def ocr_core(image_src):
"""
调用 ocr 识别图像
:param image_path: skimage 处理过的图像
:return: [[识别字符, bounding box], ...]
"""
rectification = detect_skimage(image_src)
result = []
index = 0
for ele in rectification:
index += 1
img = ele[0]
minlength = img.shape[0] if img.shape[0] < img.shape[1] else img.shape[1]
if minlength < 1280:
factor = 1280 // minlength + 1
img = cv2.resize(img, None, fx=factor, fy=factor, interpolation=cv2.INTER_CUBIC)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, th = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
if th[0][0] == 0:
th = cv2.bitwise_not(th)
img = Image.fromarray(th).convert('L')
res = crnnOcr(img)
res = segmentation.correct(res).strip()
result.append([res, ele[1]])
return result
def crnn_get(image):
sim_pred = crnnOcr(image)
print("NUMBER:")
print(sim_pred)
return sim_pred
def crnnRec(im, text_recs, ocr_mode='keras', adjust=False):
"""
crnn模型，ocr识别
@@model,
@@converter,
@@im:Array
@@text_recs:text box
"""
index = 0
results = {}
xDim, yDim = im.shape[1], im.shape[0]
for index, rec in enumerate(text_recs):
results[index] = [
rec,
]
xlength = int((rec[6] - rec[0]) * 0.1)
ylength = int((rec[7] - rec[1]) * 0.2)
if adjust:
pt1 = (max(1, rec[0] - xlength), max(1, rec[1] - ylength))
pt2 = (rec[2], rec[3])
pt3 = (min(rec[6] + xlength,
xDim - 2), min(yDim - 2, rec[7] + ylength))
pt4 = (rec[4], rec[5])
else:
pt1 = (max(1, rec[0]), max(1, rec[1]))
pt2 = (rec[2], rec[3])
pt3 = (min(rec[6], xDim - 2), min(yDim - 2, rec[7]))
pt4 = (rec[4], rec[5])
# 图像倾斜角度
degree = degrees(atan2(pt2[1] - pt1[1], pt2[0] - pt1[0]))
part_img = dump_rotate_image(im, degree, pt1, pt2, pt3, pt4)
image = Image.fromarray(part_img).convert('L')
if ocr_mode == 'keras':
sim_pred = ocr(image)
else:
sim_pred = crnnOcr(image)
# 识别文字
results[index].append(sim_pred)
return results
def crnnRec(im, text_recs, adjust=False):
"""
crnn模型，ocr识别
@@model,
@@converter,
@@im:Array
@@text_recs:text box
"""
index = 0
results = {}
xDim, yDim = im.shape[1], im.shape[0]
for index, rec in enumerate(text_recs):
results[index] = [
rec,
]
xlength = int((rec[6] - rec[0]) * 0.1)
ylength = int((rec[7] - rec[1]) * 0.2)
if adjust:
pt1 = (max(1, rec[0] - xlength), max(1, rec[1] - ylength))
pt2 = (rec[2], rec[3])
pt3 = (min(rec[6] + xlength,
xDim - 2), min(yDim - 2, rec[7] + ylength))
pt4 = (rec[4], rec[5])
else:
pt1 = (max(1, rec[0]), max(1, rec[1]))
pt2 = (rec[2], rec[3])
pt3 = (min(rec[6], xDim - 2), min(yDim - 2, rec[7]))
pt4 = (rec[4], rec[5])
degree = degrees(atan2(pt2[1] - pt1[1], pt2[0] - pt1[0])) ##图像倾斜角度
partImg = dumpRotateImage(im, degree, pt1, pt2, pt3, pt4)
# 根据ctpn进行识别出的文字区域，进行不同文字区域的crnn识别
# 图片格式转换并进行灰度处理
image = Image.fromarray(partImg).convert('L')
# 进行识别出的文字识别
sim_pred = crnnOcr(image)
results[index].append(sim_pred) ##识别文字
return results
def crnnRec(im, ocrMode='keras'):
"""
crnn模型，ocr识别
@@model,
@@converter,
@@im:Array
@@text_recs:text box
"""
index = 0
results = {}
results[index] = list()
image = Image.fromarray(im).convert('L')
# 进行识别出的文字识别
if ocrMode == 'keras':
sim_pred = ocr(image)
else:
sim_pred = crnnOcr(image)
results[index].append(sim_pred) # 识别文字
return results
def crnnRec(im,boxes,ifIm=False,leftAdjust=False,rightAdjust=False,alph=0.2):
"""
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,'cy':cy,'text':simPred,'w':newW,'h':newH,'degree':degree*180.0/np.pi})
return results
#coding:utf-8 import crnn.crnn as ocr imageFileName = "./2.jpg" preds = ocr.crnnOcr(imageFileName) print(preds)
def draw_boxes(img, image_name, boxes, opt, adjust):
base_name = image_name.split('/')[-1]
i = 0
j = 0
boxes = sorted(boxes, key=lambda x: x[1])
boxesX0 = sorted(boxes, key=lambda x: x[0])
xStart = boxesX0[0][0]
iXStart = int(xStart)
if (iXStart < 0):
iXStart = 0
boxesX2 = sorted(boxes, key=lambda x: x[2])
xEnd = boxesX2[-1][2]
previousY = 0
resultMap = {
'1_shopName': 'none',
'2_city': 'none',
'3_tel': 'none',
'4_year': 'none',
'5_total': 0,
'6_receiptNO': 'none',
'7_staffNO': 'none',
'8_pointcard': 'none',
'9_category': 0,
'a_goods': 'none',
'a_tax': 0,
'b_subtotal': 0,
'suffix_catPrice': [],
'tel_before': '',
'pos_tel_before': 0,
'pos_tel_after': 0,
'origin_result': [],
'pos_shop': 0,
'pos_time': 0,
'pos_time_after': 0,
'pos_tax_after': 0,
'pos_staff': 0,
'pos_ling': 0,
'pos_ling_after': 0,
'pos_category': 0,
'pos_total': 0,
'pos_subtotal': 0,
'pos_tax': 0,
'pos_card': 0,
'pos_mny_after': 0,
'pos_card_after': 0,
'type_shop': 0
}
for box in boxes:
if np.linalg.norm(box[0] - box[1]) < 5 or np.linalg.norm(box[3] -
box[0]) < 5:
j += 1
continue
if (i > 0 and int(box[1]) - previousY <= 11):
previousY = int(box[1])
continue
previousY = int(box[1])
i += 1
crop_img = img[int(box[1]):int(box[5]), iXStart:int(xEnd)]
# print('Y0={}, Y1={}, X0={}, X1={}'.format(int(box[1]),int(box[5]),iXStart,int(xEnd)))
if opt.develop and i <= 5:
cv2.imwrite(
os.path.join("test/results",
base_name.split('.')[0] + "_" + str(i) + ".jpg"),
crop_img)
try:
if opt.torch:
from crnn.crnn import crnnOcr
sim_pred = crnnOcr(
Image.fromarray(crop_img).convert('L')).strip()
else:
from ocr.model import predict as ocr
sim_pred = ocr(Image.fromarray(crop_img).convert('L')).strip()
except Exception as e:
print('Exception for step {}--{}'.format(i, e))
continue
sim_pred = sim_pred.strip()
sim_pred = numberUtils.removeQuote(sim_pred)
sim_pred = jaconv.z2h(sim_pred, digit=True, ascii=True)
resultMap['origin_result'].append(sim_pred)
if opt.develop and i <= 5:
with open(
os.path.join(
"test/results",
base_name.split('.')[0] + "_" + str(i) + ".txt"),
'a+') as f:
f.write(sim_pred)
commonUtils.getDrawboxResult(resultMap['origin_result'], resultMap)
return resultMap
from crnn.crnn import crnnOcr as crnnOcr
from PIL import Image
partImg = Image.open('/home/gavin/Desktop/test_ocr2.png') ##单行文本图像
partImg = partImg.convert('L')
simPred = crnnOcr(partImg) ##识别的文本
print(simPred)
