Demo

cfgs/pose_res152.yaml

@@ -1,4 +1,4 @@
-GPUS: '0'
+GPUS: (0,)
 DATA_DIR: ''
 OUTPUT_DIR: 'output'
 LOG_DIR: 'log'

evaluate.sh

@@ -4,9 +4,9 @@
 # pose_number is how many number of keypoints we want to see in a person
 # pose_threshold is the score we set to filter the keypoints whose score is small 
 # ground_truth='/export/home/cyh/mygithub/PoseGCN/data/annotations/val_2017/'
-ground_truth='${PGPT_ROOT}/data/demodataset/annotations/'
-predictions='${PGPT_ROOT}/results/demo'
-results='test'
+ground_truth='/PGPT/data/demodataset/annotations'
+predictions='/PGPT/results/demo'
+results='/PGPT/results/evaluate'
 pose_number=0
 pose_threshold=0.5
 

inference/config.py

@@ -1,31 +1,33 @@
 
 class Config():
-    root = '${PGPT_ROOT}'
+    root = '/PGPT'
+
     # save_dir is the loaction where we store the results
     save_dir = root + '/results/demo'
+
     # json_path_detection is the loaction where we store the detection results
     json_path_detection = root + '/results/demo_detection.json'
-
-    # gt_json_path is the gound truth of the validiation, all the ground_truth are in one file
+    
+    # gt_json_path is the ground truth of the validiation, all the ground_truth are in one file
     gt_json_path = root + '/data/demo_val.json'
-
+    
     # the data folder of the PoseTrack dataset
     data_folder = root + '/data/demodataset'
-
+    
     # the path of the location where we store the video
-    video_path = root 
-
+    video_path = save_dir
+    
     # the path of the track model
     track_model = root + '/models/tracker.pth'
-
+    
     # the path of the pose estimation model
     pose_model = root + '/models/pose_gcn.pth.tar'
-
+    
     # pose config file location
     pose_cfg = root + '/cfgs/pose_res152.yaml'
-
+    
     # the path of the embedding model
     embedding_model = root + '/models/embedding_model.pth'
-
+    
     def __init__(self):
         print('Using the config class at', __file__)

inference/inference.py

@@ -54,10 +54,18 @@
 config = Config()
 def parseArgs():
 	parser = argparse.ArgumentParser(description="Evaluation of Pose Estimation and Tracking (PoseTrack)")
+	#arser.add_argument('--cfg', type=str, required=True) #added by alnguyen
 	parser.add_argument("-t", "--detection_thresh",dest = 'det_thresh',required=False, default=0.4, type= float)
 	parser.add_argument("-p", "--pos_thresh",dest = 'pose_thresh',required=False, default=0, type= float)
 	parser.add_argument("-v", "--vis_flag",dest = 'vis_flag',required=False, default=False, type= bool)
-	return parser.parse_args()
+	#parser.add_argument('opts', 
+                        #help='Modify config options using the command-line',
+                        #default=None,
+                        #nargs=argparse.REMAINDER) #added by alnguyen
+
+	args = parser.parse_args()
+
+	return args
 
 class DateEncoder(json.JSONEncoder):
 	def default(self, obj):
@@ -87,24 +95,24 @@ def track_test(args, gpu_id=0):
 	if not os.path.exists(save_dir):
 		os.makedirs(save_dir)
 
-	# Load the Detection Results 
+	# Load the Detection Results (demo_detection.json)
 	with open(json_path,'r') as f:
 		bbox_dict = json.load(f)
 
 	# Create the Tracker
 	tracker = Track_And_Detect(gpu_id=gpu_id, track_model=config.track_model, pose_model=config.pose_model, embedding_model=config.embedding_model)
 
-	# Load the Ground Truth to get the right video keys
+	# Load the Ground Truth to get the right video keys (demo_val.json)
 	with open(gt_json_path,'r') as f:
 		gt_dict = json.load(f)
 
 
 	video_keys = gt_dict.keys()
 	pbar = tqdm(range(len(video_keys)))
-	for video_name in video_keys:
+	for video_name in video_keys: #in demo_val.json
 		pbar.update(1)
 		frame_dict = bbox_dict[video_name]
-		video_name = video_name.replace('.json','')
+		#video_name = video_name.replace('.json','')
 		video_json = {'annolist':[]}
 		save_path = os.path.join(save_dir, video_name+'.json')
 		idx =0
@@ -124,7 +132,7 @@ def track_test(args, gpu_id=0):
 					if not os.path.exists(video_path):
 						os.makedirs(video_path)
 					video_store_name = video_path + '/{}.mp4'
-					videoWriter = cv2.VideoWriter(video_store_name.format(video_name),fourcc,10,(im_W,im_H))
+					videoWriter = cv2.VideoWriter(video_store_name.format(video_name+'-pgpt'),fourcc,10,(im_W,im_H))
 				final_list = tracker.init_tracker(frame,det_list)
 			else:	
 				track_list = tracker.multi_track(frame)

inference/pose_estimation_graph.py

@@ -56,7 +56,7 @@
 '''
 class PoseNet(object):
 	def __init__(self, gpu_id=0, model_path=None):
-		self.cfg_file='${PGPT_ROOT}/cfgs/pose_res152.yaml'
+		self.cfg_file='/PGPT/cfgs/pose_res152.yaml'
 		self.flag = 0
 
 		update_config(self.cfg_file)

inference/skeleton_visulize.py

@@ -5,12 +5,13 @@
 import cv2
 import numpy as np
 from cv2_color import Color
+import glob
+from config import Config
 
 # The match list from the results to the test
 match_list=[13,12,14,9,8,10,7,11,6,3,2,4,1,5,0]
 color = Color(flag='bgr')
 
-
 def draw_limb(image, kps, color):
     def draw_line(head, tail):
         if head == [] or tail == []:
@@ -34,20 +35,21 @@ def draw_line(head, tail):
         draw_line(kps[h], kps[t])
 
 
-
 def demo(image_dir, result_dir, save_dir):
     """
     image_dir: the location where the frames are stored
     result_dir: the results in 2017PT fromat, each video has one file 
     save_dir: the loaction where we store the result videos
     """
 
-    json_files = os.listdir(result_dir)
+    #json_files = os.listdir(result_dir)
+    json_files = glob.glob(result_dir + "/*.json")
+    json_files = [json_file.split('/')[-1] for json_file in json_files]
 
     pbar = tqdm(range(len(json_files)))
     for json_name in json_files:
 
-        video_name = json_name.replace('.json','_new')
+        video_name = json_name.replace('.json','-pgpt')
 
         video_folder = os.path.join(save_dir, video_name)
 
@@ -58,11 +60,13 @@ def demo(image_dir, result_dir, save_dir):
             old_annolist = json.load(f)['annolist']
         pbar.set_description('Visulizing video {}'.format(video_name))
         color_list = color.get_random_color_list()
+        j = 0
         for i,annotation in enumerate(old_annolist):
             color_flag = 0
             frame_name = annotation['image'][0]['name']
-            frame_store_path = video_folder + '/{}'.format(frame_name.split('/')[-1])
-            frame_path = os.path.join(image_dir,frame_name)
+            #annotation['image_id'] #10010010103=frame_id
+            frame_store_path = os.path.join(video_folder, frame_name.split('/')[-1]) #file
+            frame_path = os.path.join(image_dir,frame_name) #file for read
             frame = cv2.imread(frame_path)
             im_H, im_W, im_C = frame.shape
             if i==0:
@@ -73,32 +77,40 @@ def demo(image_dir, result_dir, save_dir):
                 if len(anno['annopoints']) == 0:
                     continue
                 old_point_list = anno['annopoints'][0]['point']
-
                 xmin, xmax, ymin, ymax, track_id = anno['x1'][0], anno['x2'][0], anno['y1'][0], anno['y2'][0], anno['track_id'][0]
                 color_flag = int(track_id) % 16
-
                 kps = [[] for _ in range(15)]
                 for pose in old_point_list:
-
                     pose_id, pose_x, pose_y, = pose['id'][0], pose['x'][0], pose['y'][0] 
                     kps[pose_id] = (int(pose_x), int(pose_y))
-                    cv2.circle(frame,(int(pose_x),int(pose_y)), 3 ,color_list[color_flag], -1)  
+                    cv2.circle(frame,(int(pose_x),int(pose_y)), 3 ,color_list[color_flag], -1)
                 draw_limb(frame, kps, color_list[color_flag])
                 cv2.rectangle(frame, (int(xmin),int(ymin)), (int(xmax),int(ymax)), color_list[color_flag], 3)            
                 cv2.putText(frame, 'id:' + str(track_id), (int(xmin),int(ymin)), cv2.FONT_HERSHEY_SIMPLEX, 1, color_list[color_flag], 2)
             videoWriter.write(frame)
             cv2.imwrite(frame_store_path, frame)
+        #videoWriter.release()
         pbar.update(1)
     pbar.close()
 
 
 if __name__ == '__main__':
     print('Visualizing the results')
-    image_dir = '${PGPT_ROOT}/data/demodataset/'
-    result_dir = '${PGPT_ROOT}/results/demo/'
-    save_dir = '${PGPT_ROOT}/results/render/'
-    if not os.path.exists(save_dir):
-        os.makedirs(save_dir)
-    demo(image_dir, result_dir, save_dir)
+    config = Config()
+    parser = argparse.ArgumentParser(description="Visualizing the results")
+    parser.add_argument("--image_dir", type=str, default=config.data_folder) # /PGPT/data/demodataset
+    parser.add_argument("--result_dir", type=str, default=config.save_dir) # /PGPT/results/demo
+    parser.add_argument("--save_dir", type=str, default=config.video_path) # /PGPT/results/demo
+	#parser.add_argument('--cfg', type=str, required=True) #added by alnguyen
+	#parser.add_argument('opts', 
+                        #help='Modify config options using the command-line',
+                        #default=None,
+                        #nargs=argparse.REMAINDER) #added by alnguyen
+
+    args = parser.parse_args()
+
+    if not os.path.exists(args.save_dir):
+        os.makedirs(args.save_dir)
+    demo(args.image_dir, args.result_dir, args.save_dir)
 
 

inference/track_and_detect_new.py

@@ -15,7 +15,7 @@
 from tracker import SiamFCTracker
 
 from match import Matcher
-from model.nms.nms_wrapper import nms
+#from model.nms.nms_wrapper import nms
 
 class Track_And_Detect(object):
 	effective_track_thresh = 0.5

lib/poseval/py/eval_helpers.py

@@ -227,8 +227,9 @@ def process_arguments(argv):
   elif len(argv)<3 or len(argv)>4:
     help()
 
-  gt_file = argv[1]
-  pred_file = argv[2]
+  gt_dir = argv[1]
+  pred_dir = argv[2]
+  return gt_dir, pred_dir, mode
 
   if not os.path.exists(gt_file):
     help('Given ground truth directory does not exist!\n')
@@ -282,7 +283,6 @@ def load_data(argv):
 
   return gtFramesAll, prFramesAll
 
-
 def cleanupData(gtFramesAll,prFramesAll):
 
   # remove all GT frames with empty annorects and remove corresponding entries from predictions
@@ -377,6 +377,7 @@ def load_data_dir(argv):
   if not os.path.exists(pred_dir):
     help('Given prediction directory ' + pred_dir + ' does not exist!\n')
   filenames = glob.glob(gt_dir + "/*.json")
+
   gtFramesAll = []
   prFramesAll = []