Update app_utils.py

app_utils.py

@@ -88,162 +88,162 @@ def preprocess_video_and_predict(video):
 
 #to return scores
 def preprocess_video_and_rank(video):
-    return None, None, None
-#     cap = cv2.VideoCapture(video)
-#     w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-#     h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-#     fps = np.round(cap.get(cv2.CAP_PROP_FPS))
-
-#     path_save_video_face = 'result_face.mp4'
-#     vid_writer_face = cv2.VideoWriter(path_save_video_face, cv2.VideoWriter_fourcc(*'mp4v'), fps, (224, 224))
-
-#     # path_save_video_hm = 'result_hm.mp4'
-#     # vid_writer_hm = cv2.VideoWriter(path_save_video_hm, cv2.VideoWriter_fourcc(*'mp4v'), fps, (224, 224))
-
-#     lstm_features = []
-#     count_frame = 1
-#     count_face = 0
-#     probs = []
-#     frames = []
-#     last_output = None
-#     last_heatmap = None 
-#     cur_face = None
-
-#     with mp_face_mesh.FaceMesh(
-#     max_num_faces=1,
-#     refine_landmarks=False,
-#     min_detection_confidence=0.5,
-#     min_tracking_confidence=0.5) as face_mesh:
-
-#         while cap.isOpened():
-#             _, frame = cap.read()
-#             if frame is None: break
-
-#             frame_copy = frame.copy()
-#             frame_copy.flags.writeable = False
-#             frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
-#             results = face_mesh.process(frame_copy)
-#             frame_copy.flags.writeable = True
-
-#             if results.multi_face_landmarks:
-#                 for fl in results.multi_face_landmarks:
-#                     startX, startY, endX, endY  = get_box(fl, w, h)
-#                     cur_face = frame_copy[startY:endY, startX: endX]
-
-#                     if count_face%config_data.FRAME_DOWNSAMPLING == 0:
-#                         cur_face_copy = pth_processing(Image.fromarray(cur_face))
-#                         with torch.no_grad():
-#                             features = torch.nn.functional.relu(pth_model_static.extract_features(cur_face_copy)).detach().numpy()
-
-#                         # grayscale_cam = cam(input_tensor=cur_face_copy)
-#                         # grayscale_cam = grayscale_cam[0, :]
-#                         # cur_face_hm = cv2.resize(cur_face,(224,224), interpolation = cv2.INTER_AREA)
-#                         # cur_face_hm = np.float32(cur_face_hm) / 255
-#                         # heatmap = show_cam_on_image(cur_face_hm, grayscale_cam, use_rgb=False)
-#                         # last_heatmap = heatmap
+    
+    cap = cv2.VideoCapture(video)
+    w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = np.round(cap.get(cv2.CAP_PROP_FPS))
+
+    path_save_video_face = 'result_face.mp4'
+    vid_writer_face = cv2.VideoWriter(path_save_video_face, cv2.VideoWriter_fourcc(*'mp4v'), fps, (224, 224))
+
+    # path_save_video_hm = 'result_hm.mp4'
+    # vid_writer_hm = cv2.VideoWriter(path_save_video_hm, cv2.VideoWriter_fourcc(*'mp4v'), fps, (224, 224))
+
+    lstm_features = []
+    count_frame = 1
+    count_face = 0
+    probs = []
+    frames = []
+    last_output = None
+    last_heatmap = None 
+    cur_face = None
+
+    with mp_face_mesh.FaceMesh(
+    max_num_faces=1,
+    refine_landmarks=False,
+    min_detection_confidence=0.5,
+    min_tracking_confidence=0.5) as face_mesh:
+
+        while cap.isOpened():
+            _, frame = cap.read()
+            if frame is None: break
+
+            frame_copy = frame.copy()
+            frame_copy.flags.writeable = False
+            frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
+            results = face_mesh.process(frame_copy)
+            frame_copy.flags.writeable = True
+
+            if results.multi_face_landmarks:
+                for fl in results.multi_face_landmarks:
+                    startX, startY, endX, endY  = get_box(fl, w, h)
+                    cur_face = frame_copy[startY:endY, startX: endX]
+
+                    if count_face%config_data.FRAME_DOWNSAMPLING == 0:
+                        cur_face_copy = pth_processing(Image.fromarray(cur_face))
+                        with torch.no_grad():
+                            features = torch.nn.functional.relu(pth_model_static.extract_features(cur_face_copy)).detach().numpy()
+
+                        # grayscale_cam = cam(input_tensor=cur_face_copy)
+                        # grayscale_cam = grayscale_cam[0, :]
+                        # cur_face_hm = cv2.resize(cur_face,(224,224), interpolation = cv2.INTER_AREA)
+                        # cur_face_hm = np.float32(cur_face_hm) / 255
+                        # heatmap = show_cam_on_image(cur_face_hm, grayscale_cam, use_rgb=False)
+                        # last_heatmap = heatmap
         
-#                         if len(lstm_features) == 0:
-#                             lstm_features = [features]*10
-#                         else:
-#                             lstm_features = lstm_features[1:] + [features]
-
-#                         lstm_f = torch.from_numpy(np.vstack(lstm_features))
-#                         lstm_f = torch.unsqueeze(lstm_f, 0)
-#                         with torch.no_grad():
-#                             output = pth_model_dynamic(lstm_f).detach().numpy()
-#                         last_output = output
-
-#                         if count_face == 0:
-#                             count_face += 1
-
-#                     else:
-#                         if last_output is not None:
-#                             output = last_output
-#                             # heatmap = last_heatmap
-
-#                         elif last_output is None:
-#                             output = np.empty((1, 7))
-#                             output[:] = np.nan
+                        if len(lstm_features) == 0:
+                            lstm_features = [features]*10
+                        else:
+                            lstm_features = lstm_features[1:] + [features]
+
+                        lstm_f = torch.from_numpy(np.vstack(lstm_features))
+                        lstm_f = torch.unsqueeze(lstm_f, 0)
+                        with torch.no_grad():
+                            output = pth_model_dynamic(lstm_f).detach().numpy()
+                        last_output = output
+
+                        if count_face == 0:
+                            count_face += 1
+
+                    else:
+                        if last_output is not None:
+                            output = last_output
+                            # heatmap = last_heatmap
+
+                        elif last_output is None:
+                            output = np.empty((1, 7))
+                            output[:] = np.nan
                             
-#                     probs.append(output[0])
-#                     frames.append(count_frame)
-#             else:
-#                 if last_output is not None:
-#                     lstm_features = []
-#                     empty = np.empty((7))
-#                     empty[:] = np.nan
-#                     probs.append(empty)
-#                     frames.append(count_frame)                        
-
-#             if cur_face is not None:
-#                 # heatmap_f = display_info(heatmap, 'Frame: {}'.format(count_frame), box_scale=.3)
-
-#                 cur_face = cv2.cvtColor(cur_face, cv2.COLOR_RGB2BGR)
-#                 cur_face = cv2.resize(cur_face, (224,224), interpolation = cv2.INTER_AREA)
-#                 cur_face = display_info(cur_face, 'Frame: {}'.format(count_frame), box_scale=.3)
-#                 vid_writer_face.write(cur_face)
-#                 # vid_writer_hm.write(heatmap_f)
-
-#             count_frame += 1
-#             if count_face != 0:
-#                 count_face += 1
-
-#         vid_writer_face.release()
-#         # vid_writer_hm.release()
-
-#         stat = statistics_plot(frames, probs)
-
-#         if not stat:
-#             return None, None
-
-#     #for debug
-#     print(type(frames))
-#     print(frames)
-#     print(type(probs))
-#     print(probs)        
-#     # to calculate scores
-#     nan=float('nan')
-#     s1 = 0
-#     s2 = 0
-#     s3 = 0
-#     s4 = 0
-#     s5 = 0
-#     s6 = 0
-#     s7 = 0
-#     frames_len=len(frames)
-#     for i in range(frames_len):
-#         if np.isnan(probs[i][0]):
-#             frames_len=frames_len-1
-#         else: 
-#             s1=s1+probs[i][0]
-#             s2=s2+probs[i][1]
-#             s3=s3+probs[i][2]
-#             s4=s4+probs[i][3]
-#             s5=s5+probs[i][4]
-#             s6=s6+probs[i][5]
-#             s7=s7+probs[i][6]
-#     s1=s1/frames_len
-#     s2=s2/frames_len
-#     s3=s3/frames_len
-#     s4=s4/frames_len
-#     s5=s5/frames_len
-#     s6=s6/frames_len
-#     s7=s7/frames_len
-#     scores=[s1,s2,s3,s4,s5,s6,s7]
-#     scores_str=str(scores)
-#     with open("local_data/data.txt",'a', encoding="utf8") as f:
-#         f.write(scores_str+'\n')
-
-#     with open("local_data/data.txt",'r', encoding="utf8") as f:
-#         for i in f:
-#             print(i)
-
-
-#     #trans the audio file
-#     my_audio_clip = AudioFileClip(video)
-#     my_audio_clip.write_audiofile("data/audio.wav",ffmpeg_params=["-ac","1"])
+                    probs.append(output[0])
+                    frames.append(count_frame)
+            else:
+                if last_output is not None:
+                    lstm_features = []
+                    empty = np.empty((7))
+                    empty[:] = np.nan
+                    probs.append(empty)
+                    frames.append(count_frame)                        
+
+            if cur_face is not None:
+                # heatmap_f = display_info(heatmap, 'Frame: {}'.format(count_frame), box_scale=.3)
+
+                cur_face = cv2.cvtColor(cur_face, cv2.COLOR_RGB2BGR)
+                cur_face = cv2.resize(cur_face, (224,224), interpolation = cv2.INTER_AREA)
+                cur_face = display_info(cur_face, 'Frame: {}'.format(count_frame), box_scale=.3)
+                vid_writer_face.write(cur_face)
+                # vid_writer_hm.write(heatmap_f)
+
+            count_frame += 1
+            if count_face != 0:
+                count_face += 1
+
+        vid_writer_face.release()
+        # vid_writer_hm.release()
+
+        stat = statistics_plot(frames, probs)
+
+        if not stat:
+            return None, None
+
+    #for debug
+    print(type(frames))
+    print(frames)
+    print(type(probs))
+    print(probs)        
+    # to calculate scores
+    nan=float('nan')
+    s1 = 0
+    s2 = 0
+    s3 = 0
+    s4 = 0
+    s5 = 0
+    s6 = 0
+    s7 = 0
+    frames_len=len(frames)
+    for i in range(frames_len):
+        if np.isnan(probs[i][0]):
+            frames_len=frames_len-1
+        else: 
+            s1=s1+probs[i][0]
+            s2=s2+probs[i][1]
+            s3=s3+probs[i][2]
+            s4=s4+probs[i][3]
+            s5=s5+probs[i][4]
+            s6=s6+probs[i][5]
+            s7=s7+probs[i][6]
+    s1=s1/frames_len
+    s2=s2/frames_len
+    s3=s3/frames_len
+    s4=s4/frames_len
+    s5=s5/frames_len
+    s6=s6/frames_len
+    s7=s7/frames_len
+    scores=[s1,s2,s3,s4,s5,s6,s7]
+    scores_str=str(scores)
+    with open("local_data/data.txt",'a', encoding="utf8") as f:
+        f.write(scores_str+'\n')
+
+    with open("local_data/data.txt",'r', encoding="utf8") as f:
+        for i in f:
+            print(i)
+
+
+    #trans the audio file
+    my_audio_clip = AudioFileClip(video)
+    my_audio_clip.write_audiofile("data/audio.wav",ffmpeg_params=["-ac","1"])
     
-#     return stat,scores_str,"data/audio.wav"
+    return stat,scores_str,"data/audio.wav"
 
 ###########################################################################################################################
 def video_score(video):