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}')
raise Exception
# Convert To COCO Dataset
dataset = COCO_Dataset.from_ndds(ndds_dataset=ndds_dataset,
categories=hsr_categories,
naming_rule='type_object_instance_contained',
show_pbar=True,
bbox_area_threshold=1,
allow_same_instance_for_contained=True,
color_interval=5)
dataset.move_images(dst_dir,
preserve_filenames=True,
update_img_paths=True,
overwrite=True,
show_pbar=True)
dataset.save_to_path(f'{dst_dir}/output.json', overwrite=True)
dataset.save_video(
save_path=f'{dst_dir}/preview_with_mask.avi',
fps=5,
show_details=True,
kpt_idx_offset=-1,
)
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={
'seg_mark-inner_0': 'seg_bolt-roi_0',
'seg_mark-middle_0': 'seg_bolt-roi_0',
'seg_mark-outer_0': 'seg_bolt-roi_0'
}
)
bolt_roi_dst_dir = f'{target_dst_dir}/bolt_roi'
make_dir_if_not_exists(bolt_roi_dst_dir)
bolt_roi_dataset.move_images(
dst_img_dir=bolt_roi_dst_dir,
preserve_filenames=True, overwrite_duplicates=False, update_img_paths=True, overwrite=True,
show_pbar=True
)
bolt_roi_dataset.save_to_path(f'{bolt_roi_dst_dir}/output.json', overwrite=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',
fps=5,
show_details=True)
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,
overwrite=True,
show_pbar=True)
for ann_obj in frame.ndds_ann.objects:
if ann_obj.class_name.startswith('hsr'):
obj_type, obj_name = 'seg', 'hsr'
instance_name = ann_obj.class_name.replace('hsr', '')
ann_obj.class_name = f'{obj_type}_{obj_name}_{instance_name}'
elif ann_obj.class_name.startswith('point'):
obj_type, obj_name = 'kpt', 'hsr'
temp = ann_obj.class_name.replace('point', '')
instance_name, contained_name = temp[1], temp[0]
ann_obj.class_name = f'{obj_type}_{obj_name}_{instance_name}_{contained_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='hsr_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(
'/home/clayton/workspace/prj/data_keep/data/ndds/categories/hsr.json'),
naming_rule='type_object_instance_contained',
show_pbar=True,
bbox_area_threshold=1)
dataset.save_to_path('hsr_ndds2coco_test.json', overwrite=True)
dataset.display_preview(show_details=True)
ndds_dataset = NDDS_Dataset.load_from_dir(
json_dir='/home/clayton/workspace/prj/data_keep/data/ndds/NewMap2',
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:
# Note: Part numbers should be specified in the obj_type string.
if ann_obj.class_name == 'colorcone1':
obj_type, obj_name = 'seg0', 'cone'
ann_obj.class_name = f'{obj_type}_{obj_name}'
elif ann_obj.class_name == 'colorcone2':
obj_type, obj_name = 'seg1', 'cone'
ann_obj.class_name = f'{obj_type}_{obj_name}'
ndds_dataset.save_to_path(save_path='cone_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(
'/home/clayton/workspace/prj/data_keep/data/ndds/categories/cone.json'
),
naming_rule='type_object_instance_contained',
delimiter='_',
ignore_unspecified_categories=True,
show_pbar=True)
dataset.save_to_path('cone_ndds2coco_test.json', overwrite=True)
dataset.display_preview(show_details=True)
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',
'mark3': 'seg_bolt-roi'
})
# Save COCO Dataset
dataset.save_to_path('bolt-roi_dataset.json', overwrite=True)
# Preview Dataset
dataset.display_preview(show_details=True)
obj_type, obj_name, instance_name = 'seg', '80part0', '0'
ann_obj.class_name = f'{obj_type}_{obj_name}_{instance_name}'
elif ann_obj.class_name == 'part_nine0':
obj_type, obj_name, instance_name = 'seg', '90part9', '0'
ann_obj.class_name = f'{obj_type}_{obj_name}_{instance_name}'
elif ann_obj.class_name == '90part_zero9':
obj_type, obj_name, instance_name = 'seg', '90part0', '0'
ann_obj.class_name = f'{obj_type}_{obj_name}_{instance_name}'
# Convert To COCO Dataset
dataset = 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,
exclude_invalid_polygons=True)
dataset.save_to_path(result_json)
else:
dataset = COCO_Dataset.load_from_path(result_json)
dataset.remove_categories_by_name(category_names=['measure'])
dataset.images.sort(attr_name='file_name')
dataset.save_visualization(save_dir='measure_test_vis',
preserve_filenames=True,
show_details=True,
bbox_thickness=1,
