fix in case of no dir

README.md

@@ -3,14 +3,14 @@
 <img src="docs/monoloco.gif" alt="gif" />
 
 
-This library is based on three research projects for monocular/stereo 3D human localization, orientation and social distancing.
+This library is based on three research projects for monocular/stereo 3D human localization (detection), body orientation, and social distancing.
 
 > __MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization__<br /> 
 > _[L. Bertoni](https://scholar.google.com/citations?user=f-4YHeMAAAAJ&hl=en), [S. Kreiss](https://www.svenkreiss.com), 
 [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](#Todo)__
      
-<img src="docs/000840_multi.jpg" width="700"/>
+<img src="docs/out_000840_multi.jpg" width="700"/>
 
 ---
 
@@ -128,7 +128,7 @@ To show all the instances estimated by MonoLoco add the argument `show_all` to t
 
 It is also possible to run [openpifpaf](https://github.com/vita-epfl/openpifpaf) directly
 by usingt `--mode keypoints`. All the other pifpaf arguments are also supported 
-and can be checked with `python -m monstereo.run predict --help`.
+and can be checked with `python -m monoloco.run predict --help`.
 
 ![predict](docs/out_002282_pifpaf.jpg)
 
@@ -149,16 +149,17 @@ python3 -m monoloco.run predict --mode stereo \
  -o data/output  -long_edge 2500
  ```
  
-![Crowded scene](docs/out_000840.jpg)
+![Crowded scene](docs/out_000840_multi.jpg)
 
 ```
 python3 -m monoloco.run predict --glob docs/005523*.png \ --output_types multi \
 --model data/models/ms-200710-1511.pkl \
 --path_gt <to match results with ground-truths> \
- -o data/output  --long_edge 2500
+-o data/output  --long_edge 2500 \
+--instance-threshold 0.05 --seed-threshold 0.05
  ```
 
-![Occluded hard example](docs/out_005523.jpg)
+![Occluded hard example](docs/out_005523.png.multi.jpg)
 
 ## B) Social Distancing (and Talking activity)
 To visualize social distancing compliance, simply add the argument `--social-distance` to the predict command. This visualization is not supported with a stereo camera.
@@ -180,17 +181,16 @@ python -m monoloco.run predict docs/frame0038.jpg \
 <img src="docs/out_frame0038.jpg.front_bird.jpg" width="700"/>
 
 
-
 ## C) Orientation and Bounding Box dimensions 
-MonoLoco++ estimates orientation and box dimensions as well. Results are saved in a json file when using the command 
+The network 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.
 
-<br>
+<br /> 
 
 ## Training
 We train on the KITTI dataset (MonoLoco/Monoloco++/MonStereo) or the nuScenes dataset (MonoLoco) specifying the path of the json file containing the input joints. Please download them [heere](https://drive.google.com/file/d/1e-wXTO460ip_Je2NdXojxrOrJ-Oirlgh/view?usp=sharing) or follow [preprocessing instructions](#Preprocessing).
 
-Our results for MonoLoco++ are obtained with: 
+Results for MonoLoco++ are obtained with: 
 
 ```
 python -m monoloco.run train --joints data/arrays/joints-kitti-201202-1743.json --save --monocular
@@ -207,7 +207,7 @@ Finally, for a more extensive list of available parameters, run:
 
 `python -m monstereo.run train --help`
 
-<br>
+<br /> 
 
 ## Preprocessing
 Preprocessing and training step are already fully supported by the code provided, 

monoloco/predict.py

@@ -108,6 +108,7 @@ def factory_from_args(args):
     else:
         args.batch_size = 1
 
+    # Patch for stereo images with batch_size = 2
     if args.batch_size == 2 and not args.long_edge:
         args.long_edge = 1238
         LOG.info("Long-edge set to %i".format(args.long_edge))
@@ -177,7 +178,9 @@ def predict(args):
                 else:
                     file_name = os.path.basename(meta['file_name'])
                     output_path = os.path.join(args.output_directory, 'out_' + file_name)
-                print('image', batch_i, meta['file_name'], output_path)
+
+                im_name = os.path.basename(meta['file_name'])
+                print(f'{batch_i} image {im_name} saved as {output_path}')
 
             # Only for MonStereo
             else:
@@ -186,7 +189,7 @@ def predict(args):
         # 3D Predictions
         if args.mode != 'keypoints':
             im_size = (cpu_image.size[0], cpu_image.size[1])  # Original
-            kk, dic_gt = factory_for_gt(im_size, focal_length=args.focal, name=file_name, path_gt=args.path_gt)
+            kk, dic_gt = factory_for_gt(im_size, focal_length=args.focal, name=im_name, path_gt=args.path_gt)
 
             # Preprocess pifpaf outputs and run monoloco
             boxes, keypoints = preprocess_pifpaf(pifpaf_outs['left'], im_size, enlarge_boxes=False)

monoloco/run.py

@@ -45,6 +45,8 @@ def cli():
     predict_parser.add_argument('--font-size', default=0, type=int, help='annotation font size')
     predict_parser.add_argument('--monocolor-connections', default=False, action='store_true',
                                 help='use a single color per instance')
+    predict_parser.add_argument('--instance-threshold', type=float, default=None, help='threshold for entire instance')
+    predict_parser.add_argument('--seed-threshold', type=float, default=None, help='threshold for single seed')
     predict_parser.add_argument('--disable-cuda', action='store_true', help='disable CUDA')
     predict_parser.add_argument('--focal',
                                 help='focal length in mm for a sensor size of 7.2x5.4 mm. Default nuScenes sensor',