# Preprocess frame and extract features def extract_features(video_path): # Load video frames frames = load_video(video_path) # Define preprocessing and device transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Load pre-trained model and move to device model = torchvision.models.vgg16(pretrained=True) model.features = torch.nn.Sequential(*list(model.features.children())[:-1]) # Remove the last layer model.to(device) model.eval() features_all = [] for frame in frames: # Preprocess frame frame = transform(frame).unsqueeze(0).to(device) # Extract feature with torch.no_grad(): feature = model(frame) feature = torch.nn.functional.adaptive_avg_pool2d(feature, 1).squeeze() features_all.append(feature.cpu().numpy()) return features_all
# Load video def load_video(video_path): cap = cv2.VideoCapture(video_path) frames = [] while cap.isOpened(): ret, frame = cap.read() if not ret: break # Convert to RGB and add to list frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frames.append(frame) cap.release() return frames VID-20160125.mp4
import cv2 import torch import torchvision import torchvision.transforms as transforms including video preprocessing
To create a deep feature extraction from a video like "VID-20160125.mp4", we'll need to follow a process that involves several steps, including video preprocessing, feature extraction using a deep learning model, and potentially, dimensionality reduction if needed. This process can be quite complex and depends on the specific requirements of your project, such as the type of features you want to extract (e.g., frame-level features, video-level features) and the deep learning model you wish to use. VID-20160125.mp4