# Release the video capture cap.release()
features = [] while True: ret, frame = cap.read() if not ret: break # Convert to RGB (OpenCV reads in BGR format) frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frame = cv2.resize(frame, (224, 224)) # Expand dimensions and preprocess img = np.expand_dims(frame, axis=0) img = preprocess_input(img) # Get features feature = model.predict(img) feature = np.squeeze(feature) # Remove batch dimension features.append(feature) enafox - survey corps pov.mp4
import cv2 from tensorflow.keras.applications import VGG16 from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.vgg16 import preprocess_input import numpy as np # Release the video capture cap
# Load the video video_path = "enafox - survey corps pov.mp4" cap = cv2.VideoCapture(video_path) cv2.COLOR_BGR2RGB) frame = cv2.resize(frame
# Initialize the model model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))