Update app.py

app.py

@@ -73,8 +73,8 @@ def sigmoid(x):
 
 
 def preprocess_image(img, img_size):
-    img = square_pad_opencv(img)
-    img = cv2.resize(img, (img_size, img_size), interpolation=cv2.INTER_CUBIC)
+    #img = square_pad_opencv(img)
+    #img = cv2.resize(img, (img_size, img_size), interpolation=cv2.INTER_CUBIC)
     img = Image.fromarray(img)
     transforms_list = []
     transforms_list.append(transforms.ToTensor())
@@ -116,10 +116,12 @@ def inference(x, count_only_api, api_key,
             frame = all_frames[-1]  # padding will be with last frame
             break
         frame = cv2.cvtColor(np.uint8(frame), cv2.COLOR_BGR2RGB)
-        # add square padding with opencv
-        frame = square_pad_opencv(frame)
-        frame = cv2.resize(frame, (img_size, img_size), interpolation=cv2.INTER_CUBIC)
-        #img = Image.fromarray(frame)
+        frame = cv2.resize(frame, (resize_size, resize_size), interpolation=cv2.INTER_CUBIC)
+        frame_center_x = frame.shape[1] // 2
+        frame_center_y = frame.shape[0] // 2
+        crop_x = frame_center_x - img_size // 2
+        crop_y = frame_center_y - img_size // 2
+        frame = frame[crop_y:crop_y+img_size, crop_x:crop_x+img_size]
         all_frames.append(frame)
         frame_i += 1
     cap.release()
@@ -136,7 +138,7 @@ def inference(x, count_only_api, api_key,
     batch_list = []
     idx_list = []
     inference_futures = []
-    with concurrent.futures.ThreadPoolExecutor() as executor:
+    with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
         for i in tqdm(range(0, length + stride_length - stride_pad, stride_length)):
             batch = all_frames[i:i + seq_len]
             Xlist = []