def get_img_pair(self, fname_stem: str) -> Tuple[np.ndarray, np.ndarray]:
""" Load 2-tuple of image data from disk (RGB and label).
Args:
- fname_stem: string representing
Returns:
- rgb_img
- label_img
"""
# look it up in the dictionary
rgb_fpath = self.fname_to_rgbfpath_dict[fname_stem]
label_fpath = rgb_fpath.replace('/images/', '/annotations/')
label_fpath = label_fpath.replace('.jpg', '.png')
rgb_img = imageio.imread(rgb_fpath)
if rgb_img.ndim == 2:
# this image was grayscale
rgb_img = grayscale_to_color(rgb_img)
assert rgb_img.ndim == 3
h, w, ch = rgb_img.shape
assert ch == 3
label_img = imageio.imread(label_fpath)
assert label_img.ndim == 2
assert rgb_img.shape[:2] == label_img.shape[:2]
return rgb_img, label_img
def test_grayscale_to_color(): """ Convert simple 2x2 grayscale image into RGB image. """ gray_img = np.array( [ [0,255], [255,0] ], dtype=np.uint8) rgb_img = grayscale_to_color(gray_img) assert rgb_img.shape == (2,2,3) for i in range(2): assert np.allclose(rgb_img[:,:,i], gray_img)
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 get_rgb_img(self, split: str, fname_stem: str) -> np.ndarray:
"""
Args:
- split: string representing training, validation, or testing split of the data
- fname_stem:
Returns:
- rgb_img: color image.
"""
rgb_img_fpath = f'{self.coco_dataroot}/{split}2017/{fname_stem}.jpg'
rgb_img = imageio.imread(rgb_img_fpath)
if rgb_img.ndim == 2:
# this image was grayscale
rgb_img = grayscale_to_color(rgb_img)
return rgb_img
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)
