Update app.py

app.py

@@ -63,7 +63,6 @@ def perform_detection(image, interpreter, labels):
     classes = interpreter.get_tensor(output_details[classes_idx]['index'])[0]
     scores = interpreter.get_tensor(output_details[scores_idx]['index'])[0]
 
-    detections = []
     for i in range(len(scores)):
         if ((scores[i] > 0.5) and (scores[i] <= 1.0)):
             ymin = int(max(1, (boxes[i][0] * imH)))
@@ -79,7 +78,6 @@ def perform_detection(image, interpreter, labels):
             cv2.rectangle(image, (xmin, label_ymin - labelSize[1] - 10), (xmin + labelSize[0], label_ymin + baseLine - 10), (255, 255, 255), cv2.FILLED)
             cv2.putText(image, label, (xmin, label_ymin - 7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2)
 
-            detections.append([object_name, scores[i], xmin, ymin, xmax, ymax])
     return image
 
 def resize_image(image, size=640):
@@ -93,7 +91,7 @@ def detect_image(input_image):
     return Image.fromarray(result_image_resized)
 
 def detect_video(input_video):
-    cap = cv2.VideoCapture(input_video)
+    cap = cv2.VideoCapture(input_video.name)
     frames = []
 
     while cap.isOpened():
@@ -101,10 +99,8 @@ def detect_video(input_video):
         if not ret:
             break
 
-        resized_frame = resize_image(frame)
-        result_frame = perform_detection(resized_frame, interpreter, labels)
-        result_frame_resized = resize_image(result_frame)
-        frames.append(result_frame_resized)
+        result_frame = perform_detection(frame, interpreter, labels)
+        frames.append(result_frame)
 
     cap.release()
 
@@ -113,7 +109,7 @@ def detect_video(input_video):
 
     height, width, layers = frames[0].shape
     size = (width, height)
-    output_video_path = "result_" + os.path.basename(input_video)
+    output_video_path = "result_" + os.path.basename(input_video.name)
     out = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'mp4v'), 15, size)
 
     for frame in frames: