def train_ticket(filepath):
scale, maxScale = IMGSIZE[0], 2048
from run.text.keras_detect import text_detect
from run.main import TextOcrModel
angle_detect = None
model = TextOcrModel(ocr, text_detect, angle_detect)
img = cv.imread(filepath)
result, angle = model.model(
img,
scale=scale,
maxScale=maxScale,
detectAngle=False, ##是否进行文字方向检测
MAX_HORIZONTAL_GAP=100, ##字符之间的最大间隔
MIN_V_OVERLAPS=0.6,
MIN_SIZE_SIM=0.6,
TEXT_PROPOSALS_MIN_SCORE=0.1,
TEXT_PROPOSALS_NMS_THRESH=0.3,
TEXT_LINE_NMS_THRESH=0.99, ##iou值
LINE_MIN_SCORE=0.1,
leftAdjustAlph=0.01, ##
rightAdjustAlph=0.01, ##
)
res = trainTicket.trainTicket(result)
res = res.res
res = [{'text': res[key], 'name': key, 'box': {}} for key in res]
return res
def ocr_train_ticket(path, detect_angle=False):
img = cv2.imread(path) # GBR
H, W = img.shape[:2]
time_take = time.time()
_, result, angle = model.model(
img,
detectAngle=detect_angle, # 是否进行文字方向检测，通过web传参控制
config=dict(
MAX_HORIZONTAL_GAP=50, # 字符之间的最大间隔，用于文本行的合并
MIN_V_OVERLAPS=0.6,
MIN_SIZE_SIM=0.6,
TEXT_PROPOSALS_MIN_SCORE=0.1,
TEXT_PROPOSALS_NMS_THRESH=0.3,
TEXT_LINE_NMS_THRESH=0.7), # 文本行之间测iou值
leftAdjust=True, # 对检测的文本行进行向左延伸
rightAdjust=True, # 对检测的文本行进行向右延伸
alpha=0.01) # 对检测的文本行进行向右、左延伸的倍数
# print('[ocr_train_ticket] result', result)
# print('[ocr_train_ticket] angle', angle)
res = trainTicket.trainTicket(result)
res = res.res
res = [{'text': res[key], 'name': key, 'box': {}} for key in res]
print('[ocr_train_ticket] res', res)
time_take = time.time() - time_take
print('[ocr_train_ticket] time_take', time_take)
def POST(self):
data = web.data()
data = json.loads(data)
billModel = data.get('billModel', '')
imgString = data['imgString'].encode().split(b';base64,')[-1]
imgString = base64.b64decode(imgString)
jobid = uuid.uuid1().__str__()
path = '/tmp/{}.jpg'.format(jobid)
with open(path, 'wb') as f:
f.write(imgString)
img = Image.open(path).convert("RGB")
W, H = img.size
timeTake = time.time()
_, result, angle = model.model(
img,
detectAngle=DETECTANGLE, ##是否进行文字方向检测
config=dict(
MAX_HORIZONTAL_GAP=100, ##字符之间的最大间隔，用于文本行的合并
MIN_V_OVERLAPS=0.7,
MIN_SIZE_SIM=0.7,
TEXT_PROPOSALS_MIN_SCORE=0.1,
TEXT_PROPOSALS_NMS_THRESH=0.3,
TEXT_LINE_NMS_THRESH=0.99, ##文本行之间测iou值
MIN_RATIO=1.0,
LINE_MIN_SCORE=0.2,
TEXT_PROPOSALS_WIDTH=0,
MIN_NUM_PROPOSALS=0,
),
leftAdjust=True, ##对检测的文本行进行向左延伸
rightAdjust=True, ##对检测的文本行进行向右延伸
alph=0.2, ##对检测的文本行进行向右、左延伸的倍数
ifadjustDegree=False ##是否先小角度调整文字倾斜角度
)
if billModel == '' or billModel == '通用OCR':
result = union_rbox(result, 0.2)
res = [{
'text': x['text'],
'name': str(i)
} for i, x in enumerate(result)]
elif billModel == '火车票':
res = trainTicket.trainTicket(result)
res = res.res
res = [{'text': res[key], 'name': key} for key in res]
elif billModel == '身份证':
res = idcard.idcard(result)
res = res.res
res = [{'text': res[key], 'name': key} for key in res]
timeTake = time.time() - timeTake
os.remove(path)
return json.dumps({
'res': res,
'timeTake': round(timeTake, 4)
},
ensure_ascii=False)
def format_text(self, textbox, img, angle, billModel='general_OCR', CommandID= ''):
"""
格式化各种图片提取的文本
:param textbox: 提取的文本框（包括坐标和文本内容）
:param img: 原图
:param angle: 原图需要旋转的角度
:param billModel: 图片类型，方便格式化
:param CommandID: 判断来自网页的请求（文本展示），还是返回给服务器的请求
:return: res: json格式的格式化结果
"""
if billModel == '' or billModel == 'general_OCR':
result = union_rbox(textbox, 0.2)
res = [{'text': x['text'],
'name': str(i),
'box': {'cx': x['cx'],
'cy': x['cy'],
'w': x['w'],
'h': x['h'],
'angle': x['degree']
}
} for i, x in enumerate(result)]
res = adjust_box_to_origin(img, angle, res) ##修正box
elif billModel == 'trainticket':
res = trainTicket.trainTicket(textbox)
res = res.res
if CommandID != '':
res = {key: res[key] for key in res}
else:
res = [{'text': res[key], 'name': key, 'box': {}} for key in res]
elif billModel == 'idcard':
res = idcard.idcard(textbox)
res = res.res
if CommandID != '':
res = {key: res[key] for key in res}
else:
res = [{'text': res[key], 'name': key, 'box': {}} for key in res]
elif billModel == 'invoice':
res = invoice.invoice(textbox)
res = res.res
if CommandID != '':
res = {key: res[key] for key in res}
else:
res = [{'text': res[key], 'name': key, 'box': {}} for key in res]
elif billModel == 'bankcard':
res = bankcard.bankcard(textbox)
res = res.res
if CommandID != '':
res = {key: res[key] for key in res}
else:
res = [{'text': res[key], 'name': key, 'box': {}} for key in res]
elif billModel == 'licenseplate':
if CommandID != '':
res = {'carNo': list(textbox), 'picUrl': '', 'picName': ''}
else:
res = [{'text': text, 'name': 'carNo', 'box': {}} for text in list(textbox)]
return res
def result(img):
back = {}
img = Image.open(BytesIO(img)).convert('RGB')
img = np.array(img)
result = text_predict(img)
back['文本'] = list(map(lambda x: x['text'], result))
res = trainTicket.trainTicket(result)
back['火车票'] = str(res)
res = idcard.idcard(result)
back['身份证'] = str(res)
return back
def trainTicketOCR():
if 'image' not in request.files:
return {"error": "'image' not found"}
img = Image.open(request.files["image"]).convert("RGB")
img = np.array(img)
result = text_predict(img)
res = trainTicket.trainTicket(result)
res = res.res
res = [{'text': res[key], 'name':key} for key in res]
return {"result": res}
def api_root():
app.logger.info(PROJECT_HOME)
if request.method == 'POST' and request.files['image']:
starttime = time.time()
img = request.files['image']
img_cv = cv2.imdecode(np.fromstring(img.read(), np.uint8),
cv2.IMREAD_COLOR)
boxes, rec_res = text_sys(img_cv)
for box in boxes:
xy_sum = np.sum(box, axis=0) / 4.0
cx = xy_sum[0]
cy = xy_sum[1]
degree = np.arcsin(
(box[1][1] - box[0][1]) / (box[1][0] - box[0][0]))
w = abs(box[0][0] - box[1][0])
h = abs(box[0][1] - box[3][1])
x1, y1, x2, y2, x3, y3, x4, y4 = xy_rotate_box(
cx, cy, w, h, degree / 180 * np.pi)
box[0][0] = x1
box[0][1] = y1
box[1][0] = x2
box[1][1] = y2
box[2][0] = x3
box[2][1] = y3
box[3][0] = x4
box[3][1] = y4
assorted_results = [{
'box': boxes[i],
'txt': rec_res[i][0]
} for i in range(len(rec_res))]
res = trainTicket.trainTicket(assorted_results, img=img_cv)
res = res.res
elapse = time.time() - starttime
app.logger.info("Predict time : %.3fs" % elapse)
return json.dumps(res, ensure_ascii=False)
else:
return "Where is the image?"
def main():
billModel = '通用OCR'
path = 'test/t3.jpg'
img = Image.open(path).convert("RGB")
W,H = img.size
timeTake = time.time()
_,result,angle= model.model(img,
detectAngle=DETECTANGLE,##是否进行文字方向检测
config=dict(MAX_HORIZONTAL_GAP=100,##字符之间的最大间隔，用于文本行的合并
MIN_V_OVERLAPS=0.7,
MIN_SIZE_SIM=0.7,
TEXT_PROPOSALS_MIN_SCORE=0.1,
TEXT_PROPOSALS_NMS_THRESH=0.3,
TEXT_LINE_NMS_THRESH = 0.99,##文本行之间测iou值
MIN_RATIO=1.0,
LINE_MIN_SCORE=0.2,
TEXT_PROPOSALS_WIDTH=0,
MIN_NUM_PROPOSALS=0,
),
leftAdjust=True,##对检测的文本行进行向左延伸
rightAdjust=True,##对检测的文本行进行向右延伸
alph=0.2,##对检测的文本行进行向右、左延伸的倍数
ifadjustDegree=False##是否先小角度调整文字倾斜角度
)
if billModel=='' or billModel=='通用OCR':
result = union_rbox(result,0.2)
res = [{'text':x['text'],'name':str(i)} for i,x in enumerate(result)]
elif billModel=='火车票':
res = trainTicket.trainTicket(result)
res = res.res
res =[ {'text':res[key],'name':key} for key in res]
elif billModel=='身份证':
res = idcard.idcard(result)
res = res.res
res =[ {'text':res[key],'name':key} for key in res]
print(result)
def POST(self):
t = time.time()
data = web.data()
uidJob = uuid.uuid1().__str__()
data = json.loads(data)
# print(data)
billModel = data.get('billModel','')
# textAngle = data.get('textAngle',False)##文字检测
textLine = data.get('textLine',False)##只进行单行识别
imgString = data['imgString'].encode().split(b';base64,')[-1]
img = base64_to_PIL(imgString)
if img is not None:
img = np.array(img)
H,W = img.shape[:2]
while time.time()-t<=TIMEOUT:
if os.path.exists(filelock):
continue
else:
with open(filelock,'w') as f:
f.write(uidJob)
if textLine:
##单行识别
partImg = Image.fromarray(img)
text = crnn_handle.predict(partImg)
res =[ {'text':text,'name':'0','box':[0,0,W,0,W,H,0,H]} ]
os.remove(filelock)
break
else:
# detectAngle = textAngle
result= text_predict(img)
if billModel=='' or billModel=='通用OCR' :
# result = union_rbox(result,0.2)
res = [{'text':x['text'],
'name':str(i),
'box':{'cx':x['cx'],
'cy':x['cy'],
'w':x['w'],
'h':x['h'],
'angle':x['degree']
}
} for i,x in enumerate(result)]
# res = adjust_box_to_origin(img,angle, res)##修正box
elif billModel=='火车票':
res = trainTicket.trainTicket(result)
res = res.res
res =[ {'text':res[key],'name':key,'box':{}} for key in res]
elif billModel=='身份证':
res = idcard.idcard(result)
res = res.res
res =[ {'text':res[key],'name':key,'box':{}} for key in res]
os.remove(filelock)
break
timeTake = time.time()-t
return json.dumps({'res':res,'timeTake':round(timeTake,4)},ensure_ascii=False)
def result(img):
back = {}
img = Image.open(BytesIO(img)).convert('RGB')
img = np.array(img)
result = text_predict(img)
back['文本'] = list(map(lambda x: x['text'], result))
res = trainTicket.trainTicket(result)
back['火车票'] = str(res)
res = idcard.idcard(result)
back['身份证'] = str(res)
return back
if __name__ == '__main__':
img = './test/idcard-demo.jpg'
img = Image.open(img).convert('RGB')
# img.show()
img = np.array(img)
text = text_predict(img)
print('文本预测:', list(map(lambda x: x['text'], text)))
# 火车票
res = trainTicket.trainTicket(text)
print('火车票预测:', res)
# 身份证
res = idcard.idcard(text)
print('身份证预测:', res)
def POST(self):
t = time.time()
data = web.data()
uidJob = uuid.uuid1().__str__()
data = json.loads(data)
billModel = data.get('billModel', '')
textAngle = data.get('textAngle', False) ##文字检测
textLine = data.get('textLine', False) ##只进行单行识别
imgString = data['imgString'].encode().split(b';base64,')[-1]
img = base64_to_PIL(imgString)
if img is not None:
img = np.array(img)
H, W = img.shape[:2]
while time.time() - t <= TIMEOUT:
if os.path.exists(filelock):
continue
else:
with open(filelock, 'w') as f:
f.write(uidJob)
if textLine:
##单行识别
partImg = Image.fromarray(img)
text = crnn.predict(partImg.convert('L'))
res = [{
'text': text,
'name': '0',
'box': [0, 0, W, 0, W, H, 0, H]
}]
os.remove(filelock)
break
else:
detectAngle = textAngle
result, angle = model.model(
img,
scale=scale,
maxScale=maxScale,
detectAngle=detectAngle, ##是否进行文字方向检测，通过web传参控制
MAX_HORIZONTAL_GAP=100, ##字符之间的最大间隔，用于文本行的合并
MIN_V_OVERLAPS=0.6,
MIN_SIZE_SIM=0.6,
TEXT_PROPOSALS_MIN_SCORE=0.1,
TEXT_PROPOSALS_NMS_THRESH=0.3,
TEXT_LINE_NMS_THRESH=0.99, ##文本行之间测iou值
LINE_MIN_SCORE=0.1,
leftAdjustAlph=0.01, ##对检测的文本行进行向左延伸
rightAdjustAlph=0.01, ##对检测的文本行进行向右延伸
)
if billModel == '' or billModel == '通用OCR':
result = union_rbox(result, 0.2)
res = [{
'text': x['text'],
'name': str(i),
'box': {
'cx': x['cx'],
'cy': x['cy'],
'w': x['w'],
'h': x['h'],
'angle': x['degree']
}
} for i, x in enumerate(result)]
res = adjust_box_to_origin(img, angle, res) ##修正box
elif billModel == '火车票':
res = trainTicket.trainTicket(result)
res = res.res
res = [{
'text': res[key],
'name': key,
'box': {}
} for key in res]
elif billModel == '身份证':
res = idcard.idcard(result)
res = res.res
res = [{
'text': res[key],
'name': key,
'box': {}
} for key in res]
os.remove(filelock)
break
timeTake = time.time() - t
return json.dumps({
'res': res,
'timeTake': round(timeTake, 4)
},
ensure_ascii=False)
def POST(self):
t = time.time()
data = web.data()
uidJob = uuid.uuid1().__str__()
data = json.loads(data)
# print(data)
billModel = data.get("billModel", "")
# textAngle = data.get('textAngle',False)##文字检测
textLine = data.get("textLine", False) ##只进行单行识别
imgString = data["imgString"].encode().split(b";base64,")[-1]
img = base64_to_PIL(imgString)
if img is not None:
img = np.array(img)
H, W = img.shape[:2]
while time.time() - t <= TIMEOUT:
if os.path.exists(filelock):
continue
else:
with open(filelock, "w") as f:
f.write(uidJob)
if textLine:
##单行识别
partImg = Image.fromarray(img)
text = crnn_handle.predict(partImg)
res = [{
"text": text,
"name": "0",
"box": [0, 0, W, 0, W, H, 0, H]
}]
os.remove(filelock)
break
else:
# detectAngle = textAngle
result = text_predict(img)
if billModel == "" or billModel == "通用OCR":
# result = union_rbox(result,0.2)
res = [{
"text": x["text"],
"name": str(i),
"box": {
"cx": x["cx"],
"cy": x["cy"],
"w": x["w"],
"h": x["h"],
"angle": x["degree"],
},
} for i, x in enumerate(result)]
# res = adjust_box_to_origin(img,angle, res)##修正box
elif billModel == "火车票":
res = trainTicket.trainTicket(result)
res = res.res
res = [{
"text": res[key],
"name": key,
"box": {}
} for key in res]
elif billModel == "身份证":
res = idcard.idcard(result)
res = res.res
res = [{
"text": res[key],
"name": key,
"box": {}
} for key in res]
os.remove(filelock)
break
timeTake = time.time() - t
return json.dumps({
"res": res,
"timeTake": round(timeTake, 4)
},
ensure_ascii=False)
def POST(self):
data = web.data()
data = json.loads(data)
billModel = data.get('billModel', '')
textAngle = data.get('textAngle', False) # 文字检测
textLine = data.get('textLine', False) # 只进行单行识别
imgString = data['imgString'].encode().split(b';base64,')[-1]
imgString = base64.b64decode(imgString)
jobid = uuid.uuid1().__str__()
path = 'test/{}.jpg'.format(jobid)
with open(path, 'wb') as f:
f.write(imgString)
img = cv2.imread(path) # GBR
H, W = img.shape[:2]
timeTake = time.time()
if textLine:
# 单行识别
partImg = Image.fromarray(img)
text = model.crnnOcr(partImg.convert('L'))
res = [{
'text': text,
'name': '0',
'box': [0, 0, W, 0, W, H, 0, H]
}]
else:
detectAngle = textAngle # 是否进行文字方向检测
_, result, angle = model.model(
img,
file_name="test.jpg",
detectAngle=detectAngle, # 是否进行文字方向检测，通过web传参控制
config=dict(
MAX_HORIZONTAL_GAP=50, # 字符之间的最大间隔，用于文本行的合并
MIN_V_OVERLAPS=0.6,
MIN_SIZE_SIM=0.6,
TEXT_PROPOSALS_MIN_SCORE=0.1,
TEXT_PROPOSALS_NMS_THRESH=0.3,
TEXT_LINE_NMS_THRESH=0.7), # 文本行之间测iou值
leftAdjust=True, # 对检测的文本行进行向左延伸
rightAdjust=True, # 对检测的文本行进行向右延伸
alpha=0.01) # 对检测的文本行进行向右、左延伸的倍数
print('[POST] result', result)
print('[POST] angle', angle)
if billModel == '' or billModel == '通用OCR':
result = union_rbox(result, 0.2)
res = [{
'text': x['text'],
'name': str(i),
'box': {
'cx': x['cx'],
'cy': x['cy'],
'w': x['w'],
'h': x['h'],
'angle': x['degree']
}
} for i, x in enumerate(result)]
res = adjust_box_to_origin(img, angle, res) # 修正box
elif billModel == '火车票':
res = trainTicket.trainTicket(result)
res = res.res
res = [{
'text': res[key],
'name': key,
'box': {}
} for key in res]
elif billModel == '身份证':
res = idcard.idcard(result)
res = res.res
res = [{
'text': res[key],
'name': key,
'box': {}
} for key in res]
timeTake = time.time() - timeTake
os.remove(path)
return json.dumps({
'res': res,
'timeTake': round(timeTake, 4)
},
ensure_ascii=False)
def ocr():
if request.method == "POST":
request_start_time = time.time()
uid_job = uuid.uuid1().__str__()
data = request.json
# 模型参数
bill_model = data.get("billModel", "")
# 文字检测
# text_angle = data.get('textAngle', False)
# 只进行单行识别
text_line = data.get("textLine", False)
img_str = data["imgString"].encode().split(b";base64,")[-1]
img = base64_to_PIL(img_str)
if img is None:
response_time = time.time() - request_start_time
return jsonify({"res": [], "timeTake": round(response_time, 4)})
else:
img = np.array(img)
h, w = img.shape[:2]
final_result: list = []
while time.time() - request_start_time <= TIMEOUT:
if os.path.exists(file_lock):
continue
else:
with open(file_lock, "w") as f:
f.write(uid_job)
if text_line:
# 单行识别
part_img = Image.fromarray(img)
text = crnn_handle.predict(part_img)
final_result = [{
"text": text,
"name": "0",
"box": [0, 0, w, 0, w, h, 0, h]
}]
os.remove(file_lock)
break
else:
result = text_predict(img)
if bill_model == "" or bill_model == "通用OCR":
final_result = [{
"text": x["text"],
"name": str(i),
"box": {
"cx": x["cx"],
"cy": x["cy"],
"w": x["w"],
"h": x["h"],
"angle": x["degree"],
},
} for i, x in enumerate(result)]
elif bill_model == "火车票":
train_ticket_result = trainTicket.trainTicket(
result)
result = train_ticket_result.res
final_result = [{
"text": result[key],
"name": key,
"box": {}
} for key in result]
elif bill_model == "身份证":
id_card_result = idcard.idcard(result)
result = id_card_result.res
final_result = [{
"text": result[key],
"name": key,
"box": {}
} for key in result]
os.remove(file_lock)
break
response_time = time.time() - request_start_time
return jsonify({
"res": final_result,
"timeTake": round(response_time, 4)
})
def upload():
t = time.time()
parser = reqparse.RequestParser()
parser.add_argument('file')
args = parser.parse_args()
# data = request.json
uidJob = uuid.uuid1().__str__()
file = args['file']
# data = json.loads(data)
billModel = '通用OCR'
textAngle = True # 文字检测
imgString = file.encode().split(b';base64,')[-1]
img = base64_to_PIL(imgString)
if img is not None:
img = np.array(img)
else:
return 0
H, W = img.shape[:2]
while time.time() - t <= TIMEOUT:
if os.path.exists(filelock):
continue
else:
with open(filelock, 'w') as f:
f.write(uidJob)
detectAngle = textAngle
result, angle = model.model(img,
scale=scale,
maxScale=maxScale,
detectAngle=detectAngle, # 是否进行文字方向检测，通过web传参控制
MAX_HORIZONTAL_GAP=100, # 字符之间的最大间隔，用于文本行的合并
MIN_V_OVERLAPS=0.6,
MIN_SIZE_SIM=0.6,
TEXT_PROPOSALS_MIN_SCORE=0.1,
TEXT_PROPOSALS_NMS_THRESH=0.3,
TEXT_LINE_NMS_THRESH=0.99, # 文本行之间测iou值
LINE_MIN_SCORE=0.1,
leftAdjustAlph=0.01, # 对检测的文本行进行向左延伸
rightAdjustAlph=0.01, # 对检测的文本行进行向右延伸
)
if billModel == '' or billModel == '通用OCR':
result = union_rbox(result, 0.2)
res = {'name': '', 'position': '', 'company': '', 'local': '', 'email': '', 'phone': '',
'other': ''}
# 对公司进行提取
companyKeyword = ['公司', 'company', '银行', 'Bank', '集团', 'Group', '商行', 'Factory', 'CO', 'LTD']
for i, x in enumerate(result):
for ck in companyKeyword:
if ck in x['text']:
res['company'] = x['text']
result.pop(i)
break
if res['company']:
break
name = ''
name_height = 0
pop_data = 0
# 对姓名进行处理
for i, x in enumerate(result):
if 'name' in x['text'] or '姓名' in x['text']:
res['name'] = x['text'].replace('name', '')
res['name'] = res['name'].replace('姓名', '')
res['name'] = res['name'].replace(':', '')
result.pop(i)
break
else:
# 正则取中文，进行分别进行中文名和英文名的判断
text_list = re.findall(r'[\u4e00-\u9fa5]', x['text'])
if text_list:
if name_height < x['h'] and 1 < len(text_list) < 5:
name_height = x['h']
name = x['text']
pop_data = i
else:
strUpper = x['text'].upper()
if name_height < x['h'] and strUpper != x['text']:
name_height = x['h']
name = x['text']
pop_data = i
res['name'] = name
result.pop(pop_data)
# 对职位进行提取
pos = ['GM', 'VP', 'HRD', 'OD', 'MD', 'OM', 'PM', 'BM', 'DM', 'RM', 'AAD', 'ACD', 'AD', 'AE',
'AP', 'ASM', 'VC', 'CWO', 'COO', 'CXO', 'CYO', 'CZO', 'PS', 'Manager', 'Engineer']
posNum = "".join(filter(str.isdigit, x['text']))
for i, x in enumerate(result[pop_data:pop_data + 3]):
if '职位' in x['text'] or 'position' in x['text']:
res['position'] = x['text'].replace('职位', '')
res['position'] = res['position'].replace('position', '')
res['position'] = res['position'].replace(':', '')
result.pop(i)
break
if not res['position']:
for i, x in enumerate(result[pop_data:pop_data + 3]):
for ch in x['text']:
if u'\u4e00' <= ch <= u'\u9fff' and len(posNum) == 0:
res['position'] = x['text']
result.pop(i)
break
if res['position']:
break
for p in pos:
if p in x['text']:
res['position'] = x['text']
result.pop(i)
break
if res['position']:
break
# 对邮箱进行提取
for i, x in enumerate(result):
if '@' in x['text'] or '邮箱' in x['text'] or 'email' in x['text']:
res['email'] = x['text'].replace('邮箱', '')
res['email'] = res['email'].replace('email', '')
res['email'] = res['email'].replace(':', '')
result.pop(i)
break
# 对地址进行提取
localKeyword = ['市', '省', '区', '号', '路', '岛', '地址', 'sheng', 'shi', 'qu', 'hao', 'lu']
for i, x in enumerate(result):
for k in localKeyword:
if k in x['text'] or 'Add' in x['text'] or 'add' in x['text']:
res['local'] = x['text'].replace('地址', '')
res['local'] = res['local'].replace('Add', '')
res['local'] = res['local'].replace(':', '')
result.pop(i)
break
if res['local']:
break
# 对手机号码进行提取
for i, x in enumerate(result):
if '-' not in x['text'] and '-' not in x['text']:
telephone = "".join(filter(str.isdigit, x['text']))
if 11 <= len(telephone) <= 13:
res['phone'] += x['text'].replace('电话', '') + ';'
res['phone'] = res['phone'].replace('Tel', '')
res['phone'] = res['phone'].replace('手机', '')
res['phone'] = res['phone'].replace(':', '')
result.pop(i)
# 其他文本提取
for i, x in enumerate(result):
res['other'] += x['text'] + ';'
elif billModel == '火车票':
res = trainTicket.trainTicket(result)
res = res.res
res = [{'text': res[key], 'name': key, 'box': {}} for key in res]
elif billModel == '身份证':
res = idcard.idcard(result)
res = res.res
res = [{'text': res[key], 'name': key, 'box': {}} for key in res]
os.remove(filelock)
break
timeTake = time.time() - t
return jsonify({
"code": "0000",
"msg": "成功",
"data": res
})
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
import easyocr
from application import trainTicket
reader_ch = easyocr.Reader(['en', 'ch_tra']) # need to run only once to load model into memory
reader_en = easyocr.Reader(['en'])
result_en = reader_en.readtext('D:/dataset/invoice/7.jpg')
result_ch = reader_ch.readtext('D:/dataset/invoice/7.jpg')
assorted_results_ch = [{'box': bbox, 'txt': txt} for bbox, txt, _ in result_ch]
assorted_results_en = [{'box': bbox, 'txt': txt} for bbox, txt, _ in result_en]
res1 = trainTicket.trainTicket(assorted_results_ch)
res2 = trainTicket.trainTicket(assorted_results_en)
print(res1)
print(res2)
def run_ocr(img, billModel, textLine):
t = time.time()
H, W = img.shape[:2]
res = ''
uidJob = uuid.uuid1().__str__()
while time.time() - t <= TIMEOUT:
if os.path.exists(filelock):
continue
else:
with open(filelock, 'w') as f:
f.write(uidJob)
if textLine:
##单行识别
partImg = Image.fromarray(img)
text = crnn_handle.predict(partImg)
res = [{
'text': text,
'name': '0',
'box': [0, 0, W, 0, W, H, 0, H]
}]
os.remove(filelock)
break
else:
# detectAngle = textAngle
result = text_predict(img)
if billModel == '' or billModel == '通用OCR':
# result = union_rbox(result,0.2)
res = [{
'text': x['text'],
'name': str(i),
'box': {
'cx': x['cx'],
'cy': x['cy'],
'w': x['w'],
'h': x['h'],
'angle': x['degree']
}
} for i, x in enumerate(result)]
# res = adjust_box_to_origin(img,angle, res)##修正box
elif billModel == '火车票':
res = trainTicket.trainTicket(result)
res = res.res
res = [{
'text': res[key],
'name': key,
'box': {}
} for key in res]
elif billModel == '身份证':
res = idcard.idcard(result)
res = res.res
res = [{
'text': res[key],
'name': key,
'box': {}
} for key in res]
os.remove(filelock)
break
return res
