| import torchvision.transforms as transforms | |
| def preprocessingData(): | |
| transform = transforms.Compose([ | |
| transforms.ToPILImage(), # Converts the frame from a NumPy array to a PIL Image, which is required for further transformations. | |
| transforms.Resize((224, 224)), # Resizes the frame to 224x224 pixels, the input size expected by ResNet50. | |
| transforms.ToTensor(), # Converts the PIL Image to a PyTorch tensor and scales pixel values to [0, 1]. | |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # Normalizes the tensor using the mean and standard deviation of the ImageNet dataset, which ResNet50 was trained on. | |
| ]) | |
| return transform |