def run_make_train_annotation(data_root, image_set):
imageset_file_loc = os.path.join(data_root, 'image_sets', image_set)
# split = 'train1_ids_all_670'
newimage_dir = os.path.join(data_root, 'stag`e1_images', 'stage1_train')
os.makedirs(newimage_dir, exist_ok=True)
multimask_dir = os.path.join(data_root, 'stage1_images', 'multi_masks')
os.makedirs(multimask_dir, exist_ok=True)
overlays_dir = os.path.join(data_root, 'stage1_images', 'overlays')
os.makedirs(overlays_dir, exist_ok=True)
ids = read_list_from_file(imageset_file_loc, comment='#')
num_ids = len(ids)
for i in range(num_ids):
id = ids[i]
print(id)
name, folder = id.split('/')
image_loc = os.path.join(data_root, 'stage1_images', 'official',
folder, name, 'images', '*.png')
image_files = glob.glob(image_loc)
assert (len(image_files) == 1)
image_file = image_files[0]
# image
image = cv2.imread(image_file, cv2.IMREAD_COLOR)
H, W, C = image.shape
multi_mask = np.zeros((H, W), np.int32)
masks_loc = os.path.join(data_root, 'stage1_images', 'official',
folder, name, 'masks', '*.png')
mask_files = glob.glob(masks_loc)
mask_files.sort()
num_masks = len(mask_files)
for k in range(num_masks):
mask_file = mask_files[k]
mask = cv2.imread(mask_file, cv2.IMREAD_GRAYSCALE)
multi_mask[np.where(mask > 128)] = k + 1
# check
color_overlay = multi_mask_to_color_overlay(multi_mask, color='summer')
color1_overlay = multi_mask_to_contour_overlay(multi_mask,
color_overlay,
[255, 255, 255])
contour_overlay = multi_mask_to_contour_overlay(
multi_mask, image, [0, 255, 0])
all = np.hstack((
image,
contour_overlay,
color1_overlay,
)).astype(np.uint8)
cv2.imwrite(os.path.join(newimage_dir, name + '.png'), image)
np.save(os.path.join(multimask_dir, name + '.npy'), multi_mask)
cv2.imwrite(os.path.join(multimask_dir, name + '.png'), color_overlay)
cv2.imwrite(os.path.join(overlays_dir, name + '.png'), all)
def run_make_test_annotation(cfg, norm=False):
ids = read_list_from_file(os.path.join(cfg.split_dir,
cfg.annotation_test_split),
comment='#')
s_test = SourceFolder(cfg.source_test_dir)
d_test = DataFolder(os.path.join(cfg.data_dir, 'stage2_test_final'))
for i in range(len(ids)):
folder = ids[i].split('/')[0]
name = ids[i].split('/')[1]
image = s_test.get_image(name)
# show and save into image folder_name
cv2.imwrite(os.path.join(d_test.image_folder, '%s.png' % name), image)
print('\rannotate: ', i)
print('run_make_test_annotation success!')
def __init__(self, cfg, split, transform=None, mode='train'):
super(ScienceDataset, self).__init__()
start = timer()
self.cfg = cfg
self.split = split
self.transform = transform
self.mode = mode
# read split
self.ids = read_list_from_file(os.path.join(self.cfg.split_dir, split),
comment='#')
# print
print('\ttime = %0.2f min' % ((timer() - start) / 60))
print('\tnum_ids = %d' % (len(self.ids)))
print('')
def __init__(self, split, transform=None, mode='train'):
super(ScienceDataset, self).__init__()
start = timer()
self.split = split
self.transform = transform
self.mode = mode
#read split
ids = read_list_from_file(DATA_DIR + '/split/' + split, comment='#')
#save
self.ids = ids
#print
print('\ttime = %0.2f min' % ((timer() - start) / 60))
print('\tnum_ids = %d' % (len(self.ids)))
print('')
def run_make_train_annotation(cfg):
ids = read_list_from_file(os.path.join(cfg.split_dir,
cfg.annotation_train_split),
comment='#')
s_train = SourceFolder(cfg.source_train_dir)
d_train = DataFolder(os.path.join(cfg.data_dir, 'stage1_train'))
for i in range(len(ids)):
# load image and mask files
id = ids[i]
folder = id.split('/')[0]
name = id.split('/')[-1]
image = s_train.get_image(name)
multi_masks = s_train.get_masks(name)
# check
color_overlay = multi_mask_to_color_overlay(multi_masks,
color='summer')
color1_overlay = multi_mask_to_contour_overlay(multi_masks,
color_overlay,
[255, 255, 255])
contour_overlay = multi_mask_to_contour_overlay(
multi_masks, image, [0, 255, 0])
stacked_overlay = np.hstack((
image,
contour_overlay,
color1_overlay,
)).astype(np.uint8)
# dump images and multi-masks
cv2.imwrite(os.path.join(d_train.image_folder, '%s.png' % name), image)
cv2.imwrite(os.path.join(d_train.mask_folder, '%s.png' % name),
color_overlay)
np.save(os.path.join(d_train.mask_folder, '%s.npy' % name),
multi_masks)
cv2.imwrite(os.path.join(d_train.overlay_folder, '%s.png' % name),
stacked_overlay)
print('\rannotate: ', i)
print('run_make_train_annotation success!')
def run_make_test_annotation(data_root, image_set):
imageset_file_loc = os.path.join(data_root, 'image_sets', image_set)
# split = 'test1_ids_all_65'
ids = read_list_from_file(imageset_file_loc, comment='#')
newimage_dir = os.path.join(data_root, 'stage1_images', 'stage1_test')
os.makedirs(newimage_dir, exist_ok=True)
num_ids = len(ids)
for i in range(num_ids):
id = ids[i]
print(id)
folder, name = ids[i].split('/')
image_loc = os.path.join(data_root, 'stage1_images', 'official',
folder, name, 'images', '*.png')
image_files = glob.glob(image_loc)
assert (len(image_files) == 1)
image_file = image_files[0]
# image
image = cv2.imread(image_file, cv2.IMREAD_COLOR)
cv2.imwrite(os.path.join(newimage_dir, name + '.png'), image)
def run_make_extra_annotation(cfg, min_mask_threshold):
ids = read_list_from_file(os.path.join(cfg.split_dir,
cfg.annotation_extra_split),
comment='#')
s_extra = SourceFolder(cfg.source_extra_dir)
d_extra = DataFolder(os.path.join(cfg.data_dir, 'extra_data'))
extra_train_270 = []
for row in ids:
# load image and mask files
folder = row.split('/')[0]
name = row.split('/')[-1]
image = s_extra.get_image(name)
multi_masks = s_extra.get_masks(name)
for i in range(3):
for j in range(3):
x = i * 256
y = j * 256
img_idx = i * 3 + j
image_crop = image[y:y + 256, x:x + 256]
multi_mask_crop = multi_masks[y:y + 256, x:x + 256]
mask_idx = np.unique(multi_mask_crop)
assert (mask_idx[0] == 0)
mask_idx = mask_idx[1:]
re_assigned_multi_mask = np.zeros((256, 256), np.int32)
for x in range(len(mask_idx)):
mask_size = np.sum(multi_mask_crop == mask_idx[x])
if mask_size < min_mask_threshold:
continue
re_assigned_multi_mask[np.where(
multi_mask_crop == mask_idx[x])] = x + 1
# check
color_overlay = multi_mask_to_color_overlay(
re_assigned_multi_mask, color='summer')
color1_overlay = multi_mask_to_contour_overlay(
re_assigned_multi_mask, color_overlay, [255, 255, 255])
contour_overlay = multi_mask_to_contour_overlay(
re_assigned_multi_mask, image_crop, [0, 255, 0])
stacked_overlay = np.hstack((
image_crop,
contour_overlay,
color1_overlay,
)).astype(np.uint8)
# dump images and multi-masks
extra_train_270.append('%s_%s' % (name, img_idx))
cv2.imwrite(
os.path.join(d_extra.image_folder,
'%s_%s.png' % (name, img_idx)), image_crop)
cv2.imwrite(
os.path.join(d_extra.mask_folder,
'%s_%s.png' % (name, img_idx)), color_overlay)
np.save(
os.path.join(d_extra.mask_folder,
'%s_%s.npy' % (name, img_idx)),
re_assigned_multi_mask)
cv2.imwrite(
os.path.join(d_extra.overlay_folder,
'%s_%s.png' % (name, img_idx)),
stacked_overlay)
print('\r %s_%s' % (name, img_idx))
print('run_make_extra_annotation success!')
def run_make_train_annotation_fixing_masks(data_root, image_set):
imageset_file_loc = os.path.join(data_root, 'image_sets', image_set)
multimask_dir = os.path.join(data_root, 'stage1_images',
'fixed_multi_masks_watershred_debug')
os.makedirs(multimask_dir, exist_ok=True)
overlays_dir = os.path.join(data_root, 'stage1_images',
'fixed_overlays_watershred_debug')
os.makedirs(overlays_dir, exist_ok=True)
ids = read_list_from_file(imageset_file_loc, comment='#')
fixed_masks_loc = os.path.join(data_root, 'stage1_images',
'fixed_mask_new', '*')
fixed_masks_image_keys = [
loc.split('/')[-1] for loc in glob.glob(fixed_masks_loc)
]
num_ids = len(ids)
for i in range(num_ids):
id = ids[i]
print(id)
folder, name = id.split('/')
image_loc = os.path.join(data_root, 'stage1_images', 'official',
folder, name, 'images', '*.png')
image_files = glob.glob(image_loc)
assert (len(image_files) == 1)
image_file = image_files[0]
# image
image = cv2.imread(image_file, cv2.IMREAD_COLOR)
fixed_masks_instance_keys = set()
if name in fixed_masks_image_keys:
fixed_masks_loc_instances = os.path.join(data_root,
'stage1_images',
'fixed_mask_new', name,
'*')
fixed_masks_instance_keys = [
loc.split('/')[-1][:-7]
for loc in glob.glob(fixed_masks_loc_instances)
]
fixed_masks_instance_keys = set(fixed_masks_instance_keys)
print(fixed_masks_instance_keys)
H, W, C = image.shape
multi_mask = np.zeros((H, W), np.int32)
masks_loc = os.path.join(data_root, 'stage1_images', 'official',
folder, name, 'masks', '*.png')
mask_files = glob.glob(masks_loc)
mask_files.sort()
instance_count = 1
for mask_file in mask_files:
mask_key = mask_file.split('/')[-1].strip('.png')
if mask_key in fixed_masks_instance_keys:
fixed_mask_files = glob.glob(
os.path.join(data_root, 'stage1_images', 'fixed_mask_new',
name, mask_key + '*.png'))
print(fixed_mask_files)
for fixed_mask_file in fixed_mask_files:
mask = cv2.imread(fixed_mask_file, cv2.IMREAD_GRAYSCALE)
instance_contour_overlay = mask_to_inner_contour(mask)
mask = watershred_post_proess(mask,
instance_contour_overlay, 20)
multi_mask[np.where(mask > 128)] = instance_count
instance_count += 1
else:
mask = cv2.imread(mask_file, cv2.IMREAD_GRAYSCALE)
instance_contour_overlay = mask_to_inner_contour(mask)
mask = watershred_post_proess(mask, instance_contour_overlay,
20)
multi_mask[np.where(mask > 128)] = instance_count
instance_count += 1
# check
color_overlay = multi_mask_to_color_overlay(multi_mask, color='summer')
color1_overlay = multi_mask_to_contour_overlay(multi_mask,
color_overlay,
[255, 255, 255])
contour_overlay = multi_mask_to_contour_overlay(
multi_mask, image, [0, 255, 0])
all = np.hstack((
image,
contour_overlay,
color1_overlay,
)).astype(np.uint8)
np.save(os.path.join(multimask_dir, name + '.npy'), multi_mask)
cv2.imwrite(os.path.join(multimask_dir, name + '.png'), color_overlay)
cv2.imwrite(os.path.join(overlays_dir, name + '.png'), all)
def run_make_annotation():
split = 'train1_ids_all_670'
ids = read_list_from_file(DATA_DIR + '/split/' + split, comment='#')
num_ids = len(ids)
for i in range(num_ids):
id = ids[i]
# image_files = glob.glob(DATA_DIR + '/image/' + id + '/images/*.png')
image_files = list(
(DATA_DIR / ('image' + id) / 'images').glob('*.png'))
assert (len(image_files) == 1)
image_file = image_files[0]
print(id)
# ----clear old -----------------------------
if 1:
for f in [
'one_mask.png',
'one_countour_mask.png',
'one_countour_image.png',
'one_countour.png',
'overlap.png',
'one_center.png',
'/masks.npy',
'/labels.npy',
'/countour_on_image.png',
'/cut_mask.png',
'/label.npy',
'/mask.png',
'/overlay.png',
'/multi.npy',
'/multi.png',
'/instance.npy',
'/instance.png',
'/multi_instance.npy',
'/multi_instance.png',
]:
file = DATA_DIR + '/image/' + id + '/' + f
if os.path.exists(file):
os.remove(file)
# ----clear old -----------------------------
# image
image = cv2.imread(image_file, cv2.IMREAD_COLOR)
H, W, C = image.shape
multi_mask = np.zeros((H, W), np.int32)
mask = np.zeros((H, W), np.uint8)
countour = np.zeros((H, W), np.uint8)
# mask_files = glob.glob(DATA_DIR + '/image/' + id + '/masks/*.png')
mask_files = list((DATA_DIR / ('image' + id) / 'masks').glob('*.png'))
mask_files.sort()
count = len(mask_files)
for i in range(count):
mask_file = mask_files[i]
thresh = cv2.imread(mask_file, cv2.IMREAD_GRAYSCALE)
thresh = thresh > 128
index = np.where(thresh == True)
multi_mask[thresh] = i + 1
mask = np.logical_or(mask, thresh)
countour = np.logical_or(countour, thresh_to_inner_contour(thresh))
## save and show -------------------------------------------
countour_on_image = image.copy()
countour_on_image = countour[:, :, np.newaxis] * np.array(
(0, 255, 0)) + (1 - countour[:, :, np.newaxis]) * countour_on_image
countour_overlay = countour * 255
mask_overlay = mask * 255
multi_mask_overlay = multi_mask_to_overlay(multi_mask)
image_show('image', image)
image_show('mask', mask_overlay)
image_show('multi_mask', multi_mask_overlay)
image_show('countour', countour_overlay)
image_show('countour_on_image', countour_on_image)
np.save(DATA_DIR + '/image/' + id + '/multi_mask.npy', multi_mask)
cv2.imwrite(DATA_DIR + '/image/' + id + '/multi_mask.png',
multi_mask_overlay)
cv2.imwrite(DATA_DIR + '/image/' + id + '/mask.png', mask_overlay)
cv2.imwrite(DATA_DIR + '/image/' + id + '/countour.png',
countour_overlay)
cv2.imwrite(DATA_DIR + '/image/' + id + '/countour_on_image.png',
countour_on_image)
cv2.waitKey(1)