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| import cv2 | |
| import numpy as np | |
| import torch | |
| from mmdet.registry import VISUALIZERS | |
| class SegMaskHelper: | |
| def __init__(self): | |
| pass | |
| # Pad the masks to image size (bug in RTMDet config?) | |
| # @timer_func | |
| def align_masks_with_image(self, result, img): | |
| masks = list() | |
| img = img[..., ::-1].copy() | |
| for j, mask in enumerate(result.pred_instances.masks): | |
| numpy_mask = mask.cpu().numpy() | |
| mask = cv2.resize( | |
| numpy_mask.astype(np.uint8), | |
| (img.shape[1], img.shape[0]), | |
| interpolation=cv2.INTER_NEAREST, | |
| ) | |
| # Pad the mask to match the size of the image | |
| padded_mask = np.zeros((img.shape[0], img.shape[1]), dtype=np.uint8) | |
| padded_mask[: mask.shape[0], : mask.shape[1]] = mask | |
| mask = padded_mask | |
| mask = torch.from_numpy(mask) | |
| masks.append(mask) | |
| stacked_masks = torch.stack(masks) | |
| result.pred_instances.masks = stacked_masks | |
| return result | |
| # Crops the images using masks and put the cropped images on a white background | |
| # @timer_func | |
| def crop_masks(self, result, img): | |
| cropped_imgs = list() | |
| polygons = list() | |
| for j, mask in enumerate(result.pred_instances.masks): | |
| np_array = mask.cpu().numpy() | |
| contours, _ = cv2.findContours( | |
| np_array.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE | |
| ) # fix so only one contour (the largest one) is extracted | |
| largest_contour = max(contours, key=cv2.contourArea) | |
| epsilon = 0.003 * cv2.arcLength(largest_contour, True) | |
| approx_poly = cv2.approxPolyDP(largest_contour, epsilon, True) | |
| approx_poly = np.squeeze(approx_poly) | |
| approx_poly = approx_poly.tolist() | |
| polygons.append(approx_poly) | |
| x, y, w, h = cv2.boundingRect(largest_contour) | |
| # Crop masked region and put on white background | |
| masked_region = img[y : y + h, x : x + w] | |
| white_background = np.ones_like(masked_region) | |
| white_background.fill(255) | |
| masked_region_on_white = cv2.bitwise_and( | |
| white_background, masked_region, mask=np_array.astype(np.uint8)[y : y + h, x : x + w] | |
| ) | |
| cv2.bitwise_not(white_background, white_background, mask=np_array.astype(np.uint8)[y : y + h, x : x + w]) | |
| res = white_background + masked_region_on_white | |
| cropped_imgs.append(res) | |
| return cropped_imgs, polygons | |
| def visualize_result(self, result, img, model_visualizer): | |
| visualizer = VISUALIZERS.build(model_visualizer) | |
| visualizer.add_datasample("result", img, data_sample=result, draw_gt=False) | |
| return visualizer.get_image() | |
| def _translate_line_coords(self, region_mask, line_polygons): | |
| region_mask = region_mask.cpu().numpy() | |
| region_masks_binary = (region_mask > 0).astype(np.uint8) | |
| box = cv2.boundingRect(region_masks_binary) | |
| translated_line_polygons = [[[a + box[0], b + box[1]] for [a, b] in poly] for poly in line_polygons] | |
| return translated_line_polygons | |
| if __name__ == "__main__": | |
| pass | |