update readme and compatibility

docs/MonStereo.md

@@ -149,13 +149,20 @@ data/kitti/images`
 
 
 ### Annotations to preprocess
-MonStereo is trained using 2D human pose joints. To create them run pifaf over KITTI training images. 
+MonStereo is trained using 2D human pose joints. To obtain the joints the first step is to run 
-You can create them running the predict script and using `--mode pifpaf`.
+pifaf over KITTI training images, by either running the predict script and using `--mode pifpaf`,
+ or by using pifpaf code directly.
+MonStereo preprocess script expects annotations from left and right images in 2 different folders 
+with the same path apart from the suffix `_right`  for the ``right" folder. 
+For example `data/annotations` and `data/annotations_right`. 
+ Do not change name of json files created by pifpaf. For each left annotation, 
+ the code will look for the corresponding right annotation.
 
 ### Inputs joints for training
 MonoStereo is trained using 2D human pose joints matched with the ground truth location provided by
 KITTI Dataset. To create the joints run: `python3 -m monstereo.run prep` specifying:
-1. `--dir_ann` annotation directory containing Pifpaf joints of KITTI. 
+
+`--dir_ann` annotation directory containing Pifpaf joints of KITTI for the left images.
 
 
 ### Ground truth file for evaluation

monstereo/prep/prep_kitti.py

@@ -24,10 +24,9 @@ from .transforms import flip_inputs, flip_labels, height_augmentation
 class PreprocessKitti:
     """Prepare arrays with same format as nuScenes preprocessing but using ground truth txt files"""
 
-    # AV_W = 0.68
+    dir_gt = os.path.join('data', 'kitti', 'gt')
-    # AV_L = 0.75
+    dir_images = '/data/lorenzo-data/kitti/original_images/training/image_2'
-    # AV_H = 1.72
+    dir_byc_l = '/data/lorenzo-data/kitti/object_detection/left'
-    # WLH_STD = 0.1
 
     # SOCIAL DISTANCING PARAMETERS
     THRESHOLD_DIST = 2  # Threshold to check distance of people
@@ -51,9 +50,6 @@ class PreprocessKitti:
         self.dir_ann = dir_ann
         self.iou_min = iou_min
         self.monocular = monocular
-        self.dir_gt = os.path.join('data', 'kitti', 'gt')
-        self.dir_images = '/data/lorenzo-data/kitti/original_images/training/image_2'
-        self.dir_byc_l = '/data/lorenzo-data/kitti/object_detection/left'
         self.names_gt = tuple(os.listdir(self.dir_gt))
         self.dir_kk = os.path.join('data', 'kitti', 'calib')
         self.list_gt = glob.glob(self.dir_gt + '/*.txt')

monstereo/run.py

@@ -2,6 +2,7 @@
 
 import argparse
 
+from openpifpaf.network.factory import cli as openpifpaf_cli
 from openpifpaf.network import nets
 from openpifpaf import decoder
 
@@ -41,8 +42,8 @@ def cli():
     predict_parser.add_argument('--dpi', help='image resolution',  type=int, default=100)
 
     # Pifpaf
-    nets.cli(predict_parser)
+    openpifpaf_cli(predict_parser)
-    decoder.cli(predict_parser, force_complete_pose=True, instance_threshold=0.15)
+    decoder.cli(predict_parser)
     predict_parser.add_argument('--scale', default=1.0, type=float, help='change the scale of the image to preprocess')
 
     # Monoloco

monstereo/utils/kitti.py

@@ -199,11 +199,11 @@ def factory_file(path_calib, dir_ann, basename, mode='left'):
 
     if mode == 'left':
         kk, tt = p_left[:]
-        path_ann = os.path.join(dir_ann, basename + '.png.pifpaf.json')
+        path_ann = os.path.join(dir_ann, basename + '.png.predictions.json')
 
     else:
         kk, tt = p_right[:]
-        path_ann = os.path.join(dir_ann + '_right', basename + '.png.pifpaf.json')
+        path_ann = os.path.join(dir_ann + '_right', basename + '.png.predictions.json')
 
     from ..utils import open_annotations
     annotations = open_annotations(path_ann)