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HyperLRP是一个开源的、基于深度学习高性能中文车牌识别库,由北京智云视图科技有限公司开发,支持PHP、C/C++、Python语言,Windows/Mac/Linux/Android/IOS 平台。 spread retract

http://www.zeusee.com

  • C++ 69.5%
  • Java 19.7%
  • C 4.3%
  • CMake 2.3%
  • Python 2.2%
  • Other 2.0%
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HyperLPRLite.py 6.77 KB
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da-niao-dan authored 2018-06-19 16:11 . Update
#coding=utf-8
import cv2
import numpy as np
from keras import backend as K
from keras.models import *
from keras.layers import *
chars = [u"京", u"沪", u"津", u"渝", u"冀", u"晋", u"蒙", u"辽", u"吉", u"黑", u"苏", u"浙", u"皖", u"闽", u"赣", u"鲁", u"豫", u"鄂", u"湘", u"粤", u"桂",
u"琼", u"川", u"贵", u"云", u"藏", u"陕", u"甘", u"青", u"宁", u"新", u"0", u"1", u"2", u"3", u"4", u"5", u"6", u"7", u"8", u"9", u"A",
u"B", u"C", u"D", u"E", u"F", u"G", u"H", u"J", u"K", u"L", u"M", u"N", u"P", u"Q", u"R", u"S", u"T", u"U", u"V", u"W", u"X",
u"Y", u"Z",u"港",u"学",u"使",u"警",u"澳",u"挂",u"军",u"北",u"南",u"广",u"沈",u"兰",u"成",u"济",u"海",u"民",u"航",u"空"
]
class LPR():
def __init__(self,model_detection,model_finemapping,model_seq_rec):
self.watch_cascade = cv2.CascadeClassifier(model_detection)
self.modelFineMapping = self.model_finemapping()
self.modelFineMapping.load_weights(model_finemapping)
self.modelSeqRec = self.model_seq_rec(model_seq_rec)
def computeSafeRegion(self,shape,bounding_rect):
top = bounding_rect[1] # y
bottom = bounding_rect[1] + bounding_rect[3] # y + h
left = bounding_rect[0] # x
right = bounding_rect[0] + bounding_rect[2] # x + w
min_top = 0
max_bottom = shape[0]
min_left = 0
max_right = shape[1]
if top < min_top:
top = min_top
if left < min_left:
left = min_left
if bottom > max_bottom:
bottom = max_bottom
if right > max_right:
right = max_right
return [left,top,right-left,bottom-top]
def cropImage(self,image,rect):
x, y, w, h = self.computeSafeRegion(image.shape,rect)
return image[y:y+h,x:x+w]
def detectPlateRough(self,image_gray,resize_h = 720,en_scale =1.08 ,top_bottom_padding_rate = 0.05):
if top_bottom_padding_rate>0.2:
print("error:top_bottom_padding_rate > 0.2:",top_bottom_padding_rate)
exit(1)
height = image_gray.shape[0]
padding = int(height*top_bottom_padding_rate)
scale = image_gray.shape[1]/float(image_gray.shape[0])
image = cv2.resize(image_gray, (int(scale*resize_h), resize_h))
image_color_cropped = image[padding:resize_h-padding,0:image_gray.shape[1]]
image_gray = cv2.cvtColor(image_color_cropped,cv2.COLOR_RGB2GRAY)
watches = self.watch_cascade.detectMultiScale(image_gray, en_scale, 2, minSize=(36, 9),maxSize=(36*40, 9*40))
cropped_images = []
for (x, y, w, h) in watches:
x -= w * 0.14
w += w * 0.28
y -= h * 0.15
h += h * 0.3
cropped = self.cropImage(image_color_cropped, (int(x), int(y), int(w), int(h)))
cropped_images.append([cropped,[x, y+padding, w, h]])
return cropped_images
def fastdecode(self,y_pred):
results = ""
confidence = 0.0
table_pred = y_pred.reshape(-1, len(chars)+1)
res = table_pred.argmax(axis=1)
for i,one in enumerate(res):
if one<len(chars) and (i==0 or (one!=res[i-1])):
results+= chars[one]
confidence+=table_pred[i][one]
confidence/= len(results)
return results,confidence
def model_seq_rec(self,model_path):
width, height, n_len, n_class = 164, 48, 7, len(chars)+ 1
rnn_size = 256
input_tensor = Input((164, 48, 3))
x = input_tensor
base_conv = 32
for i in range(3):
x = Conv2D(base_conv * (2 ** (i)), (3, 3))(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
conv_shape = x.get_shape()
x = Reshape(target_shape=(int(conv_shape[1]), int(conv_shape[2] * conv_shape[3])))(x)
x = Dense(32)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
gru_1 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru1')(x)
gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(x)
gru1_merged = add([gru_1, gru_1b])
gru_2 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)
gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)
x = concatenate([gru_2, gru_2b])
x = Dropout(0.25)(x)
x = Dense(n_class, kernel_initializer='he_normal', activation='softmax')(x)
base_model = Model(inputs=input_tensor, outputs=x)
base_model.load_weights(model_path)
return base_model
def model_finemapping(self):
input = Input(shape=[16, 66, 3]) # change this shape to [None,None,3] to enable arbitraty shape input
x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
x = Activation("relu", name='relu1')(x)
x = MaxPool2D(pool_size=2)(x)
x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x)
x = Activation("relu", name='relu2')(x)
x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
x = Activation("relu", name='relu3')(x)
x = Flatten()(x)
output = Dense(2,name = "dense")(x)
output = Activation("relu", name='relu4')(output)
model = Model([input], [output])
return model
def finemappingVertical(self,image,rect):
resized = cv2.resize(image,(66,16))
resized = resized.astype(np.float)/255
res_raw= self.modelFineMapping.predict(np.array([resized]))[0]
res =res_raw*image.shape[1]
res = res.astype(np.int)
H,T = res
H-=3
if H<0:
H=0
T+=2;
if T>= image.shape[1]-1:
T= image.shape[1]-1
rect[2] -= rect[2]*(1-res_raw[1] + res_raw[0])
rect[0]+=res[0]
image = image[:,H:T+2]
image = cv2.resize(image, (int(136), int(36)))
return image,rect
def recognizeOne(self,src):
x_tempx = src
x_temp = cv2.resize(x_tempx,( 164,48))
x_temp = x_temp.transpose(1, 0, 2)
y_pred = self.modelSeqRec.predict(np.array([x_temp]))
y_pred = y_pred[:,2:,:]
return self.fastdecode(y_pred)
def SimpleRecognizePlateByE2E(self,image):
images = self.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
res_set = []
for j,plate in enumerate(images):
plate, rect =plate
image_rgb,rect_refine = self.finemappingVertical(plate,rect)
res,confidence = self.recognizeOne(image_rgb)
res_set.append([res,confidence,rect_refine])
return res_set

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