def __call__(self, sample):
"""Affinity label processing."""
seg = sample[self.source]
msk = get_mask(sample, self.source)
affs = list()
msks = list()
# Affinitize.
for dst in self.dst:
affs.append(tf.affinitize1(seg, dst=dst))
msks.append(tf.affinitize1_mask(msk, dst=dst))
aff = np.concatenate(affs, axis=0)
msk = np.concatenate(msks, axis=0)
# Rebalancing.
if self.rebalance:
for c in xrange(aff.shape[0]):
msk[c, ...] *= tf.rebalance_binary_class(aff[c, ...],
msk=msk[c, ...])
# Update sample.
sample[self.target] = aff
sample[self.target + '_mask'] = msk
# Crop.
if self.crop is not None:
for k, v in sample.iteritems():
sample[k] = tf.crop(v, offset=self.crop)
return sample
def generate(data_dir, batch_size=16, image_size=640, min_text_size=8, shrink_ratio=0.4, thresh_min=0.3,
thresh_max=0.7, is_training=True):
split = 'train' if is_training else 'test'
with open(osp.join(data_dir, f'{split}_list.txt')) as f:
image_fnames = f.readlines()
image_paths = [osp.join(
data_dir, f'{split}_images', image_fname.strip()) for image_fname in image_fnames]
gt_paths = [osp.join(data_dir, f'{split}_gts', image_fname.strip(
) + '.txt') for image_fname in image_fnames]
all_anns = load_all_anns(gt_paths)
transform_aug = iaa.Sequential(
[iaa.Fliplr(0.5), iaa.Affine(rotate=(-10, 10)), iaa.Resize((0.5, 3.0))])
dataset_size = len(image_paths)
indices = np.arange(dataset_size)
if is_training:
np.random.shuffle(indices)
current_idx = 0
b = 0
while True:
if current_idx >= dataset_size:
if is_training:
np.random.shuffle(indices)
current_idx = 0
if b == 0:
# Init batch arrays
batch_images = np.zeros(
[batch_size, image_size, image_size, 3], dtype=np.float32)
batch_gts = np.zeros(
[batch_size, image_size, image_size], dtype=np.float32)
batch_masks = np.zeros(
[batch_size, image_size, image_size], dtype=np.float32)
batch_thresh_maps = np.zeros(
[batch_size, image_size, image_size], dtype=np.float32)
batch_thresh_masks = np.zeros(
[batch_size, image_size, image_size], dtype=np.float32)
batch_loss = np.zeros([batch_size, ], dtype=np.float32)
i = indices[current_idx]
image_path = image_paths[i]
anns = all_anns[i]
image = cv2.imread(image_path)
# show_polys(image.copy(), anns, 'before_aug')
if is_training:
transform_aug = transform_aug.to_deterministic()
image, anns = transform(transform_aug, image, anns)
image, anns = crop(image, anns)
image, anns = resize(image_size, image, anns)
# show_polys(image.copy(), anns, 'after_aug')
# cv2.waitKey(0)
anns = [ann for ann in anns if Polygon(ann['poly']).is_valid]
gt = np.zeros((image_size, image_size), dtype=np.float32)
mask = np.ones((image_size, image_size), dtype=np.float32)
thresh_map = np.zeros((image_size, image_size), dtype=np.float32)
thresh_mask = np.zeros((image_size, image_size), dtype=np.float32)
for ann in anns:
poly = np.array(ann['poly'])
height = max(poly[:, 1]) - min(poly[:, 1])
width = max(poly[:, 0]) - min(poly[:, 0])
polygon = Polygon(poly)
# generate gt and mask
if polygon.area < 1 or min(height, width) < min_text_size or ann['text'] == '###':
cv2.fillPoly(mask, poly.astype(np.int32)[np.newaxis, :, :], 0)
continue
else:
distance = polygon.area * \
(1 - np.power(shrink_ratio, 2)) / polygon.length
subject = [tuple(l) for l in ann['poly']]
padding = pyclipper.PyclipperOffset()
padding.AddPath(subject, pyclipper.JT_ROUND,
pyclipper.ET_CLOSEDPOLYGON)
shrinked = padding.Execute(-distance)
if len(shrinked) == 0:
cv2.fillPoly(mask, poly.astype(
np.int32)[np.newaxis, :, :], 0)
continue
else:
shrinked = np.array(shrinked[0]).reshape(-1, 2)
if shrinked.shape[0] > 2 and Polygon(shrinked).is_valid:
cv2.fillPoly(gt, [shrinked.astype(np.int32)], 1)
else:
cv2.fillPoly(mask, poly.astype(
np.int32)[np.newaxis, :, :], 0)
continue
# generate thresh map and thresh mask
draw_thresh_map(ann['poly'], thresh_map,
thresh_mask, shrink_ratio=shrink_ratio)
thresh_map = thresh_map * (thresh_max - thresh_min) + thresh_min
image = image.astype(np.float32)
image[..., 0] -= mean[0]
image[..., 1] -= mean[1]
image[..., 2] -= mean[2]
batch_images[b] = image
batch_gts[b] = gt
batch_masks[b] = mask
batch_thresh_maps[b] = thresh_map
batch_thresh_masks[b] = thresh_mask
b += 1
current_idx += 1
if b == batch_size:
inputs = [batch_images, batch_gts, batch_masks,
batch_thresh_maps, batch_thresh_masks]
outputs = batch_loss
yield inputs, outputs
b = 0
def generate(cfg, train_or_val='train'):
def init_input():
batch_images = np.zeros(
[cfg.BATCH_SIZE, cfg.IMAGE_SIZE, cfg.IMAGE_SIZE, 3],
dtype=np.float32)
batch_gts = np.zeros([cfg.BATCH_SIZE, cfg.IMAGE_SIZE, cfg.IMAGE_SIZE],
dtype=np.float32)
batch_masks = np.zeros(
[cfg.BATCH_SIZE, cfg.IMAGE_SIZE, cfg.IMAGE_SIZE], dtype=np.float32)
batch_thresh_maps = np.zeros(
[cfg.BATCH_SIZE, cfg.IMAGE_SIZE, cfg.IMAGE_SIZE], dtype=np.float32)
batch_thresh_masks = np.zeros(
[cfg.BATCH_SIZE, cfg.IMAGE_SIZE, cfg.IMAGE_SIZE], dtype=np.float32)
# batch_loss = np.zeros([cfg.BATCH_SIZE, ], dtype=np.float32)
return [
batch_images, batch_gts, batch_masks, batch_thresh_maps,
batch_thresh_masks
]
data_path = cfg.TRAIN_DATA_PATH if train_or_val == 'train' else cfg.VAL_DATA_PATH
with open(data_path, encoding='utf8') as f:
data = json.load(f)
data_root_dir = data["data_root"]
data_list = data["data_list"]
image_paths = []
all_anns = []
for data_item in data_list:
img_name = data_item["img_name"]
annotations = data_item["annotations"]
anns = []
for annotation in annotations:
item = {}
text = annotation["text"]
poly = annotation["polygon"]
if len(poly) < 3:
continue
item['text'] = text
item['poly'] = poly
anns.append(item)
image_paths.append(osp.join(data_root_dir, img_name))
all_anns.append(anns)
transform_aug = iaa.Sequential(
[iaa.Affine(rotate=(-10, 10)),
iaa.Resize((0.5, 3.0))])
dataset_size = len(image_paths)
indices = np.arange(dataset_size)
if train_or_val == 'train':
np.random.shuffle(indices)
current_idx = 0
b = 0
while True:
if current_idx >= dataset_size:
if train_or_val == 'train':
np.random.shuffle(indices)
current_idx = 0
if b == 0:
batch_images, batch_gts, batch_masks, batch_thresh_maps, batch_thresh_masks = init_input(
)
i = indices[current_idx]
image_path = image_paths[i]
anns = all_anns[i]
"""
[{'text': 'chinese', 'poly': [[17.86985870232934, 29.2253341902275], [18.465581783660582, 7.2334012599376365], [525.2796724953414, 20.9621104524324], [524.6839494140104, 42.954043382722375]]},
{'text': 'chinese', 'poly': [[9.746362138723043, 329.1153286941807], [10.667025082598343, 295.12779598373265], [589.454714475228, 310.8061443514931], [588.5340515313526, 344.79367706194114]]}]
"""
image = cv2.imread(image_path)
# show_polys(image.copy(), anns, 'before_aug')
if train_or_val == 'train':
transform_aug = transform_aug.to_deterministic()
image, anns = transform(transform_aug, image, anns)
image, anns = crop(image, anns)
image, anns = resize(cfg.IMAGE_SIZE, image, anns)
# show_polys(image.copy(), anns, 'after_aug')
# cv2.waitKey(0)
anns = [ann for ann in anns if Polygon(ann['poly']).is_valid]
gt = np.zeros((cfg.IMAGE_SIZE, cfg.IMAGE_SIZE), dtype=np.float32)
mask = np.ones((cfg.IMAGE_SIZE, cfg.IMAGE_SIZE), dtype=np.float32)
thresh_map = np.zeros((cfg.IMAGE_SIZE, cfg.IMAGE_SIZE),
dtype=np.float32)
thresh_mask = np.zeros((cfg.IMAGE_SIZE, cfg.IMAGE_SIZE),
dtype=np.float32)
for ann in anns:
poly = np.array(ann['poly'])
height = max(poly[:, 1]) - min(poly[:, 1])
width = max(poly[:, 0]) - min(poly[:, 0])
polygon = Polygon(poly)
# generate gt and mask
if polygon.area < 1 or min(
height, width
) < cfg.MIN_TEXT_SIZE or ann['text'] in cfg.IGNORE_TEXT:
cv2.fillPoly(mask, poly.astype(np.int32)[np.newaxis, :, :], 0)
continue
else:
distance = polygon.area * (
1 - np.power(cfg.SHRINK_RATIO, 2)) / polygon.length
subject = [tuple(l) for l in ann['poly']]
padding = pyclipper.PyclipperOffset()
padding.AddPath(subject, pyclipper.JT_ROUND,
pyclipper.ET_CLOSEDPOLYGON)
shrinked = padding.Execute(-distance)
if len(shrinked) == 0:
cv2.fillPoly(mask,
poly.astype(np.int32)[np.newaxis, :, :], 0)
continue
else:
shrinked = np.array(shrinked[0]).reshape(-1, 2)
if shrinked.shape[0] > 2 and Polygon(shrinked).is_valid:
cv2.fillPoly(gt, [shrinked.astype(np.int32)], 1)
else:
cv2.fillPoly(mask,
poly.astype(np.int32)[np.newaxis, :, :],
0)
continue
# generate thresh map and thresh mask
draw_thresh_map(ann['poly'],
thresh_map,
thresh_mask,
shrink_ratio=cfg.SHRINK_RATIO)
thresh_map = thresh_map * (cfg.THRESH_MAX -
cfg.THRESH_MIN) + cfg.THRESH_MIN
image = image.astype(np.float32)
image -= mean
batch_images[b] = image
batch_gts[b] = gt
batch_masks[b] = mask
batch_thresh_maps[b] = thresh_map
batch_thresh_masks[b] = thresh_mask
b += 1
current_idx += 1
if b == cfg.BATCH_SIZE:
inputs = [
batch_images, batch_gts, batch_masks, batch_thresh_maps,
batch_thresh_masks
]
# outputs = batch_loss
outputs = []
yield inputs, outputs
b = 0