from IPython.display import HTML, display from base64 import b64encode def show_preds_video(video_path, model_path, output_video_path='output_video.mp4', frame_skip=5, image_size=640, conf_threshold=0.25, iou_threshold=0.45): """ Process video frames with YOLO model and annotate the frames. """ cap = cv2.VideoCapture(video_path) # Open the video file fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Define the codec for the output video out = cv2.VideoWriter(output_video_path, fourcc, 20.0, (int(cap.get(3)), int(cap.get(4)))) # Create a VideoWriter object to save the output video frame_count = 0 # Initialize frame count while cap.isOpened(): # Loop through frames success, frame = cap.read() # Read a frame from the video if success: if frame_count % frame_skip == 0: # Process every nth frame, as specified by frame_skip model = YOLO(model_path) # Load the YOLO model model.overrides['conf'] = conf_threshold # Set the confidence threshold for predictions model.overrides['iou'] = iou_threshold # Set the IoU threshold for NMS model.overrides['agnostic_nms'] = False # NMS class-agnostic setting model.overrides['max_det'] = 1000 # Maximum number of detections per frame results = model.predict(frame) # Perform inference on the frame annotated_frame = results[0].plot() # Annotate the frame with detection results out.write(annotated_frame) # Write the annotated frame to the output video else: out.write(frame) # Write the original frame to the output video if not processed frame_count += 1 # Increment the frame count else: break # Break the loop if no frame is read (end of video) cap.release() # Release the video capture object out.release() # Release the video writer object cv2.destroyAllWindows() # Close all OpenCV windows # Return the path to the saved annotated video return output_video_path def display_video(video_path): """ Display the video in the notebook. """ display(HTML(f""" <video width="600" controls> <source src="{video_path}" type="video/mp4"> </video> <a href="{video_path}" download>Download Video</a> """)) # Execution video_path = '/content/video_input.mp4' # Replace with video path model_path = 'foduucom/stockmarket-pattern-detection-yolov8' output_video_path = show_preds_video(video_path, model_path) display_video(output_video_path)

Candlestick Pattern Recognition with YOLO

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