
||||
inp = preprocess_monoloco(keypoint, kk).view(-1).tolist()
|
||||
lab = normalize_hwl(lab)
|
||||
if ys[idx_gt][10] < 0.5:
|
||||
self.dic_jo[phase]['kps'].append(keypoint.tolist())
|
||||
self.dic_jo[phase]['X'].append(inp)
|
||||
self.dic_jo[phase]['Y'].append(lab)
|
||||
self.dic_jo[phase]['names'].append(name) # One image name for each annotation
|
||||
append_cluster(self.dic_jo, phase, inp, lab, keypoint.tolist())
|
||||
cnt_mono[phase] += 1
|
||||
cnt_tot += 1
|
||||
|
||||
# Preprocess MonStereo
|
||||
else:
|
||||
zz = ys[idx_gt][2]
|
||||
stereo_matches, cnt_amb = extract_stereo_matches(keypoint, keypoints_r, zz,
|
||||
phase=phase, seed=cnt_pair_tot)
|
||||
cnt_match_l += 1 if ii < 0.1 else 0 # matched instances
|
||||
cnt_match_r += 1 if ii > 0.9 else 0
|
||||
cnt_ambiguous += cnt_amb
|
||||
|
||||
# Monitor precision of classes
|
||||
if phase == 'val':
|
||||
if ys[idx_gt][10] == cat[idx] == 1:
|
||||
correct_byc += 1
|
||||
elif ys[idx_gt][10] == cat[idx] == 0:
|
||||
correct_ped += 1
|
||||
elif ys[idx_gt][10] != cat[idx] and ys[idx_gt][10] == 1:
|
||||
wrong_byc += 1
|
||||
elif ys[idx_gt][10] != cat[idx] and ys[idx_gt][10] == 0:
|
||||
wrong_ped += 1
|
||||
|
||||
cnt_cyclist += 1 if ys[idx_gt][10] == 1 else 0
|
||||
|
||||
for num, (idx_r, s_match) in enumerate(stereo_matches):
|
||||
label = ys[idx_gt][:-1] + [s_match]
|
||||
if s_match > 0.9:
|
||||
cnt_pair += 1
|
||||
|
||||
# Remove noise of very far instances for validation
|
||||
# if (phase == 'val') and (ys[idx_gt][3] >= 50):
|
||||
# continue
|
||||
|
||||
# ---> Save only positives unless there is no positive (keep positive flip and augm)
|
||||
# if num > 0 and s_match < 0.9:
|
||||
# continue
|
||||
|
||||
# Height augmentation
|
||||
cnt_pair_tot += 1
|
||||
cnt_extra_pair += 1 if ii == 1 else 0
|
||||
flag_aug = False
|
||||
if phase == 'train' and 3 < label[2] < 30 and s_match > 0.9:
|
||||
flag_aug = True
|
||||
elif phase == 'train' and 3 < label[2] < 30 and cnt_pair_tot % 2 == 0:
|
||||
flag_aug = True
|
||||
|
||||
# Remove height augmentation
|
||||
# flag_aug = False
|
||||
|
||||
if flag_aug:
|
||||
kps_aug, labels_aug = height_augmentation(
|
||||
keypoints[idx:idx+1], keypoints_r[idx_r:idx_r+1], label, s_match,
|
||||
seed=cnt_pair_tot)
|
||||
else:
|
||||
kps_aug = [(keypoints[idx:idx+1], keypoints_r[idx_r:idx_r+1])]
|
||||
labels_aug = [label]
|
||||
|
||||
for i, lab in enumerate(labels_aug):
|
||||
(kps, kps_r) = kps_aug[i]
|
||||
input_l = preprocess_monoloco(kps, kk).view(-1)
|
||||
input_r = preprocess_monoloco(kps_r, kk).view(-1)
|
||||
keypoint = torch.cat((kps, kps_r), dim=2).tolist()
|
||||
inp = torch.cat((input_l, input_l - input_r)).tolist()
|
||||
|
||||
# Only relative distances
|
||||
# inp_x = input[::2]
|
||||
# inp = torch.cat((inp_x, input - input_r)).tolist()
|
||||
|
||||
# lab = normalize_hwl(lab)
|
||||
if ys[idx_gt][10] < 0.5:
|
||||
self.dic_jo[phase]['kps'].append(keypoint)
|
||||
self.dic_jo[phase]['X'].append(inp)
|
||||
self.dic_jo[phase]['Y'].append(lab)
|
||||
self.dic_jo[phase]['names'].append(name) # One image name for each annotation
|
||||
append_cluster(self.dic_jo, phase, inp, lab, keypoint)
|
||||
cnt_tot += 1
|
||||
if s_match > 0.9:
|
||||
cnt_stereo[phase] += 1
|
||||
else:
|
||||
cnt_mono[phase] += 1
|
||||
|
||||
with open(self.path_joints, 'w') as file:
|
||||
json.dump(self.dic_jo, file)
|
||||
with open(os.path.join(self.path_names), 'w') as file:
|
||||
json.dump(self.dic_names, file)
|
||||
|
||||
# cout
|
||||
print(cnt_30)
|
||||
print(cnt_less_30)
|
||||
print('-' * 120)
|
||||
|
||||
print("Number of GT files: {}. Files with at least one pedestrian: {}. Files not found: {}"
|
||||
.format(cnt_files, cnt_files_ped, cnt_fnf))
|
||||
print("Ground truth matches : {:.1f} % for left images (train and val) and {:.1f} % for right images (train)"
|
||||
.format(100*cnt_match_l / (cnt_gt['train'] + cnt_gt['val']), 100*cnt_match_r / cnt_gt['train']))
|
||||
print("Total annotations: {}".format(cnt_tot))
|
||||
print("Total number of cyclists: {}\n".format(cnt_cyclist))
|
||||
print("Ambiguous instances removed: {}".format(cnt_ambiguous))
|
||||
print("Extra pairs created with horizontal flipping: {}\n".format(cnt_extra_pair))
|
||||
|
||||
if self.mode == 'stereo':
|
||||
print('Instances with stereo correspondence: {:.1f}% '.format(100 * cnt_pair / cnt_pair_tot))
|
||||
for phase in ['train', 'val']:
|
||||
cnt = cnt_mono[phase] + cnt_stereo[phase]
|
||||
print("{}: annotations: {}. Stereo pairs {:.1f}% "
|
||||
.format(phase.upper(), cnt, 100 * cnt_stereo[phase] / cnt))
|
||||
|
||||
print("\nOutput files:\n{}\n{}".format(self.path_names, self.path_joints))
|
||||
print('-' * 120)
|
||||
|
||||
def prep_activity(self):
|
||||
"""Augment ground-truth with flag activity"""
|
||||
|
||||
from monoloco.activity import social_interactions
|
||||
main_dir = os.path.join('data', 'kitti')
|
||||
dir_gt = os.path.join(main_dir, 'gt')
|
||||
dir_out = os.path.join(main_dir, 'gt_activity')
|
||||
make_new_directory(dir_out)
|
||||
cnt_tp, cnt_tn = 0, 0
|
||||
|
||||
# Extract validation images for evaluation
|
||||
category = 'pedestrian'
|
||||
|
||||
for name in self.set_val:
|
||||
# Read
|
||||
path_gt = os.path.join(dir_gt, name)
|
||||
boxes_gt, ys, truncs_gt, occs_gt, lines = parse_ground_truth(path_gt, category, spherical=False,
|
||||
verbose=True)
|
||||
angles = [y[10] for y in ys]
|
||||
dds = [y[4] for y in ys]
|
||||
xz_centers = [[y[0], y[2]] for y in ys]
|
||||
|
||||
# Write
|
||||
path_out = os.path.join(dir_out, name)
|
||||
with open(path_out, "w+") as ff:
|
||||
for idx, line in enumerate(lines):
|
||||
if social_interactions(idx, xz_centers, angles, dds,
|
||||
n_samples=1,
|
||||
threshold_dist=self.THRESHOLD_DIST,
|
||||
radii=self.RADII,
|
||||
social_distance=self.SOCIAL_DISTANCE):
|
||||
activity = '1'
|
||||
cnt_tp += 1
|
||||
else:
|
||||
activity = '0'
|
||||
cnt_tn += 1
|
||||
|
||||
line_new = line[:-1] + ' ' + activity + line[-1]
|
||||
ff.write(line_new)
|
||||
|
||||
print(f'Written {len(self.set_val)} new files in {dir_out}')
|
||||
print(f'Saved {cnt_tp} positive and {cnt_tn} negative annotations')
|
||||
|
||||
def _factory_phase(self, name):
|
||||
"""Choose the phase"""
|
||||
phase = None
|
||||
flag = False
|
||||
if name in self.set_train:
|
||||
phase = 'train'
|
||||
elif name in self.set_val:
|
||||
phase = 'val'
|
||||
else:
|
||||
flag = True
|
||||
return phase, flag
|
||||
|
||||
|
||||
def crop_and_draw(im, box, keypoint):
|
||||
|
||||
box = [round(el) for el in box[:-1]]
|
||||
center = (int((keypoint[0][0])), int((keypoint[1][0])))
|
||||
radius = round((box[3]-box[1]) / 20)
|
||||
im = cv2.circle(im, center, radius, color=(0, 255, 0), thickness=1)
|
||||
crop = im[box[1]:box[3], box[0]:box[2]]
|
||||
h_crop = crop.shape[0]
|
||||
w_crop = crop.shape[1]
|
||||
|
||||
return crop, h_crop, w_crop
|
||||
@ -1,132 +0,0 @@
|
||||
"""Preprocess annotations with KITTI ground-truth"""
|
||||
|
||||
import os
|
||||
import glob
|
||||
import copy
|
||||
import logging
|
||||
from collections import defaultdict
|
||||
import json
|
||||
import datetime
|
||||
|
||||
from .transforms import transform_keypoints
|
||||
from ..utils import get_calibration, split_training, parse_ground_truth, get_iou_matches, append_cluster
|
||||
from ..network.process import preprocess_pifpaf, preprocess_monoloco
|
||||
|
||||
|
||||
class PreprocessKitti:
|
||||
"""Prepare arrays with same format as nuScenes preprocessing but using ground truth txt files"""
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
dic_jo = {'train': dict(X=[], Y=[], names=[], kps=[], boxes_3d=[], K=[],
|
||||
clst=defaultdict(lambda: defaultdict(list))),
|
||||
'val': dict(X=[], Y=[], names=[], kps=[], boxes_3d=[], K=[],
|
||||
clst=defaultdict(lambda: defaultdict(list))),
|
||||
'test': dict(X=[], Y=[], names=[], kps=[], boxes_3d=[], K=[],
|
||||
clst=defaultdict(lambda: defaultdict(list)))}
|
||||
dic_names = defaultdict(lambda: defaultdict(list))
|
||||
|
||||
def __init__(self, dir_ann, iou_min):
|
||||
|
||||
self.dir_ann = dir_ann
|
||||
self.iou_min = iou_min
|
||||
self.dir_gt = os.path.join('data', 'kitti', 'gt')
|
||||
self.names_gt = tuple(os.listdir(self.dir_gt))
|
||||
self.dir_kk = os.path.join('data', 'kitti', 'calib')
|
||||
self.list_gt = glob.glob(self.dir_gt + '/*.txt')
|
||||
assert os.path.exists(self.dir_gt), "Ground truth dir does not exist"
|
||||
assert os.path.exists(self.dir_ann), "Annotation dir does not exist"
|
||||
|
||||
now = datetime.datetime.now()
|
||||
now_time = now.strftime("%Y%m%d-%H%M")[2:]
|
||||
dir_out = os.path.join('data', 'arrays')
|
||||
self.path_joints = os.path.join(dir_out, 'joints-kitti-' + now_time + '.json')
|
||||
self.path_names = os.path.join(dir_out, 'names-kitti-' + now_time + '.json')
|
||||
path_train = os.path.join('splits', 'kitti_train.txt')
|
||||
path_val = os.path.join('splits', 'kitti_val.txt')
|
||||
self.set_train, self.set_val = split_training(self.names_gt, path_train, path_val)
|
||||
|
||||
def run(self):
|
||||
"""Save json files"""
|
||||
|
||||
cnt_gt = cnt_files = cnt_files_ped = cnt_fnf = 0
|
||||
dic_cnt = {'train': 0, 'val': 0, 'test': 0}
|
||||
|
||||
for name in self.names_gt:
|
||||
path_gt = os.path.join(self.dir_gt, name)
|
||||
basename, _ = os.path.splitext(name)
|
||||
|
||||
phase, flag = self._factory_phase(name)
|
||||
if flag:
|
||||
cnt_fnf += 1
|
||||
continue
|
||||
|
||||
# Extract keypoints
|
||||
path_txt = os.path.join(self.dir_kk, basename + '.txt')
|
||||
p_left, _ = get_calibration(path_txt)
|
||||
kk = p_left[0]
|
||||
|
||||
# Iterate over each line of the gt file and save box location and distances
|
||||
boxes_gt, boxes_3d, dds_gt = parse_ground_truth(path_gt, category='all')[:3]
|
||||
|
||||
self.dic_names[basename + '.png']['boxes'] = copy.deepcopy(boxes_gt)
|
||||
self.dic_names[basename + '.png']['dds'] = copy.deepcopy(dds_gt)
|
||||
self.dic_names[basename + '.png']['K'] = copy.deepcopy(kk)
|
||||
cnt_gt += len(boxes_gt)
|
||||
cnt_files += 1
|
||||
cnt_files_ped += min(len(boxes_gt), 1) # if no boxes 0 else 1
|
||||
|
||||
# Find the annotations if exists
|
||||
try:
|
||||
with open(os.path.join(self.dir_ann, basename + '.png.pifpaf.json'), 'r') as f:
|
||||
annotations = json.load(f)
|
||||
boxes, keypoints = preprocess_pifpaf(annotations, im_size=(1238, 374))
|
||||
keypoints_hflip = transform_keypoints(keypoints, mode='flip')
|
||||
inputs = preprocess_monoloco(keypoints, kk).tolist()
|
||||
inputs_hflip = preprocess_monoloco(keypoints_hflip, kk).tolist()
|
||||
all_keypoints = [keypoints, keypoints_hflip]
|
||||
all_inputs = [inputs, inputs_hflip]
|
||||
|
||||
except FileNotFoundError:
|
||||
boxes = []
|
||||
|
||||
# Match each set of keypoint with a ground truth
|
||||
matches = get_iou_matches(boxes, boxes_gt, self.iou_min)
|
||||
for (idx, idx_gt) in matches:
|
||||
for nn, keypoints in enumerate(all_keypoints):
|
||||
inputs = all_inputs[nn]
|
||||
self.dic_jo[phase]['kps'].append(keypoints[idx])
|
||||
self.dic_jo[phase]['X'].append(inputs[idx])
|
||||
self.dic_jo[phase]['Y'].append([dds_gt[idx_gt]]) # Trick to make it (nn,1)
|
||||
self.dic_jo[phase]['boxes_3d'].append(boxes_3d[idx_gt])
|
||||
self.dic_jo[phase]['K'].append(kk)
|
||||
self.dic_jo[phase]['names'].append(name) # One image name for each annotation
|
||||
append_cluster(self.dic_jo, phase, inputs[idx], dds_gt[idx_gt], keypoints[idx])
|
||||
dic_cnt[phase] += 1
|
||||
|
||||
with open(self.path_joints, 'w') as file:
|
||||
json.dump(self.dic_jo, file)
|
||||
with open(os.path.join(self.path_names), 'w') as file:
|
||||
json.dump(self.dic_names, file)
|
||||
for phase in ['train', 'val', 'test']:
|
||||
print("Saved {} annotations for phase {}"
|
||||
.format(dic_cnt[phase], phase))
|
||||
print("Number of GT files: {}. Files with at least one pedestrian: {}. Files not found: {}"
|
||||
.format(cnt_files, cnt_files_ped, cnt_fnf))
|
||||
print("Matched : {:.1f} % of the ground truth instances"
|
||||
.format(100 * (dic_cnt['train'] + dic_cnt['val']) / cnt_gt))
|
||||
print("\nOutput files:\n{}\n{}\n".format(self.path_names, self.path_joints))
|
||||
|
||||
def _factory_phase(self, name):
|
||||
"""Choose the phase"""
|
||||
|
||||
phase = None
|
||||
flag = False
|
||||
if name in self.set_train:
|
||||
phase = 'train'
|
||||
elif name in self.set_val:
|
||||
phase = 'val'
|
||||
else:
|
||||
flag = True
|
||||
return phase, flag
|
||||
392
monoloco/prep/preprocess_kitti.py
Normal file
392
monoloco/prep/preprocess_kitti.py
Normal file
@ -0,0 +1,392 @@
|
||||
# pylint: disable=too-many-statements, too-many-branches, too-many-nested-blocks
|
||||
|
||||
"""Preprocess annotations with KITTI ground-truth"""
|
||||
|
||||
import os
|
||||
import glob
|
||||
import copy
|
||||
import math
|
||||
import logging
|
||||
from collections import defaultdict
|
||||
import json
|
||||
import warnings
|
||||
import datetime
|
||||
from PIL import Image
|
||||
|
||||
import torch
|
||||
|
||||
from .. import __version__
|
||||
from ..utils import split_training, get_iou_matches, append_cluster, get_calibration, open_annotations, \
|
||||
extract_stereo_matches, make_new_directory, \
|
||||
check_conditions, to_spherical, correct_angle
|
||||
from ..network.process import preprocess_pifpaf, preprocess_monoloco
|
||||
from .transforms import flip_inputs, flip_labels, height_augmentation
|
||||
|
||||
|
||||
class PreprocessKitti:
|
||||
"""Prepare arrays with same format as nuScenes preprocessing but using ground truth txt files"""
|
||||
|
||||
# KITTI Dataset files
|
||||
dir_gt = os.path.join('data', 'kitti', 'gt')
|
||||
dir_images = os.path.join('data', 'kitti', 'images')
|
||||
dir_kk = os.path.join('data', 'kitti', 'calib')
|
||||
|
||||
# SOCIAL DISTANCING PARAMETERS
|
||||
THRESHOLD_DIST = 2 # Threshold to check distance of people
|
||||
RADII = (0.3, 0.5, 1) # expected radii of the o-space
|
||||
SOCIAL_DISTANCE = True
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
dic_jo = {
|
||||
'train': dict(X=[], Y=[], names=[], kps=[], K=[], clst=defaultdict(lambda: defaultdict(list))),
|
||||
'val': dict(X=[], Y=[], names=[], kps=[], K=[], clst=defaultdict(lambda: defaultdict(list))),
|
||||
'test': dict(X=[], Y=[], names=[], kps=[], K=[], clst=defaultdict(lambda: defaultdict(list))),
|
||||
'version': __version__,
|
||||
}
|
||||
dic_names = defaultdict(lambda: defaultdict(list))
|
||||
dic_std = defaultdict(lambda: defaultdict(list))
|
||||
categories_gt = dict(train=['Pedestrian', 'Person_sitting'], val=['Pedestrian'])
|
||||
|
||||
def __init__(self, dir_ann, mode='mono', iou_min=0.3, sample=False):
|
||||
|
||||
self.dir_ann = dir_ann
|
||||
self.mode = mode
|
||||
self.iou_min = iou_min
|
||||
self.sample = sample
|
||||
|
||||
assert os.path.isdir(self.dir_ann), "Annotation directory not found"
|
||||
assert any(os.scandir(self.dir_ann)), "Annotation directory empty"
|
||||
assert os.path.isdir(self.dir_gt), "Ground truth directory not found"
|
||||
assert any(os.scandir(self.dir_gt)), "Ground-truth directory empty"
|
||||
if self.mode == 'stereo':
|
||||
assert os.path.isdir(self.dir_ann + '_right'), "Annotation directory for right images not found"
|
||||
assert any(os.scandir(self.dir_ann + '_right')), "Annotation directory for right images empty"
|
||||
elif not os.path.isdir(self.dir_ann + '_right') or not any(os.scandir(self.dir_ann + '_right')):
|
||||
warnings.warn('Horizontal flipping not applied as annotation directory for right images not found/empty')
|
||||
assert self.mode in ('mono', 'stereo'), "modality not recognized"
|
||||
|
||||
self.names_gt = tuple(os.listdir(self.dir_gt))
|
||||
self.list_gt = glob.glob(self.dir_gt + '/*.txt')
|
||||
now = datetime.datetime.now()
|
||||
now_time = now.strftime("%Y%m%d-%H%M")[2:]
|
||||
dir_out = os.path.join('data', 'arrays')
|
||||
self.path_joints = os.path.join(dir_out, 'joints-kitti-' + self.mode + '-' + now_time + '.json')
|
||||
self.path_names = os.path.join(dir_out, 'names-kitti-' + self.mode + '-' + now_time + '.json')
|
||||
path_train = os.path.join('splits', 'kitti_train.txt')
|
||||
path_val = os.path.join('splits', 'kitti_val.txt')
|
||||
self.set_train, self.set_val = split_training(self.names_gt, path_train, path_val)
|
||||
self.phase, self.name = None, None
|
||||
self.stats = defaultdict(int)
|
||||
self.stats_stereo = defaultdict(int)
|
||||
|
||||
def run(self):
|
||||
# self.names_gt = ('002282.txt',)
|
||||
for self.name in self.names_gt:
|
||||
# Extract ground truth
|
||||
path_gt = os.path.join(self.dir_gt, self.name)
|
||||
basename, _ = os.path.splitext(self.name)
|
||||
self.phase, file_not_found = self._factory_phase(self.name)
|
||||
category = 'all' if self.phase == 'train' else 'pedestrian'
|
||||
if file_not_found:
|
||||
self.stats['fnf'] += 1
|
||||
continue
|
||||
|
||||
boxes_gt, labels, _, _, _ = parse_ground_truth(path_gt, category=category, spherical=True)
|
||||
self.stats['gt_' + self.phase] += len(boxes_gt)
|
||||
self.stats['gt_files'] += 1
|
||||
self.stats['gt_files_ped'] += min(len(boxes_gt), 1) # if no boxes 0 else 1
|
||||
self.dic_names[basename + '.png']['boxes'] = copy.deepcopy(boxes_gt)
|
||||
self.dic_names[basename + '.png']['ys'] = copy.deepcopy(labels)
|
||||
|
||||
# Extract annotations
|
||||
dic_boxes, dic_kps, dic_gt = self.parse_annotations(boxes_gt, labels, basename)
|
||||
if dic_boxes is None: # No annotations
|
||||
continue
|
||||
self.dic_names[basename + '.png']['K'] = copy.deepcopy(dic_gt['K'])
|
||||
self.dic_jo[self.phase]['K'].append(dic_gt['K'])
|
||||
|
||||
# Match each set of keypoint with a ground truth
|
||||
for ii, boxes_gt in enumerate(dic_boxes['gt']):
|
||||
kps, kps_r = torch.tensor(dic_kps['left'][ii]), torch.tensor(dic_kps['right'][ii])
|
||||
matches = get_iou_matches(dic_boxes['left'][ii], boxes_gt, self.iou_min)
|
||||
self.stats['flipping_match'] += len(matches) if ii == 1 else 0
|
||||
for (idx, idx_gt) in matches:
|
||||
cat_gt = dic_gt['labels'][ii][idx_gt][-1]
|
||||
if cat_gt not in self.categories_gt[self.phase]: # only for training as cyclists are also extracted
|
||||
continue
|
||||
kp = kps[idx:idx + 1]
|
||||
kk = dic_gt['K']
|
||||
label = dic_gt['labels'][ii][idx_gt][:-1]
|
||||
self.stats['match'] += 1
|
||||
assert len(label) == 10, 'dimensions of monocular label is wrong'
|
||||
|
||||
if self.mode == 'mono':
|
||||
self._process_annotation_mono(kp, kk, label)
|
||||
else:
|
||||
self._process_annotation_stereo(kp, kk, label, kps_r)
|
||||
|
||||
with open(self.path_joints, 'w') as file:
|
||||
json.dump(self.dic_jo, file)
|
||||
with open(os.path.join(self.path_names), 'w') as file:
|
||||
json.dump(self.dic_names, file)
|
||||
self._cout()
|
||||
|
||||
def parse_annotations(self, boxes_gt, labels, basename):
|
||||
|
||||
path_im = os.path.join(self.dir_images, basename + '.png')
|
||||
path_calib = os.path.join(self.dir_kk, basename + '.txt')
|
||||
min_conf = 0 if self.phase == 'train' else 0.1
|
||||
|
||||
# Check image size
|
||||
with Image.open(path_im) as im:
|
||||
width, height = im.size
|
||||
|
||||
# Extract left keypoints
|
||||
annotations, kk, _ = factory_file(path_calib, self.dir_ann, basename)
|
||||
boxes, keypoints = preprocess_pifpaf(annotations, im_size=(width, height), min_conf=min_conf)
|
||||
if not keypoints:
|
||||
return None, None, None
|
||||
|
||||
# Stereo-based horizontal flipping for training (obtaining ground truth for right images)
|
||||
self.stats['instances'] += len(keypoints)
|
||||
annotations_r, _, _ = factory_file(path_calib, self.dir_ann, basename, ann_type='right')
|
||||
boxes_r, keypoints_r = preprocess_pifpaf(annotations_r, im_size=(width, height), min_conf=min_conf)
|
||||
|
||||
if not keypoints_r: # Duplicate the left one(s)
|
||||
all_boxes_gt, all_labels = [boxes_gt], [labels]
|
||||
boxes_r, keypoints_r = boxes[0:1].copy(), keypoints[0:1].copy()
|
||||
all_boxes, all_keypoints = [boxes], [keypoints]
|
||||
all_keypoints_r = [keypoints_r]
|
||||
|
||||
elif self.phase == 'train':
|
||||
# GT)
|
||||
boxes_gt_flip, ys_flip = flip_labels(boxes_gt, labels, im_w=width)
|
||||
# New left
|
||||
boxes_flip = flip_inputs(boxes_r, im_w=width, mode='box')
|
||||
keypoints_flip = flip_inputs(keypoints_r, im_w=width)
|
||||
|
||||
# New right
|
||||
keypoints_r_flip = flip_inputs(keypoints, im_w=width)
|
||||
|
||||
# combine the 2 modes
|
||||
all_boxes_gt = [boxes_gt, boxes_gt_flip]
|
||||
all_labels = [labels, ys_flip]
|
||||
all_boxes = [boxes, boxes_flip]
|
||||
all_keypoints = [keypoints, keypoints_flip]
|
||||
all_keypoints_r = [keypoints_r, keypoints_r_flip]
|
||||
|
||||
else:
|
||||
all_boxes_gt, all_labels = [boxes_gt], [labels]
|
||||
all_boxes, all_keypoints = [boxes], [keypoints]
|
||||
all_keypoints_r = [keypoints_r]
|
||||
|
||||
dic_boxes = dict(left=all_boxes, gt=all_boxes_gt)
|
||||
dic_kps = dict(left=all_keypoints, right=all_keypoints_r)
|
||||
dic_gt = dict(K=kk, labels=all_labels)
|
||||
return dic_boxes, dic_kps, dic_gt
|
||||
|
||||
def _process_annotation_mono(self, kp, kk, label):
|
||||
"""For a single annotation, process all the labels and save them"""
|
||||
kp = kp.tolist()
|
||||
inp = preprocess_monoloco(kp, kk).view(-1).tolist()
|
||||
|
||||
# Save
|
||||
self.dic_jo[self.phase]['kps'].append(kp)
|
||||
self.dic_jo[self.phase]['X'].append(inp)
|
||||
self.dic_jo[self.phase]['Y'].append(label)
|
||||
self.dic_jo[self.phase]['names'].append(self.name) # One image name for each annotation
|
||||
append_cluster(self.dic_jo, self.phase, inp, label, kp)
|
||||
self.stats['total_' + self.phase] += 1
|
||||
|
||||
def _process_annotation_stereo(self, kp, kk, label, kps_r):
|
||||
"""For a reference annotation, combine it with some (right) annotations and save it"""
|
||||
|
||||
zz = label[2]
|
||||
stereo_matches, cnt_amb = extract_stereo_matches(kp, kps_r, zz,
|
||||
phase=self.phase,
|
||||
seed=self.stats_stereo['pair'])
|
||||
self.stats_stereo['ambiguous'] += cnt_amb
|
||||
|
||||
for idx_r, s_match in stereo_matches:
|
||||
label_s = label + [s_match] # add flag to distinguish "true pairs and false pairs"
|
||||
self.stats_stereo['true_pair'] += 1 if s_match > 0.9 else 0
|
||||
self.stats_stereo['pair'] += 1 # before augmentation
|
||||
|
||||
# ---> Remove noise of very far instances for validation
|
||||
# if (self.phase == 'val') and (label[3] >= 50):
|
||||
# continue
|
||||
|
||||
# ---> Save only positives unless there is no positive (keep positive flip and augm)
|
||||
# if num > 0 and s_match < 0.9:
|
||||
# continue
|
||||
|
||||
# Height augmentation
|
||||
flag_aug = False
|
||||
if self.phase == 'train' and 3 < label[2] < 30 and (s_match > 0.9 or self.stats_stereo['pair'] % 2 == 0):
|
||||
flag_aug = True
|
||||
|
||||
# Remove height augmentation
|
||||
# flag_aug = False
|
||||
|
||||
if flag_aug:
|
||||
kps_aug, labels_aug = height_augmentation(kp, kps_r[idx_r:idx_r + 1], label_s,
|
||||
seed=self.stats_stereo['pair'])
|
||||
else:
|
||||
kps_aug = [(kp, kps_r[idx_r:idx_r + 1])]
|
||||
labels_aug = [label_s]
|
||||
|
||||
for i, lab in enumerate(labels_aug):
|
||||
assert len(lab) == 11, 'dimensions of stereo label is wrong'
|
||||
self.stats_stereo['pair_aug'] += 1
|
||||
(kp_aug, kp_aug_r) = kps_aug[i]
|
||||
input_l = preprocess_monoloco(kp_aug, kk).view(-1)
|
||||
input_r = preprocess_monoloco(kp_aug_r, kk).view(-1)
|
||||
keypoint = torch.cat((kp_aug, kp_aug_r), dim=2).tolist()
|
||||
inp = torch.cat((input_l, input_l - input_r)).tolist()
|
||||
self.dic_jo[self.phase]['kps'].append(keypoint)
|
||||
self.dic_jo[self.phase]['X'].append(inp)
|
||||
self.dic_jo[self.phase]['Y'].append(lab)
|
||||
self.dic_jo[self.phase]['names'].append(self.name) # One image name for each annotation
|
||||
append_cluster(self.dic_jo, self.phase, inp, lab, keypoint)
|
||||
self.stats_stereo['total_' + self.phase] += 1 # including height augmentation
|
||||
|
||||
def _cout(self):
|
||||
print('-' * 100)
|
||||
print(f"Number of GT files: {self.stats['gt_files']} ")
|
||||
print(f"Files with at least one pedestrian/cyclist: {self.stats['gt_files_ped']}")
|
||||
print(f"Files not found: {self.stats['fnf']}")
|
||||
print('-' * 100)
|
||||
our = self.stats['match'] - self.stats['flipping_match']
|
||||
gt = self.stats['gt_train'] + self.stats['gt_val']
|
||||
print(f"Ground truth matches: {100 * our / gt:.1f} for left images (train and val)")
|
||||
print(f"Parsed instances: {self.stats['instances']}")
|
||||
print(f"Ground truth instances: {gt}")
|
||||
print(f"Matched instances: {our}")
|
||||
print(f"Including horizontal flipping: {self.stats['match']}")
|
||||
|
||||
if self.mode == 'stereo':
|
||||
print('-' * 100)
|
||||
print(f"Ambiguous instances removed: {self.stats_stereo['ambiguous']}")
|
||||
print(f"True pairs ratio: {100 * self.stats_stereo['true_pair'] / self.stats_stereo['pair']:.1f}% ")
|
||||
print(f"Height augmentation pairs: {self.stats_stereo['pair_aug'] - self.stats_stereo['pair']} ")
|
||||
print('-' * 100)
|
||||
total_train = self.stats_stereo['total_train'] if self.mode == 'stereo' else self.stats['total_train']
|
||||
total_val = self.stats_stereo['total_val'] if self.mode == 'stereo' else self.stats['total_val']
|
||||
print(f"Total annotations for TRAINING: {total_train}")
|
||||
print(f"Total annotations for VALIDATION: {total_val}")
|
||||
print('-' * 100)
|
||||
print(f"\nOutput files:\n{self.path_names}\n{self.path_joints}")
|
||||
print('-' * 100)
|
||||
|
||||
def process_activity(self):
|
||||
"""Augment ground-truth with flag activity"""
|
||||
|
||||
from monoloco.activity import social_interactions # pylint: disable=import-outside-toplevel
|
||||
main_dir = os.path.join('data', 'kitti')
|
||||
dir_gt = os.path.join(main_dir, 'gt')
|
||||
dir_out = os.path.join(main_dir, 'gt_activity')
|
||||
make_new_directory(dir_out)
|
||||
cnt_tp, cnt_tn = 0, 0
|
||||
|
||||
# Extract validation images for evaluation
|
||||
category = 'pedestrian'
|
||||
|
||||
for name in self.set_val:
|
||||
# Read
|
||||
path_gt = os.path.join(dir_gt, name)
|
||||
_, ys, _, _, lines = parse_ground_truth(path_gt, category, spherical=False)
|
||||
angles = [y[10] for y in ys]
|
||||
dds = [y[4] for y in ys]
|
||||
xz_centers = [[y[0], y[2]] for y in ys]
|
||||
|
||||
# Write
|
||||
path_out = os.path.join(dir_out, name)
|
||||
with open(path_out, "w+") as ff:
|
||||
for idx, line in enumerate(lines):
|
||||
if social_interactions(idx, xz_centers, angles, dds,
|
||||
n_samples=1,
|
||||
threshold_dist=self.THRESHOLD_DIST,
|
||||
radii=self.RADII,
|
||||
social_distance=self.SOCIAL_DISTANCE):
|
||||
activity = '1'
|
||||
cnt_tp += 1
|
||||
else:
|
||||
activity = '0'
|
||||
cnt_tn += 1
|
||||
|
||||
line_new = line[:-1] + ' ' + activity + line[-1]
|
||||
ff.write(line_new)
|
||||
|
||||
print(f'Written {len(self.set_val)} new files in {dir_out}')
|
||||
print(f'Saved {cnt_tp} positive and {cnt_tn} negative annotations')
|
||||
|
||||
def _factory_phase(self, name):
|
||||
"""Choose the phase"""
|
||||
phase = None
|
||||
flag = False
|
||||
if name in self.set_train:
|
||||
phase = 'train'
|
||||
elif name in self.set_val:
|
||||
phase = 'val'
|
||||
else:
|
||||
flag = True
|
||||
return phase, flag
|
||||
|
||||
|
||||
def parse_ground_truth(path_gt, category, spherical=False):
|
||||
"""Parse KITTI ground truth files"""
|
||||
|
||||
boxes_gt = []
|
||||
labels = []
|
||||
truncs_gt = [] # Float from 0 to 1
|
||||
occs_gt = [] # Either 0,1,2,3 fully visible, partly occluded, largely occluded, unknown
|
||||
lines = []
|
||||
|
||||
with open(path_gt, "r") as f_gt:
|
||||
for line_gt in f_gt:
|
||||
line = line_gt.split()
|
||||
if not check_conditions(line_gt, category, method='gt'):
|
||||
continue
|
||||
truncs_gt.append(float(line[1]))
|
||||
occs_gt.append(int(line[2]))
|
||||
boxes_gt.append([float(x) for x in line[4:8]])
|
||||
xyz = [float(x) for x in line[11:14]]
|
||||
hwl = [float(x) for x in line[8:11]]
|
||||
dd = float(math.sqrt(xyz[0] ** 2 + xyz[1] ** 2 + xyz[2] ** 2))
|
||||
yaw = float(line[14])
|
||||
assert - math.pi <= yaw <= math.pi
|
||||
alpha = float(line[3])
|
||||
sin, cos, yaw_corr = correct_angle(yaw, xyz)
|
||||
assert min(abs(-yaw_corr - alpha), (abs(yaw_corr - alpha))) < 0.15, "more than 10 degrees of error"
|
||||
if spherical:
|
||||
rtp = to_spherical(xyz)
|
||||
loc = rtp[1:3] + xyz[2:3] + rtp[0:1] # [theta, psi, z, r]
|
||||
else:
|
||||
loc = xyz + [dd]
|
||||
cat = line[0] # 'Pedestrian', or 'Person_sitting' for people
|
||||
output = loc + hwl + [sin, cos, yaw, cat]
|
||||
labels.append(output)
|
||||
lines.append(line_gt)
|
||||
return boxes_gt, labels, truncs_gt, occs_gt, lines
|
||||
|
||||
|
||||
def factory_file(path_calib, dir_ann, basename, ann_type='left'):
|
||||
"""Choose the annotation and the calibration files"""
|
||||
|
||||
assert ann_type in ('left', 'right')
|
||||
p_left, p_right = get_calibration(path_calib)
|
||||
|
||||
if ann_type == 'left':
|
||||
kk, tt = p_left[:]
|
||||
path_ann = os.path.join(dir_ann, basename + '.png.predictions.json')
|
||||
|
||||
# The right folder is called <NameOfLeftFolder>_right
|
||||
else:
|
||||
kk, tt = p_right[:]
|
||||
path_ann = os.path.join(dir_ann + '_right', basename + '.png.predictions.json')
|
||||
|
||||
annotations = open_annotations(path_ann)
|
||||
|
||||
return annotations, kk, tt
|
||||
@ -150,7 +150,6 @@ def extract_ground_truth(boxes_obj, kk, spherical=True):
|
||||
ys = []
|
||||
|
||||
for box_obj in boxes_obj:
|
||||
|
||||
# Select category
|
||||
if box_obj.name[:6] != 'animal':
|
||||
general_name = box_obj.name.split('.')[0] + '.' + box_obj.name.split('.')[1]
|
||||
@ -248,8 +247,6 @@ def extract_social(inputs, ys, keypoints, idx, matches):
|
||||
all_inputs.extend(inputs[idx])
|
||||
|
||||
indices_idx = [idx for (idx, idx_gt) in matches]
|
||||
if len(sorted_indices) > 2:
|
||||
aa = 5
|
||||
for ii in range(1, 3):
|
||||
try:
|
||||
index = sorted_indices[ii]
|
||||
@ -266,17 +263,3 @@ def extract_social(inputs, ys, keypoints, idx, matches):
|
||||
all_inputs.extend([0.] * 2)
|
||||
assert len(all_inputs) == 34 + 2 * 2
|
||||
return all_inputs
|
||||
|
||||
|
||||
# def get_jean_yaw(box_obj):
|
||||
# b_corners = box_obj.bottom_corners()
|
||||
# center = box_obj.center
|
||||
# back_point = [(b_corners[0, 2] + b_corners[0, 3]) / 2, (b_corners[2, 2] + b_corners[2, 3]) / 2]
|
||||
#
|
||||
# x = b_corners[0, :] - back_point[0]
|
||||
# y = b_corners[2, :] - back_point[1]
|
||||
#
|
||||
# angle = math.atan2((x[0] + x[1]) / 2, (y[0] + y[1]) / 2) * 180 / 3.14
|
||||
# angle = (angle + 360) % 360
|
||||
# correction = math.atan2(center[0], center[2]) * 180 / 3.14
|
||||
# return angle, correction
|
||||
|
||||
@ -1,8 +1,11 @@
|
||||
|
||||
import math
|
||||
from copy import deepcopy
|
||||
|
||||
import numpy as np
|
||||
|
||||
from ..utils import correct_angle, to_cartesian, to_spherical
|
||||
|
||||
BASELINE = 0.54
|
||||
BF = BASELINE * 721
|
||||
|
||||
@ -75,7 +78,6 @@ def flip_inputs(keypoints, im_w, mode=None):
|
||||
|
||||
def flip_labels(boxes_gt, labels, im_w):
|
||||
"""Correct x, d positions and angles after horizontal flipping"""
|
||||
from ..utils import correct_angle, to_cartesian, to_spherical
|
||||
boxes_flip = deepcopy(boxes_gt)
|
||||
labels_flip = deepcopy(labels)
|
||||
|
||||
@ -98,29 +100,28 @@ def flip_labels(boxes_gt, labels, im_w):
|
||||
yaw = label_flip[9]
|
||||
yaw_n = math.copysign(1, yaw) * (np.pi - abs(yaw)) # Horizontal flipping change of angle
|
||||
|
||||
sin, cos, yaw_corr = correct_angle(yaw_n, xyz)
|
||||
sin, cos, _ = correct_angle(yaw_n, xyz)
|
||||
label_flip[7], label_flip[8], label_flip[9] = sin, cos, yaw_n
|
||||
|
||||
return boxes_flip, labels_flip
|
||||
|
||||
|
||||
def height_augmentation(kps, kps_r, label, s_match, seed=0):
|
||||
def height_augmentation(kps, kps_r, label_s, seed=0):
|
||||
"""
|
||||
label: theta, psi, z, rho, wlh, sin, cos, yaw, cat
|
||||
label_s: theta, psi, z, rho, wlh, sin, cos, s_match
|
||||
"""
|
||||
from ..utils import to_cartesian
|
||||
n_labels = 3 if s_match > 0.9 else 1
|
||||
n_labels = 3 if label_s[-1] > 0.9 else 1
|
||||
height_min = 1.2
|
||||
height_max = 2
|
||||
av_height = 1.71
|
||||
kps_aug = [[kps.clone(), kps_r.clone()] for _ in range(n_labels+1)]
|
||||
labels_aug = [label.copy() for _ in range(n_labels+1)] # Maintain the original
|
||||
labels_aug = [label_s.copy() for _ in range(n_labels+1)] # Maintain the original
|
||||
np.random.seed(seed)
|
||||
heights = np.random.uniform(height_min, height_max, n_labels) # 3 samples
|
||||
zzs = heights * label[2] / av_height
|
||||
disp = BF / label[2]
|
||||
zzs = heights * label_s[2] / av_height
|
||||
disp = BF / label_s[2]
|
||||
|
||||
rtp = label[3:4] + label[0:2] # Originally t,p,z,r
|
||||
rtp = label_s[3:4] + label_s[0:2] # Originally t,p,z,r
|
||||
xyz = to_cartesian(rtp)
|
||||
|
||||
for i in range(n_labels):
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# pylint: disable=too-many-branches, too-many-statements
|
||||
# pylint: disable=too-many-branches, too-many-statements, import-outside-toplevel
|
||||
|
||||
import argparse
|
||||
|
||||
@ -18,6 +18,7 @@ def cli():
|
||||
# Predict (2D pose and/or 3D location from images)
|
||||
predict_parser.add_argument('images', nargs='*', help='input images')
|
||||
predict_parser.add_argument('--glob', help='glob expression for input images (for many images)')
|
||||
predict_parser.add_argument('--checkpoint', help='pifpaf model')
|
||||
predict_parser.add_argument('-o', '--output-directory', help='Output directory')
|
||||
predict_parser.add_argument('--output_types', nargs='+',
|
||||
help='what to output: json keypoints skeleton for Pifpaf'
|
||||
@ -35,9 +36,10 @@ def cli():
|
||||
predict_parser.add_argument('--instance-threshold', type=float, default=None, help='threshold for entire instance')
|
||||
predict_parser.add_argument('--seed-threshold', type=float, default=0.5, help='threshold for single seed')
|
||||
predict_parser.add_argument('--disable-cuda', action='store_true', help='disable CUDA')
|
||||
predict_parser.add_argument('--focal',
|
||||
help='focal length in mm for a sensor size of 7.2x5.4 mm. Default nuScenes sensor',
|
||||
type=float, default=5.7)
|
||||
predict_parser.add_argument('--precise-rescaling', dest='fast_rescaling', default=True, action='store_false',
|
||||
help='use more exact image rescaling (requires scipy)')
|
||||
predict_parser.add_argument('--decoder-workers', default=None, type=int,
|
||||
help='number of workers for pose decoding, 0 for windows')
|
||||
|
||||
decoder.cli(parser)
|
||||
logger.cli(parser)
|
||||
@ -58,6 +60,8 @@ def cli():
|
||||
predict_parser.add_argument('--show_all', help='only predict ground-truth matches or all', action='store_true')
|
||||
predict_parser.add_argument('--webcam', help='monstereo streaming', action='store_true')
|
||||
predict_parser.add_argument('--scale', default=0.2, type=float, help='change the scale of the webcam image')
|
||||
predict_parser.add_argument('--focal', help='focal length in mm for a sensor size of 7.2x5.4 mm. (nuScenes)',
|
||||
type=float, default=5.7)
|
||||
|
||||
# Social distancing and social interactions
|
||||
predict_parser.add_argument('--social_distance', help='social', action='store_true')
|
||||
@ -79,11 +83,12 @@ def cli():
|
||||
# Training
|
||||
training_parser.add_argument('--joints', help='Json file with input joints', required=True)
|
||||
training_parser.add_argument('--mode', help='mono, stereo', default='mono')
|
||||
training_parser.add_argument('--out', help='output_path, e.g., data/outputs/test.pkl')
|
||||
training_parser.add_argument('-e', '--epochs', type=int, help='number of epochs to train for', default=500)
|
||||
training_parser.add_argument('--bs', type=int, default=512, help='input batch size')
|
||||
training_parser.add_argument('--monocular', help='whether to train monoloco', action='store_true')
|
||||
training_parser.add_argument('--dropout', type=float, help='dropout. Default no dropout', default=0.2)
|
||||
training_parser.add_argument('--lr', type=float, help='learning rate', default=0.001)
|
||||
training_parser.add_argument('--lr', type=float, help='learning rate', default=0.002)
|
||||
training_parser.add_argument('--sched_step', type=float, help='scheduler step time (epochs)', default=30)
|
||||
training_parser.add_argument('--sched_gamma', type=float, help='Scheduler multiplication every step', default=0.98)
|
||||
training_parser.add_argument('--hidden_size', type=int, help='Number of hidden units in the model', default=1024)
|
||||
@ -142,10 +147,10 @@ def main():
|
||||
prep = PreprocessNuscenes(args.dir_ann, args.dir_nuscenes, args.dataset, args.iou_min)
|
||||
prep.run()
|
||||
else:
|
||||
from .prep.prep_kitti import PreprocessKitti
|
||||
from .prep.preprocess_kitti import PreprocessKitti
|
||||
prep = PreprocessKitti(args.dir_ann, mode=args.mode, iou_min=args.iou_min)
|
||||
if args.activity:
|
||||
prep.prep_activity()
|
||||
prep.process_activity()
|
||||
else:
|
||||
prep.run()
|
||||
|
||||
@ -173,7 +178,7 @@ def main():
|
||||
|
||||
elif args.geometric:
|
||||
assert args.joints, "joints argument not provided"
|
||||
from .network.geom_baseline import geometric_baseline
|
||||
from .eval.geom_baseline import geometric_baseline
|
||||
geometric_baseline(args.joints)
|
||||
|
||||
elif args.variance:
|
||||
|
||||
@ -60,6 +60,7 @@ class KeypointsDataset(Dataset):
|
||||
self.outputs_all = torch.tensor(dic_jo[phase]['Y'])
|
||||
self.names_all = dic_jo[phase]['names']
|
||||
self.kps_all = torch.tensor(dic_jo[phase]['kps'])
|
||||
self.version = dic_jo['version']
|
||||
|
||||
# Extract annotations divided in clusters
|
||||
self.dic_clst = dic_jo[phase]['clst']
|
||||
@ -90,3 +91,6 @@ class KeypointsDataset(Dataset):
|
||||
count = len(self.dic_clst[clst]['Y'])
|
||||
|
||||
return inputs, outputs, count
|
||||
|
||||
def get_version(self):
|
||||
return self.version
|
||||
|
||||
@ -1,9 +1,15 @@
|
||||
"""Inspired by Openpifpaf"""
|
||||
"""
|
||||
Adapted from https://github.com/openpifpaf,
|
||||
which is: 'Copyright 2019-2021 by Sven Kreiss and contributors. All rights reserved.'
|
||||
and licensed under GNU AGPLv3
|
||||
"""
|
||||
|
||||
|
||||
import math
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
from ..network import extract_labels, extract_labels_aux, extract_outputs
|
||||
|
||||
@ -180,6 +186,58 @@ class GaussianLoss(torch.nn.Module):
|
||||
return torch.sum(values)
|
||||
|
||||
|
||||
class CustomL1Loss(torch.nn.Module):
|
||||
"""
|
||||
Experimental, not used.
|
||||
L1 loss with more weight to errors at a shorter distance
|
||||
It inherits from nn.module so it supports backward
|
||||
"""
|
||||
|
||||
def __init__(self, dic_norm, device, beta=1):
|
||||
super().__init__()
|
||||
|
||||
self.dic_norm = dic_norm
|
||||
self.device = device
|
||||
self.beta = beta
|
||||
|
||||
@staticmethod
|
||||
def compute_weights(xx, beta=1):
|
||||
"""
|
||||
Return the appropriate weight depending on the distance and the hyperparameter chosen
|
||||
alpha = 1 refers to the curve of A Photogrammetric Approach for Real-time...
|
||||
It is made for unnormalized outputs (to be more understandable)
|
||||
From 70 meters on every value is weighted the same (0.1**beta)
|
||||
Alpha is optional value from Focal loss. Yet to be analyzed
|
||||
"""
|
||||
# alpha = np.maximum(1, 10 ** (beta - 1))
|
||||
alpha = 1
|
||||
ww = np.maximum(0.1, 1 - xx / 78)**beta
|
||||
|
||||
return alpha * ww
|
||||
|
||||
def print_loss(self):
|
||||
xx = np.linspace(0, 80, 100)
|
||||
y1 = self.compute_weights(xx, beta=1)
|
||||
y2 = self.compute_weights(xx, beta=2)
|
||||
y3 = self.compute_weights(xx, beta=3)
|
||||
plt.plot(xx, y1)
|
||||
plt.plot(xx, y2)
|
||||
plt.plot(xx, y3)
|
||||
plt.xlabel("Distance [m]")
|
||||
plt.ylabel("Loss function Weight")
|
||||
plt.legend(("Beta = 1", "Beta = 2", "Beta = 3"))
|
||||
plt.show()
|
||||
|
||||
def forward(self, output, target):
|
||||
|
||||
unnormalized_output = output.cpu().detach().numpy() * self.dic_norm['std']['Y'] + self.dic_norm['mean']['Y']
|
||||
weights_np = self.compute_weights(unnormalized_output, self.beta)
|
||||
weights = torch.from_numpy(weights_np).float().to(self.device) # To make weights in the same cuda device
|
||||
losses = torch.abs(output - target) * weights
|
||||
loss = losses.mean() # Mean over the batch
|
||||
return loss
|
||||
|
||||
|
||||
def angle_loss(orient, gt_orient):
|
||||
"""Only for evaluation"""
|
||||
angles = torch.atan2(orient[:, 0], orient[:, 1])
|
||||
|
||||
@ -1,8 +1,10 @@
|
||||
# pylint: disable=too-many-statements
|
||||
|
||||
"""
|
||||
Training and evaluation of a neural network which predicts 3D localization and confidence intervals
|
||||
given 2d joints
|
||||
Training and evaluation of a neural network that, given 2D joints, estimates:
|
||||
- 3D localization and confidence intervals
|
||||
- Orientation
|
||||
- Bounding box dimensions
|
||||
"""
|
||||
|
||||
import copy
|
||||
@ -12,7 +14,6 @@ import logging
|
||||
from collections import defaultdict
|
||||
import sys
|
||||
import time
|
||||
import warnings
|
||||
from itertools import chain
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
@ -20,10 +21,11 @@ import torch
|
||||
from torch.utils.data import DataLoader
|
||||
from torch.optim import lr_scheduler
|
||||
|
||||
from .. import __version__
|
||||
from .datasets import KeypointsDataset
|
||||
from .losses import CompositeLoss, MultiTaskLoss, AutoTuneMultiTaskLoss
|
||||
from ..network import extract_outputs, extract_labels
|
||||
from ..network.architectures import MonStereoModel
|
||||
from ..network.architectures import LocoModel
|
||||
from ..utils import set_logger
|
||||
|
||||
|
||||
@ -35,21 +37,16 @@ class Trainer:
|
||||
val_task = 'd'
|
||||
lambdas = (1, 1, 1, 1, 1, 1, 1, 1)
|
||||
clusters = ['10', '20', '30', '40']
|
||||
input_size = dict(mono=34, stereo=68)
|
||||
output_size = dict(mono=9, stereo=10)
|
||||
dir_figures = os.path.join('figures', 'losses')
|
||||
|
||||
def __init__(self, args):
|
||||
"""
|
||||
Initialize directories, load the data and parameters for the training
|
||||
"""
|
||||
|
||||
# Initialize directories and parameters
|
||||
dir_out = os.path.join('data', 'models')
|
||||
if not os.path.exists(dir_out):
|
||||
warnings.warn("Warning: output directory not found, the model will not be saved")
|
||||
dir_logs = os.path.join('data', 'logs')
|
||||
if not os.path.exists(dir_logs):
|
||||
warnings.warn("Warning: default logs directory not found")
|
||||
assert os.path.exists(args.joints), "Input file not found"
|
||||
|
||||
self.mode = args.mode
|
||||
self.joints = args.joints
|
||||
self.num_epochs = args.epochs
|
||||
@ -60,10 +57,22 @@ class Trainer:
|
||||
self.sched_gamma = args.sched_gamma
|
||||
self.hidden_size = args.hidden_size
|
||||
self.n_stage = args.n_stage
|
||||
self.dir_out = dir_out
|
||||
self.r_seed = args.r_seed
|
||||
self.auto_tune_mtl = args.auto_tune_mtl
|
||||
|
||||
# Select path out
|
||||
if args.out:
|
||||
self.path_out = args.out # full path without extension
|
||||
dir_out, _ = os.path.split(self.path_out)
|
||||
else:
|
||||
dir_out = os.path.join('data', 'outputs')
|
||||
name = 'monoloco_pp' if self.mode == 'mono' else 'monstereo'
|
||||
now = datetime.datetime.now()
|
||||
now_time = now.strftime("%Y%m%d-%H%M")[2:]
|
||||
name_out = name + '-' + now_time + '.pkl'
|
||||
self.path_out = os.path.join(dir_out, name_out)
|
||||
assert os.path.exists(dir_out), "Directory to save the model not found"
|
||||
print(self.path_out)
|
||||
# Select the device
|
||||
use_cuda = torch.cuda.is_available()
|
||||
self.device = torch.device("cuda" if use_cuda else "cpu")
|
||||
@ -85,45 +94,28 @@ class Trainer:
|
||||
self.mt_loss = MultiTaskLoss(losses_tr, losses_val, self.lambdas, self.tasks)
|
||||
self.mt_loss.to(self.device)
|
||||
|
||||
if not self.mode == 'stereo':
|
||||
input_size = 68
|
||||
output_size = 10
|
||||
else:
|
||||
input_size = 34
|
||||
output_size = 9
|
||||
|
||||
name = 'monoloco_pp' if self.mode == 'mono' else 'monstereo'
|
||||
now = datetime.datetime.now()
|
||||
now_time = now.strftime("%Y%m%d-%H%M")[2:]
|
||||
name_out = name + '-' + now_time
|
||||
if not self.no_save:
|
||||
self.path_model = os.path.join(dir_out, name_out + '.pkl')
|
||||
self.logger = set_logger(os.path.join(dir_logs, name_out))
|
||||
self.logger.info( # pylint: disable=logging-fstring-interpolation
|
||||
f'Training arguments: \ninput_file: {self.joints} \nmode: {self.mode} '
|
||||
f'\nlearning rate: {args.lr} \nbatch_size: {args.bs}'
|
||||
f'\nepochs: {args.epochs} \ndropout: {args.dropout} '
|
||||
f'\nscheduler step: {args.sched_step} \nscheduler gamma: {args.sched_gamma} '
|
||||
f'\ninput_size: {input_size} \noutput_size: {output_size} \nhidden_size: {args.hidden_size}'
|
||||
f' \nn_stages: {args.n_stage} \n r_seed: {args.r_seed} \nlambdas: {self.lambdas}'
|
||||
)
|
||||
else:
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
self.logger = logging.getLogger(__name__)
|
||||
|
||||
# Dataloader
|
||||
self.dataloaders = {phase: DataLoader(KeypointsDataset(self.joints, phase=phase),
|
||||
batch_size=args.bs, shuffle=True) for phase in ['train', 'val']}
|
||||
|
||||
self.dataset_sizes = {phase: len(KeypointsDataset(self.joints, phase=phase))
|
||||
for phase in ['train', 'val']}
|
||||
self.dataset_version = KeypointsDataset(self.joints, phase='train').get_version()
|
||||
|
||||
self._set_logger(args)
|
||||
|
||||
# Define the model
|
||||
self.logger.info('Sizes of the dataset: {}'.format(self.dataset_sizes))
|
||||
print(">>> creating model")
|
||||
|
||||
self.model = MonStereoModel(input_size=input_size, output_size=output_size, linear_size=args.hidden_size,
|
||||
p_dropout=args.dropout, num_stage=self.n_stage, device=self.device)
|
||||
self.model = LocoModel(
|
||||
input_size=self.input_size[self.mode],
|
||||
output_size=self.output_size[self.mode],
|
||||
linear_size=args.hidden_size,
|
||||
p_dropout=args.dropout,
|
||||
num_stage=self.n_stage,
|
||||
device=self.device,
|
||||
)
|
||||
self.model.to(self.device)
|
||||
print(">>> model params: {:.3f}M".format(sum(p.numel() for p in self.model.parameters()) / 1000000.0))
|
||||
print(">>> loss params: {}".format(sum(p.numel() for p in self.mt_loss.parameters())))
|
||||
@ -158,7 +150,7 @@ class Trainer:
|
||||
if phase == 'train':
|
||||
self.optimizer.zero_grad()
|
||||
outputs = self.model(inputs)
|
||||
loss, loss_values = self.mt_loss(outputs, labels, phase=phase)
|
||||
loss, _ = self.mt_loss(outputs, labels, phase=phase)
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 3)
|
||||
self.optimizer.step()
|
||||
@ -188,8 +180,7 @@ class Trainer:
|
||||
self.logger.info('Best validation Accuracy for {}: {:.3f}'.format(self.val_task, best_acc))
|
||||
self.logger.info('Saved weights of the model at epoch: {}'.format(best_epoch))
|
||||
|
||||
if self.print_loss:
|
||||
print_losses(epoch_losses)
|
||||
self._print_losses(epoch_losses)
|
||||
|
||||
# load best model weights
|
||||
self.model.load_state_dict(best_model_wts)
|
||||
@ -255,7 +246,7 @@ class Trainer:
|
||||
def compute_stats(self, outputs, labels, dic_err, size_eval, clst):
|
||||
"""Compute mean, bi and max of torch tensors"""
|
||||
|
||||
loss, loss_values = self.mt_loss(outputs, labels, phase='val')
|
||||
_, loss_values = self.mt_loss(outputs, labels, phase='val')
|
||||
rel_frac = outputs.size(0) / size_eval
|
||||
|
||||
tasks = self.tasks[:-1] if self.tasks[-1] == 'aux' else self.tasks # Exclude auxiliary
|
||||
@ -333,6 +324,41 @@ class Trainer:
|
||||
if epoch % 10 == 0:
|
||||
print(string.format(*format_list))
|
||||
|
||||
def _print_losses(self, epoch_losses):
|
||||
if not self.print_loss:
|
||||
return
|
||||
os.makedirs(self.dir_figures, exist_ok=True)
|
||||
for idx, phase in enumerate(epoch_losses):
|
||||
for idx_2, el in enumerate(epoch_losses['train']):
|
||||
plt.figure(idx + idx_2)
|
||||
plt.title(phase + '_' + el)
|
||||
plt.xlabel('epochs')
|
||||
plt.plot(epoch_losses[phase][el][10:], label='{} Loss: {}'.format(phase, el))
|
||||
plt.savefig(os.path.join(self.dir_figures, '{}_loss_{}.png'.format(phase, el)))
|
||||
plt.close()
|
||||
|
||||
def _set_logger(self, args):
|
||||
if self.no_save:
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
self.logger = logging.getLogger(__name__)
|
||||
else:
|
||||
self.path_model = self.path_out
|
||||
print(self.path_model)
|
||||
self.logger = set_logger(os.path.splitext(self.path_out)[0]) # remove .pkl
|
||||
self.logger.info( # pylint: disable=logging-fstring-interpolation
|
||||
f'\nVERSION: {__version__}\n'
|
||||
f'\nINPUT_FILE: {args.joints}'
|
||||
f'\nInput file version: {self.dataset_version}'
|
||||
f'\nTorch version: {torch.__version__}\n'
|
||||
f'\nTraining arguments:'
|
||||
f'\nmode: {self.mode} \nlearning rate: {args.lr} \nbatch_size: {args.bs}'
|
||||
f'\nepochs: {args.epochs} \ndropout: {args.dropout} '
|
||||
f'\nscheduler step: {args.sched_step} \nscheduler gamma: {args.sched_gamma} '
|
||||
f'\ninput_size: {self.input_size[self.mode]} \noutput_size: {self.output_size[self.mode]} '
|
||||
f'\nhidden_size: {args.hidden_size}'
|
||||
f' \nn_stages: {args.n_stage} \n r_seed: {args.r_seed} \nlambdas: {self.lambdas}'
|
||||
)
|
||||
|
||||
|
||||
def debug_plots(inputs, labels):
|
||||
inputs_shoulder = inputs.cpu().numpy()[:, 5]
|
||||
@ -347,15 +373,6 @@ def debug_plots(inputs, labels):
|
||||
plt.show()
|
||||
|
||||
|
||||
def print_losses(epoch_losses):
|
||||
for idx, phase in enumerate(epoch_losses):
|
||||
for idx_2, el in enumerate(epoch_losses['train']):
|
||||
plt.figure(idx + idx_2)
|
||||
plt.plot(epoch_losses[phase][el][10:], label='{} Loss: {}'.format(phase, el))
|
||||
plt.savefig('figures/{}_loss_{}.png'.format(phase, el))
|
||||
plt.close()
|
||||
|
||||
|
||||
def get_accuracy(outputs, labels):
|
||||
"""From Binary cross entropy outputs to accuracy"""
|
||||
|
||||
|
||||
@ -1,12 +1,13 @@
|
||||
|
||||
from .iou import get_iou_matches, reorder_matches, get_iou_matrix, get_iou_matches_matrix
|
||||
from .misc import get_task_error, get_pixel_error, append_cluster, open_annotations, make_new_directory,\
|
||||
from .iou import get_iou_matches, reorder_matches, get_iou_matrix, get_iou_matches_matrix, get_category, \
|
||||
open_annotations
|
||||
from .misc import get_task_error, get_pixel_error, append_cluster, make_new_directory,\
|
||||
normalize_hwl, average
|
||||
from .kitti import check_conditions, get_difficulty, split_training, parse_ground_truth, get_calibration, \
|
||||
factory_basename, factory_file, get_category, read_and_rewrite, find_cluster
|
||||
from .kitti import check_conditions, get_difficulty, split_training, get_calibration, \
|
||||
factory_basename, read_and_rewrite, find_cluster
|
||||
from .camera import xyz_from_distance, get_keypoints, pixel_to_camera, project_3d, open_image, correct_angle,\
|
||||
to_spherical, to_cartesian, back_correct_angles, project_to_pixels
|
||||
from .logs import set_logger
|
||||
from ..utils.nuscenes import select_categories
|
||||
from ..utils.stereo import mask_joint_disparity, average_locations, extract_stereo_matches, \
|
||||
from .nuscenes import select_categories
|
||||
from .stereo import mask_joint_disparity, average_locations, extract_stereo_matches, \
|
||||
verify_stereo, disparity_to_depth
|
||||
|
||||
@ -1,4 +1,6 @@
|
||||
|
||||
import json
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
@ -52,7 +54,7 @@ def get_iou_matches(boxes, boxes_gt, iou_min=0.3):
|
||||
for idx in indices[::-1]:
|
||||
box = boxes[idx]
|
||||
ious = []
|
||||
for idx_gt, box_gt in enumerate(boxes_gt):
|
||||
for box_gt in boxes_gt:
|
||||
iou = calculate_iou(box, box_gt)
|
||||
ious.append(iou)
|
||||
idx_gt_max = int(np.argmax(ious))
|
||||
@ -96,3 +98,48 @@ def reorder_matches(matches, boxes, mode='left_rigth'):
|
||||
matches_left = [int(idx) for (idx, _) in matches]
|
||||
|
||||
return [matches[matches_left.index(idx_boxes)] for idx_boxes in ordered_boxes if idx_boxes in matches_left]
|
||||
|
||||
|
||||
def get_category(keypoints, path_byc):
|
||||
"""Find the category for each of the keypoints"""
|
||||
|
||||
dic_byc = open_annotations(path_byc)
|
||||
boxes_byc = dic_byc['boxes'] if dic_byc else []
|
||||
boxes_ped = make_lower_boxes(keypoints)
|
||||
|
||||
matches = get_matches_bikes(boxes_ped, boxes_byc)
|
||||
list_byc = [match[0] for match in matches]
|
||||
categories = [1.0 if idx in list_byc else 0.0 for idx, _ in enumerate(boxes_ped)]
|
||||
return categories
|
||||
|
||||
|
||||
def get_matches_bikes(boxes_ped, boxes_byc):
|
||||
matches = get_iou_matches_matrix(boxes_ped, boxes_byc, thresh=0.15)
|
||||
matches_b = []
|
||||
for idx, idx_byc in matches:
|
||||
box_ped = boxes_ped[idx]
|
||||
box_byc = boxes_byc[idx_byc]
|
||||
width_ped = box_ped[2] - box_ped[0]
|
||||
width_byc = box_byc[2] - box_byc[0]
|
||||
center_ped = (box_ped[2] + box_ped[0]) / 2
|
||||
center_byc = (box_byc[2] + box_byc[0]) / 2
|
||||
if abs(center_ped - center_byc) < min(width_ped, width_byc) / 4:
|
||||
matches_b.append((idx, idx_byc))
|
||||
return matches_b
|
||||
|
||||
|
||||
def make_lower_boxes(keypoints):
|
||||
lower_boxes = []
|
||||
keypoints = np.array(keypoints)
|
||||
for kps in keypoints:
|
||||
lower_boxes.append([min(kps[0, 9:]), min(kps[1, 9:]), max(kps[0, 9:]), max(kps[1, 9:])])
|
||||
return lower_boxes
|
||||
|
||||
|
||||
def open_annotations(path_ann):
|
||||
try:
|
||||
with open(path_ann, 'r') as f:
|
||||
annotations = json.load(f)
|
||||
except FileNotFoundError:
|
||||
annotations = []
|
||||
return annotations
|
||||
|
||||
@ -1,5 +1,4 @@
|
||||
|
||||
import math
|
||||
import os
|
||||
import glob
|
||||
|
||||
@ -129,50 +128,6 @@ def split_training(names_gt, path_train, path_val):
|
||||
return set_train, set_val
|
||||
|
||||
|
||||
def parse_ground_truth(path_gt, category, spherical=False, verbose=False):
|
||||
"""Parse KITTI ground truth files"""
|
||||
from ..utils import correct_angle, to_spherical
|
||||
|
||||
boxes_gt = []
|
||||
ys = []
|
||||
truncs_gt = [] # Float from 0 to 1
|
||||
occs_gt = [] # Either 0,1,2,3 fully visible, partly occluded, largely occluded, unknown
|
||||
lines = []
|
||||
|
||||
with open(path_gt, "r") as f_gt:
|
||||
for line_gt in f_gt:
|
||||
line = line_gt.split()
|
||||
if check_conditions(line_gt, category, method='gt'):
|
||||
truncs_gt.append(float(line[1]))
|
||||
occs_gt.append(int(line[2]))
|
||||
boxes_gt.append([float(x) for x in line[4:8]])
|
||||
xyz = [float(x) for x in line[11:14]]
|
||||
hwl = [float(x) for x in line[8:11]]
|
||||
dd = float(math.sqrt(xyz[0] ** 2 + xyz[1] ** 2 + xyz[2] ** 2))
|
||||
yaw = float(line[14])
|
||||
assert - math.pi <= yaw <= math.pi
|
||||
alpha = float(line[3])
|
||||
sin, cos, yaw_corr = correct_angle(yaw, xyz)
|
||||
assert min(abs(-yaw_corr - alpha), (abs(yaw_corr - alpha))) < 0.15, "more than 10 degrees of error"
|
||||
if spherical:
|
||||
rtp = to_spherical(xyz)
|
||||
loc = rtp[1:3] + xyz[2:3] + rtp[0:1] # [theta, psi, z, r]
|
||||
else:
|
||||
loc = xyz + [dd]
|
||||
# cat = 0 if line[0] in ('Pedestrian', 'Person_sitting') else 1
|
||||
if line[0] in ('Pedestrian', 'Person_sitting'):
|
||||
cat = 0
|
||||
else:
|
||||
cat = 1
|
||||
output = loc + hwl + [sin, cos, yaw, cat]
|
||||
ys.append(output)
|
||||
if verbose:
|
||||
lines.append(line_gt)
|
||||
if verbose:
|
||||
return boxes_gt, ys, truncs_gt, occs_gt, lines
|
||||
return boxes_gt, ys, truncs_gt, occs_gt
|
||||
|
||||
|
||||
def factory_basename(dir_ann, dir_gt):
|
||||
""" Return all the basenames in the annotations folder corresponding to validation images"""
|
||||
|
||||
@ -191,64 +146,6 @@ def factory_basename(dir_ann, dir_gt):
|
||||
return set_val
|
||||
|
||||
|
||||
def factory_file(path_calib, dir_ann, basename, mode='left'):
|
||||
"""Choose the annotation and the calibration files. Stereo option with ite = 1"""
|
||||
|
||||
assert mode in ('left', 'right')
|
||||
p_left, p_right = get_calibration(path_calib)
|
||||
|
||||
if mode == 'left':
|
||||
kk, tt = p_left[:]
|
||||
path_ann = os.path.join(dir_ann, basename + '.png.predictions.json')
|
||||
|
||||
else:
|
||||
kk, tt = p_right[:]
|
||||
path_ann = os.path.join(dir_ann + '_right', basename + '.png.predictions.json')
|
||||
|
||||
from ..utils import open_annotations
|
||||
annotations = open_annotations(path_ann)
|
||||
|
||||
return annotations, kk, tt
|
||||
|
||||
|
||||
def get_category(keypoints, path_byc):
|
||||
"""Find the category for each of the keypoints"""
|
||||
|
||||
from ..utils import open_annotations
|
||||
dic_byc = open_annotations(path_byc)
|
||||
boxes_byc = dic_byc['boxes'] if dic_byc else []
|
||||
boxes_ped = make_lower_boxes(keypoints)
|
||||
|
||||
matches = get_matches_bikes(boxes_ped, boxes_byc)
|
||||
list_byc = [match[0] for match in matches]
|
||||
categories = [1.0 if idx in list_byc else 0.0 for idx, _ in enumerate(boxes_ped)]
|
||||
return categories
|
||||
|
||||
|
||||
def get_matches_bikes(boxes_ped, boxes_byc):
|
||||
from . import get_iou_matches_matrix
|
||||
matches = get_iou_matches_matrix(boxes_ped, boxes_byc, thresh=0.15)
|
||||
matches_b = []
|
||||
for idx, idx_byc in matches:
|
||||
box_ped = boxes_ped[idx]
|
||||
box_byc = boxes_byc[idx_byc]
|
||||
width_ped = box_ped[2] - box_ped[0]
|
||||
width_byc = box_byc[2] - box_byc[0]
|
||||
center_ped = (box_ped[2] + box_ped[0]) / 2
|
||||
center_byc = (box_byc[2] + box_byc[0]) / 2
|
||||
if abs(center_ped - center_byc) < min(width_ped, width_byc) / 4:
|
||||
matches_b.append((idx, idx_byc))
|
||||
return matches_b
|
||||
|
||||
|
||||
def make_lower_boxes(keypoints):
|
||||
lower_boxes = []
|
||||
keypoints = np.array(keypoints)
|
||||
for kps in keypoints:
|
||||
lower_boxes.append([min(kps[0, 9:]), min(kps[1, 9:]), max(kps[0, 9:]), max(kps[1, 9:])])
|
||||
return lower_boxes
|
||||
|
||||
|
||||
def read_and_rewrite(path_orig, path_new):
|
||||
"""Read and write same txt file. If file not found, create open file"""
|
||||
try:
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
import json
|
||||
import shutil
|
||||
import os
|
||||
|
||||
@ -44,15 +43,6 @@ def get_pixel_error(zz_gt):
|
||||
return error
|
||||
|
||||
|
||||
def open_annotations(path_ann):
|
||||
try:
|
||||
with open(path_ann, 'r') as f:
|
||||
annotations = json.load(f)
|
||||
except FileNotFoundError:
|
||||
annotations = []
|
||||
return annotations
|
||||
|
||||
|
||||
def make_new_directory(dir_out):
|
||||
"""Remove the output directory if already exists (avoid residual txt files)"""
|
||||
if os.path.exists(dir_out):
|
||||
|
||||
@ -12,7 +12,16 @@ D_MAX = BF / z_min
|
||||
|
||||
|
||||
def extract_stereo_matches(keypoint, keypoints_r, zz, phase='train', seed=0, method=None):
|
||||
"""Return binaries representing the match between the pose in the left and the ones in the right"""
|
||||
"""
|
||||
Return:
|
||||
1) a list of tuples that indicates, for a reference pose in the left image:
|
||||
- the index of the right pose
|
||||
- weather the right pose corresponds to the same person as the left pose (stereo match) or not
|
||||
For example: [(0,0), (1,0), (2,1)] means there are three right poses in the image
|
||||
and the third one is the same person as the reference pose
|
||||
2) a flag indicating whether a match has been found
|
||||
3) number of ambiguous instances, for which is not possible to define whether there is a correspondence
|
||||
"""
|
||||
|
||||
stereo_matches = []
|
||||
cnt_ambiguous = 0
|
||||
@ -70,20 +79,10 @@ def extract_stereo_matches(keypoint, keypoints_r, zz, phase='train', seed=0, met
|
||||
if idx_matches[num] not in used:
|
||||
stereo_matches.append((idx_matches[num], 0))
|
||||
|
||||
# elif len(stereo_matches) < 1:
|
||||
# stereo_matches.append((idx_match, 0))
|
||||
|
||||
# Easy-negative
|
||||
# elif len(idx_matches) > len(stereo_matches):
|
||||
# stereo_matches.append((idx_matches[-1], 0))
|
||||
# break # matches are ordered
|
||||
else:
|
||||
break
|
||||
used.append(idx_match)
|
||||
|
||||
# Make sure each left has at least a negative match
|
||||
# if not stereo_matches:
|
||||
# stereo_matches.append((idx_matches[0], 0))
|
||||
return stereo_matches, cnt_ambiguous
|
||||
|
||||
|
||||
@ -191,7 +190,7 @@ def verify_stereo(zz_stereo, zz_mono, disparity_x, disparity_y):
|
||||
y_max_difference = (80 / zz_mono)
|
||||
z_max_difference = 1 * zz_mono
|
||||
|
||||
cov = float(np.nanstd(disparity_x) / np.abs(np.nanmean(disparity_x))) # Coefficient of variation
|
||||
cov = float(np.nanstd(disparity_x) / np.abs(np.nanmean(disparity_x))) # pylint: disable=unused-variable
|
||||
avg_disparity_y = np.nanmedian(disparity_y)
|
||||
|
||||
return abs(zz_stereo - zz_mono) < z_max_difference and avg_disparity_y < y_max_difference and 1 < zz_stereo < 80
|
||||
|
||||
@ -7,6 +7,11 @@ import os
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.patches import Ellipse
|
||||
try:
|
||||
import pandas as pd
|
||||
DATAFRAME = pd.DataFrame
|
||||
except ImportError:
|
||||
DATAFRAME = None
|
||||
|
||||
from ..utils import get_task_error, get_pixel_error
|
||||
|
||||
@ -33,7 +38,7 @@ def show_results(dic_stats, clusters, net, dir_fig, show=False, save=False):
|
||||
excl_clusters = ['all', 'easy', 'moderate', 'hard', '49']
|
||||
clusters = [clst for clst in clusters if clst not in excl_clusters]
|
||||
styles = printing_styles(net)
|
||||
for idx_style, style in enumerate(styles.items()):
|
||||
for idx_style in styles:
|
||||
plt.figure(idx_style, figsize=FIGSIZE)
|
||||
plt.grid(linewidth=GRID_WIDTH)
|
||||
plt.xlim(x_min, x_max)
|
||||
@ -183,7 +188,6 @@ def show_method(save, dir_out='data/figures'):
|
||||
|
||||
|
||||
def show_box_plot(dic_errors, clusters, dir_fig, show=False, save=False):
|
||||
import pandas as pd
|
||||
excl_clusters = ['all', 'easy', 'moderate', 'hard']
|
||||
clusters = [int(clst) for clst in clusters if clst not in excl_clusters]
|
||||
methods = ('monstereo', 'pseudo-lidar', '3dop', 'monoloco')
|
||||
@ -192,7 +196,7 @@ def show_box_plot(dic_errors, clusters, dir_fig, show=False, save=False):
|
||||
xxs = get_distances(clusters)
|
||||
labels = [str(xx) for xx in xxs]
|
||||
for idx, method in enumerate(methods):
|
||||
df = pd.DataFrame([dic_errors[method][str(clst)] for clst in clusters[:-1]]).T
|
||||
df = DATAFRAME([dic_errors[method][str(clst)] for clst in clusters[:-1]]).T
|
||||
df.columns = labels
|
||||
|
||||
plt.figure(idx, figsize=FIGSIZE) # with 200 dpi it becomes 1920x1440
|
||||
@ -289,16 +293,16 @@ def expandgrid(*itrs):
|
||||
return combinations
|
||||
|
||||
|
||||
def get_percentile(dist_gmm):
|
||||
dd_gt = 1000
|
||||
mu_gmm = np.mean(dist_gmm)
|
||||
dist_d = dd_gt * mu_gmm / dist_gmm
|
||||
perc_d, _ = np.nanpercentile(dist_d, [18.5, 81.5]) # Laplace bi => 63%
|
||||
perc_d2, _ = np.nanpercentile(dist_d, [23, 77])
|
||||
mu_d = np.mean(dist_d)
|
||||
# mm_bi = (mu_d - perc_d) / mu_d
|
||||
# mm_test = (mu_d - perc_d2) / mu_d
|
||||
# mad_d = np.mean(np.abs(dist_d - mu_d))
|
||||
# def get_percentile(dist_gmm):
|
||||
# dd_gt = 1000
|
||||
# mu_gmm = np.mean(dist_gmm)
|
||||
# dist_d = dd_gt * mu_gmm / dist_gmm
|
||||
# perc_d, _ = np.nanpercentile(dist_d, [18.5, 81.5]) # Laplace bi => 63%
|
||||
# perc_d2, _ = np.nanpercentile(dist_d, [23, 77])
|
||||
# mu_d = np.mean(dist_d)
|
||||
# # mm_bi = (mu_d - perc_d) / mu_d
|
||||
# # mm_test = (mu_d - perc_d2) / mu_d
|
||||
# # mad_d = np.mean(np.abs(dist_d - mu_d))
|
||||
|
||||
|
||||
def printing_styles(net):
|
||||
|
||||
@ -1,5 +1,9 @@
|
||||
|
||||
# File adapted from https://github.com/vita-epfl/openpifpaf
|
||||
"""
|
||||
Adapted from https://github.com/openpifpaf,
|
||||
which is: 'Copyright 2019-2021 by Sven Kreiss and contributors. All rights reserved.'
|
||||
and licensed under GNU AGPLv3
|
||||
"""
|
||||
|
||||
from contextlib import contextmanager
|
||||
import math
|
||||
@ -13,9 +17,8 @@ try:
|
||||
from matplotlib.patches import Circle, FancyArrow
|
||||
import scipy.ndimage as ndimage
|
||||
except ImportError:
|
||||
matplotlib = None
|
||||
plt = None
|
||||
ndimage = None
|
||||
plt = None
|
||||
|
||||
|
||||
COCO_PERSON_SKELETON = [
|
||||
@ -71,7 +74,7 @@ def load_image(path, scale=1.0):
|
||||
return image
|
||||
|
||||
|
||||
class KeypointPainter(object):
|
||||
class KeypointPainter:
|
||||
def __init__(self, *,
|
||||
skeleton=None,
|
||||
xy_scale=1.0, y_scale=1.0, highlight=None, highlight_invisible=False,
|
||||
|
||||
@ -10,11 +10,11 @@ def correct_boxes(boxes, hwls, xyzs, yaws, path_calib):
|
||||
p2_list = [float(xx) for xx in p2_str]
|
||||
P = np.array(p2_list).reshape(3, 4)
|
||||
boxes_new = []
|
||||
for idx, box in enumerate(boxes):
|
||||
for idx in range(boxes):
|
||||
hwl = hwls[idx]
|
||||
xyz = xyzs[idx]
|
||||
yaw = yaws[idx]
|
||||
corners_2d, corners_3d = compute_box_3d(hwl, xyz, yaw, P)
|
||||
corners_2d, _ = compute_box_3d(hwl, xyz, yaw, P)
|
||||
box_new = project_8p_to_4p(corners_2d).reshape(-1).tolist()
|
||||
boxes_new.append(box_new)
|
||||
return boxes_new
|
||||
@ -58,7 +58,6 @@ def compute_box_3d(hwl, xyz, ry, P):
|
||||
return corners_2d, np.transpose(corners_3d)
|
||||
|
||||
|
||||
|
||||
def roty(t):
|
||||
""" Rotation about the y-axis. """
|
||||
c = np.cos(t)
|
||||
@ -66,7 +65,6 @@ def roty(t):
|
||||
return np.array([[c, 0, s], [0, 1, 0], [-s, 0, c]])
|
||||
|
||||
|
||||
|
||||
def project_to_image(pts_3d, P):
|
||||
""" Project 3d points to image plane.
|
||||
Usage: pts_2d = projectToImage(pts_3d, P)
|
||||
@ -87,7 +85,6 @@ def project_to_image(pts_3d, P):
|
||||
return pts_2d[:, 0:2]
|
||||
|
||||
|
||||
|
||||
def project_8p_to_4p(pts_2d):
|
||||
x0 = np.min(pts_2d[:, 0])
|
||||
x1 = np.max(pts_2d[:, 0])
|
||||
|
||||
38
pyproject.toml
Normal file
38
pyproject.toml
Normal file
@ -0,0 +1,38 @@
|
||||
[build-system]
|
||||
build-backend = "setuptools.build_meta"
|
||||
requires = ["setuptools", "versioneer-518"]
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
minversion = "6.0"
|
||||
testpaths = ["tests"]
|
||||
markers = ["slow: marks tests as slow (deselect with '-m \"not slow\"')"]
|
||||
|
||||
[tool.pylint.BASIC]
|
||||
class-const-naming-style = "snake_case" # checked since pylint 2.7.3
|
||||
|
||||
[tool.pylint.IMPORTS]
|
||||
allow-any-import-level = []
|
||||
|
||||
[tool.pylint.SIMILARITIES]
|
||||
min-similarity-lines = 15 # Minimum lines number of a similarity.
|
||||
ignore-comments = "yes" # Ignore comments when computing similarities.
|
||||
ignore-docstrings = "yes" # Ignore docstrings when computing similarities.
|
||||
ignore-imports = "yes" # Ignore imports when computing similarities.
|
||||
|
||||
[tool.pylint.TYPECHECK]
|
||||
generated-members = ["numpy.*", "torch.*", "cv2.*", "openpifpaf.functional.*"]
|
||||
ignored-modules = ["openpifpaf.functional"]
|
||||
disable = [
|
||||
"missing-docstring",
|
||||
"too-many-arguments",
|
||||
"too-many-instance-attributes",
|
||||
"too-many-locals",
|
||||
"too-few-public-methods",
|
||||
"unsubscriptable-object",
|
||||
"not-callable", # for torch tensors
|
||||
"invalid-name",
|
||||
"logging-format-interpolation",
|
||||
]
|
||||
[tool.pylint.FORMAT]
|
||||
max-line-length = 120
|
||||
|
||||
13
setup.cfg
Normal file
13
setup.cfg
Normal file
@ -0,0 +1,13 @@
|
||||
|
||||
[versioneer]
|
||||
VCS = git
|
||||
style = pep440
|
||||
versionfile_source = monoloco/_version.py
|
||||
versionfile_build = monoloco/_version.py
|
||||
tag_prefix = v
|
||||
#parentdir_prefix =
|
||||
|
||||
[pycodestyle]
|
||||
max-line-length=120
|
||||
ignore=E731,E741,W503
|
||||
exclude=monoloco/__init__.py
|
||||
24
setup.py
24
setup.py
@ -1,13 +1,18 @@
|
||||
|
||||
from setuptools import setup
|
||||
|
||||
# extract version from __init__.py
|
||||
with open('monoloco/__init__.py', 'r') as f:
|
||||
VERSION_LINE = [l for l in f if l.startswith('__version__')][0]
|
||||
VERSION = VERSION_LINE.split('=')[1].strip()[1:-1]
|
||||
# This is needed for versioneer to be importable when building with PEP 517.
|
||||
# See <https://github.com/warner/python-versioneer/issues/193> and links
|
||||
# therein for more information.
|
||||
|
||||
import os, sys
|
||||
sys.path.append(os.path.dirname(__file__))
|
||||
import versioneer
|
||||
|
||||
setup(
|
||||
name='monoloco',
|
||||
version=VERSION,
|
||||
version=versioneer.get_version(),
|
||||
cmdclass=versioneer.get_cmdclass(),
|
||||
packages=[
|
||||
'monoloco',
|
||||
'monoloco.network',
|
||||
@ -29,15 +34,18 @@ setup(
|
||||
install_requires=[
|
||||
'openpifpaf>=v0.12.1',
|
||||
'matplotlib',
|
||||
'gdown',
|
||||
],
|
||||
extras_require={
|
||||
'test': [
|
||||
'pylint',
|
||||
'pytest',
|
||||
'gdown',
|
||||
'scipy', # for social distancing gaussian blur
|
||||
],
|
||||
'eval': [
|
||||
'tabulate',
|
||||
'sklearn',
|
||||
'pandas',
|
||||
'pylint',
|
||||
'pytest',
|
||||
],
|
||||
'prep': [
|
||||
'nuscenes-devkit==1.0.2',
|
||||
|
||||
File diff suppressed because one or more lines are too long
1
tests/sample_joints-kitti-mono.json
Normal file
1
tests/sample_joints-kitti-mono.json
Normal file
File diff suppressed because one or more lines are too long
1
tests/sample_joints-kitti-stereo.json
Normal file
1
tests/sample_joints-kitti-stereo.json
Normal file
File diff suppressed because one or more lines are too long
@ -1,89 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import math\n",
|
||||
"def calculate_iou(box1, box2):\n",
|
||||
"\n",
|
||||
" # Calculate the (x1, y1, x2, y2) coordinates of the intersection of box1 and box2. Calculate its Area.\n",
|
||||
" xi1 = max(box1[0], box2[0])\n",
|
||||
" yi1 = max(box1[1], box2[1])\n",
|
||||
" xi2 = min(box1[2], box2[2])\n",
|
||||
" yi2 = min(box1[3], box2[3])\n",
|
||||
" inter_area = max((xi2 - xi1), 0) * max((yi2 - yi1), 0) # Max keeps into account not overlapping box\n",
|
||||
"\n",
|
||||
" # Calculate the Union area by using Formula: Union(A,B) = A + B - Inter(A,B)\n",
|
||||
" box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])\n",
|
||||
" box2_area = (box2[2] - box2[0]) * (box2[3] - box2[1])\n",
|
||||
" union_area = box1_area + box2_area - inter_area\n",
|
||||
"\n",
|
||||
" # compute the IoU\n",
|
||||
" iou = inter_area / union_area\n",
|
||||
"\n",
|
||||
" return iou"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 64,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"15.0\n",
|
||||
"[8.450052369622647, 12.393410142113215, 88.45005236962265, 77.39341014211321]\n",
|
||||
"0.4850460596873889\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"x1 = 75\n",
|
||||
"y1 = 60\n",
|
||||
"\n",
|
||||
"box1 = [0, 0, x1, y1]\n",
|
||||
"alpha = math.atan2(110,75) # good number\n",
|
||||
"diag = 15\n",
|
||||
"x_cateto = diag * math.cos(alpha)\n",
|
||||
"y_cateto = diag * math.sin(alpha)\n",
|
||||
"print(math.sqrt(x_cateto**2 + y_cateto**2))\n",
|
||||
"box2 = [x_cateto, y_cateto, x1 + x_cateto + 5, y1 + y_cateto+ 5]\n",
|
||||
"print(box2)\n",
|
||||
"print(calculate_iou(box1, box2))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -1,69 +0,0 @@
|
||||
"""Test if the main modules of the package run correctly"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
|
||||
# Python does not consider the current directory to be a package
|
||||
sys.path.insert(0, os.path.join('..', 'monoloco'))
|
||||
|
||||
from PIL import Image
|
||||
|
||||
from stereoloco.train import Trainer
|
||||
from stereoloco.network import MonoLoco
|
||||
from stereoloco.network.process import preprocess_pifpaf, factory_for_gt
|
||||
from stereoloco.visuals.printer import Printer
|
||||
|
||||
JOINTS = 'tests/joints_sample.json'
|
||||
PIFPAF_KEYPOINTS = 'tests/002282.png.pifpaf.json'
|
||||
IMAGE = 'docs/002282.png'
|
||||
|
||||
|
||||
def tst_trainer(joints):
|
||||
trainer = Trainer(joints=joints, epochs=150, lr=0.01)
|
||||
_ = trainer.train()
|
||||
dic_err, model = trainer.evaluate()
|
||||
return dic_err['val']['all']['mean'], model
|
||||
|
||||
|
||||
def tst_prediction(model, path_keypoints):
|
||||
with open(path_keypoints, 'r') as f:
|
||||
pifpaf_out = json.load(f)
|
||||
|
||||
kk, _ = factory_for_gt(im_size=[1240, 340])
|
||||
|
||||
# Preprocess pifpaf outputs and run monoloco
|
||||
boxes, keypoints = preprocess_pifpaf(pifpaf_out)
|
||||
monoloco = MonoLoco(model)
|
||||
outputs, varss = monoloco.forward(keypoints, kk)
|
||||
dic_out = monoloco.post_process(outputs, varss, boxes, keypoints, kk)
|
||||
return dic_out, kk
|
||||
|
||||
|
||||
def tst_printer(dic_out, kk, image_path):
|
||||
"""Draw a fake figure"""
|
||||
with open(image_path, 'rb') as f:
|
||||
pil_image = Image.open(f).convert('RGB')
|
||||
printer = Printer(image=pil_image, output_path='tests/test_image', kk=kk, output_types=['multi'], z_max=15)
|
||||
figures, axes = printer.factory_axes()
|
||||
printer.draw(figures, axes, dic_out, pil_image, save=True)
|
||||
|
||||
|
||||
def test_package():
|
||||
|
||||
# Training test
|
||||
val_acc, model = tst_trainer(JOINTS)
|
||||
assert val_acc < 2.5
|
||||
|
||||
# Prediction test
|
||||
dic_out, kk = tst_prediction(model, PIFPAF_KEYPOINTS)
|
||||
assert dic_out['boxes'] and kk
|
||||
|
||||
# Visualization test
|
||||
tst_printer(dic_out, kk, IMAGE)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
79
tests/test_train_mono.py
Normal file
79
tests/test_train_mono.py
Normal file
@ -0,0 +1,79 @@
|
||||
|
||||
"""
|
||||
Adapted from https://github.com/openpifpaf/openpifpaf/blob/main/tests/test_train.py,
|
||||
which is: 'Copyright 2019-2021 by Sven Kreiss and contributors. All rights reserved.'
|
||||
and licensed under GNU AGPLv3
|
||||
"""
|
||||
|
||||
import os
|
||||
import subprocess
|
||||
|
||||
import gdown
|
||||
|
||||
OPENPIFPAF_MODEL = 'https://drive.google.com/uc?id=1b408ockhh29OLAED8Tysd2yGZOo0N_SQ'
|
||||
|
||||
TRAIN_COMMAND = [
|
||||
'python3', '-m', 'monoloco.run',
|
||||
'train',
|
||||
'--joints', 'tests/sample_joints-kitti-mono.json',
|
||||
'--lr=0.001',
|
||||
'-e=10',
|
||||
]
|
||||
|
||||
|
||||
PREDICT_COMMAND = [
|
||||
'python3', '-m', 'monoloco.run',
|
||||
'predict',
|
||||
'docs/002282.png',
|
||||
'--output_types', 'multi', 'json',
|
||||
'--decoder-workers=0' # for windows
|
||||
]
|
||||
|
||||
PREDICT_COMMAND_SOCIAL_DISTANCE = [
|
||||
'python3', '-m', 'monoloco.run',
|
||||
'predict',
|
||||
'docs/frame0032.jpg',
|
||||
'--social_distance',
|
||||
'--output_types', 'front', 'bird',
|
||||
'--decoder-workers=0' # for windows'
|
||||
]
|
||||
|
||||
|
||||
def test_train_mono(tmp_path):
|
||||
# train a model
|
||||
train_cmd = TRAIN_COMMAND + ['--out={}'.format(os.path.join(tmp_path, 'train_test.pkl'))]
|
||||
print(' '.join(train_cmd))
|
||||
subprocess.run(train_cmd, check=True, capture_output=True)
|
||||
print(os.listdir(tmp_path))
|
||||
|
||||
# find the trained model checkpoint and download pifpaf one
|
||||
final_model = next(iter(f for f in os.listdir(tmp_path) if f.endswith('.pkl')))
|
||||
pifpaf_model = os.path.join(tmp_path, 'pifpaf_model.pkl')
|
||||
print('Downloading OpenPifPaf model in temporary folder')
|
||||
gdown.download(OPENPIFPAF_MODEL, pifpaf_model)
|
||||
|
||||
# run predictions with that model
|
||||
model = os.path.join(tmp_path, final_model)
|
||||
|
||||
print(model)
|
||||
predict_cmd = PREDICT_COMMAND + [
|
||||
'--model={}'.format(model),
|
||||
'--checkpoint={}'.format(pifpaf_model),
|
||||
'-o={}'.format(tmp_path),
|
||||
]
|
||||
print(' '.join(predict_cmd))
|
||||
subprocess.run(predict_cmd, check=True, capture_output=True)
|
||||
print(os.listdir(tmp_path))
|
||||
assert 'out_002282.png.multi.png' in os.listdir(tmp_path)
|
||||
assert 'out_002282.png.monoloco.json' in os.listdir(tmp_path)
|
||||
|
||||
predict_cmd_sd = PREDICT_COMMAND_SOCIAL_DISTANCE + [
|
||||
'--model={}'.format(model),
|
||||
'--checkpoint={}'.format(pifpaf_model),
|
||||
'-o={}'.format(tmp_path),
|
||||
]
|
||||
print(' '.join(predict_cmd_sd))
|
||||
subprocess.run(predict_cmd_sd, check=True, capture_output=True)
|
||||
print(os.listdir(tmp_path))
|
||||
assert 'out_frame0032.jpg.front.png' in os.listdir(tmp_path)
|
||||
assert 'out_frame0032.jpg.bird.png' in os.listdir(tmp_path)
|
||||
59
tests/test_train_stereo.py
Normal file
59
tests/test_train_stereo.py
Normal file
@ -0,0 +1,59 @@
|
||||
|
||||
"""
|
||||
Adapted from https://github.com/openpifpaf/openpifpaf/blob/main/tests/test_train.py,
|
||||
which is: 'Copyright 2019-2021 by Sven Kreiss and contributors. All rights reserved.'
|
||||
and licensed under GNU AGPLv3
|
||||
"""
|
||||
|
||||
import os
|
||||
import subprocess
|
||||
|
||||
import gdown
|
||||
|
||||
OPENPIFPAF_MODEL = 'https://drive.google.com/uc?id=1b408ockhh29OLAED8Tysd2yGZOo0N_SQ'
|
||||
|
||||
TRAIN_COMMAND = [
|
||||
'python3', '-m', 'monoloco.run',
|
||||
'train',
|
||||
'--mode=stereo',
|
||||
'--joints', 'tests/sample_joints-kitti-stereo.json',
|
||||
'--lr=0.001',
|
||||
'-e=20',
|
||||
]
|
||||
|
||||
|
||||
PREDICT_COMMAND = [
|
||||
'python3', '-m', 'monoloco.run',
|
||||
'predict',
|
||||
'--mode=stereo',
|
||||
'--glob', 'docs/000840*.png',
|
||||
'--output_types', 'multi', 'json',
|
||||
'--decoder-workers=0', # for windows'
|
||||
]
|
||||
|
||||
|
||||
def test_train_stereo(tmp_path):
|
||||
# train a model
|
||||
train_cmd = TRAIN_COMMAND + ['--out={}'.format(os.path.join(tmp_path, 'train_test.pkl'))]
|
||||
print(' '.join(train_cmd))
|
||||
subprocess.run(train_cmd, check=True, capture_output=True)
|
||||
print(os.listdir(tmp_path))
|
||||
|
||||
# find the trained model checkpoint
|
||||
final_model = next(iter(f for f in os.listdir(tmp_path) if f.endswith('.pkl')))
|
||||
pifpaf_model = os.path.join(tmp_path, 'pifpaf_model.pkl')
|
||||
print('Downloading OpenPifPaf model in temporary folder')
|
||||
gdown.download(OPENPIFPAF_MODEL, pifpaf_model)
|
||||
|
||||
# run predictions with that model
|
||||
model = os.path.join(tmp_path, final_model)
|
||||
|
||||
predict_cmd = PREDICT_COMMAND + [
|
||||
'--model={}'.format(model),
|
||||
'--checkpoint={}'.format(pifpaf_model),
|
||||
'-o={}'.format(tmp_path),
|
||||
]
|
||||
print(' '.join(predict_cmd))
|
||||
subprocess.run(predict_cmd, check=True, capture_output=True)
|
||||
print(os.listdir(tmp_path))
|
||||
assert 'out_000840.png.multi.png' in os.listdir(tmp_path)
|
||||
@ -1,12 +1,14 @@
|
||||
import os
|
||||
import sys
|
||||
|
||||
from monoloco.utils import pixel_to_camera
|
||||
from monoloco.utils import get_iou_matrix
|
||||
|
||||
# Python does not consider the current directory to be a package
|
||||
sys.path.insert(0, os.path.join('..', 'monoloco'))
|
||||
|
||||
|
||||
def test_iou():
|
||||
from stereoloco.utils import get_iou_matrix
|
||||
boxes_pred = [[1, 100, 1, 200]]
|
||||
boxes_gt = [[100., 120., 150., 160.],[12, 110, 130., 160.]]
|
||||
iou_matrix = get_iou_matrix(boxes_pred, boxes_gt)
|
||||
@ -14,7 +16,6 @@ def test_iou():
|
||||
|
||||
|
||||
def test_pixel_to_camera():
|
||||
from stereoloco.utils import pixel_to_camera
|
||||
kk = [[718.3351, 0., 600.3891], [0., 718.3351, 181.5122], [0., 0., 1.]]
|
||||
zz = 10
|
||||
uv_vector = [1000., 400.]
|
||||
|
||||
@ -1,23 +0,0 @@
|
||||
import os
|
||||
import sys
|
||||
from collections import defaultdict
|
||||
|
||||
from PIL import Image
|
||||
|
||||
# Python does not consider the current directory to be a package
|
||||
sys.path.insert(0, os.path.join('..', 'monoloco'))
|
||||
|
||||
|
||||
def test_printer():
|
||||
"""Draw a fake figure"""
|
||||
from stereoloco.visuals.printer import Printer
|
||||
test_list = [[718.3351, 0., 600.3891], [0., 718.3351, 181.5122], [0., 0., 1.]]
|
||||
boxes = [xx + [0] for xx in test_list]
|
||||
kk = test_list
|
||||
dict_ann = defaultdict(lambda: [1., 2., 3.], xyz_real=test_list, xyz_pred=test_list, uv_shoulders=test_list,
|
||||
boxes=boxes, boxes_gt=boxes)
|
||||
with open('docs/002282.png', 'rb') as f:
|
||||
pil_image = Image.open(f).convert('RGB')
|
||||
printer = Printer(image=pil_image, output_path=None, kk=kk, output_types=['combined'])
|
||||
figures, axes = printer.factory_axes()
|
||||
printer.draw(figures, axes, dict_ann, pil_image)
|
||||
1855
versioneer.py
Normal file
1855
versioneer.py
Normal file
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue
Block a user