ComfyUI Node: Load Video Frame

Load Video Frame:

The LoadVideoFrame node is designed to extract a specific frame from a video file and convert it into an image tensor that can be used in various AI and machine learning applications. This node is particularly useful for AI artists who need to work with video data, allowing them to easily access and manipulate individual frames. By specifying the video path and the desired frame number, you can retrieve the exact frame you need. Additionally, the node supports frame rate control, which can be useful for synchronizing frame extraction with other processes. The primary goal of this node is to simplify the process of working with video frames, making it accessible even to those without a deep technical background.

Load Video Frame Input Parameters:

video_path

The video_path parameter specifies the location of the video file from which you want to extract a frame. This should be a string representing the full path to the video file. The default value is an empty string, meaning you need to provide a valid path for the node to function correctly. This parameter is crucial as it directs the node to the source video.

frame

The frame parameter indicates the specific frame number you wish to extract from the video. It is an integer value with a default of 1, a minimum of 1, and a maximum of 100000. This parameter is essential for pinpointing the exact frame you need. The frame count starts at 1, so setting this to 1 will extract the first frame of the video.

frameRate

The frameRate parameter allows you to control the rate at which frames are processed. It is an integer value with a default of 0, a minimum of 0, and a maximum of 144. A value of 0 means no delay between frames, while any other value will introduce a delay of 1/frameRate seconds. This can be useful for synchronizing frame extraction with other timed processes.

Load Video Frame Output Parameters:

IMAGE

The IMAGE output parameter is a tensor representing the extracted video frame. This tensor is in a format suitable for further processing in AI and machine learning models. The image is converted to RGB format and normalized to a range of 0 to 1, making it ready for use in various applications. This output is crucial for any task that requires frame-by-frame analysis or manipulation of video data.

Load Video Frame Usage Tips:

• Ensure the video_path is correct and points to a valid video file to avoid errors during frame extraction.
• Use the frame parameter to specify the exact frame you need, especially when working with large videos where processing all frames would be inefficient.
• Adjust the frameRate parameter if you need to synchronize frame extraction with other processes or if you want to introduce a delay between frame extractions.

Load Video Frame Common Errors and Solutions:

Error reading video frame: <error_message>

• Explanation: This error occurs when the node fails to read the specified frame from the video file. Possible reasons include an incorrect video path, a non-existent frame number, or issues with the video file itself.
• Solution: Verify that the video_path is correct and points to a valid video file. Ensure that the frame number is within the range of available frames in the video. Check the video file for any corruption or format issues.

cv2.error: <error_message>

• Explanation: This error is related to the OpenCV library used for video processing. It can occur due to various reasons, such as unsupported video formats or issues with the OpenCV installation.
• Solution: Ensure that the video file is in a supported format (e.g., MP4, AVI). Check your OpenCV installation and update it if necessary. If the problem persists, consider converting the video to a different format using a video conversion tool.