Dark theme only with webcam (#65)

* Only dark theme with webcam

* Fixed axes colors

* Update printer.py

* Fixed axes in printer.py

* Fixed test images names

* Fixed README

* Fixed README

* Fixed bird view

* Linting

* Linting

.github/workflows/tests.yml

@@ -10,14 +10,16 @@ on:
     paths:
       - 'monoloco/**'
       - 'test/**'
-      - 'docs/test*'
+      - 'docs/00*.png'
+      - 'docs/frame0032.jpg'
       - '.github/workflows/tests.yml'
 
   pull_request:
     paths:
       - 'monoloco/**'
       - 'test/**'
-      - 'docs/test*'
+      - 'docs/00*.png'
+      - 'docs/frame0032.jpg'
       - '.github/workflows/tests.yml'
 
 jobs:

README.md

@@ -17,7 +17,7 @@ This library is based on three research projects for monocular/stereo 3D human l
 [T. Mordan](https://people.epfl.ch/taylor.mordan/?lang=en), [A. Alahi](https://scholar.google.com/citations?user=UIhXQ64AAAAJ&hl=en)_, ICRA 2021 <br /> 
 __[Article](https://arxiv.org/abs/2008.10913)__  &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;   __[Citation](#Citation)__  &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; __[Video](https://www.youtube.com/watch?v=pGssROjckHU)__
      
-<img src="docs/out_test_000840_multi.jpg" width="700"/>
+<img src="docs/out_000840_multi.jpg" width="700"/>
 
 ---
 
@@ -125,24 +125,24 @@ If you provide a ground-truth json file to compare the predictions of the networ
 For an example image, run the following command:
 
 ```sh
-python3 -m monoloco.run predict docs/test_002282.png \
+python3 -m monoloco.run predict docs/002282.png \
 --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>
 ```
 
-![predict](docs/out_test_002282.png.multi.jpg)
+![predict](docs/out_002282.png.multi.jpg)
 
 To show all the instances estimated by MonoLoco add the argument `--show_all` to the above command.
 
-![predict_all](docs/out_test_002282.png.multi_all.jpg)
+![predict_all](docs/out_002282.png.multi_all.jpg)
 
 It is also possible to run [openpifpaf](https://github.com/vita-epfl/openpifpaf) directly
 by using `--mode keypoints`. All the other pifpaf arguments are also supported 
 and can be checked with `python3 -m monoloco.run predict --help`.
 
-![predict](docs/out_test_002282_pifpaf.jpg)
+![predict](docs/out_002282_pifpaf.jpg)
 
 
 **Stereo Examples** <br /> 
@@ -156,12 +156,12 @@ You can load one or more image pairs using glob expressions. For example:
 
 ```sh
 python3 -m monoloco.run predict --mode stereo \
---glob docs/test_000840*.png
+--glob docs/000840*.png
  --path_gt <to match results with ground-truths> \
  -o data/output  -long_edge 2500
  ```
  
-![Crowded scene](docs/out_test_000840_multi.jpg)
+![Crowded scene](docs/out_000840_multi.jpg)
 
 ```sh
 python3 -m monoloco.run predict --glob docs/005523*.png \ --output_types multi \
@@ -183,7 +183,7 @@ For more info, run:
 **Examples** <br>
 An example from the Collective Activity Dataset is provided below.
 
-<img src="docs/test_frame0032.jpg" width="500"/>
+<img src="docs/frame0032.jpg" width="500"/>
 
 To visualize social distancing run the below, command:
 
@@ -192,11 +192,11 @@ pip3 install scipy
 ```
 
 ```sh
-python3 -m monoloco.run predict docs/test_frame0032.jpg \
+python3 -m monoloco.run predict docs/frame0032.jpg \
 --activities social_distance --output_types front bird 
 ```
 
-<img src="docs/out_test_frame0032_front_bird.jpg" width="700"/>
+<img src="docs/out_frame0032_front_bird.jpg" width="700"/>
 
 ## C) Hand-raising detection
 To detect raised hand, you can add the argument `--activities raise_hand` to the prediction command.

monoloco/visuals/printer.py

@@ -114,7 +114,8 @@ class Printer:
     def factory_axes(self, dic_out):
         """Create axes for figures: front bird multi"""
 
-        plt.style.use('dark_background')
+        if self.webcam:
+            plt.style.use('dark_background')
 
         axes = []
         figures = []
@@ -217,7 +218,8 @@ class Printer:
     def _bird_loop(self, iterator, axes, colors, number):
         for idx in iterator:
             if any(xx in self.output_types for xx in ['bird', 'multi']) and self.zz_pred[idx] > 0:
-                draw_orientation(axes[1], self.xz_centers, [], self.angles, colors, mode='bird')
+                draw_orientation(axes[1], self.xz_centers[:len(iterator)], [],
+                                self.angles[:len(iterator)], colors, mode='bird')
                 # Draw ground truth and uncertainty
                 self._draw_uncertainty(axes, idx)
 
@@ -424,17 +426,19 @@ class Printer:
             ax.get_yaxis().set_visible(False)
 
         else:
+            line_style = 'w--' if self.webcam else 'k--'
             uv_max = [0., float(self.height)]
             xyz_max = pixel_to_camera(uv_max, self.kk, self.z_max)
             x_max = abs(xyz_max[0]) # shortcut to avoid oval circles in case of different kk
             corr = round(float(x_max / 3))
-            ax.plot([0, x_max], [0, self.z_max], 'w--')
-            ax.plot([0, -x_max], [0, self.z_max], 'w--')
+            ax.plot([0, x_max], [0, self.z_max], line_style)
+            ax.plot([0, -x_max], [0, self.z_max], line_style)
             ax.set_xlim(-x_max + corr, x_max - corr)
             ax.set_ylim(0, self.z_max + 1)
             ax.set_xlabel("X [m]")
-            ax.set_box_aspect(.8)
-            plt.xlim((-x_max, x_max))
+            if self.webcam:
+                ax.set_box_aspect(.8)
+                plt.xlim((-x_max, x_max))
             plt.xticks(fontsize=self.attr['fontsize_ax'])
             plt.yticks(fontsize=self.attr['fontsize_ax'])
         return ax

tests/test_train_mono.py

@@ -24,7 +24,7 @@ TRAIN_COMMAND = [
 PREDICT_COMMAND = [
     'python3', '-m', 'monoloco.run',
     'predict',
-    'docs/test_002282.png',
+    'docs/002282.png',
     '--output_types', 'multi', 'json',
     '--decoder-workers=0'  # for windows
 ]
@@ -32,7 +32,7 @@ PREDICT_COMMAND = [
 PREDICT_COMMAND_SOCIAL_DISTANCE = [
     'python3', '-m', 'monoloco.run',
     'predict',
-    'docs/test_frame0032.jpg',
+    'docs/frame0032.jpg',
     '--activities', 'social_distance',
     '--output_types', 'front', 'bird',
     '--decoder-workers=0'  # for windows'
@@ -64,8 +64,8 @@ def test_train_mono(tmp_path):
     print(' '.join(predict_cmd))
     subprocess.run(predict_cmd, check=True, capture_output=True)
     print(os.listdir(tmp_path))
-    assert 'out_test_002282.png.multi.png' in os.listdir(tmp_path)
-    assert 'out_test_002282.png.monoloco.json' in 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),
@@ -75,5 +75,5 @@ def test_train_mono(tmp_path):
     print(' '.join(predict_cmd_sd))
     subprocess.run(predict_cmd_sd, check=True, capture_output=True)
     print(os.listdir(tmp_path))
-    assert 'out_test_frame0032.jpg.front.png' in os.listdir(tmp_path)
-    assert 'out_test_frame0032.jpg.bird.png' in 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)

tests/test_train_stereo.py

@@ -26,7 +26,7 @@ PREDICT_COMMAND = [
     'python3', '-m', 'monoloco.run',
     'predict',
     '--mode=stereo',
-    '--glob', 'docs/test_000840*.png',
+    '--glob', 'docs/000840*.png',
     '--output_types', 'multi', 'json',
     '--decoder-workers=0',  # for windows'
 ]
@@ -56,4 +56,4 @@ def test_train_stereo(tmp_path):
     print(' '.join(predict_cmd))
     subprocess.run(predict_cmd, check=True, capture_output=True)
     print(os.listdir(tmp_path))
-    assert 'out_test_000840.png.multi.png' in os.listdir(tmp_path)
+    assert 'out_000840.png.multi.png' in os.listdir(tmp_path)