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import time
import copy
import base64
import cv2
import numpy as np
from io import BytesIO
from PIL import Image
from paddleocr import PaddleOCR
from paddleocr.ppocr.utils.logging import get_logger
from paddleocr.ppocr.utils.utility import check_and_read, alpha_to_color, binarize_img
from paddleocr.tools.infer.utility import draw_ocr_box_txt, get_rotate_crop_image, get_minarea_rect_crop
logger = get_logger()
def img_decode(content: bytes):
np_arr = np.frombuffer(content, dtype=np.uint8)
return cv2.imdecode(np_arr, cv2.IMREAD_UNCHANGED)
def check_img(img):
if isinstance(img, bytes):
img = img_decode(img)
if isinstance(img, str):
image_file = img
img, flag_gif, flag_pdf = check_and_read(image_file)
if not flag_gif and not flag_pdf:
with open(image_file, 'rb') as f:
img_str = f.read()
img = img_decode(img_str)
if img is None:
try:
buf = BytesIO()
image = BytesIO(img_str)
im = Image.open(image)
rgb = im.convert('RGB')
rgb.save(buf, 'jpeg')
buf.seek(0)
image_bytes = buf.read()
data_base64 = str(base64.b64encode(image_bytes),
encoding="utf-8")
image_decode = base64.b64decode(data_base64)
img_array = np.frombuffer(image_decode, np.uint8)
img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
except:
logger.error("error in loading image:{}".format(image_file))
return None
if img is None:
logger.error("error in loading image:{}".format(image_file))
return None
if isinstance(img, np.ndarray) and len(img.shape) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
return img
def sorted_boxes(dt_boxes):
"""
Sort text boxes in order from top to bottom, left to right
args:
dt_boxes(array):detected text boxes with shape [4, 2]
return:
sorted boxes(array) with shape [4, 2]
"""
num_boxes = dt_boxes.shape[0]
sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
_boxes = list(sorted_boxes)
for i in range(num_boxes - 1):
for j in range(i, -1, -1):
if abs(_boxes[j + 1][0][1] - _boxes[j][0][1]) < 10 and \
(_boxes[j + 1][0][0] < _boxes[j][0][0]):
tmp = _boxes[j]
_boxes[j] = _boxes[j + 1]
_boxes[j + 1] = tmp
else:
break
return _boxes
def formula_in_text(mf_bbox, text_bbox):
x1, y1, x2, y2 = mf_bbox
x3, y3 = text_bbox[0]
x4, y4 = text_bbox[2]
left_box, right_box = None, None
same_line = abs((y1+y2)/2 - (y3+y4)/2) / abs(y4-y3) < 0.2
if not same_line:
return False, left_box, right_box
else:
drop_origin = False
left_x = x1 - 1
right_x = x2 + 1
if x3 < x1 and x2 < x4:
drop_origin = True
left_box = np.array([text_bbox[0], [left_x, text_bbox[1][1]], [left_x, text_bbox[2][1]], text_bbox[3]]).astype('float32')
right_box = np.array([[right_x, text_bbox[0][1]], text_bbox[1], text_bbox[2], [right_x, text_bbox[3][1]]]).astype('float32')
if x3 < x1 and x1 <= x4 <= x2:
drop_origin = True
left_box = np.array([text_bbox[0], [left_x, text_bbox[1][1]], [left_x, text_bbox[2][1]], text_bbox[3]]).astype('float32')
if x1 <= x3 <= x2 and x2 < x4:
drop_origin = True
right_box = np.array([[right_x, text_bbox[0][1]], text_bbox[1], text_bbox[2], [right_x, text_bbox[3][1]]]).astype('float32')
if x1 <= x3 < x4 <= x2:
drop_origin = True
return drop_origin, left_box, right_box
def update_det_boxes(dt_boxes, mfdetrec_res):
new_dt_boxes = dt_boxes
for mf_box in mfdetrec_res:
flag, left_box, right_box = False, None, None
for idx, text_box in enumerate(new_dt_boxes):
ret, left_box, right_box = formula_in_text(mf_box['bbox'], text_box)
if ret:
new_dt_boxes.pop(idx)
if left_box is not None:
new_dt_boxes.append(left_box)
if right_box is not None:
new_dt_boxes.append(right_box)
break
return new_dt_boxes
class ModifiedPaddleOCR(PaddleOCR):
def ocr(self, img, det=True, rec=True, cls=True, bin=False, inv=False, mfd_res=None, alpha_color=(255, 255, 255)):
"""
OCR with PaddleOCR
args：
img: img for OCR, support ndarray, img_path and list or ndarray
det: use text detection or not. If False, only rec will be exec. Default is True
rec: use text recognition or not. If False, only det will be exec. Default is True
cls: use angle classifier or not. Default is True. If True, the text with rotation of 180 degrees can be recognized. If no text is rotated by 180 degrees, use cls=False to get better performance. Text with rotation of 90 or 270 degrees can be recognized even if cls=False.
bin: binarize image to black and white. Default is False.
inv: invert image colors. Default is False.
alpha_color: set RGB color Tuple for transparent parts replacement. Default is pure white.
"""
assert isinstance(img, (np.ndarray, list, str, bytes))
if isinstance(img, list) and det == True:
logger.error('When input a list of images, det must be false')
exit(0)
if cls == True and self.use_angle_cls == False:
pass
# logger.warning(
# 'Since the angle classifier is not initialized, it will not be used during the forward process'
# )
img = check_img(img)
# for infer pdf file
if isinstance(img, list):
if self.page_num > len(img) or self.page_num == 0:
self.page_num = len(img)
imgs = img[:self.page_num]
else:
imgs = [img]
def preprocess_image(_image):
_image = alpha_to_color(_image, alpha_color)
if inv:
_image = cv2.bitwise_not(_image)
if bin:
_image = binarize_img(_image)
return _image
if det and rec:
ocr_res = []
for idx, img in enumerate(imgs):
img = preprocess_image(img)
dt_boxes, rec_res, _ = self.__call__(img, cls, mfd_res=mfd_res)
if not dt_boxes and not rec_res:
ocr_res.append(None)
continue
tmp_res = [[box.tolist(), res]
for box, res in zip(dt_boxes, rec_res)]
ocr_res.append(tmp_res)
return ocr_res
elif det and not rec:
ocr_res = []
for idx, img in enumerate(imgs):
img = preprocess_image(img)
dt_boxes, elapse = self.text_detector(img)
if not dt_boxes:
ocr_res.append(None)
continue
tmp_res = [box.tolist() for box in dt_boxes]
ocr_res.append(tmp_res)
return ocr_res
else:
ocr_res = []
cls_res = []
for idx, img in enumerate(imgs):
if not isinstance(img, list):
img = preprocess_image(img)
img = [img]
if self.use_angle_cls and cls:
img, cls_res_tmp, elapse = self.text_classifier(img)
if not rec:
cls_res.append(cls_res_tmp)
rec_res, elapse = self.text_recognizer(img)
ocr_res.append(rec_res)
if not rec:
return cls_res
return ocr_res
def __call__(self, img, cls=True, mfd_res=None):
time_dict = {'det': 0, 'rec': 0, 'cls': 0, 'all': 0}
if img is None:
logger.debug("no valid image provided")
return None, None, time_dict
start = time.time()
ori_im = img.copy()
dt_boxes, elapse = self.text_detector(img)
time_dict['det'] = elapse
if dt_boxes is None:
logger.debug("no dt_boxes found, elapsed : {}".format(elapse))
end = time.time()
time_dict['all'] = end - start
return None, None, time_dict
else:
logger.debug("dt_boxes num : {}, elapsed : {}".format(
len(dt_boxes), elapse))
img_crop_list = []
dt_boxes = sorted_boxes(dt_boxes)
if mfd_res:
bef = time.time()
dt_boxes = update_det_boxes(dt_boxes, mfd_res)
aft = time.time()
logger.debug("split text box by formula, new dt_boxes num : {}, elapsed : {}".format(
len(dt_boxes), aft-bef))
for bno in range(len(dt_boxes)):
tmp_box = copy.deepcopy(dt_boxes[bno])
if self.args.det_box_type == "quad":
img_crop = get_rotate_crop_image(ori_im, tmp_box)
else:
img_crop = get_minarea_rect_crop(ori_im, tmp_box)
img_crop_list.append(img_crop)
if self.use_angle_cls and cls:
img_crop_list, angle_list, elapse = self.text_classifier(
img_crop_list)
time_dict['cls'] = elapse
logger.debug("cls num : {}, elapsed : {}".format(
len(img_crop_list), elapse))
rec_res, elapse = self.text_recognizer(img_crop_list)
time_dict['rec'] = elapse
logger.debug("rec_res num : {}, elapsed : {}".format(
len(rec_res), elapse))
if self.args.save_crop_res:
self.draw_crop_rec_res(self.args.crop_res_save_dir, img_crop_list,
rec_res)
filter_boxes, filter_rec_res = [], []
for box, rec_result in zip(dt_boxes, rec_res):
text, score = rec_result
if score >= self.drop_score:
filter_boxes.append(box)
filter_rec_res.append(rec_result)
end = time.time()
time_dict['all'] = end - start
return filter_boxes, filter_rec_res, time_dict |