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| import cv2 | |
| import numpy as np | |
| import torch | |
| from torchvision.transforms import Compose, Normalize, ToTensor | |
| def preprocess_image(img: np.ndarray, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) -> torch.Tensor: | |
| preprocessing = Compose([ | |
| ToTensor(), | |
| Normalize(mean=mean, std=std) | |
| ]) | |
| return preprocessing(img.copy()).unsqueeze(0) | |
| def deprocess_image(img): | |
| """ see https://github.com/jacobgil/keras-grad-cam/blob/master/grad-cam.py#L65 """ | |
| img = img - np.mean(img) | |
| img = img / (np.std(img) + 1e-5) | |
| img = img * 0.1 | |
| img = img + 0.5 | |
| img = np.clip(img, 0, 1) | |
| return np.uint8(img * 255) | |
| def show_cam_on_image(img: np.ndarray, | |
| mask: np.ndarray, | |
| use_rgb: bool = False, | |
| colormap: int = cv2.COLORMAP_JET) -> np.ndarray: | |
| """ This function overlays the cam mask on the image as an heatmap. | |
| By default the heatmap is in BGR format. | |
| :param img: The base image in RGB or BGR format. | |
| :param mask: The cam mask. | |
| :param use_rgb: Whether to use an RGB or BGR heatmap, this should be set to True if 'img' is in RGB format. | |
| :param colormap: The OpenCV colormap to be used. | |
| :returns: The default image with the cam overlay. | |
| """ | |
| heatmap = cv2.applyColorMap(np.uint8(255 * mask), colormap) | |
| if use_rgb: | |
| heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB) | |
| heatmap = np.float32(heatmap) / 255 | |
| if np.max(img) > 1: | |
| raise Exception( | |
| "The input image should np.float32 in the range [0, 1]") | |
| cam = heatmap + img | |
| cam = cam / np.max(cam) | |
| return np.uint8(255 * cam) | |
| def scale_cam_image(cam, target_size=None): | |
| result = [] | |
| for img in cam: | |
| img = img - np.min(img) | |
| img = img / (1e-7 + np.max(img)) | |
| if target_size is not None: | |
| img = cv2.resize(img, target_size) | |
| result.append(img) | |
| result = np.float32(result) | |
| return result | |
| def scale_accross_batch_and_channels(tensor, target_size): | |
| batch_size, channel_size = tensor.shape[:2] | |
| reshaped_tensor = tensor.reshape( | |
| batch_size * channel_size, *tensor.shape[2:]) | |
| result = scale_cam_image(reshaped_tensor, target_size) | |
| result = result.reshape( | |
| batch_size, | |
| channel_size, | |
| target_size[1], | |
| target_size[0]) | |
| return result | |