def find_matches(self, examples):
""" """
print(f'Finding matches for {self.dname}')
save_dir = f'temp_files/target_visual_check_{self.dname}'
for split in ['train', 'val']:
split_txt_fpath = f'{_ROOT}/mseg/dataset_lists/{self.dname}/list/{split}.txt'
pairs = generate_all_img_label_pair_fpaths(self.dataroot,
split_txt_fpath)
for (title, annot_fname, data_split) in examples:
parent, fname_stem, _ = get_unique_mask_identifiers(
self.dname, annot_fname, data_split)
for (rgb_fpath, label_fpath) in pairs:
if Path(rgb_fpath).stem == fname_stem and Path(
label_fpath).parts[-2] == parent:
rgb_img = imageio.imread(rgb_fpath)
label_img = imageio.imread(label_fpath)
save_fpath = f'{save_dir}/{self.dname}_{title}_{annot_fname}'
save_classnames_in_image_maxcardinality(
rgb_img,
label_img,
self.id_to_classname_map,
font_color=(255, 255, 255), # (0,0,0),
save_to_disk=True,
save_fpath=save_fpath)
def verify_all_relabeled_dataset_segments(num_processes: int, mld: Any,
dname: str, dataroot: str,
update_records,
require_strict_boundaries: bool):
"""
By using remap.py, we already have converted label img from original taxonomy, to universal taxonomy.
Args:
- num_processes: number of processes to launch; shouldn't exceed number of cores on machine
- mld: Mask Level Dataset
- dname: string representing name of a dataset taxonomy
- dataroot: string representing path to a file directory
- update_records
Returns:
- None
"""
classname_to_id_map = get_classname_to_dataloaderid_map(
dname, include_ignore_idx_cls=True)
# Check for incorrect class names.
for rec in update_records:
valid_orig = rec.orig_class in classname_to_id_map.keys()
valid_relabeled = rec.relabeled_class in classname_to_id_map.keys()
if not (valid_orig and valid_relabeled):
print(rec.__dict__)
print(
f'Invalid universal classname: {rec.orig_class}, {rec.relabeled_class}'
)
quit()
for split in ['train', 'val']:
# Create for each split separately, since SUNRGBD has same file names for different images in train vs. val
parent_fname_to_updatelist_dict = form_fname_to_updatelist_dict(
dname, update_records, split)
split_txt_fpath = f'{_ROOT}/mseg/dataset_lists/{dname}/list/{split}.txt'
# load up the data root and all absolute paths from the txt file
img_label_pairs = generate_all_img_label_pair_fpaths(
data_root=dataroot, split_txt_fpath=split_txt_fpath)
if num_processes > 1:
send_list_to_workers(
num_processes=num_processes,
list_to_split=img_label_pairs,
worker_func_ptr=verify_mask_worker,
parent_fname_to_updatelist_dict=parent_fname_to_updatelist_dict,
mld=mld,
classname_to_id_map=classname_to_id_map,
require_strict_boundaries=require_strict_boundaries,
split=split)
elif num_processes == 1:
# useful for debugging in a single thread
for (img_fpath, label_img_fpath) in img_label_pairs:
verify_label_img_masks(img_fpath, label_img_fpath,
parent_fname_to_updatelist_dict, mld,
classname_to_id_map,
require_strict_boundaries, split)
def visual_sanitychecks():
"""
Save every 1000th image of each dataset, with classnames embedded.
"""
save_dir = 'temp_files/MSeg_verify'
for dname, d_info in infos.items():
print(f'Writing visual sanity checks for {dname}...')
if dname in [
'coco-panoptic-inst-201', 'mapillary-public66',
'ade20k-151-inst'
]:
continue # is in RGB format and not comparable
id_to_classname_map = get_dataloader_id_to_classname_map(dname)
splits = ['train', 'val']
split_lists = [d_info.trainlist, d_info.vallist]
for split, split_list in zip(splits, split_lists):
pairs = generate_all_img_label_pair_fpaths(d_info.dataroot,
split_list)
# Save 5 examples from each dataset split
step_sz = math.floor(len(pairs) // 5)
for i, (rgb_fpath, label_fpath) in enumerate(pairs[::step_sz]):
print(f'On {i} of {dname}')
rgb_img = cv2_imread_rgb(rgb_fpath)
label_img = imageio.imread(label_fpath)
fname_stem = Path(rgb_fpath).stem
save_fpath = f'{save_dir}/{dname}_{fname_stem}.jpg'
blend_save_fpath = f'{save_dir}/{dname}_{fname_stem}_blended.jpg'
if rgb_img.ndim == 2:
# this image was grayscale
rgb_img = grayscale_to_color(rgb_img)
form_mask_triple_embedded_classnames(rgb_img,
label_img,
id_to_classname_map,
save_fpath,
save_to_disk=True)
frame_visualizer = Visualizer(rgb_img, metadata=None)
output_img = frame_visualizer.overlay_instances(
label_map=label_img,
id_to_class_name_map=id_to_classname_map)
imageio.imwrite(blend_save_fpath, output_img)
def verify_all_dataset_paths_exist():
"""
Loop through all of the datasets and ensure that the absolute paths exist
and are valid.
"""
for dname, d_info in infos.items():
print(f'Verifying {dname}...')
if dname in ['ade20k-151-inst']:
# no such explicit list
continue
for split_list in [d_info.trainlist, d_info.vallist]:
pairs = generate_all_img_label_pair_fpaths(d_info.dataroot,
split_list)
for (rgb_fpath, label_fpath) in pairs:
assert Path(rgb_fpath).exists()
assert Path(label_fpath).exists()
print(f'Verified {dname}.')
def extract_semantic_imgs(self, num_processes: int) -> None:
"""
Args:
- num_processes: number of workers to exploit
Returns:
- None
"""
for split in ['train', 'val']:
split_txt_fpath = f'{ROOT}/dataset_lists/{self.orig_dname}/list/{split}.txt'
# load up the data root and all absolute paths from the txt file
img_label_pairs = generate_all_img_label_pair_fpaths(self.orig_dataroot, split_txt_fpath)
send_list_to_workers(
num_processes=num_processes,
list_to_split=img_label_pairs,
worker_func_ptr=semantic_extractor_worker,
cse=self,
split=split)
def find_matches(self, examples):
""" """
print(f'Finding matches for {self.dname}...')
save_dir = f'temp_files/target_visual_check_{self.dname}'
for split in ['train', 'val']:
split_txt_fpath = f'{_ROOT}/mseg/dataset_lists/{self.dname}/list/{split}.txt'
pairs = generate_all_img_label_pair_fpaths(self.dataroot,
split_txt_fpath)
for (title, annot_fname, data_split, coords) in examples:
parent, fname_stem, _ = get_unique_mask_identifiers(
self.dname, annot_fname, data_split)
for (rgb_fpath, label_fpath) in pairs:
if Path(rgb_fpath).stem == fname_stem and Path(
label_fpath).parts[-2] == parent:
y, x = coords
rgb_img = imageio.imread(rgb_fpath)
label_img = imageio.imread(label_fpath)
assert label_img[
y, x] == self.get_classname_to_id_map[title]
def main():
"""
"""
save_dir = 'temp_files'
# beforehand
# orig_dname = 'cityscapes-19'
# orig_dataroot = '/export/share/Datasets/cityscapes'
# rel_dname = 'cityscapes-19'
# rel_dataroot = '/export/share/Datasets/MSeg/mseg_dataset/Cityscapes'
orig_dname = 'coco-panoptic-133'
orig_dname_lists = 'coco-panoptic-133-temp'
orig_dataroot = '/export/share/Datasets/COCO2017'
rel_dname = 'coco-panoptic-133'
rel_dataroot = '/export/share/Datasets/MSeg/mseg_dataset/COCOPanoptic'
orig_id_to_classname_map = get_dataloader_id_to_classname_map(orig_dname)
rel_id_to_classname_map = get_dataloader_id_to_classname_map(rel_dname)
for split in ['train', 'val']:
orig_split_txt_fpath = f'../mseg/dataset_lists/{orig_dname_lists}/list/{split}.txt'
orig_pairs = generate_all_img_label_pair_fpaths(orig_dataroot, orig_split_txt_fpath)
rel_split_txt_fpath = f'../mseg/dataset_lists/{rel_dname}/list/{split}.txt'
rel_pairs = generate_all_img_label_pair_fpaths(rel_dataroot, rel_split_txt_fpath)
for i in range(len(orig_pairs))[::100]:
orig_pair = orig_pairs[i]
orig_rgb_fpath, orig_label_fpath = orig_pair
orig_rgb_img = imageio.imread(orig_rgb_fpath)
orig_label_img = imageio.imread(orig_label_fpath)
rel_pair = rel_pairs[i]
rel_rgb_fpath, rel_label_fpath = rel_pair
rel_rgb_img = imageio.imread(rel_rgb_fpath)
rel_label_img = imageio.imread(rel_label_fpath)
if not np.allclose(orig_label_img,rel_label_img):
pdb.set_trace()
fname_stem = Path(orig_rgb_fpath).stem
orig_save_fpath = f'{save_dir}/{split}_{i}_noguide_orig_{fname_stem}.png'
orig_guide_save_fpath = f'{save_dir}/{split}_{i}_guide_orig_{fname_stem}.png'
rel_save_fpath = f'{save_dir}/{split}_{i}_noguide_rel_{fname_stem}.png'
rel_guide_save_fpath = f'{save_dir}/{split}_{i}_guide_rel_{fname_stem}.png'
if orig_rgb_img.ndim == 2:
# this image was grayscale
orig_rgb_img = grayscale_to_color(orig_rgb_img)
if rel_rgb_img.ndim == 2:
# this image was grayscale
rel_rgb_img = grayscale_to_color(rel_rgb_img)
#save_classnames_in_image(img_rgb, label_img, id_to_class_name_map, save_to_disk=True, save_fpath=save_fpath)
form_mask_triple_embedded_classnames(orig_rgb_img, orig_label_img, orig_id_to_classname_map, orig_save_fpath, save_to_disk=True)
save_mask_triple_with_color_guide(orig_rgb_img, orig_label_img, orig_id_to_classname_map,fname_stem,save_dir, orig_guide_save_fpath)
form_mask_triple_embedded_classnames(rel_rgb_img, rel_label_img, rel_id_to_classname_map, rel_save_fpath, save_to_disk=True)
save_mask_triple_with_color_guide(rel_rgb_img, rel_label_img, rel_id_to_classname_map,fname_stem,save_dir, rel_guide_save_fpath)
