def crnnRec(im, boxes, leftAdjust=False, rightAdjust=False, alph=0.2, f=1.0):
"""
crnn模型，ocr识别
@@model,
@@converter,
@@im:Array
@@text_recs:text box
@@ifIm:是否输出box对应的img
"""
results = []
im = Image.fromarray(im)
for index, box in enumerate(boxes):
degree, w, h, cx, cy = solve(box)
partImg, newW, newH = rotate_cut_img(im, degree, box, w, h, leftAdjust,
rightAdjust, alph)
newBox = xy_rotate_box(cx, cy, newW, newH, degree)
# partImg_ = partImg.convert('L')
# simPred = crnnOcr(partImg_)##识别的文本
# if simPred.strip()!=u'':
# results.append({'cx':cx*f,'cy':cy*f,'text':simPred,'w':newW*f,'h':newH*f,'degree':degree*180.0/np.pi})
results.append({
'cx': cx * f,
'cy': cy * f,
'text': '',
'w': newW * f,
'h': newH * f,
'degree': degree * 180.0 / np.pi
})
return results
def genGT(file) :
with open(file, 'r', encoding = 'utf-8') as f :
gt = []
lines = f.readlines()
for line in lines :
line = line.split(' ')
points = line[0]
text = re.sub(r'\n', '', ''.join(line[1 :]))
box = [float(p) for p in points.split(',')]
box = np.array(box).astype('int32')
box = box.reshape(4, 2)
box = order_point(box)
box = box.reshape(-1)
angle, w, h, cx, cy = solve(box)
gt.append({'angle' : angle, 'w' : w, 'h' : h, 'cx' : cx, 'cy' : cy, 'text' : text})
basic = getBasics(gt)
buyer = getBuyer(gt)
content = getContent(gt)
seller = getSeller(gt)
summation = getSummation(gt)
groundtruth = [{'title' : r'发票基本信息', 'items' : basic},
{'title' : r'购买方', 'items' : buyer},
{'title' : r'销售方', 'items' : seller},
{'title' : r'货物或应税劳务、服务', 'items' : content},
{'title' : r'合计', 'items' : summation}]
return groundtruth
def rotate_box(box):
angle, w, h, cx, cy = solve(box)
angle = round(angle, 4)
w = round(w, 4)
h = round(h, 4)
cx = round(cx, 4)
cy = round(cy, 4)
return ((cx, cy), (w, h), angle)
def evaluate(file, errfile, canfile, predict = None) :
with open(file, 'r', encoding = 'utf-8') as f :
gt = []
precision = []
lines = f.readlines()
# print("file:",file)
# print("len:",len(lines))
# i = 1
for line in lines :
line = line.split(',')
flag = len(line)
if flag==8:# 空格
points = line[:7]
line = line[7].split(' ')
points.append(line[0])
text = ''
for i in range(1,len(line)):
text += line[i]
text = re.sub(r'\n', '', ''.join(text))
# print("points:",points)
box = [float(p) for p in points]
else:#逗号
points = line[:8]
text = line[8:]
text = re.sub(r'\n','',''.join(text))
box = [float(p) for p in points]
pass
box = np.array(box).astype('int32')
box = box.reshape(4, 2)
box = order_point(box)
box = box.reshape(-1)
angle, w, h, cx, cy = solve(box)
gt.append({'angle' : angle, 'w' : w, 'h' : h, 'cx' : cx, 'cy' : cy, 'text' : text})
basic = getBasics(gt)
buyer = getBuyer(gt)
content = getContent(gt)
seller = getSeller(gt)
summation = getSummation(gt)
gt_len = len(gt)
groundtruth = [{'title' : r'发票基本信息', 'items' : basic},
{'title' : r'购买方', 'items' : buyer},
{'title' : r'销售方', 'items' : seller},
{'title' : r'货物或应税劳务、服务', 'items' : content},
{'title' : r'合计', 'items' : summation}]
precision1 = calc_precision1(predict, groundtruth, errfile, canfile)
# precision2 = calc_precision2(gt_len, errfile) # 不准
precision.append(precision1)
# precision.append(precision2)
return precision
def crnnRec(im, boxes, leftAdjust=False, rightAdjust=False, alph=0.2, f=1.0):
"""
crnn模型，ocr识别
leftAdjust,rightAdjust 是否左右调整box 边界误差，解决文字漏检
"""
results = []
im = Image.fromarray(im)
for index, box in enumerate(boxes):
degree, w, h, cx, cy = solve(box)
partImg, newW, newH = rotate_cut_img(im, degree, box, w, h, leftAdjust, rightAdjust, alph)
text = crnnOcr(partImg.convert('L'))
if text.strip() != u'':
results.append({'cx': cx * f, 'cy': cy * f, 'text': text, 'w': newW * f, 'h': newH * f,
'degree': degree * 180.0 / np.pi})
return results
def crnnRec(im,
boxes,
leftAdjust=False,
rightAdjust=False,
alph=0.2,
f=1.0,
save=True):
"""
crnn模型，ocr识别
leftAdjust, rightAdjust 是否左右调整box 边界误差，解决文字漏检
"""
results = []
# im = Image.fromarray(im)
im = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
for index, box in enumerate(boxes):
degree, w, h, cx, cy = solve(box)
# 按照box大小，裁剪图片
partImg, newW, newH = rotate_cut_img(im, degree, box, w, h, leftAdjust,
rightAdjust, alph)
# partImg, newW, newH = cut_img = ()
image_cv = cv2.cvtColor(numpy.asarray(partImg), cv2.COLOR_RGB2BGR)
cv2.imshow("crnnRec", image_cv)
# partImg = Image.fromarray(cv2.cvtColor(partImg, cv2.COLOR_BGR2RGB))
# 图片会转灰度图，进行识别
# print('crnnRec', partImg.size)
text, w = crnnOcr(partImg.convert('L'))
# text, w = crnnOcr(partImg)
if save:
image = partImg.resize((w, 32), Image.BILINEAR)
save_image(image, text)
# text = crnnOcr(partImg)
if text.strip() != u'':
results.append({
'cx': cx * f,
'cy': cy * f,
'text': text,
'w': newW * f,
'h': newH * f,
'degree': degree * 180.0 / np.pi
})
return results
def crnnRec(im,
boxes,
leftAdjust=False,
rightAdjust=False,
alph=0.2,
f=1.0,
tp_groups=None,
boxAll=None,
scoreAll=None):
"""
crnn模型，ocr识别
leftAdjust,rightAdjust 是否左右调整box 边界误差，解决文字漏检
"""
results = []
im = Image.fromarray(im)
for index, box in enumerate(boxes):
degree, w, h, cx, cy = solve(box)
partImg, newW, newH = rotate_cut_img(im, degree, box, w, h, leftAdjust,
rightAdjust, alph)
text = crnnOcr(partImg.convert('L'))
detailbox = boxAll[tp_groups[index]]
detailscore = scoreAll[tp_groups[index]]
detaildex = tp_groups[index]
if text.strip() != u'':
results.append({
'cx': cx * f,
'cy': cy * f,
'text': text,
'w': newW * f,
'h': newH * f,
'degree': degree * 180.0 / np.pi,
'detailbox': detailbox,
'detailscore': detailscore,
'detaildex': detaildex
})
#degree表示顺时针转多少度.
return results
for imgPath in imglist:
# print('img_count: ', img_count)
img = Image.open(imgPath).convert("RGB")
config['img'] = img
text_recs = text_detect(**config)
# print(text_recs)
boxes = sorted(text_recs, key=lambda x: sum([x[1], x[3], x[5], x[7]]))
i = 0
filename = ntpath.basename(imgPath)
ori_filename = romvChinese(filename)
for index, box in enumerate(boxes):
filename = ori_filename
degree, w, h, cx, cy = solve(box)
partImg, newW, newH = rotate_cut_img(img,
degree,
box,
w,
h,
leftAdjust=True,
rightAdjust=True,
alph=0.2)
if partImg.size[1] < 32:
scale = partImg.size[1] * 1.0 / 32
w = partImg.size[0] / scale
w = int(w)
partImg = partImg.resize((w, 32), Image.BILINEAR)
filename = filename[:-4] + '_' + str(i) + '_.jpg'
partImgPath = os.path.join(subdir, filename)
