add description social distancing

docs/MonoLoco++.md

@@ -57,7 +57,25 @@ To show all the instances estimated by MonoLoco add the argument `show_all` to t
 ![predict_all](out_002282.png.multi_all.jpg)
 
 ### Social Distancing
-WIP
+To visualize social distancing compliance, simply add the argument `--social-distance` to the predict command.
+
+An example from the Collective Activity Dataset is provided below.
+
+<img src="frame0038.jpg" width="400"/>
+
+To visualize social distancing run the below, command:
+```
+python -m monstereo.run predict \
+docs/frame0038.jpg \
+--net monoloco_pp  \
+--social_distance \
+--output_types front bird --show_all \
+--model data/models/monoloco_pp-201203-1424.pkl -o <output directory> 
+```
+<img src="out_frame0038.jpg.front.png" width="300"/>
+
+
+<img src="out_frame0038.jpg.bird.png" width="300"/>
 
 ### Orientation and Bounding Box dimensions
 MonoLoco++ estimates orientation and box dimensions as well. Results are saved in a json file when using the command 

monstereo/activity.py

@@ -251,7 +251,8 @@ def show_social(args, image_t, output_path, annotations, dic_out):
             draw_orientation(ax, uv_centers, sizes, angles, colors, mode='front')
 
     if 'bird' in args.output_types:
-        with bird_canvas(args, output_path) as ax1:
+        z_max = min(args.z_max, 4 + max([el[1] for el in xz_centers]))
+        with bird_canvas(output_path, z_max) as ax1:
             draw_orientation(ax1, xz_centers, [], angles, colors, mode='bird')
             draw_uncertainty(ax1, xz_centers, stds)
 
@@ -273,14 +274,14 @@ def get_pifpaf_outputs(annotations):
 
 
 @contextmanager
-def bird_canvas(args, output_path):
+def bird_canvas(output_path, z_max):
     fig, ax = plt.subplots(1, 1)
     fig.set_tight_layout(True)
     output_path = output_path + '.bird.png'
-    x_max = args.z_max / 1.5
-    ax.plot([0, x_max], [0, args.z_max], 'k--')
-    ax.plot([0, -x_max], [0, args.z_max], 'k--')
-    ax.set_ylim(0, args.z_max + 1)
+    x_max = z_max / 1.5
+    ax.plot([0, x_max], [0, z_max], 'k--')
+    ax.plot([0, -x_max], [0, z_max], 'k--')
+    ax.set_ylim(0, z_max + 1)
     yield ax
     fig.savefig(output_path)
     plt.close(fig)

monstereo/predict.py

@@ -37,7 +37,7 @@ def factory_from_args(args):
     if not args.checkpoint:
         args.checkpoint = 'data/models/shufflenetv2k30-201104-224654-cocokp-d75ed641.pkl'  # Default model
 
-    # Decices
+    # Devices
     args.device = torch.device('cpu')
     args.disable_cuda = False
     args.pin_memory = False
@@ -98,8 +98,8 @@ def predict(args):
     for batch_i, (image_tensors_batch, _, meta_batch) in enumerate(data_loader):
         pred_batch = processor.batch(model, image_tensors_batch, device=args.device)
 
-        # unbatch
-        for pred, meta in zip(pred_batch, meta_batch):
+        # unbatch (only for MonStereo)
+        for idx, (pred, meta) in enumerate(zip(pred_batch, meta_batch)):
             LOG.info('batch %d: %s', batch_i, meta['file_name'])
             pred = preprocess.annotations_inverse(pred, meta)
 
@@ -112,7 +112,7 @@ def predict(args):
             print('image', batch_i, meta['file_name'], output_path)
             pifpaf_out = [ann.json_data() for ann in pred]
 
-            if batch_i == 0:
+            if idx == 0:
                 pifpaf_outputs = pred  # to only print left image for stereo
                 pifpaf_outs = {'left': pifpaf_out}
                 with open(meta_batch[0]['file_name'], 'rb') as f:
@@ -120,7 +120,7 @@ def predict(args):
             else:
                 pifpaf_outs['right'] = pifpaf_out
 
-        # Load the original image
+        # 3D Predictions
         if args.net in ('monoloco_pp', 'monstereo'):
 
             im_name = os.path.basename(meta['file_name'])

monstereo/run.py

@@ -53,18 +53,16 @@ def cli():
     predict_parser.add_argument('--path_gt', help='path of json file with gt 3d localization',
                                 default='data/arrays/names-kitti-200615-1022.json')
     predict_parser.add_argument('--transform', help='transformation for the pose', default='None')
-    predict_parser.add_argument('--z_max', type=int, help='maximum meters distance for predictions', default=30)
+    predict_parser.add_argument('--z_max', type=int, help='maximum meters distance for predictions', default=100)
     predict_parser.add_argument('--n_dropout', type=int, help='Epistemic uncertainty evaluation', default=0)
     predict_parser.add_argument('--dropout', type=float, help='dropout parameter', default=0.2)
     predict_parser.add_argument('--show_all', help='only predict ground-truth matches or all', action='store_true')
 
     # Social distancing and social interactions
     predict_parser.add_argument('--social_distance', help='social', action='store_true')
-    predict_parser.add_argument('--json_dir', help='for social')
     predict_parser.add_argument('--threshold_prob', type=float, help='concordance for samples', default=0.25)
-    predict_parser.add_argument('--threshold_dist', type=float, help='min distance of people', default=2)
-    predict_parser.add_argument('--margin', type=float, help='conservative for noise in orientation', default=1.5)
-    predict_parser.add_argument('--radii', type=tuple, help='o-space radii', default=(0.25, 1, 2))
+    predict_parser.add_argument('--threshold_dist', type=float, help='min distance of people', default=2.5)
+    predict_parser.add_argument('--radii', type=tuple, help='o-space radii', default=(0.3, 0.5, 1))
 
     # Training
     training_parser.add_argument('--joints', help='Json file with input joints',