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| import cv2 | |
| from pycocotools.coco import COCO | |
| from pycocotools import mask as maskUtils | |
| # coco id: https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ | |
| all_instances_ids = [ | |
| 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, | |
| 11, 13, 14, 15, 16, 17, 18, 19, 20, | |
| 21, 22, 23, 24, 25, 27, 28, | |
| 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, | |
| 41, 42, 43, 44, 46, 47, 48, 49, 50, | |
| 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, | |
| 61, 62, 63, 64, 65, 67, 70, | |
| 72, 73, 74, 75, 76, 77, 78, 79, 80, | |
| 81, 82, 84, 85, 86, 87, 88, 89, 90, | |
| ] | |
| all_stuff_ids = [ | |
| 92, 93, 94, 95, 96, 97, 98, 99, 100, | |
| 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, | |
| 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, | |
| 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, | |
| 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, | |
| 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, | |
| 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, | |
| 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, | |
| 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, | |
| 181, 182, | |
| # other | |
| 183, | |
| # unlabeled | |
| 0, | |
| ] | |
| # panoptic id: https://github.com/cocodataset/panopticapi/blob/master/panoptic_coco_categories.json | |
| panoptic_stuff_ids = [ | |
| 92, 93, 95, 100, | |
| 107, 109, | |
| 112, 118, 119, | |
| 122, 125, 128, 130, | |
| 133, 138, | |
| 141, 144, 145, 147, 148, 149, | |
| 151, 154, 155, 156, 159, | |
| 161, 166, 168, | |
| 171, 175, 176, 177, 178, 180, | |
| 181, 184, 185, 186, 187, 188, 189, 190, | |
| 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, | |
| # unlabeled | |
| 0, | |
| ] | |
| def getCocoIds(name = 'semantic'): | |
| if 'instances' == name: | |
| return all_instances_ids | |
| elif 'stuff' == name: | |
| return all_stuff_ids | |
| elif 'panoptic' == name: | |
| return all_instances_ids + panoptic_stuff_ids | |
| else: # semantic | |
| return all_instances_ids + all_stuff_ids | |
| def getMappingId(index, name = 'semantic'): | |
| ids = getCocoIds(name = name) | |
| return ids[index] | |
| def getMappingIndex(id, name = 'semantic'): | |
| ids = getCocoIds(name = name) | |
| return ids.index(id) | |
| # convert ann to rle encoded string | |
| def annToRLE(ann, img_size): | |
| h, w = img_size | |
| segm = ann['segmentation'] | |
| if list == type(segm): | |
| # polygon -- a single object might consist of multiple parts | |
| # we merge all parts into one mask rle code | |
| rles = maskUtils.frPyObjects(segm, h, w) | |
| rle = maskUtils.merge(rles) | |
| elif list == type(segm['counts']): | |
| # uncompressed RLE | |
| rle = maskUtils.frPyObjects(segm, h, w) | |
| else: | |
| # rle | |
| rle = ann['segmentation'] | |
| return rle | |
| # decode ann to mask martix | |
| def annToMask(ann, img_size): | |
| rle = annToRLE(ann, img_size) | |
| m = maskUtils.decode(rle) | |
| return m | |
| # convert mask to polygans | |
| def convert_to_polys(mask): | |
| # opencv 3.2 | |
| contours, hierarchy = cv2.findContours((mask).astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) | |
| # before opencv 3.2 | |
| # contours, hierarchy = cv2.findContours((mask).astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) | |
| segmentation = [] | |
| for contour in contours: | |
| contour = contour.flatten().tolist() | |
| if 4 < len(contour): | |
| segmentation.append(contour) | |
| return segmentation | |