computer vision 1

app.py

@@ -6,7 +6,8 @@ import os
 from ultralytics import YOLO
 
 file_urls = [
-    'https://lh3.googleusercontent.com/a2iyhpYl4Jgzc0r7MYgXQI1BGwkutp3rKuauNpkEbD3Z_HP-gf29M-wugKebKJQdl8ILtKWN-vOZAS9r1qMsI88=w16383'    
+    'https://lh3.googleusercontent.com/a2iyhpYl4Jgzc0r7MYgXQI1BGwkutp3rKuauNpkEbD3Z_HP-gf29M-wugKebKJQdl8ILtKWN-vOZAS9r1qMsI88=w16383',
+    'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1'    
 ]
 
 def download_file(url, save_name):
@@ -50,42 +51,42 @@ interface_image = gr.Interface(
     cache_examples=False,
 )
 
-# def show_preds_video(video_path):
-#     cap = cv2.VideoCapture(video_path)
-#     while(cap.isOpened()):
-#         ret, frame = cap.read()
-#         if ret:
-#             frame_copy = frame.copy()
-#             outputs = model.predict(source=frame)
-#             results = outputs[0].cpu().numpy()
-#             for i, det in enumerate(results.boxes.xyxy):
-#                 cv2.rectangle(
-#                     frame_copy,
-#                     (int(det[0]), int(det[1])),
-#                     (int(det[2]), int(det[3])),
-#                     color=(0, 0, 255),
-#                     thickness=2,
-#                     lineType=cv2.LINE_AA
-#                 )
-#             yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
+def show_preds_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    while(cap.isOpened()):
+        ret, frame = cap.read()
+        if ret:
+            frame_copy = frame.copy()
+            outputs = model.predict(source=frame)
+            results = outputs[0].cpu().numpy()
+            for i, det in enumerate(results.boxes.xyxy):
+                cv2.rectangle(
+                    frame_copy,
+                    (int(det[0]), int(det[1])),
+                    (int(det[2]), int(det[3])),
+                    color=(0, 0, 255),
+                    thickness=2,
+                    lineType=cv2.LINE_AA
+                )
+            yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
 
-# inputs_video = [
-#     gr.components.Video(type="filepath", label="Input Video"),
+inputs_video = [
+    gr.components.Video(type="filepath", label="Input Video"),
 
-# ]
-# outputs_video = [
-#     gr.components.Image(type="numpy", label="Output Image"),
-# ]
-# interface_video = gr.Interface(
-#     fn=show_preds_video,
-#     inputs=inputs_video,
-#     outputs=outputs_video,
-#     title="Pothole detector",
-#     examples=video_path,
-#     cache_examples=False,
-# )
+]
+outputs_video = [
+    gr.components.Image(type="numpy", label="Output Image"),
+]
+interface_video = gr.Interface(
+    fn=show_preds_video,
+    inputs=inputs_video,
+    outputs=outputs_video,
+    title="Video Processing ",
+    examples=video_path,
+    cache_examples=False,
+)
 
-# gr.TabbedInterface(
-#     [interface_image, interface_video],
-#     tab_names=['Image inference', 'Video inference']
-# ).queue().launch()
+gr.TabbedInterface(
+    [interface_image, interface_video],
+    tab_names=['Image inference', 'Video inference']
+).queue().launch()