![]() ![]() The CenterCrop transform crops the given image at the center. New_image = resize_transform(torch_image) Resize_transform = tv.transforms.Resize(size=(50, 100)) (size, interpolation=, max_size=None, antialias=None) Vertically flip the given image randomly with a given probability. Horizontally flip the given image randomly with a given probability. The Pad transform fills image borders with some pixel values. ![]() Some example transformations from torchvision: (padding, fill=0, padding_mode='constant') Note: We can control the random seed of torch via torch.manual_seed(seed). Torchvision: A selection of transformationsįirst we need to convert the np.ndarray into a suitable torch tensor # %% Original_image = np.flip(original_image, axis=2) # "Convert" from BlueGreenRed (BGR) to RGB (RedGreenBlue) Original_image = cv2.imread(filename, cv2.IMREAD_COLOR) Original_image = cv2.imread(filename, cv2.IMREAD_GRAYSCALE) But may be we want no color anyway ( options can be found here ): # %% Load it via cv2.imread( filename) -> retvalįilename: str = "data_augmentation_test_image.jpg"Īs you can see (not very well I might add) is that the color channels are wrong. (size, padding=None, pad_if_needed=False, fill=0, padding_mode='constant').(degrees, translate=None, scale=None, shear=None, interpolation= , fill=0, fillcolor=None, resample=None).(size, interpolation= , max_size=None, antialias=None).(padding, fill=0, padding_mode='constant').Torchvision: A selection of transformations.Loading an example image (with opencv2). ![]()
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