def convert_cityscapes_instance_only(data_dir, out_dir):
"""Convert from cityscapes format to COCO instance seg format - polygons"""
sets = [
#'gtFine_val'
'gtFine_train',
# 'gtFine_test',
# 'gtCoarse_train',
# 'gtCoarse_val',
# 'gtCoarse_train_extra'
]
ann_dirs = [
#'gtFine_trainvaltest/gtFine/val'
'gtFine_trainvaltest/gtFine/train',
# 'gtFine_trainvaltest/gtFine/test',
# 'gtCoarse/train',
# 'gtCoarse/train_extra',
# 'gtCoarse/val'
]
json_name = 'instancesonly_filtered_%s.json'
ends_in = '%s_polygons.json'
img_id = 0
ann_id = 0
cat_id = 1
category_dict = {}
category_instancesonly = [
'person',
'rider',
'car',
'truck',
'bus',
'caravan',
'trailer',
'train',
'motorcycle',
'bicycle',
]
for data_set, ann_dir in zip(sets, ann_dirs):
print('Starting %s' % data_set)
ann_dict = {}
images = []
annotations = []
ann_dir = os.path.join(data_dir, ann_dir)
for root, _, files in os.walk(ann_dir):
for filename in files:
#print("*******", filename)
if filename.endswith(ends_in % data_set.split('_')[0]):
if len(images) % 50 == 0:
print("Processed %s images, %s annotations" %
(len(images), len(annotations)))
json_ann = json.load(open(os.path.join(root, filename)))
image = {}
image['id'] = img_id
img_id += 1
image['width'] = json_ann['imgWidth']
image['height'] = json_ann['imgHeight']
image['file_name'] = filename[:-len(
ends_in % data_set.split('_')[0])] + 'leftImg8bit.png'
image['seg_file_name'] = filename[:-len(
ends_in % data_set.split('_')[0]
)] + '%s_instanceIds.png' % data_set.split('_')[0]
images.append(image)
fullname = os.path.join(
root, image['seg_file_name']) # instance name
objects = cs.instances2dict_with_polygons(
[fullname], False)[os.path.abspath(fullname)]
#print(fullname)
#print(objects)
for object_cls in objects:
if object_cls not in category_instancesonly:
continue # skip non-instance categories
for obj in objects[object_cls]:
if obj['contours'] == []:
print('Warning: empty contours.')
continue # skip non-instance categories
len_p = [len(p) for p in obj['contours']]
if min(len_p) <= 4:
print('Warning: invalid contours.')
continue # skip non-instance categories
ann = {}
ann['id'] = ann_id
ann_id += 1
ann['image_id'] = image['id']
ann['segmentation'] = obj['contours']
if object_cls not in category_dict:
category_dict[object_cls] = cat_id
cat_id += 1
ann['category_id'] = category_dict[object_cls]
ann['iscrowd'] = 0
ann['area'] = obj['pixelCount']
ann['bbox'] = bboxs_util.xyxy_to_xywh(
segms_util.polys_to_boxes(
[ann['segmentation']])).tolist()[0]
annotations.append(ann)
ann_dict['images'] = images
categories = [{
"id": category_dict[name],
"name": name
} for name in category_dict]
ann_dict['categories'] = categories
ann_dict['annotations'] = annotations
print("Num categories: %s" % len(categories))
print("Num images: %s" % len(images))
print("Num annotations: %s" % len(annotations))
with open(os.path.join(out_dir, json_name % data_set), 'w') as outfile:
outfile.write(json.dumps(ann_dict))
def convert_cityscapes_instance_only(
data_dir, out_dir):
"""Convert from cityscapes format to COCO instance seg format - polygons"""
sets = [
'gtFine_val',
# 'gtFine_train',
# 'gtFine_test',
# 'gtCoarse_train',
# 'gtCoarse_val',
# 'gtCoarse_train_extra'
]
ann_dirs = [
'gtFine_trainvaltest/gtFine/val',
# 'gtFine_trainvaltest/gtFine/train',
# 'gtFine_trainvaltest/gtFine/test',
# 'gtCoarse/train',
# 'gtCoarse/train_extra',
# 'gtCoarse/val'
]
json_name = 'instancesonly_filtered_%s.json'
ends_in = '%s_polygons.json'
img_id = 0
ann_id = 0
cat_id = 1
category_dict = {}
category_instancesonly = [
'person',
'rider',
'car',
'truck',
'bus',
'train',
'motorcycle',
'bicycle',
]
for data_set, ann_dir in zip(sets, ann_dirs):
print('Starting %s' % data_set)
ann_dict = {}
images = []
annotations = []
ann_dir = os.path.join(data_dir, ann_dir)
for root, _, files in os.walk(ann_dir):
for filename in files:
if filename.endswith(ends_in % data_set.split('_')[0]):
if len(images) % 50 == 0:
print("Processed %s images, %s annotations" % (
len(images), len(annotations)))
json_ann = json.load(open(os.path.join(root, filename)))
image = {}
image['id'] = img_id
img_id += 1
image['width'] = json_ann['imgWidth']
image['height'] = json_ann['imgHeight']
image['file_name'] = filename[:-len(
ends_in % data_set.split('_')[0])] + 'leftImg8bit.png'
image['seg_file_name'] = filename[:-len(
ends_in % data_set.split('_')[0])] + \
'%s_instanceIds.png' % data_set.split('_')[0]
images.append(image)
fullname = os.path.join(root, image['seg_file_name'])
objects = cs.instances2dict_with_polygons(
[fullname], verbose=False)[fullname]
for object_cls in objects:
if object_cls not in category_instancesonly:
continue # skip non-instance categories
for obj in objects[object_cls]:
if obj['contours'] == []:
print('Warning: empty contours.')
continue # skip non-instance categories
len_p = [len(p) for p in obj['contours']]
if min(len_p) <= 4:
print('Warning: invalid contours.')
continue # skip non-instance categories
ann = {}
ann['id'] = ann_id
ann_id += 1
ann['image_id'] = image['id']
ann['segmentation'] = obj['contours']
if object_cls not in category_dict:
category_dict[object_cls] = cat_id
cat_id += 1
ann['category_id'] = category_dict[object_cls]
ann['iscrowd'] = 0
ann['area'] = obj['pixelCount']
ann['bbox'] = bboxs_util.xyxy_to_xywh(
segms_util.polys_to_boxes(
[ann['segmentation']])).tolist()[0]
annotations.append(ann)
ann_dict['images'] = images
categories = [{"id": category_dict[name], "name": name} for name in
category_dict]
ann_dict['categories'] = categories
ann_dict['annotations'] = annotations
print("Num categories: %s" % len(categories))
print("Num images: %s" % len(images))
print("Num annotations: %s" % len(annotations))
with open(os.path.join(out_dir, json_name % data_set), 'wb') as outfile:
outfile.write(json.dumps(ann_dict))
def convert_cityscapes_instance_only(data_dir, out_dir):
"""Convert from cityscapes format to COCO instance seg format - polygons"""
sets = [
"gtFine_val",
"gtFine_train",
"gtFine_test",
# 'gtCoarse_train',
# 'gtCoarse_val',
# 'gtCoarse_train_extra'
]
ann_dirs = [
"gtFine_trainvaltest/gtFine/val",
"gtFine_trainvaltest/gtFine/train",
"gtFine_trainvaltest/gtFine/test",
# 'gtCoarse/train',
# 'gtCoarse/train_extra',
# 'gtCoarse/val'
]
json_name = "instancesonly_filtered_%s.json"
ends_in = "%s_polygons.json"
img_id = 0
ann_id = 0
cat_id = 1
category_dict = {}
category_instancesonly = [
"person",
"rider",
"car",
"truck",
"bus",
"train",
"motorcycle",
"bicycle",
]
for data_set, ann_dir in zip(sets, ann_dirs):
print("Starting %s" % data_set)
ann_dict = {}
images = []
annotations = []
ann_dir = os.path.join(data_dir, ann_dir)
for root, _, files in os.walk(ann_dir):
for filename in files:
if filename.endswith(ends_in % data_set.split("_")[0]):
if len(images) % 50 == 0:
print("Processed %s images, %s annotations" %
(len(images), len(annotations)))
json_ann = json.load(open(os.path.join(root, filename)))
image = {}
image["id"] = img_id
img_id += 1
image["width"] = json_ann["imgWidth"]
image["height"] = json_ann["imgHeight"]
image["file_name"] = (
filename[:-len(ends_in % data_set.split("_")[0])] +
"leftImg8bit.png")
image["seg_file_name"] = (
filename[:-len(ends_in % data_set.split("_")[0])] +
"%s_instanceIds.png" % data_set.split("_")[0])
images.append(image)
fullname = os.path.join(root, image["seg_file_name"])
objects = cs.instances2dict_with_polygons(
[fullname], verbose=False)[fullname]
for object_cls in objects:
if object_cls not in category_instancesonly:
continue # skip non-instance categories
for obj in objects[object_cls]:
if obj["contours"] == []:
print("Warning: empty contours.")
continue # skip non-instance categories
len_p = [len(p) for p in obj["contours"]]
if min(len_p) <= 4:
print("Warning: invalid contours.")
continue # skip non-instance categories
ann = {}
ann["id"] = ann_id
ann_id += 1
ann["image_id"] = image["id"]
ann["segmentation"] = obj["contours"]
if object_cls not in category_dict:
category_dict[object_cls] = cat_id
cat_id += 1
ann["category_id"] = category_dict[object_cls]
ann["iscrowd"] = 0
ann["area"] = obj["pixelCount"]
ann["bbox"] = bboxs_util.xyxy_to_xywh(
segms_util.polys_to_boxes(
[ann["segmentation"]])).tolist()[0]
annotations.append(ann)
ann_dict["images"] = images
categories = [{
"id": category_dict[name],
"name": name
} for name in category_dict]
ann_dict["categories"] = categories
ann_dict["annotations"] = annotations
print("Num categories: %s" % len(categories))
print("Num images: %s" % len(images))
print("Num annotations: %s" % len(annotations))
with open(os.path.join(out_dir, json_name % data_set), "w") as outfile:
outfile.write(json.dumps(ann_dict))
def convert_cityscapes_car_only(
data_dir, out_dir):
"""Convert from cityscapes format to COCO instance seg format - polygons"""
sets = [
'gtFine_val',
'gtFine_train',
# 'gtFine_test',
# 'gtCoarse_train',
# 'gtCoarse_val',
# 'gtCoarse_train_extra'
]
ann_dirs = [
'gtFine_trainvaltest/gtFine/val',
'gtFine_trainvaltest/gtFine/train',
# 'gtFine_trainvaltest/gtFine/test',
# 'gtCoarse/train',
# 'gtCoarse/train_extra',
# 'gtCoarse/val'
]
json_name = 'caronly_filtered_unlabeled_%s.json'
ends_in = '%s_polygons.json'
img_id = 0
ann_id = 0
cat_id = 1
category_dict = {}
category_instancesonly = [
'car',
]
category_dict['car'] = cat_id
for data_set, ann_dir in zip(sets, ann_dirs):
print('Starting %s' % data_set)
ann_dict = {}
images = []
annotations = []
ann_dir = os.path.join(data_dir, ann_dir)
for root, _, files in os.walk(ann_dir):
for filename in files:
if filename.endswith(ends_in % data_set.split('_')[0]):
if len(images) % 50 == 0:
print("Processed %s images, %s annotations" % (
len(images), len(annotations)))
json_ann = json.load(open(os.path.join(root, filename)))
image = {}
image['id'] = img_id
img_id += 1
image['width'] = json_ann['imgWidth']
image['height'] = json_ann['imgHeight']
image['file_name'] = filename[:-len(
ends_in % data_set.split('_')[0])] + 'leftImg8bit.png'
image['seg_file_name'] = filename[:-len(
ends_in % data_set.split('_')[0])] + \
'%s_instanceIds.png' % data_set.split('_')[0]
images.append(image)
fullname = os.path.join(root, image['seg_file_name'])
objects = cs.instances2dict_with_polygons(
[fullname], verbose=False)[fullname]
bbox = []
#x
bbox.append(0)
#y
bbox.append(0)
#w
bbox.append(int(json_ann['imgWidth']))
#h
bbox.append(int(json_ann['imgHeight']))
seg = []
#bbox[] is x,y,w,h
#left_top
seg.append(bbox[0])
seg.append(bbox[1])
#left_bottom
seg.append(bbox[0])
seg.append(bbox[1] + bbox[3])
#right_bottom
seg.append(bbox[0] + bbox[2])
seg.append(bbox[1] + bbox[3])
#right_top
seg.append(bbox[0] + bbox[2])
seg.append(bbox[1])
ann = {}
ann['id'] = ann_id
ann_id += 1
ann['image_id'] = image['id']
ann['segmentation'] = [seg]
ann['category_id'] = 1
ann['iscrowd'] = 0
ann['area'] = bbox[2]*bbox[3]
ann['bbox'] = bbox
annotations.append(ann)
ann_dict['images'] = images
categories = [{"id": category_dict[name], "name": name} for name in
category_dict]
ann_dict['categories'] = categories
ann_dict['annotations'] = annotations
print("Num categories: %s" % len(categories))
print("Num images: %s" % len(images))
print("Num annotations: %s" % len(annotations))
print(categories)
with open(os.path.join(out_dir, json_name % data_set), 'wb') as outfile:
outfile.write(json.dumps(ann_dict))
