Tomo_4.mp4 May 2026

# 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.

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

import matplotlib.pyplot as plt

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

# Simple example: visualize the feature space using PCA from sklearn.decomposition import PCA tomo_4.mp4

# Load the VGG16 model for feature extraction model = VGG16(weights='imagenet', include_top=False, pooling='avg')

# Load the video cap = cv2.VideoCapture('tomo_4.mp4') # Extract features from all frames features =

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