edited

app.py

@@ -49,9 +49,8 @@ def read_frames(dest_path):
     images.append(preprocess(image))
   return original_images, images
 
-def process_video_parallel(url, skip_frames, dest_path, process_number):
-    cap = cv2.VideoCapture(url)
-    num_processes = os.cpu_count()
+def process_video_parallel(video, skip_frames, dest_path, num_processes, process_number):
+    cap = cv2.VideoCapture(video)
     chunks_per_process = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) // (num_processes * skip_frames)
     count =  skip_frames * chunks_per_process * process_number
     print(f"worker: {process_number}, process frames {count} ~ {skip_frames * chunks_per_process * (process_number + 1)}")
@@ -90,7 +89,7 @@ def vid2frames(url, sampling_interval=1, ext='mp4'):
     print('extracting frames...')
     n_workers = os.cpu_count()
     with Pool(n_workers) as pool:
-        pool.map(partial(process_video_parallel, video, skip_frames, dest_path), range(n_workers))
+        pool.map(partial(process_video_parallel, video, skip_frames, dest_path, n_workers), range(n_workers))
     return dest_path
 
 
@@ -138,7 +137,7 @@ def run_inference(url, sampling_interval, search_query):
     return(title, image_output)
 
 inputs = [gr.inputs.Textbox(label="Give us the link to your youtube video!"),
-          gr.Number(5),
+          gr.Number(5,label='sampling interval (seconds)'),
           gr.inputs.Textbox(label="What do you want to search?")]
 outputs = [
     gr.outputs.HTML(label=""),  # To be used as title

packages.txt

@@ -1,2 +1 @@
-python3-opencv
-libssl-dev
+ffmpeg

requirements.txt

@@ -1,4 +1,4 @@
 git+https://github.com/openai/CLIP.git
 torch
 youtube_dl
-opencv-python
+opencv-python-headless