def _imgETL(self, imgBase64):
# Convert the base64 to PIL image
__img = readImage(imgBase64, outFormat='PIL')
__img = __img.convert("RGB")
# Resize the image to the given width
(x, y) = __img.size
if x < y:
x_s = 600
y_s = int(y * x_s / x)
else:
y_s = 600
x_s = int(x * y_s / y)
__img0 = __img.resize((x_s, y_s), Image.ANTIALIAS)
# Image Skew handling
angle = self._deskew.determine_skew(__img0)
logging.info('Image angle = ' + str(angle))
__img1 = self._deskew.deskew(__img0, angle)
__imgGrey = __img1.convert('L')
# Capture Face image
__faceImg = captureFace(__img0)
# Get Doc Type by running predict model
docType, confidence = self._getDocType(__imgGrey)
return __imgGrey, __faceImg, docType, confidence
def _docTypePredict(self, imgBase64):
# Convert the base64 to PIL image
__img = readImage(imgBase64, outFormat='PIL')
__imgGrey = __img.convert('L')
# Get Doc Type by running predict model
docType, confidence = _docClass.predict(__imgGrey)
return docType, confidence
def _getDocType(self, imgBase64):
params = {
"api_id": "demo_id",
"timestamp": get_current_timestamp(),
"image": readImage(imgBase64, outFormat='Base64'),
"sign": "123"
}
params['sign'] = self._sign._sign(params)
url = 'http://127.0.0.1:8089/v1/docType'
headers = {'content-type': "application/json"}
response = requests.post(url, data=json.dumps(params), headers=headers)
__rslt = json.loads(response.content.decode())
return __rslt['data']['docType'], __rslt['data']['confidence']
def _getOcrResult(self, vendor, img):
ocrBoxes = []
if vendor == 'Baidu':
tmpImg = readImage(img, outFormat='Bytes')
__ai = baidu_ai()
__rslt = __ai.ocr_general(tmpImg)
if 'words_result_num' in __rslt.keys():
if __rslt['words_result_num'] > 0:
for box in __rslt['words_result']:
if box['words']:
ocrBoxes.append([
int(box['location']['left']),
int(box['location']['top']),
int(box['location']['width']),
int(box['location']['height']), box['words']
])
elif vendor == 'Tencent':
tmpImg = readImage(img, outFormat='Base64')
__ai = tencent_ai()
__ret, __rslt = __ai.ocr_generalocr(tmpImg)
if __ret == 0:
for box in __rslt['data']['item_list']:
if box['itemstring']:
ocrBoxes.append([
int(box['itemcoord'][0]['x']),
int(box['itemcoord'][0]['y']),
int(box['itemcoord'][0]['width']),
int(box['itemcoord'][0]['height']),
box['itemstring']
])
elif vendor == 'JD':
tmpImg = readImage(img, outFormat='Base64')
__ai = jd_ai()
__ret, __rslt = __ai.ocr_generalocr_v2(tmpImg)
if 'result' in __rslt.keys():
if __rslt['result']['code'] == 0:
for box in __rslt['result']['resultData']:
if box['text']:
ocrBoxes.append([
int(box['location']['x']),
int(box['location']['y']),
int(box['location']['width']),
int(box['location']['height']), box['text']
])
elif vendor == 'Face++':
tmpImg = readImage(img, outFormat='Base64')
__ai = faceplusplus_ai()
__rslt = __ai.recognizeText(imgBase64=tmpImg)
if __rslt['status'] == 'success':
for box in __rslt['result']:
_tmp_X1 = box['child-objects'][0]['position'][0]['x']
_tmp_Y1 = box['child-objects'][0]['position'][0]['y']
_tmp_X2 = box['child-objects'][-1]['position'][2]['x']
_tmp_Y2 = box['child-objects'][-1]['position'][2]['y']
ocrBoxes.append([
int(_tmp_X1),
int(_tmp_Y1),
int(_tmp_X2 - _tmp_X1),
int(_tmp_Y2 - _tmp_Y1), box['value']
])
elif vendor == 'Face++_Template':
if str(self._docType) in FACEPLUSPLUS_TEMPLATE.keys():
tmpImg = readImage(img, outFormat='Base64')
__ai = faceplusplus_ai()
__rslt = __ai.OCRTemplate(templateID=FACEPLUSPLUS_TEMPLATE[str(
self._docType)],
imgBase64=tmpImg)
if __rslt['status'] == 'success':
for box in __rslt['result']:
ocrBoxes.append(
[box['key'], ''.join(box['value']['text'])])
elif vendor == 'Netease':
tmpImg = readImage(img, outFormat='Base64')
__ai = netease_ai()
__rslt = __ai.ocr_generalocr(tmpImg)
if __rslt['status'] == '000000':
for box in __rslt['detail']['ocr']:
_position = box['position'].split(",")
_tmp_X1 = int(float(_position[0]))
_tmp_Y1 = int(float(_position[1]))
_tmp_X2 = int(float(_position[2]))
_tmp_Y2 = int(float(_position[3]))
ocrBoxes.append([
_tmp_X1, _tmp_Y1, _tmp_X2 - _tmp_X1, _tmp_Y2 - _tmp_Y1,
box['recognition']
])
ocrBoxes = sorted(ocrBoxes, key=itemgetter(1, 0))
logging.info(vendor + ' OCR Boxes = ' + str(ocrBoxes))
return ocrBoxes
self._setParams(__params, 'appId', self._app_id)
self._setParams(__params, 'timestamp', timestamps)
self._setParams(__params, 'nonce', timestamps)
self._setParams(__params, 'image', imageBase64)
self._setParams(__params, 'type', 'base64')
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
'cache-control': 'no-cache',
'appId': self._app_id,
'nonce': timestamps,
'timestamp': timestamps,
'sign': self._genSignString(__params)
}
__params.pop('appId')
__params.pop('timestamp')
__params.pop('nonce')
__rslt = self._invoke(url=__url, data=__params, headers=headers)
return __rslt
if __name__ == '__main__':
ai = netease_ai()
image_data = readImage(r"..\..\templates\Samples\14 - AU Driver - NSW.jpg",
outFormat='Base64')
print(ai.ocr_generalocr(image_data))
__rslt = self._request(self.__recognizeText, data)
return __rslt
@logging_elapsed_time('Face++ OCR - OCRTemplate')
def OCRTemplate(self, templateID, imgPath=None, imgBase64=None):
"""
调用者提供图片文件或者图片URL，进行图片分析，找出图片中出现的文字信息
"""
data = {}
data['template_id'] = templateID
if imgBase64:
data['image_base64'] = imgBase64
else:
data['image_base64'] = self._get_img_content(imgPath)
return self._request(self.__ocrTemplate, data)
if __name__ == "__main__":
ai = faceplusplus_ai()
_img = readImage(r"..\..\templates\Samples\04 - MACAU_ID_0021.jpg",
outFormat='Base64')
# rslt = ai.recognizeText(imgBase64=_img)
rslt = ai.OCRTemplate(templateID=1573524413, imgBase64=_img)
print(rslt)
'''
======================================
银行卡OCR识别 - 识别银行卡上面的字段信息
======================================
'''
@logging_elapsed_time('Baidu OCR - Bank Card')
def ocr_creditcardocr(self, image):
# 定义参数变量
options = {}
# 调用人脸属性检测接口
result = self.ocr.bankcard(image, options)
json_rslt = self.load_json(result)
return json_rslt
if __name__ == '__main__':
imgFile = r'..\..\templates\Samples\001.jpg'
img = Image.open(imgFile)
img = img.convert('RGB')
imgBase64 = readImage(img, outFormat='Base64')
__ai = baidu_ai()
__rslt = __ai.ocr_general(base64.b64decode(imgBase64))
print(__rslt)