from logger import logger
from annotation_utils.ndds.structs import NDDS_Dataset
from annotation_utils.coco.structs import COCO_Dataset, COCO_Category_Handler
from common_utils.file_utils import make_dir_if_not_exists, delete_all_files_in_dir
src_dir = '/home/clayton/workspace/prj/data_keep/data/ndds/20200924_yagura5_bright_1-colored'
dst_dir = 'nihonbashi_debug'
make_dir_if_not_exists(dst_dir)
delete_all_files_in_dir(dst_dir, ask_permission=False)
hsr_categories = COCO_Category_Handler.load_from_path(
'/home/clayton/workspace/prj/data_keep/data/toyota/dataset/config/categories/hsr_categories.json'
)
# 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.lower() == 'nihonbashi':
obj_type, obj_name = 'seg', 'hsr'
instance_name = '0'
ann_obj.class_name = f'{obj_type}_{obj_name}_{instance_name}'
elif ann_obj.class_name.lower() in list('abcdefghijkl'):
obj_type, obj_name = 'kpt', 'hsr'
instance_name, contained_name = '0', ann_obj.class_name
ann_obj.class_name = f'{obj_type}_{obj_name}_{instance_name}_{contained_name}'
else:
logger.error(f'Unknown ann_obj.class_name: {ann_obj.class_name}')
from common_utils.file_utils import file_exists
from annotation_utils.coco.structs import COCO_Dataset, COCO_Category_Handler, COCO_Category
from annotation_utils.labelme.structs import LabelmeAnnotationHandler
# Define Labelme Directory Paths
img_dir = '/path/to/labelme/img/dir'
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(
dst_dir = 'crescent_dataset_val'
# 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)
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)
from annotation_utils.coco.structs import COCO_Category_Handler, COCO_Category
# Simple Non-Keypoint Example
handler = COCO_Category_Handler([
COCO_Category(id=0, supercategory='bird', name='duck'),
COCO_Category(id=1, supercategory='bird', name='sparrow'),
COCO_Category(id=1, supercategory='bird', name='pigeon')
])
handler.save_to_path('birds0.json')
# Simple Non-Keypoint Example using for loop
handler = COCO_Category_Handler()
for name in ['duck', 'sparrow', 'pigeon']:
handler.append(
COCO_Category(id=len(handler), supercategory='bird', name=name))
handler.save_to_path('birds1.json')
# Keypoint Example
handler = COCO_Category_Handler([
COCO_Category(
id=0,
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
],
temp_parts = temp.split('_')
if len(temp_parts) == 2:
obj_type, obj_name, instance_name = target_obj_type, temp_parts[
1], temp_parts[0]
ann_obj.class_name = f'{obj_type}_{obj_name}_{instance_name}'
elif len(temp_parts) == 3:
obj_type, obj_name, instance_name = target_obj_type, f'{temp_parts[1]}part{temp_parts[2]}', '0'
ann_obj.class_name = f'{obj_type}_{obj_name}_{instance_name}'
else:
raise Exception
# Convert To COCO Dataset
dataset = Measure_COCO_Dataset.from_ndds(
ndds_dataset=ndds_dataset,
categories=COCO_Category_Handler.load_from_path(
'/home/clayton/workspace/prj/data_keep/data/ndds/categories/measure_all.json'
),
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={'measure': 0.01},
allow_unfound_seg=True,
class_merge_map={
'mark_10th_place': 'seg_measure',
'marking_bottom': 'seg_measure',
'marking_top': 'seg_measure',
'hook': 'seg_measure'
})
# printj.yellow( ann_obj.class_name)
else:
logger.error(f'ann_obj.class_name: {ann_obj.class_name}')
# raise Exception
# Delete Duplicate Objects
frame.ndds_ann.objects.delete_duplicates(verbose=True,
verbose_ref=frame.img_path)
# ndds_dataset.save_to_path(save_path=f'{coco_data_dir}/hook_fixed_ndds.json', overwrite=True)
# Convert To COCO Dataset
dataset = COCO_Dataset.from_ndds(
ndds_dataset=ndds_dataset,
# categories=COCO_Category_Handler.load_from_path(f'/home/jitesh/3d/data/categories/hook_7ckpt.json'),
categories=COCO_Category_Handler.load_from_path(
f'/home/jitesh/3d/data/categories/tropicana.json'),
naming_rule='type_object_instance_contained',
ignore_unspecified_categories=True,
show_pbar=True,
bbox_area_threshold=1,
default_visibility_threshold=-1,
allow_unfound_seg=True,
)
make_dir_if_not_exists(coco_data_dir)
img_path = f'{coco_data_dir}/img'
make_dir_if_not_exists(coco_data_dir)
ann_dir = f'{coco_data_dir}/json'
make_dir_if_not_exists(ann_dir)
dataset.move_images(dst_img_dir=img_path,
preserve_filenames=False,
update_img_paths=True,
elif ann_obj.class_name.startswith('kpt-ob'):
obj_type, obj_name = 'kpt', 'mark-outer'
contained_name = 'ob'
instance_name = str(0)
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,
from annotation_utils.labelme.structs import LabelmeAnnotationHandler
from annotation_utils.coco.structs import COCO_Dataset, COCO_Category_Handler
# Path Variables
img_dir = ''
json_dir = ''
categories_conf_path = ''
# Load Labelme Annotation Handler
labelme_handler = LabelmeAnnotationHandler.load_from_dir(load_dir=json_dir)
# Load COCO Dataset from Labelme Annotation Handler
coco_dataset = COCO_Dataset.from_labelme(
labelme_handler=labelme_handler,
categories=COCO_Category_Handler.load_from_path(categories_conf_path),
img_dir=img_dir
)
# Save COCO Dataset to a file
coco_dataset.save_to_path(save_path='', overwrite=True)
# Preview COCO Dataset
coco_dataset.display_preview(show_details=True)
coco_dataset.save_visualization(show_details=True)
# coco_dataset.save_video(show_details=True)
dst_dir = 'cup_dataset'
# 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)
from annotation_utils.coco.structs import COCO_Dataset
# 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',
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