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while cap.isOpened(): ret, frame = cap.read() if not ret: break # Save frame cv2.imwrite(os.path.join(frame_dir, f'frame_{frame_count}.jpg'), frame) frame_count += 1
def extract_features(frame_path): img = image.load_img(frame_path, target_size=(224, 224)) img_data = image.img_to_array(img) img_data = np.expand_dims(img_data, axis=0) img_data = preprocess_input(img_data) features = model.predict(img_data) return features shkd257 avi
import numpy as np
# Video file path video_path = 'shkd257.avi' while cap
import numpy as np from tensorflow.keras.applications import VGG16 from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.vgg16 import preprocess_input while cap.isOpened(): ret
# Load the VGG16 model for feature extraction model = VGG16(weights='imagenet', include_top=False, pooling='avg')
pip install tensorflow opencv-python numpy You'll need to extract frames from your video. Here's a simple way to do it:
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