json_dir = '/path/to/labelme/json/dir'
# Load Labelme Handler
labelme_handler = LabelmeAnnotationHandler.load_from_dir(load_dir=json_dir)
# Define COCO Categories Before Conversion
if not file_exists('categories_example.json'): # Save a new categories json if it doesn't already exist.
categories = COCO_Category_Handler()
categories.append( # Standard Keypoint Example
COCO_Category(
id=len(categories),
supercategory='pet',
name='dog',
keypoints=[ # The keypoint labels are defined here
'left_eye', 'right_eye', # 0, 1
'mouth_left', 'mouth_center', 'mouth_right' # 2, 3, 4
],
skeleton=[ # The connections between keypoints are defined with indecies here
[0, 1],
[2, 3], [3,4]
]
)
)
categories.append( # Simple Keypoint Example
COCO_Category.from_label_skeleton(
id=len(categories),
supercategory='pet',
name='cat',
label_skeleton=[
['left_eye', 'right_eye'],
['mouth_left', 'mouth_center'], ['mouth_center', 'mouth_right']
# Load NDDS Dataset
ndds_dataset = NDDS_Dataset.load_from_dir(json_dir=src_dir, show_pbar=True)
# Fix NDDS Dataset naming so that it follows convention. (This is not necessary if the NDDS dataset already follows the naming convention.)
for frame in ndds_dataset.frames:
# Fix Naming Convention
for ann_obj in frame.ndds_ann.objects:
if ann_obj.class_name.startswith('crescent'):
obj_type, obj_name = 'seg', 'crescent'
instance_name = '0'
ann_obj.class_name = f'{obj_type}_{obj_name}_{instance_name}'
# Convert To COCO Dataset
crescent_categories = COCO_Category_Handler()
crescent_categories.append(
COCO_Category(id=len(crescent_categories), name='crescent'))
dataset = COCO_Dataset.from_ndds(ndds_dataset=ndds_dataset,
categories=crescent_categories,
naming_rule='type_object_instance_contained',
show_pbar=True,
bbox_area_threshold=-1,
allow_unfound_seg=True)
dataset.move_images(dst_img_dir=dst_dir,
preserve_filenames=True,
overwrite_duplicates=False,
update_img_paths=True,
overwrite=True,
show_pbar=True)
dataset.save_to_path(f'{dst_dir}/output.json', overwrite=True)
# dataset.display_preview(show_details=True)
dataset.save_video(save_path=f'{dst_dir}/preview.mp4',
from annotation_utils.coco.structs import COCO_Category_Handler, COCO_Category
handler = COCO_Category_Handler()
handler.append(COCO_Category(id=len(handler), name='garbage'))
output_dir = '/home/clayton/workspace/prj/data_keep/data/ndds/categories'
handler.save_to_path(f'{output_dir}/garbage.json', overwrite=True)
# Load NDDS Dataset
ndds_dataset = NDDS_Dataset.load_from_dir(json_dir=src_dir, show_pbar=True)
# Fix NDDS Dataset naming so that it follows convention. (This is not necessary if the NDDS dataset already follows the naming convention.)
for frame in ndds_dataset.frames:
# Fix Naming Convention
for ann_obj in frame.ndds_ann.objects:
if ann_obj.class_name.startswith('cup'):
obj_type, obj_name = 'seg', 'cup'
instance_name = '0'
ann_obj.class_name = f'{obj_type}_{obj_name}_{instance_name}'
# Convert To COCO Dataset
cup_categories = COCO_Category_Handler()
cup_categories.append(COCO_Category(id=len(cup_categories), name='cup'))
dataset = COCO_Dataset.from_ndds(ndds_dataset=ndds_dataset,
categories=cup_categories,
naming_rule='type_object_instance_contained',
show_pbar=True,
bbox_area_threshold=-1,
allow_unfound_seg=True)
dataset.move_images(dst_img_dir=dst_dir,
preserve_filenames=True,
overwrite_duplicates=False,
update_img_paths=True,
overwrite=True,
show_pbar=True)
dataset.save_to_path(f'{dst_dir}/output.json', overwrite=True)
# dataset.display_preview(show_details=True)
dataset.save_video(save_path=f'{dst_dir}/preview.mp4',
ann_obj.class_name = f'{obj_type}_{obj_name}_{instance_name}_{contained_name}'
for idx in delete_idx_list[::-1]:
del ndds_dataset.frames[idx]
print(f"ID deleted {idx}")
# Bolt ROI Dataset Creation
logger.info('Creating Bolt ROI Dataset')
bolt_roi_categories = COCO_Category_Handler()
print(f"Bolt_roi_categories :{bolt_roi_categories}")
bolt_roi_categories.append(
COCO_Category(
id=len(bolt_roi_categories),
name='bolt-roi'
)
)
print(f"Bolt_roi_categories :{bolt_roi_categories}")
bolt_roi_dataset = COCO_Dataset.from_ndds(
ndds_dataset=ndds_dataset,
categories=bolt_roi_categories,
naming_rule='type_object_instance_contained', delimiter='_',
ignore_unspecified_categories=True,
show_pbar=True,
bbox_area_threshold=1,
default_visibility_threshold=0.01,
visibility_threshold_dict={'bolt-roi': 0.01},
allow_unfound_seg=False,
class_merge_map={
from logger import logger from annotation_utils.coco.structs import COCO_Category coco_cat = COCO_Category(id=0, supercategory=1.0, name=2) assert type(coco_cat.supercategory) is str assert type(coco_cat.name) is str coco_cat0 = COCO_Category.from_dict(coco_cat.to_dict()) assert type(coco_cat0.supercategory) is str assert type(coco_cat0.name) is str coco_cat = COCO_Category(id=0, supercategory='a', name='b') assert type(coco_cat.supercategory) is str assert type(coco_cat.name) is str coco_cat0 = COCO_Category.from_dict(coco_cat.to_dict()) assert type(coco_cat0.supercategory) is str assert type(coco_cat0.name) is str logger.good(f'Test passed')
from annotation_utils.coco.structs import COCO_Dataset, \
COCO_License, COCO_Image, COCO_Annotation, COCO_Category
from common_utils.common_types.bbox import BBox
from logger import logger
dataset = COCO_Dataset.new(description='Test')
dataset.categories.append(
COCO_Category(id=len(dataset.categories),
supercategory='test_category',
name='category_a'))
dataset.categories.append(
COCO_Category(id=len(dataset.categories),
supercategory='test_category',
name='category_b'))
dataset.categories.append(
COCO_Category(id=len(dataset.categories),
supercategory='test_category',
name='category_c'))
for i in range(10):
dataset.licenses.append(
COCO_License(url=f'test_license_{i}',
name=f'Test License {i}',
id=len(dataset.licenses)))
for i in range(20):
dataset.images.append(
COCO_Image(license_id=i % len(dataset.licenses),
file_name=f'{i}.jpg',
coco_url=f'/path/to/{i}.jpg',
height=500,
width=500,
# Load NDDS Dataset
ndds_dataset = NDDS_Dataset.load_from_dir(
json_dir='/home/clayton/workspace/prj/data_keep/data/ndds/type1',
show_pbar=True)
# Fix NDDS Dataset naming so that it follows convention. (This is not necessary if the NDDS dataset already follows the naming convention.)
for frame in ndds_dataset.frames:
for ann_obj in frame.ndds_ann.objects:
if ann_obj.class_name == 'bolt':
obj_type, obj_name = 'seg', 'bolt-roi'
ann_obj.class_name = f'{obj_type}_{obj_name}'
# Define COCO Categories
categories = COCO_Category_Handler()
categories.append(COCO_Category(id=len(categories), name='bolt-roi'))
# Convert To COCO Dataset
dataset = COCO_Dataset.from_ndds(ndds_dataset=ndds_dataset,
categories=categories,
naming_rule='type_object_instance_contained',
delimiter='_',
ignore_unspecified_categories=True,
show_pbar=True,
bbox_area_threshold=1,
default_visibility_threshold=0.10,
visibility_threshold_dict={'bolt-roi': 0.01},
allow_unfound_seg=True,
class_merge_map={
'mark1': 'seg_bolt-roi',
'mark2': 'seg_bolt-roi',
from annotation_utils.coco.structs import COCO_Category_Handler, COCO_Category
handler = COCO_Category_Handler()
handler.append(
COCO_Category(supercategory='screw',
name='screw',
keypoints=[],
skeleton=[],
id=len(handler)))
handler.append(
COCO_Category(supercategory='hole',
name='hole',
keypoints=[],
skeleton=[],
id=len(handler)))
handler.save_to_path('interphone_ng_object_categories.json')
from annotation_utils.coco.structs import COCO_Category_Handler, COCO_Category
handler = COCO_Category_Handler()
handler.append(
COCO_Category.from_label_skeleton(
supercategory='', # Supercategory Name
name='bracket_light', # Object Name
id=len(handler),
label_skeleton=''
)
)
handler.save_to_path(save_path='hien_all_2_symbol_categories.json', overwrite=True) # Save Categorie