computer vision 1

app.py

@@ -6,8 +6,7 @@ import os
 from ultralytics import YOLO
 
 file_urls = [
-    'https://github.com/pirahansiah/farshid/blob/eda35793771c298696dba7eadc0fbc74931ef8fd/OpenCV_book/5/AIoT.png',
-    'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1'    
+    'https://onedrive.live.com/embed?resid=CEE9ECC964C491B8%2123411&authkey=%21AFboK-sTQYxPuQo'    
 ]
 
 def download_file(url, save_name):
@@ -17,16 +16,10 @@ def download_file(url, save_name):
         open(save_name, 'wb').write(file.content)
 
 for i, url in enumerate(file_urls):
-    if 'mp4' in file_urls[i]:
-        download_file(
-            file_urls[i],
-            f"video.mp4"
-        )
-    else:
-        download_file(
-            file_urls[i],
-            f"image_{i}.png"
-        )
+    download_file(
+        file_urls[i],
+        f"image_{i}.png"
+    )
 
 
 path  = [['image_0.png']]
@@ -54,44 +47,4 @@ interface_image = gr.Interface(
 gr.TabbedInterface(
     [interface_image],
     tab_names=['Image Processing']
-).queue().launch()
-
-# 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"),
-
-# ]
-# 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()
+).queue().launch(share=True)