def convert_single_core(proc_id,
image_set,
categories,
source_folder,
segmentations_folder,
VOID=0):
annotations = []
for working_idx, image_info in enumerate(image_set):
if working_idx % 100 == 0:
print('Core: {}, {} from {} images converted'.format(
proc_id, working_idx, len(image_set)))
file_name = '{}.png'.format(image_info['file_name'].rsplit('.')[0])
try:
original_format = np.array(Image.open(
os.path.join(source_folder, file_name)),
dtype=np.uint32)
except IOError:
raise KeyError('no prediction png file for id: {}'.format(
image_info['id']))
pan = OFFSET * original_format[:, :, 0] + original_format[:, :, 1]
pan_format = np.zeros(
(original_format.shape[0], original_format.shape[1], 3),
dtype=np.uint8)
id_generator = IdGenerator(categories)
l = np.unique(pan)
segm_info = []
for el in l:
sem = el // OFFSET
if sem == VOID:
continue
if sem not in categories:
raise KeyError('Unknown semantic label {}'.format(sem))
mask = pan == el
segment_id, color = id_generator.get_id_and_color(sem)
pan_format[mask] = color
segm_info.append({"id": int(segment_id), "category_id": int(sem)})
annotations.append({
'image_id': image_info['id'],
'file_name': file_name,
"segments_info": segm_info
})
Image.fromarray(pan_format).save(
os.path.join(segmentations_folder, file_name))
print('Core: {}, all {} images processed'.format(proc_id, len(image_set)))
return annotations
def convert_detection_to_panoptic_coco_format_single_core(
proc_id, coco_detection, img_ids, categories, segmentations_folder):
id_generator = IdGenerator(categories)
annotations_panoptic = []
for working_idx, img_id in enumerate(img_ids):
if working_idx % 100 == 0:
print('Core: {}, {} from {} images processed'.format(
proc_id, working_idx, len(img_ids)))
img = coco_detection.loadImgs(int(img_id))[0]
pan_format = np.zeros((img['height'], img['width'], 3), dtype=np.uint8)
overlaps_map = np.zeros((img['height'], img['width']), dtype=np.uint32)
anns_ids = coco_detection.getAnnIds(img_id)
anns = coco_detection.loadAnns(anns_ids)
panoptic_record = {}
panoptic_record['image_id'] = img_id
file_name = '{}.png'.format(img['file_name'].rsplit('.')[0])
panoptic_record['file_name'] = file_name
segments_info = []
for ann in anns:
if ann['category_id'] not in categories:
raise Exception(
'Panoptic coco categories file does not contain \
category with id: {}'.format(ann['category_id']))
segment_id, color = id_generator.get_id_and_color(
ann['category_id'])
mask = coco_detection.annToMask(ann)
overlaps_map += mask
pan_format[mask == 1] = color
ann.pop('segmentation')
ann.pop('image_id')
ann['id'] = segment_id
segments_info.append(ann)
if np.sum(overlaps_map > 1) != 0:
raise Exception(
"Segments for image {} overlap each other.".format(img_id))
panoptic_record['segments_info'] = segments_info
annotations_panoptic.append(panoptic_record)
Image.fromarray(pan_format).save(
os.path.join(segmentations_folder, file_name))
print('Core: {}, all {} images processed'.format(proc_id, len(img_ids)))
return annotations_panoptic
def panoptic_converter(original_format_folder, out_folder, out_file):
if not os.path.isdir(out_folder):
print("Creating folder {} for panoptic segmentation PNGs".format(
out_folder))
os.mkdir(out_folder)
categories = []
for idx, el in enumerate(labels):
if el.ignoreInEval:
continue
categories.append({
'id': el.id,
'name': el.name,
'color': el.color,
'supercategory': el.category,
'isthing': 1 if el.hasInstances else 0
})
categories_dict = {cat['id']: cat for cat in categories}
file_list = sorted(
glob.glob(
os.path.join(original_format_folder,
'*/*_gtFine_instanceIds.png')))
images = []
annotations = []
for working_idx, f in enumerate(file_list):
if working_idx % 10 == 0:
print(working_idx, len(file_list))
original_format = np.array(Image.open(f))
file_name = f.split('/')[-1]
image_id = file_name.rsplit('_', 2)[0]
image_filename = '{}_leftImg8bit.png'.format(image_id)
# image entry, id for image is its filename without extension
images.append({
"id": image_id,
"width": original_format.shape[1],
"height": original_format.shape[0],
"file_name": image_filename
})
pan_format = np.zeros(
(original_format.shape[0], original_format.shape[1], 3),
dtype=np.uint8)
id_generator = IdGenerator(categories_dict)
idx = 0
l = np.unique(original_format)
segm_info = []
for el in l:
if el < 1000:
semantic_id = el
is_crowd = 1
else:
semantic_id = el // 1000
is_crowd = 0
if semantic_id not in categories_dict:
continue
if categories_dict[semantic_id]['isthing'] == 0:
is_crowd = 0
mask = original_format == el
segment_id, color = id_generator.get_id_and_color(semantic_id)
pan_format[mask] = color
area = np.sum(mask) # segment area computation
# bbox computation for a segment
hor = np.sum(mask, axis=0)
hor_idx = np.nonzero(hor)[0]
x = hor_idx[0]
width = hor_idx[-1] - x + 1
vert = np.sum(mask, axis=1)
vert_idx = np.nonzero(vert)[0]
y = vert_idx[0]
height = vert_idx[-1] - y + 1
bbox = [x, y, width, height]
segm_info.append({
"id": int(segment_id),
"category_id": int(semantic_id),
"area": area,
"bbox": bbox,
"iscrowd": is_crowd
})
annotations.append({
'image_id': image_id,
'file_name': file_name,
"segments_info": segm_info
})
Image.fromarray(pan_format).save(os.path.join(out_folder, file_name))
d = {
'images': images,
'annotations': annotations,
'categories': categories,
}
with open(out_file, 'w') as f:
json.dump(d, f)