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@ -19,7 +19,7 @@ Our vision-based system (i) is privacy-safe, (ii) works with any fixed or moving
This readme is in Beta version and refers to the `update` branch. It is currently under development. This readme is in Beta version and refers to the `update` branch. It is currently under development.
## Predictions ## Predictions
For a quick setup download a pifpaf and a MonoLoco++ models from here and save them into `data/models`. For a quick setup download a pifpaf and a MonoLoco++ models from TODO and save them into `data/models`.
### 3D Localization ### 3D Localization
The predict script receives an image (or an entire folder using glob expressions), The predict script receives an image (or an entire folder using glob expressions),
@ -28,20 +28,34 @@ and runs Monoloco++ for 3d location of the detected poses.
The command `--net` defines if saving pifpaf outputs, MonoLoco++ outputs or MonStereo ones. The command `--net` defines if saving pifpaf outputs, MonoLoco++ outputs or MonStereo ones.
You can check all commands for Pifpaf at [openpifpaf](https://github.com/vita-epfl/openpifpaf). You can check all commands for Pifpaf at [openpifpaf](https://github.com/vita-epfl/openpifpaf).
Output options include json files and/or visualization of the predictions on the image in *frontal mode*, Output options include json files and/or visualization of the predictions on the image in *frontal mode*,
*birds-eye-view mode* or *combined mode* and can be specified with `--output_types` *birds-eye-view mode* or *combined mode* and can be specified with `--output_types`
Below an example image and comparison with ground-truth. Ground-truth KITTI files for comparing results can be downloaded from
Ground-truth KITTI files for comparing results can be downloaded from here and should be saved into `data/arrays` [here](https://drive.google.com/drive/folders/1jZToVMBEZQMdLB5BAIq2CdCLP5kzNo9t?usp=sharing)
Ground-truth files can also be generated, more info in the preprocessing section (file called *names-kitti*) and should be saved into `data/arrays`
Ground-truth files can also be generated, more info in the preprocessing section.
For an example image, run the following command:
``` ```
python -m monstereo.run predict --net monoloco_pp --glob docs/002282.png --output_types multi python -m monstereo.run predict \
--model data/models/monoloco_pp-201203-1424.pkl -o <desired output directory> --long-edge 2500 --n_dropout 50 docs/002282.png \
--net monoloco_pp \
--output_types multi \
--model data/models/monoloco_pp-201203-1424.pkl \
--path_gt data/arrays/names-kitti-200615-1022.json \
-o <output directory> \
--long-edge <rescale the image by providing dimension of long side. If None original resolution>
--n_dropout <50 to include epistemic uncertainty, 0 otherwise>
``` ```
![predict_ground_truth](docs/out_002282.png.multi.jpg)
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.
![predict_all](docs/out_002282.png.multi_all.jpg)
### Social Distancing ### Social Distancing
WIP WIP