TensorFlow识别复杂验证码以及搭建生产环境（8）—— 基于Python的调用

0x00 基本环境

grpc, tensorflow-serving-client 都要装好

0x01 client.py

import sys
sys.path.insert(0, "./")
from tensorflow_serving_client.protos import predict_pb2, prediction_service_pb2
# import cv2
from grpc.beta import implementations
import tensorflow as tf
from tensorflow.python.framework import dtypes
import time
from PIL import Image
import numpy as np

def convert2gray(img):
if len(img.shape) > 2:
gray = np.mean(img, -1)
# 上面的转法较快，正规转法如下
# r, g, b = img[:,:,0], img[:,:,1], img[:,:,2]
# gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
# int gray = (int) (0.3 * r + 0.59 * g + 0.11 * b);
return gray
else:
return img

#注意，如果在windows下测试，文件名可能需要写成：im_name = r"测试文件目录\文件名"
if __name__ == '__main__':
#文件读取和处理
# im = cv2.imread(im_name)
# re_im = cv2.resize(im, (224, 224), interpolation=cv2.INTER_CUBIC)

captcha_image = Image.open('D:\\jwxt\\new_test\\4am7.jpg')
captcha_image = np.array(captcha_image)
captcha_image = convert2gray(captcha_image)
captcha_image = captcha_image.flatten() / 255
#记个时
start_time = time.time()
#建立连接
channel = implementations.insecure_channel("192.168.190.128", 9000)
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
request = predict_pb2.PredictRequest()
#这里由保存和运行时定义，第一个是运行时配置的模型名，第二个是保存时输入的方法名
request.model_spec.name = "crack_captcha"
#入参参照入参定义
request.inputs["image"].ParseFromString(tf.contrib.util.make_tensor_proto(captcha_image, dtype=dtypes.float32, shape=[1, 60, 180, 1]).SerializeToString())
#第二个参数是最大等待时间，因为这里是block模式访问的
response = stub.Predict(request, 40.0)
results = {}
for key in response.outputs:
tensor_proto = response.outputs[key]
nd_array = tf.contrib.util.make_ndarray(tensor_proto)
results[key] = nd_array
#print("cost %ss to predict: " % (time.time() - start_time))
tmp = ''
for i in results['out']:
# print(i)
if i < 10:
char_code = i + ord('0')
elif i < 36:
char_code = i - 10 + ord('a')
tmp = tmp + chr(char_code)
print(tmp)

0x02 输出

0x03 扩展

from PIL import Image
import numpy as np
import time
from grpc.beta import implementations
from tensorflow_serving_client.protos import predict_pb2, prediction_service_pb2
import tensorflow as tf
from tensorflow.python.framework import dtypes

def convert2gray(img):
if len(img.shape) > 2:
gray = np.mean(img, -1)
# 上面的转法较快，正规转法如下
# r, g, b = img[:,:,0], img[:,:,1], img[:,:,2]
# gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
# int gray = (int) (0.3 * r + 0.59 * g + 0.11 * b);
return gray
else:
return img

def crack_jwxt_captcha(file_name):
#文件读取和处理
# im = cv2.imread(im_name)
# re_im = cv2.resize(im, (224, 224), interpolation=cv2.INTER_CUBIC)

captcha_image = Image.open(file_name)
captcha_image = np.array(captcha_image)
captcha_image = convert2gray(captcha_image)
captcha_image = captcha_image.flatten() / 255
#记个时
start_time = time.time()
#建立连接
channel = implementations.insecure_channel("192.168.190.128", 9000)
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
request = predict_pb2.PredictRequest()
#这里由保存和运行时定义，第一个是运行时配置的模型名，第二个是保存时输入的方法名
request.model_spec.name = "crack_captcha"
#入参参照入参定义
request.inputs["image"].ParseFromString(tf.contrib.util.make_tensor_proto(captcha_image, dtype=dtypes.float32, shape=[1, 60, 180, 1]).SerializeToString())
#第二个参数是最大等待时间，因为这里是block模式访问的
response = stub.Predict(request, 40.0)
results = {}
for key in response.outputs:
tensor_proto = response.outputs[key]
nd_array = tf.contrib.util.make_ndarray(tensor_proto)
results[key] = nd_array
print("cost %ss to predict: " % (time.time() - start_time))
tmp = ''
for i in results['out']:
print(i)
if i < 10:
char_code = i + ord('0')
elif i < 36:
char_code = i - 10 + ord('a')
tmp = tmp + chr(char_code)
return tmp

这样就能很方便的被其他模块调用

在控制器这块这么写就行

@csrf_exempt
def upload_jwxt_captcha(req):
if req.method == 'POST':
image = req.FILES.get('image', None)
if not image:
result = {'rst': -2, 'msg': '文件上传失败'}
else:
import time
file_name = str(int(time.time() * 1000000)) + '.jpg'
destination = open(os.path.join("./upload", file_name), 'wb+')
for chunk in image.chunks():  # 分块写入文件
destination.write(chunk)
destination.close()

result = {'rst': 0, 'msg': 'ok', 'data': crack_jwxt_captcha("./upload/" + file_name)}
return HttpResponse(json.dumps(result), content_type="application/json")
else:
result = {'rst': -1, 'msg': '当前请求不支持GET方式'}
return HttpResponse(json.dumps(result), content_type="application/json")