fix post processing and add description

docs/MonoLoco++.md

@@ -18,9 +18,40 @@ 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.
 
+##  Predictions
+For a quick setup download a pifpaf and a MonoLoco++ models from here and save them into `data/models`.
+
+### 3D Localization
+The predict script receives an image (or an entire folder using glob expressions), 
+calls PifPaf for 2d human pose detection over the image
+and runs Monoloco++ for 3d location of the detected poses.
+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).
+
+
+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`
+
+Below an example image and comparison with ground-truth. 
+Ground-truth KITTI files for comparing results can be downloaded from here and should be saved into `data/arrays`
+Ground-truth files can also be generated, more info in the preprocessing section
+```
+python -m monstereo.run predict --net monoloco_pp --glob docs/002282.png --output_types multi 
+--model data/models/monoloco_pp-201203-1424.pkl -o <desired output directory> --long-edge 2500 --n_dropout 50
+```
+
+To show all the instances estimated by MonoLoco add the argument `show_all` to the above command.
+
+### Social Distancing
+WIP
+
+### Orientation and Bounding Box dimensions
+MonoLoco++ 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.
+
 ## Preprocessing
 
-# Kitti
+### Kitti
 Annotations from a pose detector needs to be stored in a folder.
 For example by using [openpifpaf](https://github.com/vita-epfl/openpifpaf):
 ```
@@ -134,6 +165,3 @@ python -m monstereo.run eval
 --model <path to the model>   
 --dir_ann <annotation directory>
 ```
-
-###  Predictions
-Currently under development. For the moment, please refer to the master branch

monstereo/network/net.py

@@ -163,7 +163,7 @@ class Loco:
                 print("found {} matches with ground-truth".format(len(matches)))
 
             # Keep track of instances non-matched
-            idxs_matches = (el[0] for el in matches)
+            idxs_matches = [el[0] for el in matches]
             not_matches = [idx for idx, _ in enumerate(boxes) if idx not in idxs_matches]
 
         else:

monstereo/predict.py

@@ -26,32 +26,29 @@ LOG = logging.getLogger(__name__)
 
 def factory_from_args(args):
 
-    # Merge the model_pifpaf argument
-    if not args.checkpoint:
-        args.checkpoint = 'shufflenetv2k30'  # Default model
-    # glob
+    # Data
     if args.glob:
         args.images += glob.glob(args.glob)
     if not args.images:
         raise Exception("no image files given")
 
-    # add args.device
+    # Model
+    if not args.checkpoint:
+        args.checkpoint = 'data/models/shufflenetv2k30-201104-224654-cocokp-d75ed641.pkl'  # Default model
+
+    # Decices
     args.device = torch.device('cpu')
     args.disable_cuda = False
     args.pin_memory = False
     if torch.cuda.is_available():
         args.device = torch.device('cuda')
         args.pin_memory = True
-
-    # Add num_workers
     args.loader_workers = 8
 
     # Add visualization defaults
     args.figure_width = 10
     args.dpi_factor = 1.0
 
-    # TODO
-    args.long_edge = None
     if args.net == 'monstereo':
         args.batch_size = 2
     else:
@@ -59,7 +56,7 @@ def factory_from_args(args):
 
     # Make default pifpaf argument
     args.force_complete_pose = True
-    args.instance_threshold = 0.15
+    print("Force complete pose is active")
 
     # Configure
     decoder.configure(args)

monstereo/run.py

@@ -37,13 +37,14 @@ def cli():
                                      'json bird front or multi for MonStereo')
     predict_parser.add_argument('--no_save', help='to show images', action='store_true')
     predict_parser.add_argument('--dpi', help='image resolution',  type=int, default=100)
+    predict_parser.add_argument('--long-edge', default=None, type=int,
+                                help='rescale the long side of the image (aspect ratio maintained)')
 
     # Pifpaf parsers
     decoder.cli(predict_parser)
     network.cli(predict_parser)
     show.cli(predict_parser)
     visualizer.cli(predict_parser)
-    predict_parser.add_argument('--scale', default=1.0, type=float, help='change the scale of the image to preprocess')
 
     # Monoloco
     predict_parser.add_argument('--net', help='Choose network: monoloco, monoloco_p, monoloco_pp, monstereo')

monstereo/utils/iou.py

@@ -57,7 +57,7 @@ def get_iou_matches(boxes, boxes_gt, iou_min=0.3):
             ious.append(iou)
         idx_gt_max = int(np.argmax(ious))
         if (ious[idx_gt_max] >= iou_min) and (idx_gt_max not in used):
-            matches.append((idx, idx_gt_max))
+            matches.append((int(idx), idx_gt_max))
             used.append(idx_gt_max)
     return matches
 
@@ -93,6 +93,6 @@ def reorder_matches(matches, boxes, mode='left_rigth'):
 
     # Order the boxes based on the left-right position in the image and
     ordered_boxes = np.argsort([box[0] for box in boxes])  # indices of boxes ordered from left to right
-    matches_left = [idx for (idx, _) in matches]
+    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]