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907462b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 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 | import cv2
import pickle
import numpy as np
from pathlib import Path
from tqdm import tqdm
from isegm.data.datasets import LvisDataset, CocoDataset
from isegm.utils.misc import get_bbox_from_mask, get_bbox_iou
from scripts.annotations_conversion.common import get_masks_hierarchy, get_iou, encode_masks
def create_annotations(lvis_path: Path, coco_path: Path, dataset_split='train', min_object_area=80):
lvis_dataset = LvisDataset(lvis_path, split=dataset_split)
lvis_samples = lvis_dataset.dataset_samples
lvis_annotations = lvis_dataset.annotations
coco_dataset = CocoDataset(coco_path, split=dataset_split + '2017')
coco_lvis_mapping = []
lvis_images = {x['coco_url'].split('/')[-1].split('.')[0]: lvis_indx
for lvis_indx, x in enumerate(lvis_samples)}
for indx, coco_sample in enumerate(coco_dataset.dataset_samples):
lvis_indx = lvis_images.get(coco_sample['file_name'].split('.')[0], None)
if lvis_indx is not None:
coco_lvis_mapping.append((indx, lvis_indx))
output_masks_path = lvis_path / dataset_split / 'masks'
output_masks_path.mkdir(parents=True, exist_ok=True)
hlvis_annotation = dict()
for coco_indx, lvis_indx in tqdm(coco_lvis_mapping):
coco_sample = get_coco_sample(coco_dataset, coco_indx)
lvis_info = lvis_samples[lvis_indx]
lvis_annotation = lvis_annotations[lvis_info['id']]
empty_mask = np.zeros((lvis_info['height'], lvis_info['width']))
image_name = lvis_info['coco_url'].split('/')[-1].split('.')[0]
lvis_masks = []
lvis_bboxes = []
for obj_annotation in lvis_annotation:
obj_mask = lvis_dataset.get_mask_from_polygon(obj_annotation, empty_mask)
obj_mask = obj_mask == 1
if obj_mask.sum() >= min_object_area:
lvis_masks.append(obj_mask)
lvis_bboxes.append(get_bbox_from_mask(obj_mask))
coco_bboxes = []
coco_masks = []
for inst_id in coco_sample['instances_info'].keys():
obj_mask = coco_sample['instances_mask'] == inst_id
if obj_mask.sum() >= min_object_area:
coco_masks.append(obj_mask)
coco_bboxes.append(get_bbox_from_mask(obj_mask))
masks = []
for coco_j, coco_bbox in enumerate(coco_bboxes):
for lvis_i, lvis_bbox in enumerate(lvis_bboxes):
if get_bbox_iou(lvis_bbox, coco_bbox) > 0.70 and \
get_iou(lvis_masks[lvis_i], coco_masks[coco_j]) > 0.70:
break
else:
masks.append(coco_masks[coco_j])
for ti, (lvis_mask, lvis_bbox) in enumerate(zip(lvis_masks, lvis_bboxes)):
for tj_mask, tj_bbox in zip(lvis_masks[ti + 1:], lvis_bboxes[ti + 1:]):
bbox_iou = get_bbox_iou(lvis_bbox, tj_bbox)
if bbox_iou > 0.7 and get_iou(lvis_mask, tj_mask) > 0.85:
break
else:
masks.append(lvis_mask)
masks_meta = [(get_bbox_from_mask(x), x.sum()) for x in masks]
if not masks:
continue
hierarchy = get_masks_hierarchy(masks, masks_meta)
for obj_id, obj_info in list(hierarchy.items()):
if obj_info['parent'] is None and len(obj_info['children']) == 0:
hierarchy[obj_id] = None
merged_mask = np.max(masks, axis=0)
num_instance_masks = len(masks)
for obj_id in coco_sample['semantic_info'].keys():
obj_mask = coco_sample['semantic_map'] == obj_id
obj_mask = np.logical_and(obj_mask, np.logical_not(merged_mask))
if obj_mask.sum() > 500:
masks.append(obj_mask)
hlvis_annotation[image_name] = {
'num_instance_masks': num_instance_masks,
'hierarchy': hierarchy
}
with open(output_masks_path / f'{image_name}.pickle', 'wb') as f:
pickle.dump(encode_masks(masks), f)
with open(lvis_path / dataset_split / 'hannotation.pickle', 'wb') as f:
pickle.dump(hlvis_annotation, f, protocol=pickle.HIGHEST_PROTOCOL)
def get_coco_sample(dataset, index):
dataset_sample = dataset.dataset_samples[index]
image_path = dataset.images_path / dataset.get_image_name(dataset_sample['file_name'])
label_path = dataset.labels_path / dataset_sample['file_name']
image = cv2.imread(str(image_path))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
label = cv2.imread(str(label_path), cv2.IMREAD_UNCHANGED).astype(np.int32)
label = 256 * 256 * label[:, :, 0] + 256 * label[:, :, 1] + label[:, :, 2]
instance_map = np.full_like(label, 0)
semantic_map = np.full_like(label, 0)
semantic_info = dict()
instances_info = dict()
for segment in dataset_sample['segments_info']:
class_id = segment['category_id']
obj_id = segment['id']
if class_id not in dataset._things_labels_set:
semantic_map[label == obj_id] = obj_id
semantic_info[obj_id] = {'ignore': False}
continue
instance_map[label == obj_id] = obj_id
ignore = segment['iscrowd'] == 1
instances_info[obj_id] = {
'ignore': ignore
}
sample = {
'image': image,
'instances_mask': instance_map,
'instances_info': instances_info,
'semantic_map': semantic_map,
'semantic_info': semantic_info
}
return sample
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