Tomo_4.mp4 ✪ (TRUSTED)

# 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)

# Check if video file was opened successfully if not cap.isOpened(): print("Error opening video file")

# 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. tomo_4.mp4

pca = PCA(n_components=2) pca_features = pca.fit_transform(features)

import matplotlib.pyplot as plt

# Load the video cap = cv2.VideoCapture('tomo_4.mp4')

# 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 # Read and display video frames frames = [] while cap

cap.release() For extracting features, you can use a pre-trained model like VGG16. We'll use TensorFlow/Keras for this.