def test_opencv_crop():
dummy_img = np.ones((600, 600, 3), dtype=np.uint8)
dummy_box = [20, 20, 120, 20, 120, 40, 20, 40]
cropped_img = warp_img(dummy_img, dummy_box)
with pytest.raises(AssertionError):
warp_img(dummy_img, [])
with pytest.raises(AssertionError):
warp_img(dummy_img, [20, 40, 40, 20])
assert math.isclose(cropped_img.shape[0], 20)
assert math.isclose(cropped_img.shape[1], 100)
def __call__(self, results):
if 'img_info' not in results:
return results
crop_flag = True
box = []
for key in self.box_keys:
if key not in results['img_info']:
crop_flag = False
break
box.append(float(results['img_info'][key]))
if not crop_flag:
return results
jitter_flag = np.random.random() > self.jitter_prob
kwargs = dict(jitter_flag=jitter_flag,
jitter_ratio_x=self.max_jitter_ratio_x,
jitter_ratio_y=self.max_jitter_ratio_y)
crop_img = warp_img(results['img'], box, **kwargs)
results['img'] = crop_img
results['img_shape'] = crop_img.shape
return results
def parse_labelme_json(json_file,
img_dir,
out_dir,
tasks,
ignore_marker='###',
recog_format='jsonl',
warp_flag=False):
invalid_res = [[], [], []]
json_obj = mmcv.load(json_file)
img_file = osp.basename(json_obj['imagePath'])
img_full_path = osp.join(img_dir, img_file)
img_width = json_obj['imageWidth']
img_height = json_obj['imageHeight']
if 'recog' in tasks:
src_img = mmcv.imread(img_full_path)
img_basename = osp.splitext(img_file)[0]
sub_dir = osp.join(out_dir, 'crops', img_basename)
mmcv.mkdir_or_exist(sub_dir)
det_line_json_list = []
recog_crop_line_str_list = []
recog_warp_line_str_list = []
shape_info = json_obj['shapes']
idx = 0
annos = []
for box_info in shape_info:
shape = box_info['shape_type']
if shape not in ['rectangle', 'polygon']:
msg = 'Only \'rectangle\' and \'polygon\' boxes are supported. '
msg += f'Boxes with {shape} will be discarded.'
warnings.warn(msg)
return invalid_res
poly = []
box_points = box_info['points']
for point in box_points:
poly.extend([int(x) for x in point])
x_list = poly[0::2]
y_list = poly[1::2]
quad = []
if shape == 'rectangle':
warp_flag = False
quad = [
poly[0], poly[1], poly[2], poly[1], poly[2], poly[3], poly[0],
poly[3]
]
else:
if len(poly) < 8 or len(poly) % 2 != 0:
msg = f'Invalid polygon {poly}. '
msg += 'The polygon is expected to have 8 or more than 8 '
msg += 'even number of coordinates in MMOCR.'
warnings.warn(msg)
return invalid_res
if len(poly) == 8:
quad = poly
else:
warp_flag = False
x_min, x_max, y_min, y_max = min(x_list), max(x_list), min(
y_list), max(y_list)
quad = [x_min, y_min, x_max, y_min, x_max, y_max, x_min, y_max]
text_label = box_info['label']
# for textdet
anno = {}
anno['iscrowd'] = 0 if text_label != ignore_marker else 1
anno['category_id'] = 1
w = max(x_list) - min(x_list)
h = max(y_list) - min(y_list)
anno['bbox'] = [min(x_list), min(y_list), w, h]
if shape == 'rectangle':
anno['segmentation'] = [quad]
else:
anno['segmentation'] = [poly]
anno['text'] = text_label
annos.append(anno)
# for textrecog
if 'recog' in tasks:
if text_label == ignore_marker or len(text_label) == 0:
continue
cropped_img = crop_img(src_img, quad)
img_path_cropped_img = osp.join(sub_dir, f'crop_{idx}.jpg')
mmcv.imwrite(cropped_img, img_path_cropped_img)
if recog_format == 'txt':
recog_crop_line_str_list.append(
f'{img_path_cropped_img} {text_label}')
elif recog_format == 'jsonl':
recog_crop_line_str_list.append(
json.dumps({
'filename': img_path_cropped_img,
'text': text_label
}))
else:
raise NotImplementedError
if warp_flag:
warpped_img = warp_img(src_img, quad)
img_path_warpped_img = osp.join(sub_dir, f'warp_{idx}.jpg')
mmcv.imwrite(warpped_img, img_path_warpped_img)
if recog_format == 'txt':
recog_warp_line_str_list.append(
f'{img_path_warpped_img} {text_label}')
elif recog_format == 'jsonl':
recog_warp_line_str_list.append(
json.dumps({
'filename': img_path_cropped_img,
'text': text_label
}))
idx += 1
line_json = {
'file_name': img_file,
'height': img_height,
'width': img_width,
'annotations': annos
}
det_line_json_list.append(json.dumps(line_json, ensure_ascii=False))
return [
det_line_json_list, recog_crop_line_str_list, recog_warp_line_str_list
]
