# Video file path video_path = 'shkd257.avi'
video_features = aggregate_features(frame_dir) print(f"Aggregated video features shape: {video_features.shape}") np.save('video_features.npy', video_features) This example demonstrates a basic pipeline. Depending on your specific requirements, you might want to adjust the preprocessing, the model used for feature extraction, or how you aggregate features from multiple frames. shkd257 avi
cap.release() print(f"Extracted {frame_count} frames.") Now, let's use a pre-trained VGG16 model to extract features from these frames. # Video file path video_path = 'shkd257
import cv2 import os
# Video capture cap = cv2.VideoCapture(video_path) frame_count = 0 the model used for feature extraction
import numpy as np from tensorflow.keras.applications import VGG16 from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.vgg16 import preprocess_input
pip install tensorflow opencv-python numpy You'll need to extract frames from your video. Here's a simple way to do it:
# Video file path video_path = 'shkd257.avi'
video_features = aggregate_features(frame_dir) print(f"Aggregated video features shape: {video_features.shape}") np.save('video_features.npy', video_features) This example demonstrates a basic pipeline. Depending on your specific requirements, you might want to adjust the preprocessing, the model used for feature extraction, or how you aggregate features from multiple frames.
cap.release() print(f"Extracted {frame_count} frames.") Now, let's use a pre-trained VGG16 model to extract features from these frames.
import cv2 import os
# Video capture cap = cv2.VideoCapture(video_path) frame_count = 0
import numpy as np from tensorflow.keras.applications import VGG16 from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.vgg16 import preprocess_input
pip install tensorflow opencv-python numpy You'll need to extract frames from your video. Here's a simple way to do it: