humpydonkey
commented
1 week ago hi @akushonkamen ,
The error suggests that your code runtime environment is missing a system library called ffmpeg. Could you try installing ffmpeg and then run it again?
I think you were running on Vision Agent locally on a Mac. If so, you can run "brew install ffmpeg" to install it. Ref: https://formulae.brew.sh/formula/ffmpeg
How to verify ffmpeg is installed?
Try the command in a terminal: ffmpeg -version
You can also try our free web app which has a working code environment: https://va.landing.ai/
I was trying the example code and trying to look for number of cars appeared in a file, but the agent seemed to stuck on an error with a loop,
{'code': 'from typing import \nfrom pillow_heif import register_heif_opener\nregister_heif_opener()\nimport vision_agent as va\nfrom vision_agent.tools import register_tool\n\nfrom vision_agent.tools import extract_frames, owl_v2\n\n# Ensure ffmpeg is installed\n!apt-get update\n!apt-get install -y ffmpeg\n\n\ndef count_cars_in_video(video_path: str, fps: float = 0.5, debug: bool = False) -> int:\n """\n Count the number of cars displayed in the given video.\n\n Parameters:\n video_path (str): The path to the video file.\n fps (float, optional): The frame rate per second to extract the frames. Defaults to 0.5.\n debug (bool, optional): If True, print debug information. Defaults to False.\n\n Returns:\n int: The total number of cars detected in the video.\n """\n # Step 1: Extract frames from the video\n frames_with_timestamps = extract_frames(video_path, fps)\n \n if debug:\n print(f"Extracted {len(frames_with_timestamps)} frames from the video.")\n\n # Step 2: Initialize the car counter\n total_car_count = 0\n\n # Step 3: Detect cars in each frame\n for frame, timestamp in frames_with_timestamps:\n # Detect cars in the current frame\n detections = owl_v2("car", frame)\n \n # Count the number of cars detected\n car_count = sum(1 for detection in detections if detection[\'label\'] == \'car\')\n \n if debug:\n print(f"Frame at {timestamp}s: Detected {car_count} cars.")\n \n # Add to the total car count\n total_car_count += car_count\n\n # Step 4: Return the total car count\n return total_car_count\n\n# Example usage (do not call this in the final submission):\n# video_path = "/Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4"\n# print(count_cars_in_video(video_path, fps=0.5, debug=True))\n', 'test': 'from vision_agent.tools import extract_frames, owl_v2\n\n# Ensure ffmpeg is installed\n!apt-get update\n!apt-get install -y ffmpeg\n\n\ndef test_count_cars_in_video():\n """\n Test case for the count_cars_in_video function.\n \n This test case verifies the fundamental functionality of the function under normal conditions.\n It uses the provided video file path and checks the output format and data structure.\n """\n # Given video file path\n video_path = "/Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4"\n \n # Call the function with the given video path\n car_count = count_cars_in_video(video_path, fps=0.5, debug=True)\n \n # Print the output\n print(car_count)\n \n # Return the output for further validation if needed\n return car_count\n\n# Run the test case\ntest_count_cars_in_video()\n', 'test_result': Execution(results=[], logs=Logs(stdout=[], stderr=[]), error=Error(name='CellExecutionError', value='An error occurred while executing the following cell:\n------------------\nfrom typing import \nfrom pillow_heif import register_heif_opener\nregister_heif_opener()\nimport vision_agent as va\nfrom vision_agent.tools import register_tool\nfrom vision_agent.tools import extract_frames, owl_v2\n\n# Ensure ffmpeg is installed\n!apt-get update\n!apt-get install -y ffmpeg\n\n\ndef count_cars_in_video(video_path: str, fps: float = 0.5, debug: bool = False) -> int:\n """\n Count the number of cars displayed in the given video.\n\n Parameters:\n video_path (str): The path to the video file.\n fps (float, optional): The frame rate per second to extract the frames. Defaults to 0.5.\n debug (bool, optional): If True, print debug information. Defaults to False.\n\n Returns:\n int: The total number of cars detected in the video.\n """\n # Step 1: Extract frames from the video\n frames_with_timestamps = extract_frames(video_path, fps)\n \n if debug:\n print(f"Extracted {len(frames_with_timestamps)} frames from the video.")\n\n # Step 2: Initialize the car counter\n total_car_count = 0\n\n # Step 3: Detect cars in each frame\n for frame, timestamp in frames_with_timestamps:\n # Detect cars in the current frame\n detections = owl_v2("car", frame)\n \n # Count the number of cars detected\n car_count = sum(1 for detection in detections if detection[\'label\'] == \'car\')\n \n if debug:\n print(f"Frame at {timestamp}s: Detected {car_count} cars.")\n \n # Add to the total car count\n total_car_count += car_count\n\n # Step 4: Return the total car count\n return total_car_count\n\n# Example usage (do not call this in the final submission):\n# video_path = "/Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4"\n# print(count_cars_in_video(video_path, fps=0.5, debug=True))\n\nfrom vision_agent.tools import extract_frames, owl_v2\n\n# Ensure ffmpeg is installed\n!apt-get update\n!apt-get install -y ffmpeg\n\n\ndef test_count_cars_in_video():\n """\n Test case for the count_cars_in_video function.\n \n This test case verifies the fundamental functionality of the function under normal conditions.\n It uses the provided video file path and checks the output format and data structure.\n """\n # Given video file path\n video_path = "/Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4"\n \n # Call the function with the given video path\n car_count = count_cars_in_video(video_path, fps=0.5, debug=True)\n \n # Print the output\n print(car_count)\n \n # Return the output for further validation if needed\n return car_count\n\n# Run the test case\ntest_count_cars_in_video()\n\n------------------\n\n----- stdout -----\nzsh:1: command not found: apt-get\n----- stdout -----\nzsh:1: command not found: apt-get\n----- stdout -----\nzsh:1: command not found: apt-get\n----- stdout -----\nzsh:1: command not found: apt-get\n------------------\n\n\x1b[0;31m---------------------------------------------------------------------------\x1b[0m\n\x1b[0;31mFileNotFoundError\x1b[0m Traceback (most recent call last)\nCell \x1b[0;32mIn[1], line 82\x1b[0m\n\x1b[1;32m 79\x1b[0m \x1b[38;5;28;01mreturn\x1b[39;00m car_count\n\x1b[1;32m 81\x1b[0m \x1b[38;5;66;03m# Run the test case\x1b[39;00m\n\x1b[0;32m---> 82\x1b[0m \x1b[43mtest_count_cars_in_video\x1b[49m\x1b[43m(\x1b[49m\x1b[43m)\x1b[49m\n\nCell \x1b[0;32mIn[1], line 73\x1b[0m, in \x1b[0;36mtest_count_cars_in_video\x1b[0;34m()\x1b[0m\n\x1b[1;32m 70\x1b[0m video_path \x1b[38;5;241m=\x1b[39m \x1b[38;5;124m"\x1b[39m\x1b[38;5;124m/Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4\x1b[39m\x1b[38;5;124m"\x1b[39m\n\x1b[1;32m 72\x1b[0m \x1b[38;5;66;03m# Call the function with the given video path\x1b[39;00m\n\x1b[0;32m---> 73\x1b[0m car_count \x1b[38;5;241m=\x1b[39m \x1b[43mcount_cars_in_video\x1b[49m\x1b[43m(\x1b[49m\x1b[43mvideo_path\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mfps\x1b[49m\x1b[38;5;241;43m=\x1b[39;49m\x1b[38;5;241;43m0.5\x1b[39;49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mdebug\x1b[49m\x1b[38;5;241;43m=\x1b[39;49m\x1b[38;5;28;43;01mTrue\x1b[39;49;00m\x1b[43m)\x1b[49m\n\x1b[1;32m 75\x1b[0m \x1b[38;5;66;03m# Print the output\x1b[39;00m\n\x1b[1;32m 76\x1b[0m \x1b[38;5;28mprint\x1b[39m(car_count)\n\nCell \x1b[0;32mIn[1], line 26\x1b[0m, in \x1b[0;36mcount_cars_in_video\x1b[0;34m(video_path, fps, debug)\x1b[0m\n\x1b[1;32m 14\x1b[0m \x1b[38;5;250m\x1b[39m\x1b[38;5;124;03m"""\x1b[39;00m\n\x1b[1;32m 15\x1b[0m \x1b[38;5;124;03mCount the number of cars displayed in the given video.\x1b[39;00m\n\x1b[1;32m 16\x1b[0m \n\x1b[0;32m (...)\x1b[0m\n\x1b[1;32m 23\x1b[0m \x1b[38;5;124;03m int: The total number of cars detected in the video.\x1b[39;00m\n\x1b[1;32m 24\x1b[0m \x1b[38;5;124;03m"""\x1b[39;00m\n\x1b[1;32m 25\x1b[0m \x1b[38;5;66;03m# Step 1: Extract frames from the video\x1b[39;00m\n\x1b[0;32m---> 26\x1b[0m frames_with_timestamps \x1b[38;5;241m=\x1b[39m \x1b[43mextract_frames\x1b[49m\x1b[43m(\x1b[49m\x1b[43mvideo_path\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mfps\x1b[49m\x1b[43m)\x1b[49m\n\x1b[1;32m 28\x1b[0m \x1b[38;5;28;01mif\x1b[39;00m debug:\n\x1b[1;32m 29\x1b[0m \x1b[38;5;28mprint\x1b[39m(\x1b[38;5;124mf\x1b[39m\x1b[38;5;124m"\x1b[39m\x1b[38;5;124mExtracted \x1b[39m\x1b[38;5;132;01m{\x1b[39;00m\x1b[38;5;28mlen\x1b[39m(frames_with_timestamps)\x1b[38;5;132;01m}\x1b[39;00m\x1b[38;5;124m frames from the video.\x1b[39m\x1b[38;5;124m"\x1b[39m)\n\nFile \x1b[0;32m~/anaconda3/envs/VIsionAgent/lib/python3.10/site-packages/vision_agent/tools/tools.py:264\x1b[0m, in \x1b[0;36mextract_frames\x1b[0;34m(video_uri, fps)\x1b[0m\n\x1b[1;32m 242\x1b[0m \x1b[38;5;28;01mdef\x1b[39;00m \x1b[38;5;21mextract_frames\x1b[39m(\n\x1b[1;32m 243\x1b[0m video_uri: Union[\x1b[38;5;28mstr\x1b[39m, Path], fps: \x1b[38;5;28mfloat\x1b[39m \x1b[38;5;241m=\x1b[39m \x1b[38;5;241m0.5\x1b[39m\n\x1b[1;32m 244\x1b[0m ) \x1b[38;5;241m-\x1b[39m\x1b[38;5;241m>\x1b[39m List[Tuple[np\x1b[38;5;241m.\x1b[39mndarray, \x1b[38;5;28mfloat\x1b[39m]]:\n\x1b[1;32m 245\x1b[0m \x1b[38;5;250m \x1b[39m\x1b[38;5;124;03m"""\'extract_frames\' extracts frames from a video, returns a list of tuples (frame,\x1b[39;00m\n\x1b[1;32m 246\x1b[0m \x1b[38;5;124;03m timestamp), where timestamp is the relative time in seconds where the frame was\x1b[39;00m\n\x1b[1;32m 247\x1b[0m \x1b[38;5;124;03m captured. The frame is a numpy array.\x1b[39;00m\n\x1b[0;32m (...)\x1b[0m\n\x1b[1;32m 261\x1b[0m \x1b[38;5;124;03m [(frame1, 0.0), (frame2, 0.5), ...]\x1b[39;00m\n\x1b[1;32m 262\x1b[0m \x1b[38;5;124;03m """\x1b[39;00m\n\x1b[0;32m--> 264\x1b[0m \x1b[38;5;28;01mreturn\x1b[39;00m \x1b[43mextract_frames_from_video\x1b[49m\x1b[43m(\x1b[49m\x1b[38;5;28;43mstr\x1b[39;49m\x1b[43m(\x1b[49m\x1b[43mvideo_uri\x1b[49m\x1b[43m)\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mfps\x1b[49m\x1b[43m)\x1b[49m\n\nFile \x1b[0;32m~/anaconda3/envs/VIsionAgent/lib/python3.10/site-packages/vision_agent/utils/video.py:70\x1b[0m, in \x1b[0;36mextract_frames_from_video\x1b[0;34m(video_uri, fps, motion_detection_threshold)\x1b[0m\n\x1b[1;32m 50\x1b[0m \x1b[38;5;28;01mdef\x1b[39;00m \x1b[38;5;21mextract_frames_from_video\x1b[39m(\n\x1b[1;32m 51\x1b[0m video_uri: \x1b[38;5;28mstr\x1b[39m, fps: \x1b[38;5;28mfloat\x1b[39m \x1b[38;5;241m=\x1b[39m \x1b[38;5;241m0.5\x1b[39m, motion_detection_threshold: \x1b[38;5;28mfloat\x1b[39m \x1b[38;5;241m=\x1b[39m \x1b[38;5;241m0.0\x1b[39m\n\x1b[1;32m 52\x1b[0m ) \x1b[38;5;241m-\x1b[39m\x1b[38;5;241m>\x1b[39m List[Tuple[np\x1b[38;5;241m.\x1b[39mndarray, \x1b[38;5;28mfloat\x1b[39m]]:\n\x1b[1;32m 53\x1b[0m \x1b[38;5;250m \x1b[39m\x1b[38;5;124;03m"""Extract frames from a video\x1b[39;00m\n\x1b[1;32m 54\x1b[0m \n\x1b[1;32m 55\x1b[0m \x1b[38;5;124;03m Parameters:\x1b[39;00m\n\x1b[0;32m (...)\x1b[0m\n\x1b[1;32m 68\x1b[0m \x1b[38;5;124;03m the video. The frames are sorted by the timestamp in ascending order.\x1b[39;00m\n\x1b[1;32m 69\x1b[0m \x1b[38;5;124;03m """\x1b[39;00m\n\x1b[0;32m---> 70\x1b[0m \x1b[38;5;28;01mwith\x1b[39;00m \x1b[43mVideoFileClip\x1b[49m\x1b[43m(\x1b[49m\x1b[43mvideo_uri\x1b[49m\x1b[43m)\x1b[49m \x1b[38;5;28;01mas\x1b[39;00m video:\n\x1b[1;32m 71\x1b[0m video_duration: \x1b[38;5;28mfloat\x1b[39m \x1b[38;5;241m=\x1b[39m video\x1b[38;5;241m.\x1b[39mduration\n\x1b[1;32m 72\x1b[0m num_workers \x1b[38;5;241m=\x1b[39m os\x1b[38;5;241m.\x1b[39mcpu_count()\n\nFile \x1b[0;32m~/anaconda3/envs/VIsionAgent/lib/python3.10/site-packages/moviepy/video/io/VideoFileClip.py:88\x1b[0m, in \x1b[0;36mVideoFileClip.init\x1b[0;34m(self, filename, has_mask, audio, audio_buffersize, target_resolution, resize_algorithm, audio_fps, audio_nbytes, verbose, fps_source)\x1b[0m\n\x1b[1;32m 86\x1b[0m \x1b[38;5;66;03m# Make a reader\x1b[39;00m\n\x1b[1;32m 87\x1b[0m pix_fmt \x1b[38;5;241m=\x1b[39m \x1b[38;5;124m"\x1b[39m\x1b[38;5;124mrgba\x1b[39m\x1b[38;5;124m"\x1b[39m \x1b[38;5;28;01mif\x1b[39;00m has_mask \x1b[38;5;28;01melse\x1b[39;00m \x1b[38;5;124m"\x1b[39m\x1b[38;5;124mrgb24\x1b[39m\x1b[38;5;124m"\x1b[39m\n\x1b[0;32m---> 88\x1b[0m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mreader \x1b[38;5;241m=\x1b[39m \x1b[43mFFMPEG_VideoReader\x1b[49m\x1b[43m(\x1b[49m\x1b[43mfilename\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mpix_fmt\x1b[49m\x1b[38;5;241;43m=\x1b[39;49m\x1b[43mpix_fmt\x1b[49m\x1b[43m,\x1b[49m\n\x1b[1;32m 89\x1b[0m \x1b[43m \x1b[49m\x1b[43mtarget_resolution\x1b[49m\x1b[38;5;241;43m=\x1b[39;49m\x1b[43mtarget_resolution\x1b[49m\x1b[43m,\x1b[49m\n\x1b[1;32m 90\x1b[0m \x1b[43m \x1b[49m\x1b[43mresize_algo\x1b[49m\x1b[38;5;241;43m=\x1b[39;49m\x1b[43mresize_algorithm\x1b[49m\x1b[43m,\x1b[49m\n\x1b[1;32m 91\x1b[0m \x1b[43m \x1b[49m\x1b[43mfps_source\x1b[49m\x1b[38;5;241;43m=\x1b[39;49m\x1b[43mfps_source\x1b[49m\x1b[43m)\x1b[49m\n\x1b[1;32m 93\x1b[0m \x1b[38;5;66;03m# Make some of the reader\'s attributes accessible from the clip\x1b[39;00m\n\x1b[1;32m 94\x1b[0m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mduration \x1b[38;5;241m=\x1b[39m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mreader\x1b[38;5;241m.\x1b[39mduration\n\nFile \x1b[0;32m~/anaconda3/envs/VIsionAgent/lib/python3.10/site-packages/moviepy/video/io/ffmpeg_reader.py:35\x1b[0m, in \x1b[0;36mFFMPEG_VideoReader.init\x1b[0;34m(self, filename, print_infos, bufsize, pix_fmt, check_duration, target_resolution, resize_algo, fps_source)\x1b[0m\n\x1b[1;32m 33\x1b[0m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mfilename \x1b[38;5;241m=\x1b[39m filename\n\x1b[1;32m 34\x1b[0m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mproc \x1b[38;5;241m=\x1b[39m \x1b[38;5;28;01mNone\x1b[39;00m\n\x1b[0;32m---> 35\x1b[0m infos \x1b[38;5;241m=\x1b[39m \x1b[43mffmpeg_parse_infos\x1b[49m\x1b[43m(\x1b[49m\x1b[43mfilename\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mprint_infos\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mcheck_duration\x1b[49m\x1b[43m,\x1b[49m\n\x1b[1;32m 36\x1b[0m \x1b[43m \x1b[49m\x1b[43mfps_source\x1b[49m\x1b[43m)\x1b[49m\n\x1b[1;32m 37\x1b[0m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mfps \x1b[38;5;241m=\x1b[39m infos[\x1b[38;5;124m\'\x1b[39m\x1b[38;5;124mvideo_fps\x1b[39m\x1b[38;5;124m\'\x1b[39m]\n\x1b[1;32m 38\x1b[0m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39msize \x1b[38;5;241m=\x1b[39m infos[\x1b[38;5;124m\'\x1b[39m\x1b[38;5;124mvideo_size\x1b[39m\x1b[38;5;124m\'\x1b[39m]\n\nFile \x1b[0;32m~/anaconda3/envs/VIsionAgent/lib/python3.10/site-packages/moviepy/video/io/ffmpeg_reader.py:257\x1b[0m, in \x1b[0;36mffmpeg_parse_infos\x1b[0;34m(filename, print_infos, check_duration, fps_source)\x1b[0m\n\x1b[1;32m 254\x1b[0m \x1b[38;5;28;01mif\x1b[39;00m os\x1b[38;5;241m.\x1b[39mname \x1b[38;5;241m==\x1b[39m \x1b[38;5;124m"\x1b[39m\x1b[38;5;124mnt\x1b[39m\x1b[38;5;124m"\x1b[39m:\n\x1b[1;32m 255\x1b[0m popen_params[\x1b[38;5;124m"\x1b[39m\x1b[38;5;124mcreationflags\x1b[39m\x1b[38;5;124m"\x1b[39m] \x1b[38;5;241m=\x1b[39m \x1b[38;5;241m0x08000000\x1b[39m\n\x1b[0;32m--> 257\x1b[0m proc \x1b[38;5;241m=\x1b[39m \x1b[43msp\x1b[49m\x1b[38;5;241;43m.\x1b[39;49m\x1b[43mPopen\x1b[49m\x1b[43m(\x1b[49m\x1b[43mcmd\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[38;5;241;43m\x1b[39;49m\x1b[38;5;241;43m\x1b[39;49m\x1b[43mpopen_params\x1b[49m\x1b[43m)\x1b[49m\n\x1b[1;32m 258\x1b[0m (output, error) \x1b[38;5;241m=\x1b[39m proc\x1b[38;5;241m.\x1b[39mcommunicate()\n\x1b[1;32m 259\x1b[0m infos \x1b[38;5;241m=\x1b[39m error\x1b[38;5;241m.\x1b[39mdecode(\x1b[38;5;124m\'\x1b[39m\x1b[38;5;124mutf8\x1b[39m\x1b[38;5;124m\'\x1b[39m)\n\nFile \x1b[0;32m~/anaconda3/envs/VIsionAgent/lib/python3.10/subprocess.py:971\x1b[0m, in \x1b[0;36mPopen.init\x1b[0;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask, pipesize)\x1b[0m\n\x1b[1;32m 967\x1b[0m \x1b[38;5;28;01mif\x1b[39;00m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mtext_mode:\n\x1b[1;32m 968\x1b[0m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mstderr \x1b[38;5;241m=\x1b[39m io\x1b[38;5;241m.\x1b[39mTextIOWrapper(\x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mstderr,\n\x1b[1;32m 969\x1b[0m encoding\x1b[38;5;241m=\x1b[39mencoding, errors\x1b[38;5;241m=\x1b[39merrors)\n\x1b[0;32m--> 971\x1b[0m \x1b[38;5;28;43mself\x1b[39;49m\x1b[38;5;241;43m.\x1b[39;49m\x1b[43m_execute_child\x1b[49m\x1b[43m(\x1b[49m\x1b[43margs\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mexecutable\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mpreexec_fn\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mclose_fds\x1b[49m\x1b[43m,\x1b[49m\n\x1b[1;32m 972\x1b[0m \x1b[43m \x1b[49m\x1b[43mpass_fds\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mcwd\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43menv\x1b[49m\x1b[43m,\x1b[49m\n\x1b[1;32m 973\x1b[0m \x1b[43m \x1b[49m\x1b[43mstartupinfo\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mcreationflags\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mshell\x1b[49m\x1b[43m,\x1b[49m\n\x1b[1;32m 974\x1b[0m \x1b[43m \x1b[49m\x1b[43mp2cread\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mp2cwrite\x1b[49m\x1b[43m,\x1b[49m\n\x1b[1;32m 975\x1b[0m \x1b[43m \x1b[49m\x1b[43mc2pread\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mc2pwrite\x1b[49m\x1b[43m,\x1b[49m\n\x1b[1;32m 976\x1b[0m \x1b[43m \x1b[49m\x1b[43merrread\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43merrwrite\x1b[49m\x1b[43m,\x1b[49m\n\x1b[1;32m 977\x1b[0m \x1b[43m \x1b[49m\x1b[43mrestore_signals\x1b[49m\x1b[43m,\x1b[49m\n\x1b[1;32m 978\x1b[0m \x1b[43m \x1b[49m\x1b[43mgid\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mgids\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43muid\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mumask\x1b[49m\x1b[43m,\x1b[49m\n\x1b[1;32m 979\x1b[0m \x1b[43m \x1b[49m\x1b[43mstart_new_session\x1b[49m\x1b[43m)\x1b[49m\n\x1b[1;32m 980\x1b[0m \x1b[38;5;28;01mexcept\x1b[39;00m:\n\x1b[1;32m 981\x1b[0m \x1b[38;5;66;03m# Cleanup if the child failed starting.\x1b[39;00m\n\x1b[1;32m 982\x1b[0m \x1b[38;5;28;01mfor\x1b[39;00m f \x1b[38;5;129;01min\x1b[39;00m \x1b[38;5;28mfilter\x1b[39m(\x1b[38;5;28;01mNone\x1b[39;00m, (\x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mstdin, \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mstdout, \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mstderr)):\n\nFile \x1b[0;32m~/anaconda3/envs/VIsionAgent/lib/python3.10/subprocess.py:1863\x1b[0m, in \x1b[0;36mPopen._execute_child\x1b[0;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, gid, gids, uid, umask, start_new_session)\x1b[0m\n\x1b[1;32m 1861\x1b[0m \x1b[38;5;28;01mif\x1b[39;00m errno_num \x1b[38;5;241m!=\x1b[39m \x1b[38;5;241m0\x1b[39m:\n\x1b[1;32m 1862\x1b[0m err_msg \x1b[38;5;241m=\x1b[39m os\x1b[38;5;241m.\x1b[39mstrerror(errno_num)\n\x1b[0;32m-> 1863\x1b[0m \x1b[38;5;28;01mraise\x1b[39;00m child_exception_type(errno_num, err_msg, err_filename)\n\x1b[1;32m 1864\x1b[0m \x1b[38;5;28;01mraise\x1b[39;00m child_exception_type(err_msg)\n\n\x1b[0;31mFileNotFoundError\x1b[0m: [Errno 2] No such file or directory: \'/usr/local/bin/ffmpeg\'\n', traceback_raw=['Traceback (most recent call last):', ' File "/Users/yp1017/anaconda3/envs/VIsionAgent/lib/python3.10/site-packages/vision_agent/utils/execute.py", line 531, in exec_cell', ' self.nb_client.execute_cell(cell, len(self.nb.cells) - 1)', ' File "/Users/yp1017/anaconda3/envs/VIsionAgent/lib/python3.10/site-packages/jupyter_core/utils/init.py", line 159, in wrapped', ' return _runner_map[name].run(inner)', ' File "/Users/yp1017/anaconda3/envs/VIsionAgent/lib/python3.10/site-packages/jupyter_core/utils/init.py", line 126, in run', ' return fut.result(None)', ' File "/Users/yp1017/anaconda3/envs/VIsionAgent/lib/python3.10/concurrent/futures/_base.py", line 458, in result', ' return self.get_result()', ' File "/Users/yp1017/anaconda3/envs/VIsionAgent/lib/python3.10/concurrent/futures/_base.py", line 403, in get_result', ' raise self._exception', ' File "/Users/yp1017/anaconda3/envs/VIsionAgent/lib/python3.10/site-packages/nbclient/client.py", line 1058, in async_execute_cell', ' await self._check_raise_for_error(cell, cell_index, exec_reply)', ' File "/Users/yp1017/anaconda3/envs/VIsionAgent/lib/python3.10/site-packages/nbclient/client.py", line 914, in _check_raise_for_error', ' raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)', 'nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:', '------------------', 'from typing import *', 'from pillow_heif import register_heif_opener', 'register_heif_opener()', 'import vision_agent as va', 'from vision_agent.tools import register_tool', 'from vision_agent.tools import extract_frames, owl_v2', '', '# Ensure ffmpeg is installed', '!apt-get update', '!apt-get install -y ffmpeg', '', '', 'def count_cars_in_video(video_path: str, fps: float = 0.5, debug: bool = False) -> int:', ' """', ' Count the number of cars displayed in the given video.', '', ' Parameters:', ' video_path (str): The path to the video file.', ' fps (float, optional): The frame rate per second to extract the frames. Defaults to 0.5.', ' debug (bool, optional): If True, print debug information. Defaults to False.', '', ' Returns:', ' int: The total number of cars detected in the video.', ' """', ' # Step 1: Extract frames from the video', ' frames_with_timestamps = extract_frames(video_path, fps)', ' ', ' if debug:', ' print(f"Extracted {len(frames_with_timestamps)} frames from the video.")', '', ' # Step 2: Initialize the car counter', ' total_car_count = 0', '', ' # Step 3: Detect cars in each frame', ' for frame, timestamp in frames_with_timestamps:', ' # Detect cars in the current frame', ' detections = owl_v2("car", frame)', ' ', ' # Count the number of cars detected', " car_count = sum(1 for detection in detections if detection['label'] == 'car')", ' ', ' if debug:', ' print(f"Frame at {timestamp}s: Detected {car_count} cars.")', ' ', ' # Add to the total car count', ' total_car_count += car_count', '', ' # Step 4: Return the total car count', ' return total_car_count', '', '# Example usage (do not call this in the final submission):', '# video_path = "/Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4"', '# print(count_cars_in_video(video_path, fps=0.5, debug=True))', '', 'from vision_agent.tools import extract_frames, owl_v2', '', '# Ensure ffmpeg is installed', '!apt-get update', '!apt-get install -y ffmpeg', '', '', 'def test_count_cars_in_video():', ' """', ' Test case for the count_cars_in_video function.', ' ', ' This test case verifies the fundamental functionality of the function under normal conditions.', ' It uses the provided video file path and checks the output format and data structure.', ' """', ' # Given video file path', ' video_path = "/Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4"', ' ', ' # Call the function with the given video path', ' car_count = count_cars_in_video(video_path, fps=0.5, debug=True)', ' ', ' # Print the output', ' print(car_count)', ' ', ' # Return the output for further validation if needed', ' return car_count', '', '# Run the test case', 'test_count_cars_in_video()', '', '------------------', '', '----- stdout -----', 'zsh:1: command not found: apt-get', '----- stdout -----', 'zsh:1: command not found: apt-get', '----- stdout -----', 'zsh:1: command not found: apt-get', '----- stdout -----', 'zsh:1: command not found: apt-get', '------------------', '', '\x1b[0;31m---------------------------------------------------------------------------\x1b[0m', '\x1b[0;31mFileNotFoundError\x1b[0m Traceback (most recent call last)', 'Cell \x1b[0;32mIn[1], line 82\x1b[0m', '\x1b[1;32m 79\x1b[0m \x1b[38;5;28;01mreturn\x1b[39;00m car_count', '\x1b[1;32m 81\x1b[0m \x1b[38;5;66;03m# Run the test case\x1b[39;00m', '\x1b[0;32m---> 82\x1b[0m \x1b[43mtest_count_cars_in_video\x1b[49m\x1b[43m(\x1b[49m\x1b[43m)\x1b[49m', '', 'Cell \x1b[0;32mIn[1], line 73\x1b[0m, in \x1b[0;36mtest_count_cars_in_video\x1b[0;34m()\x1b[0m', '\x1b[1;32m 70\x1b[0m video_path \x1b[38;5;241m=\x1b[39m \x1b[38;5;124m"\x1b[39m\x1b[38;5;124m/Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4\x1b[39m\x1b[38;5;124m"\x1b[39m', '\x1b[1;32m 72\x1b[0m \x1b[38;5;66;03m# Call the function with the given video path\x1b[39;00m', '\x1b[0;32m---> 73\x1b[0m car_count \x1b[38;5;241m=\x1b[39m \x1b[43mcount_cars_in_video\x1b[49m\x1b[43m(\x1b[49m\x1b[43mvideo_path\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mfps\x1b[49m\x1b[38;5;241;43m=\x1b[39;49m\x1b[38;5;241;43m0.5\x1b[39;49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mdebug\x1b[49m\x1b[38;5;241;43m=\x1b[39;49m\x1b[38;5;28;43;01mTrue\x1b[39;49;00m\x1b[43m)\x1b[49m', '\x1b[1;32m 75\x1b[0m \x1b[38;5;66;03m# Print the output\x1b[39;00m', '\x1b[1;32m 76\x1b[0m \x1b[38;5;28mprint\x1b[39m(car_count)', '', 'Cell \x1b[0;32mIn[1], line 26\x1b[0m, in \x1b[0;36mcount_cars_in_video\x1b[0;34m(video_path, fps, debug)\x1b[0m', '\x1b[1;32m 14\x1b[0m \x1b[38;5;250m\x1b[39m\x1b[38;5;124;03m"""\x1b[39;00m', '\x1b[1;32m 15\x1b[0m \x1b[38;5;124;03mCount the number of cars displayed in the given video.\x1b[39;00m', '\x1b[1;32m 16\x1b[0m ', '\x1b[0;32m (...)\x1b[0m', '\x1b[1;32m 23\x1b[0m \x1b[38;5;124;03m int: The total number of cars detected in the video.\x1b[39;00m', '\x1b[1;32m 24\x1b[0m \x1b[38;5;124;03m"""\x1b[39;00m', '\x1b[1;32m 25\x1b[0m \x1b[38;5;66;03m# Step 1: Extract frames from the video\x1b[39;00m', '\x1b[0;32m---> 26\x1b[0m frames_with_timestamps \x1b[38;5;241m=\x1b[39m \x1b[43mextract_frames\x1b[49m\x1b[43m(\x1b[49m\x1b[43mvideo_path\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mfps\x1b[49m\x1b[43m)\x1b[49m', '\x1b[1;32m 28\x1b[0m \x1b[38;5;28;01mif\x1b[39;00m debug:', '\x1b[1;32m 29\x1b[0m \x1b[38;5;28mprint\x1b[39m(\x1b[38;5;124mf\x1b[39m\x1b[38;5;124m"\x1b[39m\x1b[38;5;124mExtracted \x1b[39m\x1b[38;5;132;01m{\x1b[39;00m\x1b[38;5;28mlen\x1b[39m(frames_with_timestamps)\x1b[38;5;132;01m}\x1b[39;00m\x1b[38;5;124m frames from the video.\x1b[39m\x1b[38;5;124m"\x1b[39m)', '', 'File \x1b[0;32m~/anaconda3/envs/VIsionAgent/lib/python3.10/site-packages/vision_agent/tools/tools.py:264\x1b[0m, in \x1b[0;36mextract_frames\x1b[0;34m(video_uri, fps)\x1b[0m', '\x1b[1;32m 242\x1b[0m \x1b[38;5;28;01mdef\x1b[39;00m \x1b[38;5;21mextract_frames\x1b[39m(', '\x1b[1;32m 243\x1b[0m video_uri: Union[\x1b[38;5;28mstr\x1b[39m, Path], fps: \x1b[38;5;28mfloat\x1b[39m \x1b[38;5;241m=\x1b[39m \x1b[38;5;241m0.5\x1b[39m', '\x1b[1;32m 244\x1b[0m ) \x1b[38;5;241m-\x1b[39m\x1b[38;5;241m>\x1b[39m List[Tuple[np\x1b[38;5;241m.\x1b[39mndarray, \x1b[38;5;28mfloat\x1b[39m]]:', '\x1b[1;32m 245\x1b[0m \x1b[38;5;250m \x1b[39m\x1b[38;5;124;03m"""\'extract_frames\' extracts frames from a video, returns a list of tuples (frame,\x1b[39;00m', '\x1b[1;32m 246\x1b[0m \x1b[38;5;124;03m timestamp), where timestamp is the relative time in seconds where the frame was\x1b[39;00m', '\x1b[1;32m 247\x1b[0m \x1b[38;5;124;03m captured. The frame is a numpy array.\x1b[39;00m', '\x1b[0;32m (...)\x1b[0m', '\x1b[1;32m 261\x1b[0m \x1b[38;5;124;03m [(frame1, 0.0), (frame2, 0.5), ...]\x1b[39;00m', '\x1b[1;32m 262\x1b[0m \x1b[38;5;124;03m """\x1b[39;00m', '\x1b[0;32m--> 264\x1b[0m \x1b[38;5;28;01mreturn\x1b[39;00m \x1b[43mextract_frames_from_video\x1b[49m\x1b[43m(\x1b[49m\x1b[38;5;28;43mstr\x1b[39;49m\x1b[43m(\x1b[49m\x1b[43mvideo_uri\x1b[49m\x1b[43m)\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mfps\x1b[49m\x1b[43m)\x1b[49m', '', 'File \x1b[0;32m~/anaconda3/envs/VIsionAgent/lib/python3.10/site-packages/vision_agent/utils/video.py:70\x1b[0m, in \x1b[0;36mextract_frames_from_video\x1b[0;34m(video_uri, fps, motion_detection_threshold)\x1b[0m', '\x1b[1;32m 50\x1b[0m \x1b[38;5;28;01mdef\x1b[39;00m \x1b[38;5;21mextract_frames_from_video\x1b[39m(', '\x1b[1;32m 51\x1b[0m video_uri: \x1b[38;5;28mstr\x1b[39m, fps: \x1b[38;5;28mfloat\x1b[39m \x1b[38;5;241m=\x1b[39m \x1b[38;5;241m0.5\x1b[39m, motion_detection_threshold: \x1b[38;5;28mfloat\x1b[39m \x1b[38;5;241m=\x1b[39m \x1b[38;5;241m0.0\x1b[39m', '\x1b[1;32m 52\x1b[0m ) \x1b[38;5;241m-\x1b[39m\x1b[38;5;241m>\x1b[39m List[Tuple[np\x1b[38;5;241m.\x1b[39mndarray, \x1b[38;5;28mfloat\x1b[39m]]:', '\x1b[1;32m 53\x1b[0m \x1b[38;5;250m \x1b[39m\x1b[38;5;124;03m"""Extract frames from a video\x1b[39;00m', '\x1b[1;32m 54\x1b[0m ', '\x1b[1;32m 55\x1b[0m \x1b[38;5;124;03m Parameters:\x1b[39;00m', '\x1b[0;32m (...)\x1b[0m', '\x1b[1;32m 68\x1b[0m \x1b[38;5;124;03m the video. The frames are sorted by the timestamp in ascending order.\x1b[39;00m', '\x1b[1;32m 69\x1b[0m \x1b[38;5;124;03m """\x1b[39;00m', '\x1b[0;32m---> 70\x1b[0m \x1b[38;5;28;01mwith\x1b[39;00m \x1b[43mVideoFileClip\x1b[49m\x1b[43m(\x1b[49m\x1b[43mvideo_uri\x1b[49m\x1b[43m)\x1b[49m \x1b[38;5;28;01mas\x1b[39;00m video:', '\x1b[1;32m 71\x1b[0m video_duration: \x1b[38;5;28mfloat\x1b[39m \x1b[38;5;241m=\x1b[39m video\x1b[38;5;241m.\x1b[39mduration', '\x1b[1;32m 72\x1b[0m num_workers \x1b[38;5;241m=\x1b[39m os\x1b[38;5;241m.\x1b[39mcpu_count()', '', 'File \x1b[0;32m~/anaconda3/envs/VIsionAgent/lib/python3.10/site-packages/moviepy/video/io/VideoFileClip.py:88\x1b[0m, in \x1b[0;36mVideoFileClip.init\x1b[0;34m(self, filename, has_mask, audio, audio_buffersize, target_resolution, resize_algorithm, audio_fps, audio_nbytes, verbose, fps_source)\x1b[0m', '\x1b[1;32m 86\x1b[0m \x1b[38;5;66;03m# Make a reader\x1b[39;00m', '\x1b[1;32m 87\x1b[0m pix_fmt \x1b[38;5;241m=\x1b[39m \x1b[38;5;124m"\x1b[39m\x1b[38;5;124mrgba\x1b[39m\x1b[38;5;124m"\x1b[39m \x1b[38;5;28;01mif\x1b[39;00m has_mask \x1b[38;5;28;01melse\x1b[39;00m \x1b[38;5;124m"\x1b[39m\x1b[38;5;124mrgb24\x1b[39m\x1b[38;5;124m"\x1b[39m', '\x1b[0;32m---> 88\x1b[0m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mreader \x1b[38;5;241m=\x1b[39m \x1b[43mFFMPEG_VideoReader\x1b[49m\x1b[43m(\x1b[49m\x1b[43mfilename\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mpix_fmt\x1b[49m\x1b[38;5;241;43m=\x1b[39;49m\x1b[43mpix_fmt\x1b[49m\x1b[43m,\x1b[49m', '\x1b[1;32m 89\x1b[0m \x1b[43m \x1b[49m\x1b[43mtarget_resolution\x1b[49m\x1b[38;5;241;43m=\x1b[39;49m\x1b[43mtarget_resolution\x1b[49m\x1b[43m,\x1b[49m', '\x1b[1;32m 90\x1b[0m \x1b[43m \x1b[49m\x1b[43mresize_algo\x1b[49m\x1b[38;5;241;43m=\x1b[39;49m\x1b[43mresize_algorithm\x1b[49m\x1b[43m,\x1b[49m', '\x1b[1;32m 91\x1b[0m \x1b[43m \x1b[49m\x1b[43mfps_source\x1b[49m\x1b[38;5;241;43m=\x1b[39;49m\x1b[43mfps_source\x1b[49m\x1b[43m)\x1b[49m', "\x1b[1;32m 93\x1b[0m \x1b[38;5;66;03m# Make some of the reader's attributes accessible from the clip\x1b[39;00m", '\x1b[1;32m 94\x1b[0m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mduration \x1b[38;5;241m=\x1b[39m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mreader\x1b[38;5;241m.\x1b[39mduration', '', 'File \x1b[0;32m~/anaconda3/envs/VIsionAgent/lib/python3.10/site-packages/moviepy/video/io/ffmpeg_reader.py:35\x1b[0m, in \x1b[0;36mFFMPEG_VideoReader.init\x1b[0;34m(self, filename, print_infos, bufsize, pix_fmt, check_duration, target_resolution, resize_algo, fps_source)\x1b[0m', '\x1b[1;32m 33\x1b[0m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mfilename \x1b[38;5;241m=\x1b[39m filename', '\x1b[1;32m 34\x1b[0m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mproc \x1b[38;5;241m=\x1b[39m \x1b[38;5;28;01mNone\x1b[39;00m', '\x1b[0;32m---> 35\x1b[0m infos \x1b[38;5;241m=\x1b[39m \x1b[43mffmpeg_parse_infos\x1b[49m\x1b[43m(\x1b[49m\x1b[43mfilename\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mprint_infos\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mcheck_duration\x1b[49m\x1b[43m,\x1b[49m', '\x1b[1;32m 36\x1b[0m \x1b[43m \x1b[49m\x1b[43mfps_source\x1b[49m\x1b[43m)\x1b[49m', "\x1b[1;32m 37\x1b[0m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mfps \x1b[38;5;241m=\x1b[39m infos[\x1b[38;5;124m'\x1b[39m\x1b[38;5;124mvideo_fps\x1b[39m\x1b[38;5;124m'\x1b[39m]", "\x1b[1;32m 38\x1b[0m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39msize \x1b[38;5;241m=\x1b[39m infos[\x1b[38;5;124m'\x1b[39m\x1b[38;5;124mvideo_size\x1b[39m\x1b[38;5;124m'\x1b[39m]", '', 'File \x1b[0;32m~/anaconda3/envs/VIsionAgent/lib/python3.10/site-packages/moviepy/video/io/ffmpeg_reader.py:257\x1b[0m, in \x1b[0;36mffmpeg_parse_infos\x1b[0;34m(filename, print_infos, check_duration, fps_source)\x1b[0m', '\x1b[1;32m 254\x1b[0m \x1b[38;5;28;01mif\x1b[39;00m os\x1b[38;5;241m.\x1b[39mname \x1b[38;5;241m==\x1b[39m \x1b[38;5;124m"\x1b[39m\x1b[38;5;124mnt\x1b[39m\x1b[38;5;124m"\x1b[39m:', '\x1b[1;32m 255\x1b[0m popen_params[\x1b[38;5;124m"\x1b[39m\x1b[38;5;124mcreationflags\x1b[39m\x1b[38;5;124m"\x1b[39m] \x1b[38;5;241m=\x1b[39m \x1b[38;5;241m0x08000000\x1b[39m', '\x1b[0;32m--> 257\x1b[0m proc \x1b[38;5;241m=\x1b[39m \x1b[43msp\x1b[49m\x1b[38;5;241;43m.\x1b[39;49m\x1b[43mPopen\x1b[49m\x1b[43m(\x1b[49m\x1b[43mcmd\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[38;5;241;43m\x1b[39;49m\x1b[38;5;241;43m\x1b[39;49m\x1b[43mpopen_params\x1b[49m\x1b[43m)\x1b[49m', '\x1b[1;32m 258\x1b[0m (output, error) \x1b[38;5;241m=\x1b[39m proc\x1b[38;5;241m.\x1b[39mcommunicate()', "\x1b[1;32m 259\x1b[0m infos \x1b[38;5;241m=\x1b[39m error\x1b[38;5;241m.\x1b[39mdecode(\x1b[38;5;124m'\x1b[39m\x1b[38;5;124mutf8\x1b[39m\x1b[38;5;124m'\x1b[39m)", '', 'File \x1b[0;32m~/anaconda3/envs/VIsionAgent/lib/python3.10/subprocess.py:971\x1b[0m, in \x1b[0;36mPopen.init\x1b[0;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask, pipesize)\x1b[0m', '\x1b[1;32m 967\x1b[0m \x1b[38;5;28;01mif\x1b[39;00m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mtext_mode:', '\x1b[1;32m 968\x1b[0m \x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mstderr \x1b[38;5;241m=\x1b[39m io\x1b[38;5;241m.\x1b[39mTextIOWrapper(\x1b[38;5;28mself\x1b[39m\x1b[38;5;241m.\x1b[39mstderr,', '\x1b[1;32m 969\x1b[0m encoding\x1b[38;5;241m=\x1b[39mencoding, errors\x1b[38;5;241m=\x1b[39merrors)', '\x1b[0;32m--> 971\x1b[0m \x1b[38;5;28;43mself\x1b[39;49m\x1b[38;5;241;43m.\x1b[39;49m\x1b[43m_execute_child\x1b[49m\x1b[43m(\x1b[49m\x1b[43margs\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mexecutable\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mpreexec_fn\x1b[49m\x1b[43m,\x1b[49m\x1b[43m \x1b[49m\x1b[43mclose_fds\x1b[49m\x1b[43m,\x1b[49m', '\x1b[1;32m 972\x1b[0m \x1b[43m \x1b[49m\x1b[43mpass_fds\x1b[49m\x1b[43m,\x1b[49m\x1b[43m 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start_new_session)\x1b[0m', '\x1b[1;32m 1861\x1b[0m \x1b[38;5;28;01mif\x1b[39;00m errno_num \x1b[38;5;241m!=\x1b[39m \x1b[38;5;241m0\x1b[39m:', '\x1b[1;32m 1862\x1b[0m err_msg \x1b[38;5;241m=\x1b[39m os\x1b[38;5;241m.\x1b[39mstrerror(errno_num)', '\x1b[0;32m-> 1863\x1b[0m \x1b[38;5;28;01mraise\x1b[39;00m child_exception_type(errno_num, err_msg, err_filename)', '\x1b[1;32m 1864\x1b[0m \x1b[38;5;28;01mraise\x1b[39;00m child_exception_type(err_msg)', '', "\x1b[0;31mFileNotFoundError\x1b[0m: [Errno 2] No such file or directory: '/usr/local/bin/ffmpeg'", ''])), 'plan': [{'code': 'from vision_agent.tools import extract_frames, owl_v2\n\n# Ensure ffmpeg is installed\n!apt-get update\n!apt-get install -y ffmpeg\n\n\ndef count_cars_in_video(video_path: str, fps: float = 0.5, debug: bool = False) -> int:\n """\n Count the number of cars displayed in the given video.\n\n Parameters:\n video_path (str): The path to the video file.\n fps (float, optional): The frame rate per second to extract the frames. Defaults to 0.5.\n debug (bool, optional): If True, print debug information. Defaults to False.\n\n Returns:\n int: The total number of cars detected in the video.\n """\n # Step 1: Extract frames from the video\n frames_with_timestamps = extract_frames(video_path, fps)\n \n if debug:\n print(f"Extracted {len(frames_with_timestamps)} frames from the video.")\n\n # Step 2: Initialize the car counter\n total_car_count = 0\n\n # Step 3: Detect cars in each frame\n for frame, timestamp in frames_with_timestamps:\n # Detect cars in the current frame\n detections = owl_v2("car", frame)\n \n # Count the number of cars detected\n car_count = sum(1 for detection in detections if detection[\'label\'] == \'car\')\n \n if debug:\n print(f"Frame at {timestamp}s: Detected {car_count} cars.")\n \n # Add to the total car count\n total_car_count += car_count\n\n # Step 4: Return the total car count\n return total_car_count\n\n# Example usage (do not call this in the final submission):\n# video_path = "/Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4"\n# print(count_cars_in_video(video_path, fps=0.5, debug=True))\n', 'test': 'from vision_agent.tools import extract_frames, owl_v2\n\n# Ensure ffmpeg is installed\n!apt-get update\n!apt-get install -y ffmpeg\n\n\ndef test_count_cars_in_video():\n """\n Test case for the count_cars_in_video function.\n \n This test case verifies the fundamental functionality of the function under normal conditions.\n It uses the provided video file path and checks the output format and data structure.\n """\n # Given video file path\n video_path = "/Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4"\n \n # Call the function with the given video path\n car_count = count_cars_in_video(video_path, fps=0.5, debug=True)\n \n # Print the output\n print(car_count)\n \n # Return the output for further validation if needed\n return car_count\n\n# Run the test case\ntest_count_cars_in_video()\n', 'plan': [{'instructions': 'Extract frames from the video located at /Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4 at a rate of 0.5 frames per second using the extract_frames tool.'}, {'instructions': "For each extracted frame, use the owl_v2 tool with the prompt 'car' to detect and count the number of cars in the frame."}, {'instructions': 'Sum the counts of cars detected in each frame to get the total number of cars displayed in the video.'}, {'instructions': 'Output the total count of cars displayed in the video.'}]}], 'working_memory': [{'code': '\nfrom vision_agent.tools import extract_frames, owl_v2\n\ndef count_cars_in_video(video_path: str, fps: float = 0.5, debug: bool = False) -> int:\n """\n Count the number of cars displayed in the given video.\n\n Parameters:\n video_path (str): The path to the video file.\n fps (float, optional): The frame rate per second to extract the frames. Defaults to 0.5.\n debug (bool, optional): If True, print debug information. Defaults to False.\n\n Returns:\n int: The total number of cars detected in the video.\n """\n # Step 1: Extract frames from the video\n frames_with_timestamps = extract_frames(video_path, fps)\n \n if debug:\n print(f"Extracted {len(frames_with_timestamps)} frames from the video.")\n\n # Step 2: Initialize the car counter\n total_car_count = 0\n\n # Step 3: Detect cars in each frame\n for frame, timestamp in frames_with_timestamps:\n # Detect cars in the current frame\n detections = owl_v2("car", frame)\n \n # Count the number of cars detected\n car_count = sum(1 for detection in detections if detection[\'label\'] == \'car\')\n \n if debug:\n print(f"Frame at {timestamp}s: Detected {car_count} cars.")\n \n # Add to the total car count\n total_car_count += car_count\n\n # Step 4: Return the total car count\n return total_car_count\n\n# Example usage (do not call this in the final submission):\n# video_path = "/Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4"\n# print(count_cars_in_video(video_path, fps=0.5, debug=True))\n\n\nfrom vision_agent.tools import extract_frames, owl_v2\n\ndef test_count_cars_in_video():\n """\n Test case for the count_cars_in_video function.\n \n This test case verifies the fundamental functionality of the function under normal conditions.\n It uses the provided video file path and checks the output format and data structure.\n """\n # Given video file path\n video_path = "/Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4"\n \n # Call the function with the given video path\n car_count = count_cars_in_video(video_path, fps=0.5, debug=True)\n \n # Print the output\n print(car_count)\n \n # Return the output for further validation if needed\n return car_count\n\n# Run the test case\ntest_count_cars_in_video()\n', 'feedback': "The error message indicates that the 'ffmpeg' executable is not found in the specified directory. This is a common issue when working with video processing libraries like moviepy, which rely on ffmpeg for reading and writing video files. To fix this, we need to install ffmpeg and ensure it is accessible from the system's PATH.", 'edits': ''}, {'code': '\n\nfrom vision_agent.tools import extract_frames, owl_v2\n\n# Ensure ffmpeg is installed\n!apt-get update\n!apt-get install -y ffmpeg\n\n\ndef count_cars_in_video(video_path: str, fps: float = 0.5, debug: bool = False) -> int:\n """\n Count the number of cars displayed in the given video.\n\n Parameters:\n video_path (str): The path to the video file.\n fps (float, optional): The frame rate per second to extract the frames. Defaults to 0.5.\n debug (bool, optional): If True, print debug information. Defaults to False.\n\n Returns:\n int: The total number of cars detected in the video.\n """\n # Step 1: Extract frames from the video\n frames_with_timestamps = extract_frames(video_path, fps)\n \n if debug:\n print(f"Extracted {len(frames_with_timestamps)} frames from the video.")\n\n # Step 2: Initialize the car counter\n total_car_count = 0\n\n # Step 3: Detect cars in each frame\n for frame, timestamp in frames_with_timestamps:\n # Detect cars in the current frame\n detections = owl_v2("car", frame)\n \n # Count the number of cars detected\n car_count = sum(1 for detection in detections if detection[\'label\'] == \'car\')\n \n if debug:\n print(f"Frame at {timestamp}s: Detected {car_count} cars.")\n \n # Add to the total car count\n total_car_count += car_count\n\n # Step 4: Return the total car count\n return total_car_count\n\n# Example usage (do not call this in the final submission):\n# video_path = "/Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4"\n# print(count_cars_in_video(video_path, fps=0.5, debug=True))\n\n\nfrom vision_agent.tools import extract_frames, owl_v2\n\n# Ensure ffmpeg is installed\n!apt-get update\n!apt-get install -y ffmpeg\n\n\ndef test_count_cars_in_video():\n """\n Test case for the count_cars_in_video function.\n \n This test case verifies the fundamental functionality of the function under normal conditions.\n It uses the provided video file path and checks the output format and data structure.\n """\n # Given video file path\n video_path = "/Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4"\n \n # Call the function with the given video path\n car_count = count_cars_in_video(video_path, fps=0.5, debug=True)\n \n # Print the output\n print(car_count)\n \n # Return the output for further validation if needed\n return car_count\n\n# Run the test case\ntest_count_cars_in_video()\n', 'feedback': "The error message indicates that the 'ffmpeg' executable is not found in the specified directory. This is a common issue when working with video processing libraries like moviepy, which rely on ffmpeg for reading and writing video files. To fix this, we need to install ffmpeg and ensure it is accessible from the system's PATH. We can use the
!apt-get installcommand to install ffmpeg in a notebook environment.", 'edits': '--- \n+++ \n@@ -1,5 +1,11 @@\n+\n \n from vision_agent.tools import extract_frames, owl_v2\n+\n+# Ensure ffmpeg is installed\n+!apt-get update\n+!apt-get install -y ffmpeg\n+\n \n def count_cars_in_video(video_path: str, fps: float = 0.5, debug: bool = False) -> int:\n """\n@@ -46,6 +52,11 @@\n \n from vision_agent.tools import extract_frames, owl_v2\n \n+# Ensure ffmpeg is installed\n+!apt-get update\n+!apt-get install -y ffmpeg\n+\n+\n def test_count_cars_in_video():\n """\n Test case for the count_cars_in_video function.\n'}, {'code': 'from vision_agent.tools import extract_frames, owl_v2\n\n# Ensure ffmpeg is installed\n!apt-get update\n!apt-get install -y ffmpeg\n\n\ndef count_cars_in_video(video_path: str, fps: float = 0.5, debug: bool = False) -> int:\n """\n Count the number of cars displayed in the given video.\n\n Parameters:\n video_path (str): The path to the video file.\n fps (float, optional): The frame rate per second to extract the frames. Defaults to 0.5.\n debug (bool, optional): If True, print debug information. Defaults to False.\n\n Returns:\n int: The total number of cars detected in the video.\n """\n # Step 1: Extract frames from the video\n frames_with_timestamps = extract_frames(video_path, fps)\n \n if debug:\n print(f"Extracted {len(frames_with_timestamps)} frames from the video.")\n\n # Step 2: Initialize the car counter\n total_car_count = 0\n\n # Step 3: Detect cars in each frame\n for frame, timestamp in frames_with_timestamps:\n # Detect cars in the current frame\n detections = owl_v2("car", frame)\n \n # Count the number of cars detected\n car_count = sum(1 for detection in detections if detection[\'label\'] == \'car\')\n \n if debug:\n print(f"Frame at {timestamp}s: Detected {car_count} cars.")\n \n # Add to the total car count\n total_car_count += car_count\n\n # Step 4: Return the total car count\n return total_car_count\n\n# Example usage (do not call this in the final submission):\n# video_path = "/Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4"\n# print(count_cars_in_video(video_path, fps=0.5, debug=True))\n\nfrom vision_agent.tools import extract_frames, owl_v2\n\n# Ensure ffmpeg is installed\n!apt-get update\n!apt-get install -y ffmpeg\n\n\ndef test_count_cars_in_video():\n """\n Test case for the count_cars_in_video function.\n \n This test case verifies the fundamental functionality of the function under normal conditions.\n It uses the provided video file path and checks the output format and data structure.\n """\n # Given video file path\n video_path = "/Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4"\n \n # Call the function with the given video path\n car_count = count_cars_in_video(video_path, fps=0.5, debug=True)\n \n # Print the output\n print(car_count)\n \n # Return the output for further validation if needed\n return car_count\n\n# Run the test case\ntest_count_cars_in_video()\n', 'feedback': "The error message indicates that the 'ffmpeg' executable is not found in the specified directory. This is a common issue when working with video processing libraries like moviepy, which rely on ffmpeg for reading and writing video files. To fix this, we need to install ffmpeg and ensure it is accessible from the system's PATH. We can use the!apt-get installcommand to install ffmpeg in a notebook environment. Additionally, the installation commands should be placed outside the function definitions to ensure they are executed before the functions are called.", 'edits': '--- \n+++ \n@@ -1,5 +1,3 @@\n-\n-\n from vision_agent.tools import extract_frames, owl_v2\n \n # Ensure ffmpeg is installed\n@@ -49,7 +47,6 @@\n # video_path = "/Applications/Polyspace/R2020a/toolbox/driving/drivingdata/pedtracking.mp4"\n # print(count_cars_in_video(video_path, fps=0.5, debug=True))\n \n-\n from vision_agent.tools import extract_frames, owl_v2\n \n # Ensure ffmpeg is installed\n'}]}