def check_if_good_boundary(self, boundary, norm_height, norm_width,
color_img):
preprocess_bg_mask = PreprocessBackgroundMask(boundary)
char_w = norm_width / 20
remove_noise = PreprocessRemoveNonCharNoise(char_w)
id_card_img_mask = preprocess_bg_mask.do(color_img)
id_card_img_mask[0:int(norm_height * 0.05), :] = 0
id_card_img_mask[int(norm_height * 0.95):, :] = 0
id_card_img_mask[:, 0:int(norm_width * 0.05)] = 0
id_card_img_mask[:, int(norm_width * 0.95):] = 0
remove_noise.do(id_card_img_mask)
# se1 = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
# se2 = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))
# mask = cv2.morphologyEx(id_card_img_mask, cv2.MORPH_CLOSE, se1)
# id_card_img_mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, se2)
## remove right head profile
left_half_id_card_img_mask = np.copy(id_card_img_mask)
left_half_id_card_img_mask[:, int(norm_width / 2):] = 0
## Try to find text lines and chars
horizontal_sum = np.sum(left_half_id_card_img_mask, axis=1)
line_ranges = extract_peek_ranges_from_array(horizontal_sum)
return len(line_ranges) >= 5 and len(line_ranges) <= 7
def check_if_good_boundary(self, boundary, norm_height, norm_width, color_img):
preprocess_bg_mask = PreprocessBackgroundMask(boundary)
char_w = norm_width / 20
remove_noise = PreprocessRemoveNonCharNoise(char_w)
id_card_img_mask = preprocess_bg_mask.do(color_img)
id_card_img_mask[0:int(norm_height*0.05),:] = 0
id_card_img_mask[int(norm_height*0.95): ,:] = 0
id_card_img_mask[:, 0:int(norm_width*0.05)] = 0
id_card_img_mask[:, int(norm_width*0.95):] = 0
remove_noise.do(id_card_img_mask)
# se1 = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
# se2 = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))
# mask = cv2.morphologyEx(id_card_img_mask, cv2.MORPH_CLOSE, se1)
# id_card_img_mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, se2)
#
## remove right head profile
left_half_id_card_img_mask = np.copy(id_card_img_mask)
left_half_id_card_img_mask[:, norm_width/2:] = 0
## Try to find text lines and chars
horizontal_sum = np.sum(left_half_id_card_img_mask, axis=1)
line_ranges = extract_peek_ranges_from_array(horizontal_sum)
return len(line_ranges) >= 5 and len(line_ranges) <= 7
def check_if_good_boundary(boundary, norm_height, norm_width, color_img):
preprocess_bg_mask = PreprocessBackgroundMask(boundary)
char_w = norm_width / 20
remove_noise = PreprocessRemoveNonCharNoise(char_w)
id_card_img_mask = preprocess_bg_mask.do(color_img)
id_card_img_mask[0:int(norm_height*0.05),:] = 0
id_card_img_mask[int(norm_height*0.95): ,:] = 0
id_card_img_mask[:, 0:int(norm_width*0.05)] = 0
id_card_img_mask[:, int(norm_width*0.95):] = 0
remove_noise.do(id_card_img_mask)
## remove right head profile
left_half_id_card_img_mask = np.copy(id_card_img_mask)
left_half_id_card_img_mask[:, norm_width/2:] = 0
## Try to find text lines and chars
horizontal_sum = np.sum(left_half_id_card_img_mask, axis=1)
peek_ranges = extract_peek_ranges_from_array(horizontal_sum)
return len(peek_ranges) >= 5 and len(peek_ranges) <= 7
def do(self, color_img):
shape = color_img.shape
norm_height = shape[0]
norm_width = shape[1]
gray_id_card_img = cv2.cvtColor(color_img, cv2.COLOR_BGR2GRAY)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
gray_id_card_img = clahe.apply(gray_id_card_img)
gray_id_card_img = 255 - gray_id_card_img
best_boundary = None
for boundary in self.boundaries:
if self.check_if_good_boundary(
boundary,
norm_height, norm_width,
color_img):
best_boundary = boundary
break
if best_boundary is None:
return {}
boundary = best_boundary
## boundary = ([0, 0, 0], [100, 100, 100])
preprocess_bg_mask = PreprocessBackgroundMask(boundary)
id_card_img_mask = preprocess_bg_mask.do(color_img)
id_card_img_mask[0:int(norm_height*0.05),:] = 0
id_card_img_mask[int(norm_height*0.95): ,:] = 0
id_card_img_mask[:, 0:int(norm_width*0.05)] = 0
id_card_img_mask[:, int(norm_width*0.95):] = 0
# se1 = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
# se2 = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))
# mask = cv2.morphologyEx(id_card_img_mask, cv2.MORPH_CLOSE, se1)
# id_card_img_mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, se2)
## remove right head profile
left_half_id_card_img_mask = np.copy(id_card_img_mask)
left_half_id_card_img_mask[:, norm_width/2:] = 0
## Try to find text lines and chars
vertical_peek_ranges2d = []
horizontal_sum = np.sum(left_half_id_card_img_mask, axis=1)
line_ranges = extract_peek_ranges_from_array(horizontal_sum)
## char extraction
for line_range in line_ranges:
start_y, end_y = line_range
end_y += 1
line_img = id_card_img_mask[start_y: end_y]
vertical_sum = np.sum(line_img, axis=0)
vertical_peek_ranges = extract_peek_ranges_from_array(
vertical_sum,
minimun_val=40,
minimun_range=1)
vertical_peek_ranges2d.append(vertical_peek_ranges)
vertical_peek_ranges2d = merge_chars_into_line_segments(vertical_peek_ranges2d)
img_gray_texts = cv2.bitwise_and(gray_id_card_img,
gray_id_card_img,
mask=id_card_img_mask)
key_to_segmentation = {}
try:
## name extraction
range_y = line_ranges[0]
range_x = vertical_peek_ranges2d[0][0]
start_x, end_x = range_x
start_y, end_y = range_y
w = end_x - start_x
h = end_y - start_y
key_to_segmentation["name"] = [(start_x, start_y, w, h), ]
## sex extraction
range_y = line_ranges[1]
range_x = vertical_peek_ranges2d[1][0]
start_x, end_x = range_x
start_y, end_y = range_y
w = end_x - start_x
h = end_y - start_y
key_to_segmentation["sex"] = [(start_x, start_y, w, h), ]
## minzu extraction
range_y = line_ranges[1]
range_x = vertical_peek_ranges2d[1][1]
start_x, end_x = range_x
start_y, end_y = range_y
w = end_x - start_x
h = end_y - start_y
key_to_segmentation["minzu"] = [(start_x, start_y, w, h), ]
## year extraction
range_y = line_ranges[2]
range_x = vertical_peek_ranges2d[2][0]
start_x, end_x = range_x
start_y, end_y = range_y
w = end_x - start_x
h = end_y - start_y
key_to_segmentation["year"] = [(start_x, start_y, w, h), ]
## month extraction
range_y = line_ranges[2]
range_x = vertical_peek_ranges2d[2][1]
start_x, end_x = range_x
start_y, end_y = range_y
w = end_x - start_x
h = end_y - start_y
key_to_segmentation["month"] = [(start_x, start_y, w, h), ]
## day extraction
range_y = line_ranges[2]
range_x = vertical_peek_ranges2d[2][2]
start_x, end_x = range_x
start_y, end_y = range_y
w = end_x - start_x
h = end_y - start_y
key_to_segmentation["day"] = [(start_x, start_y, w, h), ]
## address extraction
key_to_segmentation["address"] = []
first_line = line_ranges[3][0]
first_line_range_x = vertical_peek_ranges2d[3][0]
first_line_start_x = first_line_range_x[0]
first_line_w = first_line_range_x[1] - first_line_start_x
for i, line_range in enumerate(line_ranges):
if i >= 3:
range_y = line_range
range_x = vertical_peek_ranges2d[i][0]
start_x, end_x = range_x
start_y, end_y = range_y
if abs(first_line_start_x - start_x)> int(first_line_w * 0.05):
break
w = end_x - start_x
h = end_y - start_y
key_to_segmentation["address"].append((start_x, start_y, w, h))
## id extraction
range_y = line_ranges[-1]
range_x = vertical_peek_ranges2d[-1][0]
start_x, end_x = range_x
start_y, end_y = range_y
w = end_x - start_x
h = end_y - start_y
key_to_segmentation["id"] = [(start_x, start_y, w, h), ]
except:
print "Exception in user code:"
print '-' * 60
traceback.print_exc(file=sys.stdout)
print '-' * 60
key_to_segmentation = {}
debug_path = self.debug_path
if debug_path is not None:
import random
if os.path.isdir(debug_path):
shutil.rmtree(debug_path)
os.makedirs(debug_path)
debug_image_path = os.path.join(debug_path, "01_origin_img.jpg")
debug_gray_image_path = os.path.join(debug_path, "01_gray_img.jpg")
debug_image_mask_path = os.path.join(debug_path, "02_mask.jpg")
debug_image_mask_text_lines_path = os.path.join(debug_path, "03_mask_text_lines.jpg")
debug_image_left_mask_path = os.path.join(debug_path, "04_left_mask.jpg")
debug_image_gray_texts_path = os.path.join(debug_path, "05_gray_texts.jpg")
debug_image_chars_path = os.path.join(debug_path, "06_origin_img_chars.jpg")
debug_image_key_to_segments_path = os.path.join(debug_path, "07_origin_img_key_to_segments.jpg")
cv2.imwrite(debug_image_path, color_img)
cv2.imwrite(debug_gray_image_path, 255 - gray_id_card_img)
id_card_img_chars = np.copy(color_img)
cv2.imwrite(debug_image_mask_path, id_card_img_mask)
id_card_img_mask_text_lines = np.copy(id_card_img_mask)
for i, line_range in enumerate(line_ranges):
start_y, end_y = line_range
id_card_img_mask_text_lines[start_y, :] = 255
id_card_img_mask_text_lines[end_y, :] = 255
color = (255, 0, 0)
for i, line_range in enumerate(line_ranges):
start_y, end_y = line_range
for vertical_peek_range in vertical_peek_ranges2d[i]:
start_x, end_x = vertical_peek_range
cv2.rectangle(id_card_img_chars,
(start_x, start_y),
(end_x+1, end_y+1),
color)
key_to_segments_img = np.copy(color_img)
for key in key_to_segmentation:
color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
rects = key_to_segmentation[key]
for rect in rects:
pt1 = (rect[0], rect[1])
pt2 = (rect[0] + rect[2], rect[1] + rect[3])
cv2.rectangle(key_to_segments_img, pt1, pt2, color)
cv2.imwrite(debug_image_mask_text_lines_path, id_card_img_mask_text_lines)
cv2.imwrite(debug_image_left_mask_path, left_half_id_card_img_mask)
cv2.imwrite(debug_image_gray_texts_path, img_gray_texts)
cv2.imwrite(debug_image_chars_path, id_card_img_chars)
cv2.imwrite(debug_image_key_to_segments_path, key_to_segments_img)
return key_to_segmentation
def upload_file():
if request.method == 'POST':
f = request.files['file']
f.save('/home/ygh/flask/id_card_img.jpg')
## path_img = os.path.expanduser("/home/ygh/deep_ocr/data/id_card_img.jpg")
path_img = os.path.expanduser("/home/ygh/flask/id_card_img.jpg")
debug_path = os.path.expanduser("/home/ygh/deep_ocr_workspace/debug")
if debug_path is not None:
if os.path.isdir(debug_path):
shutil.rmtree(debug_path)
os.makedirs(debug_path)
cls_dir_sim = os.path.expanduser(
"/home/ygh/deep_ocr_workspace/data/chongdata_caffe_cn_sim_digits_64_64"
)
cls_dir_ua = os.path.expanduser(
"/home/ygh/deep_ocr_workspace/data/chongdata_train_ualpha_digits_64_64"
)
caffe_cls_builder = CaffeClsBuilder()
cls_sim = caffe_cls_builder.build(cls_dir=cls_dir_sim, )
cls_ua = caffe_cls_builder.build(cls_dir=cls_dir_ua, )
caffe_classifiers = {"sim": cls_sim, "ua": cls_ua}
seg_norm_width = 600
seg_norm_height = 600
preprocess_resize = PreprocessResizeKeepRatio(seg_norm_width,
seg_norm_height)
id_card_img = cv2.imread(path_img)
id_card_img = preprocess_resize.do(id_card_img)
segmentation = Segmentation(debug_path)
key_to_segmentation = segmentation.do(id_card_img)
boundaries = [
((0, 0, 0), (100, 100, 100)),
]
boundary2binimgs = []
for boundary in boundaries:
preprocess_bg_mask = PreprocessBackgroundMask(boundary)
id_card_img_mask = preprocess_bg_mask.do(id_card_img)
boundary2binimgs.append((boundary, id_card_img_mask))
char_set = CharSet()
char_set_data = char_set.get()
rect_img_clf = RectImageClassifier(None,
None,
char_set,
caffe_cls_width=64,
caffe_cls_height=64)
reco_text_line = RecoTextLine(rect_img_clf)
key_ocr_res = {}
for key in key_to_segmentation:
key_ocr_res[key] = []
print("=" * 64)
print(key)
for i, segment in enumerate(key_to_segmentation[key]):
if debug_path is not None:
line_debug_path = "key_%s_%i" % (key, i)
line_debug_path = os.path.join(debug_path, line_debug_path)
reco_text_line.debug_path = line_debug_path
reco_text_line.char_set = char_set_data[key]
## 初始化模型
caffe_cls = caffe_classifiers[char_set_data[key]["caffe_cls"]]
## 输入到模型中进行识别
ocr_res = reco_text_line.do(boundary2binimgs, segment, caffe_cls)
## 将结果输出到列表中
key_ocr_res[key].append(ocr_res)
print("ocr res:")
for key in key_ocr_res:
print("=" * 60)
print(key)
for res_i in key_ocr_res[key]:
print(res_i.encode("utf-8"))
if debug_path is not None:
path_debug_image_mask = os.path.join(debug_path,
"reco_debug_01_image_mask.jpg")
cv2.imwrite(path_debug_image_mask, id_card_img_mask)
## 返回结果 将其封装成json的键值对的格式
data = [{
"result": "sucess",
"response": {
"name": key_ocr_res["name"],
"address": key_ocr_res["address"],
"month": key_ocr_res["month"],
"minzu": key_ocr_res["minzu"],
"year": key_ocr_res["year"],
"sex": key_ocr_res["sex"],
"id": key_ocr_res["id"],
"day": key_ocr_res["day"]
}
}]
## data = '{"result":"sucess"}
## result = json.loads(data)
return json.dumps(data,
skipkeys=True,
ensure_ascii=False,
encoding="utf-8")
else:
data2 = [{"result": "error"}]
## result2 = json.loads(data2)
return json.dumps(data2)
seg_norm_width = 600
seg_norm_height = 600
preprocess_resize = PreprocessResizeKeepRatio(seg_norm_width,
seg_norm_height)
id_card_img = cv2.imread(path_img)
id_card_img = preprocess_resize.do(id_card_img)
segmentation = Segmentation(debug_path)
key_to_segmentation = segmentation.do(id_card_img)
boundaries = [
((0, 0, 0), (100, 100, 100)),
]
boundary2binimgs = []
for boundary in boundaries:
preprocess_bg_mask = PreprocessBackgroundMask(boundary)
id_card_img_mask = preprocess_bg_mask.do(id_card_img)
boundary2binimgs.append((boundary, id_card_img_mask))
char_set = CharSet()
char_set_data = char_set.get()
rect_img_clf = RectImageClassifier(None,
None,
char_set,
caffe_cls_width=64,
caffe_cls_height=64)
reco_text_line = RecoTextLine(rect_img_clf)
## just test id card num
def do(self, color_img):
shape = color_img.shape
norm_height = shape[0]
norm_width = shape[1]
gray_id_card_img = cv2.cvtColor(color_img, cv2.COLOR_BGR2GRAY)
#
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
gray_id_card_img = clahe.apply(gray_id_card_img)
gray_id_card_img = 255 - gray_id_card_img
best_boundary = None
for boundary in self.boundaries:
if self.check_if_good_boundary(boundary, norm_height, norm_width,
color_img):
best_boundary = boundary
break
if best_boundary is None:
return {}
boundary = best_boundary
## boundary = ([0, 0, 0], [100, 100, 100])
preprocess_bg_mask = PreprocessBackgroundMask(boundary)
id_card_img_mask = preprocess_bg_mask.do(color_img)
id_card_img_mask[0:int(norm_height * 0.05), :] = 0
id_card_img_mask[int(norm_height * 0.95):, :] = 0
id_card_img_mask[:, 0:int(norm_width * 0.05)] = 0
id_card_img_mask[:, int(norm_width * 0.95):] = 0
# se1 = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
# se2 = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))
# mask = cv2.morphologyEx(id_card_img_mask, cv2.MORPH_CLOSE, se1)
# id_card_img_mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, se2)
## remove right head profile
left_half_id_card_img_mask = np.copy(id_card_img_mask)
left_half_id_card_img_mask[:, int(norm_width / 2):] = 0
## Try to find text lines and chars
vertical_peek_ranges2d = []
horizontal_sum = np.sum(left_half_id_card_img_mask, axis=1)
line_ranges = extract_peek_ranges_from_array(horizontal_sum)
## char extraction
for line_range in line_ranges:
start_y, end_y = line_range
end_y += 1
line_img = id_card_img_mask[start_y:end_y]
vertical_sum = np.sum(line_img, axis=0)
vertical_peek_ranges = extract_peek_ranges_from_array(
vertical_sum, minimun_val=40, minimun_range=1)
vertical_peek_ranges2d.append(vertical_peek_ranges)
vertical_peek_ranges2d = merge_chars_into_line_segments(
vertical_peek_ranges2d)
img_gray_texts = cv2.bitwise_and(gray_id_card_img,
gray_id_card_img,
mask=id_card_img_mask)
key_to_segmentation = {}
try:
## name extraction
range_y = line_ranges[0]
range_x = vertical_peek_ranges2d[0][0]
start_x, end_x = range_x
start_y, end_y = range_y
w = end_x - start_x
h = end_y - start_y
key_to_segmentation["name"] = [
(start_x, start_y, w, h),
]
## sex extraction
range_y = line_ranges[1]
range_x = vertical_peek_ranges2d[1][0]
start_x, end_x = range_x
start_y, end_y = range_y
w = end_x - start_x
h = end_y - start_y
key_to_segmentation["sex"] = [
(start_x, start_y, w, h),
]
## minzu extraction
range_y = line_ranges[1]
range_x = vertical_peek_ranges2d[1][1]
start_x, end_x = range_x
start_y, end_y = range_y
w = end_x - start_x
h = end_y - start_y
key_to_segmentation["minzu"] = [
(start_x, start_y, w, h),
]
## year extraction
range_y = line_ranges[2]
range_x = vertical_peek_ranges2d[2][0]
start_x, end_x = range_x
start_y, end_y = range_y
w = end_x - start_x
h = end_y - start_y
key_to_segmentation["year"] = [
(start_x, start_y, w, h),
]
## month extraction
range_y = line_ranges[2]
range_x = vertical_peek_ranges2d[2][1]
start_x, end_x = range_x
start_y, end_y = range_y
w = end_x - start_x
h = end_y - start_y
key_to_segmentation["month"] = [
(start_x, start_y, w, h),
]
## day extraction
range_y = line_ranges[2]
range_x = vertical_peek_ranges2d[2][2]
start_x, end_x = range_x
start_y, end_y = range_y
w = end_x - start_x
h = end_y - start_y
key_to_segmentation["day"] = [
(start_x, start_y, w, h),
]
## address extraction
key_to_segmentation["address"] = []
first_line = line_ranges[3][0]
first_line_range_x = vertical_peek_ranges2d[3][0]
first_line_start_x = first_line_range_x[0]
first_line_w = first_line_range_x[1] - first_line_start_x
for i, line_range in enumerate(line_ranges):
if i >= 3:
range_y = line_range
range_x = vertical_peek_ranges2d[i][0]
start_x, end_x = range_x
start_y, end_y = range_y
if abs(first_line_start_x - start_x) > int(
first_line_w * 0.05):
break
w = end_x - start_x
h = end_y - start_y
key_to_segmentation["address"].append(
(start_x, start_y, w, h))
## id extraction
range_y = line_ranges[-1]
range_x = vertical_peek_ranges2d[-1][0]
start_x, end_x = range_x
start_y, end_y = range_y
w = end_x - start_x
h = end_y - start_y
key_to_segmentation["id"] = [
(start_x, start_y, w, h),
]
except:
print("Exception in user code:")
print('-' * 60)
traceback.print_exc(file=sys.stdout)
print('-' * 60)
key_to_segmentation = {}
debug_path = self.debug_path
if debug_path is not None:
import random
if os.path.isdir(debug_path):
shutil.rmtree(debug_path)
os.makedirs(debug_path)
debug_image_path = os.path.join(debug_path, "01_origin_img.jpg")
debug_gray_image_path = os.path.join(debug_path, "01_gray_img.jpg")
debug_image_mask_path = os.path.join(debug_path, "02_mask.jpg")
debug_image_mask_text_lines_path = os.path.join(
debug_path, "03_mask_text_lines.jpg")
debug_image_left_mask_path = os.path.join(debug_path,
"04_left_mask.jpg")
debug_image_gray_texts_path = os.path.join(debug_path,
"05_gray_texts.jpg")
debug_image_chars_path = os.path.join(debug_path,
"06_origin_img_chars.jpg")
debug_image_key_to_segments_path = os.path.join(
debug_path, "07_origin_img_key_to_segments.jpg")
cv2.imwrite(debug_image_path, color_img)
cv2.imwrite(debug_gray_image_path, 255 - gray_id_card_img)
id_card_img_chars = np.copy(color_img)
cv2.imwrite(debug_image_mask_path, id_card_img_mask)
id_card_img_mask_text_lines = np.copy(id_card_img_mask)
for i, line_range in enumerate(line_ranges):
start_y, end_y = line_range
id_card_img_mask_text_lines[start_y, :] = 255
id_card_img_mask_text_lines[end_y, :] = 255
color = (255, 0, 0)
for i, line_range in enumerate(line_ranges):
start_y, end_y = line_range
for vertical_peek_range in vertical_peek_ranges2d[i]:
start_x, end_x = vertical_peek_range
cv2.rectangle(id_card_img_chars, (start_x, start_y),
(end_x + 1, end_y + 1), color)
key_to_segments_img = np.copy(color_img)
for key in key_to_segmentation:
color = (random.randint(0, 255), random.randint(0, 255),
random.randint(0, 255))
rects = key_to_segmentation[key]
for rect in rects:
pt1 = (rect[0], rect[1])
pt2 = (rect[0] + rect[2], rect[1] + rect[3])
cv2.rectangle(key_to_segments_img, pt1, pt2, color)
cv2.imwrite(debug_image_mask_text_lines_path,
id_card_img_mask_text_lines)
cv2.imwrite(debug_image_left_mask_path, left_half_id_card_img_mask)
cv2.imwrite(debug_image_gray_texts_path, img_gray_texts)
cv2.imwrite(debug_image_chars_path, id_card_img_chars)
cv2.imwrite(debug_image_key_to_segments_path, key_to_segments_img)
return key_to_segmentation
lower_blue = np.array([77, 43, 46])
upper_blue = np.array([120,255,255])
hsv = cv2.cvtColor(image_color, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, lower_blue, upper_blue)
res = cv2.bitwise_and(hsv,hsv, mask= mask)
#cv2.imshow("res",res)
brim = np.column_stack(np.where(res != 0))[::3,0:2]
for temp in range(len(brim)):
color_img[brim[temp][0],brim[temp][1]] = 255
#cv2.imshow("color_img",color_img)
#通过三通道的hsv值找到位置
#换成平均颜色
boundary = best_boundary
preprocess_bg_mask = PreprocessBackgroundMask(boundary)
if True:
id_card_img_mask = preprocess_bg_mask.do(color_img)
id_card_img_mask[0:int(norm_height*0.05),:] = 0
id_card_img_mask[int(norm_height*0.95): ,:] = 0
id_card_img_mask[:, 0:int(norm_width*0.05)] = 0
id_card_img_mask[:, int(norm_width*0.95):] = 0
left_half_id_card_img_mask = np.copy(id_card_img_mask)
left_half_id_card_img_mask[:norm_height*1/2, 3*norm_width/5:] = 0
#using mask to conceal the photo
#cv2.imshow("left_half_id_card_img_mask",left_half_id_card_img_mask)