def prepare_model(): model = models.video.slowfast_r50_2x16x32_featurizer(pretrained=True) model.eval() # Set the model to evaluation mode return model
features = extract_features(model, frames_tensor) print(features.shape) You might want to save these features for later use: 22241mp4
model = prepare_model() To extract features, we first need to preprocess the video. This involves loading the video, possibly resizing it, and converting it into a tensor that the model can process. def prepare_model(): model = models
def load_video(video_path, target_resolution=(224, 224), frame_rate=16): cap = cv2.VideoCapture(video_path) frames = [] while cap.isOpened(): ret, frame = cap.read() if not ret: break frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frame = cv2.resize(frame, target_resolution) frames.append(frame) cap.release() # Select every frame_rate-th frame selected_frames = frames[::int(30/frame_rate)] # Stack and convert to tensor frames_tensor = torch.from_numpy(np.stack(selected_frames)).permute(0, 3, 1, 2).float() / 255. return frames_tensor return frames_tensor video_path = '22241
video_path = '22241.mp4' frames_tensor = load_video(video_path) def extract_features(model, video_tensor): # This may need to be adjusted based on the model and the input requirements inputs = video_tensor.unsqueeze(0) # Add batch dimension with torch.no_grad(): features = model(inputs) return features.squeeze()
import torch import torchvision import torchvision.transforms as transforms from torchvision import models
For simplicity and effectiveness, let's outline a method using PyTorch and a pre-trained model. We'll use a model pre-trained on the Kinetics dataset, which is a common benchmark for video action recognition tasks. Specifically, we can leverage the SlowFast model, which has shown excellent performance on various video understanding tasks. Ensure you have PyTorch and torchvision installed. If not, you can install them via pip: