set xlim and convert images to jpeg

README.md

@@ -5,14 +5,14 @@ This repository contains the code for two research projects:
 1. **MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization**   
 [README](https://github.com/vita-epfl/monstereo/tree/master/docs/MonStereo.md) & [Article](https://arxiv.org/abs/2008.10913)
      
-     ![monstereo 1](docs/000840_multi.png)
+     ![monstereo 1](docs/000840_multi.jpg)
     
 2.  **Perceiving Humans: from Monocular 3D Localization to Social Distancing (MonoLoco++)**         
  [README](https://github.com/vita-epfl/monstereo/tree/master/docs/MonoLoco_pp.md) & [Article](https://arxiv.org/abs/2009.00984)
  
-    ![social distancing](docs/pull_sd.png)
+    ![social distancing](docs/social_distancing.jpg)
  
-    ![monoloco_pp](docs/truck.png)
+    ![monoloco_pp](docs/truck.jpg)
 
 Both projects has been built upon the CVPR'19 project [Openpifpaf](https://github.com/vita-epfl/openpifpaf) 
 for 2D pose estimation and the ICCV'19 project [MonoLoco](https://github.com/vita-epfl/monoloco) for monocular 3D localization. 

docs/MonStereo.md

@@ -66,13 +66,13 @@ After downloading model and ground-truth file, a demo can be tested with the fol
  --model data/models/ms-200710-1511.pkl --z_max 30 --checkpoint resnet152 --path_gt data/arrays/names-kitti-200615-1022.json
  -o data/output`
  
-![Crowded scene](out_000840.png)
+![Crowded scene](out_000840.jpg)
 
 `python3 -m monstereo.run predict --glob docs/005523*.png --output_types multi --scale 2
  --model data/models/ms-200710-1511.pkl --z_max 30 --checkpoint resnet152 --path_gt data/arrays/names-kitti-200615-1022.json
  -o data/output`
 
-![Occluded hard example](out_005523.png)
+![Occluded hard example](out_005523.jpg)
 
 # Preprocessing
 Preprocessing and training step are already fully supported by the code provided, 

monstereo/visuals/figures.py

@@ -28,7 +28,7 @@ def show_results(dic_stats, clusters, net, dir_fig, show=False, save=False):
     x_max = 31
     y_min = 0
     # y_max = 2.2
-    y_max = 3.5 if net == 'monstereo' else 2.6
+    y_max = 3.5 if net == 'monstereo' else 2.7
     xx = np.linspace(x_min, x_max, 100)
     excl_clusters = ['all', 'easy', 'moderate', 'hard', '49']
     clusters = [clst for clst in clusters if clst not in excl_clusters]
@@ -76,10 +76,10 @@ def show_spread(dic_stats, clusters, net, dir_fig, show=False, save=False):
 
     assert net in ('monoloco_pp', 'monstereo'), "network not recognized"
     phase = 'test'
-    excl_clusters = ['all', 'easy', 'moderate', 'hard']
+    excl_clusters = ['all', 'easy', 'moderate', 'hard', '49']
     clusters = [clst for clst in clusters if clst not in excl_clusters]
     x_min = 3
-    x_max = 42
+    x_max = 31
     y_min = 0
 
     plt.figure(2, figsize=FIGSIZE)
@@ -87,10 +87,10 @@ def show_spread(dic_stats, clusters, net, dir_fig, show=False, save=False):
     bbs = np.array([dic_stats[phase][net][key]['std_ale'] for key in clusters[:-1]])
     xx = np.linspace(x_min, x_max, 100)
     if net == 'monoloco_pp':
-        y_max = 5
+        y_max = 2.7
         color = 'deepskyblue'
         epis = np.array([dic_stats[phase][net][key]['std_epi'] for key in clusters[:-1]])
-        plt.plot(xxs, epis, marker='o', color='coral', label="Combined uncertainty (\u03C3)")
+        plt.plot(xxs, epis, marker='o', color='coral', linewidth=4, markersize=8, label="Combined uncertainty (\u03C3)")
     else:
         y_max = 3.5
         color = 'b'