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Video to Frames Conversion: The code takes a video file as input. It uses the FFmpeg tool to convert the video into a series of image frames. The frames are saved in a temporary directory. It specifies options such as video scaling and image quality. Load Pre-trained VGG16 Model: It loads the VGG16 model, pre-trained on ImageNet, using Keras. The model is set up to exclude the top classification layers, leaving only the feature extraction layers. Image Loading and Feature Extraction: It reads the image frames from the temporary directory. The frames are resized to a specified dimension (e.g., 224x224 pixels). The pre-trained VGG16 model is used to extract dense features from the images. These features are then saved as NumPy arrays. Output and Cleanup: The extracted features are saved in a specified directory in NumPy format. The temporary directory containing image frames is deleted.
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Video to Frames Conversion:
The code takes a video file as input.
It uses the FFmpeg tool to convert the video into a series of image frames. The frames are saved in a temporary directory. It specifies options such as video scaling and image quality. Load Pre-trained VGG16 Model:
It loads the VGG16 model, pre-trained on ImageNet, using Keras. The model is set up to exclude the top classification layers, leaving only the feature extraction layers. Image Loading and Feature Extraction:
It reads the image frames from the temporary directory. The frames are resized to a specified dimension (e.g., 224x224 pixels). The pre-trained VGG16 model is used to extract dense features from the images. These features are then saved as NumPy arrays.
Output and Cleanup:
The extracted features are saved in a specified directory in NumPy format. The temporary directory containing image frames is deleted.