add factory method

src/features/preprocess_ki.py

@@ -1,13 +1,13 @@
-"""Preprocess annnotations with KITTI ground-truth"""
+"""Preprocess annotations with KITTI ground-truth"""
 
 import os
 import glob
-import math
+import copy
 import logging
 from collections import defaultdict
 import json
 import datetime
-from utils.kitti import get_calibration, check_conditions, split_training
+from utils.kitti import get_calibration, split_training, parse_ground_truth
 from utils.pifpaf import get_input_data, preprocess_pif
 from utils.misc import get_idx_max, append_cluster
 
@@ -47,23 +47,18 @@ class PreprocessKitti:
         self.set_train, self.set_val = split_training(self.names_gt, path_train, path_val)
 
     def run(self):
+        """Save json files"""
 
         cnt_gt = 0
         cnt_fnf = 0
         dic_cnt = {'train': 0, 'val': 0, 'test': 0}
 
         for name in self.names_gt:
-            # Extract ground truth
             path_gt = os.path.join(self.dir_gt, name)
             basename, _ = os.path.splitext(name)
-            boxes_gt = []
-            dds = []
 
-            if name in self.set_train:
+            phase, flag = self._factory_phase(name)
-                phase = 'train'
+            if flag:
-            elif name in self.set_val:
-                phase = 'val'
-            else:
                 cnt_fnf += 1
                 continue
 
@@ -72,18 +67,11 @@ class PreprocessKitti:
             kk, tt = get_calibration(path_txt)
 
             # Iterate over each line of the gt file and save box location and distances
-            with open(path_gt, "r") as f_gt:
+            boxes_gt, dds_gt, _, _ = parse_ground_truth(path_gt)
-                for line_gt in f_gt:
+            self.dic_names[basename + '.png']['boxes'] = copy.deepcopy(boxes_gt)
-                    if check_conditions(line_gt, mode='gt'):
+            self.dic_names[basename + '.png']['dds'] = copy.deepcopy(dds_gt)
-                        box = [float(x) for x in line_gt.split()[4:8]]
+            self.dic_names[basename + '.png']['K'] = copy.deepcopy(kk.tolist())
-                        boxes_gt.append(box)
+            cnt_gt += len(boxes_gt)
-                        loc_gt = [float(x) for x in line_gt.split()[11:14]]
-                        dd = math.sqrt(loc_gt[0] ** 2 + loc_gt[1] ** 2 + loc_gt[2] ** 2)
-                        dds.append(dd)
-                        self.dic_names[basename + '.png']['boxes'].append(box)
-                        self.dic_names[basename + '.png']['dds'].append(dd)
-                        self.dic_names[basename + '.png']['K'] = kk.tolist()
-                        cnt_gt += 1
 
             # Find the annotations if exists
             try:
@@ -103,13 +91,13 @@ class PreprocessKitti:
 
                     self.dic_jo[phase]['kps'].append(uv_kps[ii])
                     self.dic_jo[phase]['X'].append(inputs[ii])
-                    self.dic_jo[phase]['Y'].append([dds[idx_max]])  # Trick to make it (nn,1)
+                    self.dic_jo[phase]['Y'].append([dds_gt[idx_max]])  # Trick to make it (nn,1)
                     self.dic_jo[phase]['K'] = kk.tolist()
                     self.dic_jo[phase]['names'].append(name)  # One image name for each annotation
-                    append_cluster(self.dic_jo, phase, inputs[ii], dds[idx_max], uv_kps[ii])
+                    append_cluster(self.dic_jo, phase, inputs[ii], dds_gt[idx_max], uv_kps[ii])
                     dic_cnt[phase] += 1
                     boxes_gt.pop(idx_max)
-                    dds.pop(idx_max)
+                    dds_gt.pop(idx_max)
 
         with open(self.path_joints, 'w') as file:
             json.dump(self.dic_jo, file)
@@ -122,5 +110,16 @@ class PreprocessKitti:
               .format(cnt_gt, cnt_fnf))
         print("\nOutput files:\n{}\n{}\n".format(self.path_names, self.path_joints))
 
+    def _factory_phase(self, name):
+        """Choose the phase"""
 
+        phase = None
+        flag = False
+        if name in self.set_train:
+            phase = 'train'
+        elif name in self.set_val:
+            phase = 'val'
+        else:
+            flag = True
+        return phase, flag
 

src/utils/kitti.py

@@ -161,5 +161,4 @@ def parse_ground_truth(path_gt):
                 loc_gt = [float(x) for x in line_gt.split()[11:14]]
                 dds_gt.append(math.sqrt(loc_gt[0] ** 2 + loc_gt[1] ** 2 + loc_gt[2] ** 2))
 
-    return boxes_gt, dds_gt, truncs_gt, occs_gt
+    return boxes_gt, dds_gt, truncs_gt, occs_gt
-

src/visuals/results.py

@@ -1,6 +1,5 @@
 
 import os
-import time
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.patches import Ellipse