def general_ocr():
upload_image = request.form['image']
# print(upload_image)
# 是否进行方向检测
detect_direction = request.form['detect_direction']
# print(detect_direction)
image = base64.b64decode(upload_image)
image = np.fromstring(image, np.uint8)
# image = cv2.imdecode(image, cv2.IMREAD_COLOR)
# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = io.BytesIO(image)
image = Image.open(image).convert('RGB')
image = np.array(image)
# print(image)
'''
jobid = uuid.uuid1().__str__()
path = 'test/{}.jpg'.format(jobid)
with open(path, 'wb') as f:
f.write(image)
img = cv2.imread(path) ##GBR
'''
timeTake = time.time()
_, result, angle = model.model(
image,
detectAngle=detect_direction, ##是否进行文字方向检测，通过web传参控制
config=dict(
MAX_HORIZONTAL_GAP=200, ##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=0.2, # 是文字的概率
TEXT_PROPOSALS_NMS_THRESH=0.3,
TEXT_LINE_NMS_THRESH=0.99, ##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
alph=0.1, ##对检测的文本行进行向右、左延伸的倍数
)
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(image, angle, res) ##修正box
timeTake = time.time() - timeTake
print({'res': res, 'timeTake': round(timeTake, 4)})
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 getTextList(self, img, angle, result):
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(np.copy(img), angle, res)##修正box
textStrings = ''
for each in res:
textStrings += each["text"] + ' '
return textStrings
def find_word(imgpath):
time.sleep(0.5)
img = cv2.imread(imgpath)
_, result, angle = model.model(img,
detectAngle=True,
config=dict(MAX_HORIZONTAL_GAP=50,
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),
leftAdjust=True,
rightAdjust=True,
alph=0.01)
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
txtpath = os.path.join('result',
imgpath.split('/')[1].split('.')[0] + '.txt')
print(res)
with open(txtpath, 'w') as f:
for n in res:
str_temp = n['text']
f.write(str_temp)
f.write('\n')
f.close()
if os.path.exists(imgpath):
os.remove(imgpath)
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):
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 getWordRecognition(self, img_file, bill_model):
billModel = bill_model
textAngle = True ##文字检测
textLine = False ##只进行单行识别
img = cv2.imread(img_file) ##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
_, result, angle = model(
img,
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
alph=0.01, ##对检测的文本行进行向右、左延伸的倍数
)
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 == '身份证':
res = idcard.idcard(result)
res = res.res
res = [{
'text': res[key],
'name': key,
'box': {}
} for key in res]
elif billModel == '驾驶证':
res = drivinglicense.drivinglicense(result)
res = res.res
res = [{
'text': res[key],
'name': key,
'box': {}
} for key in res]
elif billModel == '行驶证':
res = vehiclelicense.vehiclelicense(result)
res = res.res
res = [{
'text': res[key],
'name': key,
'box': {}
} for key in res]
elif billModel == '银行卡':
res = bankcard.bankcard(result)
res = res.res
res = [{
'text': res[key],
'name': key,
'box': {}
} for key in res]
elif billModel == '手写体':
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 == '车牌':
res = vehicleplate.vehicleplate(result)
res = res.res
res = [{
'text': res[key],
'name': key,
'box': {}
} for key in res]
timeTake = time.time() - timeTake
return {'res': res, 'timeTake': round(timeTake, 4)}
def getWordRecognition(self, img_file, bill_model):
billModel = bill_model
textAngle = True ##文字检测
textLine = False ##只进行单行识别
text = ''
file_name = os.path.basename(img_file)
file_path = os.path.dirname(img_file)
# img = cv2.imread(img_file)##GBR
img,is_exif,H,W = self.setExif(img_file)
if is_exif is True:
textAngle = False
# 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
_,result,angle= model(img,
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
alph=0.01,##对检测的文本行进行向右、左延伸的倍数
)
if billModel=='' or billModel=='通用OCR' :
text = self.getTextList(img,angle, result)
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(np.copy(img),angle, res)##修正box
com_res = res
elif billModel=='身份证':
res = idcard.idcard(result)
res = res.res
res =[ {'text':res[key],'name':key,'box':{}} for key in res]
text = self.getTextList(img,angle, result)
result = union_rbox(result,0.2)
com_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)]
com_res = adjust_box_to_origin(np.copy(img),angle, com_res)##修正box
elif billModel=='驾驶证':
res = drivinglicense.drivinglicense(result)
res = res.res
res =[ {'text':res[key],'name':key,'box':{}} for key in res]
text = self.getTextList(img,angle, result)
result = union_rbox(result,0.2)
com_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)]
com_res = adjust_box_to_origin(np.copy(img),angle, com_res)##修正box
elif billModel=='行驶证':
res = vehiclelicense.vehiclelicense(result)
res = res.res
res =[ {'text':res[key],'name':key,'box':{}} for key in res]
text = self.getTextList(img,angle, result)
result = union_rbox(result,0.2)
com_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)]
com_res = adjust_box_to_origin(np.copy(img),angle, com_res)##修正box
elif billModel=='营业执照':
res = businesslicense.businesslicense(result)
res = res.res
res =[ {'text':res[key],'name':key,'box':{}} for key in res]
text = self.getTextList(img,angle, result)
result = union_rbox(result,0.2)
com_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)]
com_res = adjust_box_to_origin(np.copy(img),angle, com_res)##修正box
elif billModel=='银行卡':
res = bankcard.bankcard(result)
res = res.res
res =[ {'text':res[key],'name':key,'box':{}} for key in res]
text = self.getTextList(img,angle, result)
result = union_rbox(result,0.2)
com_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)]
com_res = adjust_box_to_origin(np.copy(img),angle, com_res)##修正box
elif billModel=='手写体':
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(np.copy(img),angle, res)##修正box
text = self.getTextList(img,angle, result)
elif billModel=='车牌':
res = vehicleplate.vehicleplate(result)
res = res.res
res =[ {'text':res[key],'name':key,'box':{}} for key in res]
text = self.getTextList(img,angle, result)
result = union_rbox(result,0.2)
com_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)]
com_res = adjust_box_to_origin(np.copy(img),angle, com_res)##修正box
elif billModel=='名片':
res = businesscard.businesscard(result)
res = res.res
res =[ {'text':res[key],'name':key,'box':{}} for key in res]
text = self.getTextList(img,angle, result)
result = union_rbox(result,0.2)
com_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)]
com_res = adjust_box_to_origin(np.copy(img),angle, com_res)##修正box
timeTake = time.time()-timeTake
#draw box in to original image
drawBoxes = []
draw_filename = file_name.split('.')[0] + '_drawed.' + file_name.split('.')[1]
drawPath = os.path.join(file_path,draw_filename)
drawUrl = settings.FILE_URL + settings.MEDIA_URL + 'photos' + '/' + draw_filename
# img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
if len(com_res) > 0:
for arr in com_res:
drawBoxes.append(arr["box"])
drawImg = draw_boxes(np.array(img),drawBoxes)
cv2.imwrite(drawPath, drawImg)
return {'res':res,'timeTake':round(timeTake,4), 'text':text, 'com_res': com_res, 'drawUrl': drawUrl}
def currency_invoice(img, arg, arg2, model_flg):
from main import TextOcrModel
angle_detect = None
model = TextOcrModel(ocr, text_detect, angle_detect)
# print(arg[12],'---------------------',type(arg[12]))
if model_flg == True:
model = TextOcrModel(ocr, text_detect, angle_detect)
rotate_img = color_filter(img, color_thre=255, mode='less')
img = color_filter(rotate_img, color_thre=50, mode='more')
if lab == '-1':
result, angle, im_show = model.model_CRAFT(
img,
scale=arg[0],
detectAngle=False, ##是否进行文字方向检测
MAX_HORIZONTAL_GAP=arg[2], ##字符之间的最大间隔 #30
MIN_V_OVERLAPS=arg[3], #0.5
TEXT_PROPOSALS_MIN_SCORE=arg[4], #[0.5, 0.2, 0.7],
leftAdjustAlph=arg[6], ##
rightAdjustAlph=arg[7], ##
Adjustbox=arg[5], ##
pixel_filter=arg[8], ##
batch_by_1=arg[9],
scoremap_enhance_pixel=arg[10],
)
elif lab == '0':
result, angle, im_show = model.model_CRAFT(
img,
scale=arg[0],
detectAngle=False, ##是否进行文字方向检测
MAX_HORIZONTAL_GAP=arg[2], ##字符之间的最大间隔 #30
MIN_V_OVERLAPS=arg[3], #0.5
TEXT_PROPOSALS_MIN_SCORE=arg[4], #[0.5, 0.2, 0.7],
leftAdjustAlph=arg[6], ##
rightAdjustAlph=arg[7], ##
Adjustbox=arg[5], ##
pixel_filter=arg[8], ## 参数一是过滤宽，第二个参数是过滤高
batch_by_1=arg[9],
scoremap_enhance_pixel=arg[10],
)
# ti2 = time.asctime(time.localtime(time.time()))
# print("本地时间为 :", ti2)
else:
model = TextOcrModel(ocr, text_detect_p, angle_detect)
rotate_img = color_filter(img, color_thre=255, mode='less')
img = color_filter(rotate_img, color_thre=50, mode='more')
# ------------------------变换---------------------------
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
edges = cv.Canny(gray, 50, 150, apertureSize=3)
# 霍夫变换
lines = cv.HoughLines(edges, 1, np.pi / 180, 0)
for rho, theta in lines[0]:
a = np.cos(theta)
b = np.sin(theta)
x0 = a * rho
y0 = b * rho
x1 = int(x0 + 1000 * (-b))
y1 = int(y0 + 1000 * (a))
x2 = int(x0 - 1000 * (-b))
y2 = int(y0 - 1000 * (a))
# print(x1, '---------', x2, '---------', x2, '---------', y2)
if x1 == x2 or y1 == y2 or y2 == -1000:
rotate_img = img
else:
t = float(y2 - y1) / (x2 - x1)
rotate_angle = math.degrees(math.atan(t))
if rotate_angle > 45:
rotate_angle = -90 + rotate_angle
elif rotate_angle < -45:
rotate_angle = 90 + rotate_angle
rotate_img = ndimage.rotate(img, rotate_angle)
result, angle, im_show = model.model_PSENET(
rotate_img,
scale=arg2[0],
maxScale=arg2[1],
detectAngle=arg2[2], ##是否进行文字方向检测
MAX_HORIZONTAL_GAP=arg2[3], ##字符之间的最大间隔
MIN_V_OVERLAPS=arg2[4],
TEXT_PROPOSALS_MIN_SCORE=arg2[5],
leftAdjustAlph=arg2[6], ##
rightAdjustAlph=arg2[7], ##
Adjustbox=arg2[8], ##
)
# 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
return res, im_show
