Convert the images into numerical arrays (tensors). 4. Extract the Global Feature Vector
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 Download: video5179512026745012956.mp4 (5.75 MB)
If you have the file locally, you can use PyTorch and OpenCV to get the feature: Convert the images into numerical arrays (tensors)
Subtract the mean and divide by the standard deviation (specific to the dataset the model was trained on). Load pre-trained ResNet model = models
Use a 3D CNN like I3D or VideoMAE which processes temporal data. 3. Pre-process the Data
To prepare a "deep feature" (a high-dimensional vector representation) for the video file video5179512026745012956.mp4 , you will typically follow a computer vision pipeline using a pre-trained deep learning model. 1. Extract Representative Frames
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").