Resolving the numpy.ndarray has no attribute read Error in Video Processing with OpenCV

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Understanding the Problem

When working with video files in Python using OpenCV, it’s important to utilize the correct methods and object types. The error you’re encountering often occurs because the read() method is being called on a NumPy array (an object resulting from image extraction) rather than on the appropriate video capture object. This misunderstanding can halt your progress, particularly when you’re looking to annotate and save videos or display them.

The Solution

Now, let’s outline a structured approach to resolving this issue. The following sections will guide you through a corrected version of your video processing script.

Step 1: Initialize VideoCapture and VideoWriter

Make sure to correctly set up the video capture object to read from the video file and initialize the video writer for output.

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Step 2: Process Each Frame in a Loop

Utilize a loop to read each frame from the video, passing it into your detection model. Note that the frame should be expanded in dimensions to fit the input requirements of your model.

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Annotate the Frames

Now that you have the detections, use a visualization utility to annotate the boxes and labels on the frames.

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Write Frames to VideoWriter

Resize the annotated frame and write it to the output file. Ensure this step occurs within the loop for each frame processed.

[[See Video to Reveal this Text or Code Snippet]]

Step 5: Clean Up Resources

Once you finish processing all frames, it’s crucial to release the resources used by the video capture and writer.

[[See Video to Reveal this Text or Code Snippet]]

Final Thoughts

Feel free to experiment with different visualizations and processing techniques to further enhance your video analysis projects!