Download:: Video5179512026745012956.mp4 (5.75 Mb)

Depending on what you want the "feature" to represent, choose a model:

Instead of the final classification layer (which would say "dog" or "running"), you extract the output from the (often called the "bottleneck" or "pooling layer"). Download: video5179512026745012956.mp4 (5.75 MB)

import torch import torchvision.models as models import torchvision.transforms as T from PIL import Image import cv2 # 1. Load pre-trained ResNet model = models.resnet50(pretrained=True) model = torch.nn.Sequential(*(list(model.children())[:-1])) # Remove last layer model.eval() # 2. Define Transform preprocess = T.Compose([ T.Resize(256), T.CenterCrop(224), T.ToTensor(), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) # 3. Process a frame from video5179512026745012956.mp4 cap = cv2.VideoCapture('video5179512026745012956.mp4') ret, frame = cap.read() if ret: img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) input_tensor = preprocess(img).unsqueeze(0) with torch.no_grad(): deep_feature = model(input_tensor) # This is your feature vector Use code with caution. Copied to clipboard AI responses may include mistakes. Learn more Depending on what you want the "feature" to

If you have the file locally, you can use PyTorch and OpenCV to get the feature: Define Transform preprocess = T

Convert the images into numerical arrays (tensors). 4. Extract the Global Feature Vector