Tomo_4.mp4

# Read and display video frames frames = [] while cap.isOpened(): ret, frame = cap.read() if not ret: break # Convert to RGB (OpenCV reads in BGR format) frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frames.append(frame_rgb)

from tensorflow.keras.applications import VGG16 from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.vgg16 import preprocess_input tomo_4.mp4

# Extract features from all frames features = extract_features(frames) print(features.shape) The analysis depends on your specific goals, such as clustering, classification, or visualization. # Read and display video frames frames = [] while cap

import matplotlib.pyplot as plt

pip install tensorflow opencv-python numpy You'll need to load the video, extract frames, and then feed these frames into a deep learning model to extract features. such as clustering

# Define a function to extract features from frames def extract_features(frames): # Convert frames to batch frames_batch = np.array(frames) # Preprocess for VGG16 frames_batch = preprocess_input(frames_batch) # Extract features features = model.predict(frames_batch) return features