@@ -8,15 +8,18 @@ HyperLPR是一个使用深度学习针对对中文车牌识别的实现,与较 | |||
### 更新 | |||
+ 添加端到端的序列识别模型识别率大幅度提升 | |||
+ 添加的端到端模型可以识别 新能源车牌,教练车牌,白色警用车牌 | |||
+ 更新Windows版本的Visual Studio工程。(2017.11.15) | |||
+ 增加cpp版本,目前仅支持标准蓝牌(需要依赖OpenCV 3.3)。 | |||
+ 添加了简单的Android实现 (骁龙835 (*720*x*1280*) 200ms)。 | |||
+ 增加cpp版本,目前仅支持标准蓝牌(需要依赖OpenCV 3.3) | |||
+ 添加了简单的Android实现 (骁龙835 (*720*x*1280*) 200ms) | |||
### 特性 | |||
+ 速度快 720p ,单核 Intel 2.2G CPU (macbook Pro 2015)识别时间 <=140ms 。 | |||
+ 基于端到端的车牌识别无需进行字符分割。 | |||
+ 识别率高 EasyPR数据集上0-error达到 81.75%, 1-error识别率达到 94.1%。 | |||
+ 轻量 总代码量不超1k行。 | |||
@@ -81,13 +84,14 @@ sudo make -j | |||
- [x] 单行蓝牌 | |||
- [x] 单行黄牌 | |||
- [ ] 新能源车牌 | |||
- [x] 新能源车牌 | |||
- [x] 白色警用车牌 | |||
- [x] 使馆/港澳车牌 | |||
- [x] 教练车牌 | |||
- [ ] 双层黄牌 | |||
- [ ] 双层武警 | |||
- [ ] 双层军牌 | |||
- [ ] 农用车牌 | |||
- [ ] 白色警用车牌 | |||
- [ ] 使馆/港澳车牌 | |||
- [ ] 民航车牌 | |||
- [ ] 个性化车牌 | |||
@@ -0,0 +1,64 @@ | |||
#coding=utf-8 | |||
from keras import backend as K | |||
from keras.models import load_model | |||
from keras.layers import * | |||
from captcha.image import ImageCaptcha | |||
import numpy as np | |||
import random | |||
import string | |||
import cv2 | |||
import e2emodel as model | |||
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" | |||
]; | |||
pred_model = model.construct_model("./model/ocr_plate_all_w_rnn_2.h5",) | |||
import time | |||
def fastdecode(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 recognizeOne(src): | |||
# x_tempx= cv2.imread(src) | |||
x_tempx = src | |||
# x_tempx = cv2.bitwise_not(x_tempx) | |||
x_temp = cv2.resize(x_tempx,( 160,40)) | |||
x_temp = x_temp.transpose(1, 0, 2) | |||
t0 = time.time() | |||
y_pred = pred_model.predict(np.array([x_temp])) | |||
y_pred = y_pred[:,2:,:] | |||
# plt.imshow(y_pred.reshape(16,66)) | |||
# plt.show() | |||
# | |||
# cv2.imshow("x_temp",x_tempx) | |||
# cv2.waitKey(0) | |||
return fastdecode(y_pred) | |||
# | |||
# | |||
# import os | |||
# | |||
# path = "/Users/yujinke/PycharmProjects/HyperLPR_Python_web/cache/finemapping" | |||
# for filename in os.listdir(path): | |||
# if filename.endswith(".png") or filename.endswith(".jpg") or filename.endswith(".bmp"): | |||
# x = os.path.join(path,filename) | |||
# recognizeOne(x) | |||
# # print time.time() - t0 | |||
# | |||
# # cv2.imshow("x",x) | |||
# # cv2.waitKey() |
@@ -0,0 +1,35 @@ | |||
from keras import backend as K | |||
from keras.models import * | |||
from keras.layers import * | |||
from captcha.image import ImageCaptcha | |||
import e2e | |||
def ctc_lambda_func(args): | |||
y_pred, labels, input_length, label_length = args | |||
y_pred = y_pred[:, 2:, :] | |||
return K.ctc_batch_cost(labels, y_pred, input_length, label_length) | |||
def construct_model(model_path): | |||
input_tensor = Input((None, 40, 3)) | |||
x = input_tensor | |||
base_conv = 32 | |||
for i in range(3): | |||
x = Conv2D(base_conv * (2 ** (i)), (3, 3),padding="same")(x) | |||
x = BatchNormalization()(x) | |||
x = Activation('relu')(x) | |||
x = MaxPooling2D(pool_size=(2, 2))(x) | |||
x = Conv2D(256, (5, 5))(x) | |||
x = BatchNormalization()(x) | |||
x = Activation('relu')(x) | |||
x = Conv2D(1024, (1, 1))(x) | |||
x = BatchNormalization()(x) | |||
x = Activation('relu')(x) | |||
x = Conv2D(len(e2e.chars)+1, (1, 1))(x) | |||
x = Activation('softmax')(x) | |||
base_model = Model(inputs=input_tensor, outputs=x) | |||
base_model.load_weights(model_path) | |||
return base_model |
@@ -22,7 +22,7 @@ sys.setdefaultencoding("utf-8") | |||
fontC = ImageFont.truetype("./Font/platech.ttf", 14, 0); | |||
import e2e | |||
#寻找车牌左右边界 | |||
def find_edge(image): | |||
@@ -91,7 +91,7 @@ def horizontalSegmentation(image): | |||
#打上boundingbox和标签 | |||
def drawRectBox(image,rect,addText): | |||
cv2.rectangle(image, (int(rect[0]), int(rect[1])), (int(rect[0] + rect[2]), int(rect[1] + rect[3])), (0,0, 255), 2,cv2.LINE_AA) | |||
cv2.rectangle(image, (int(rect[0]-1), int(rect[1])-16), (int(rect[0] + 80), int(rect[1])), (0, 0, 255), -1, | |||
cv2.rectangle(image, (int(rect[0]-1), int(rect[1])-16), (int(rect[0] + 115), int(rect[1])), (0, 0, 255), -1, | |||
cv2.LINE_AA) | |||
img = Image.fromarray(image) | |||
@@ -129,7 +129,7 @@ def RecognizePlateJson(image): | |||
ptype = td.SimplePredict(plate) | |||
if ptype>0 and ptype<5: | |||
if ptype>0 and ptype<4: | |||
plate = cv2.bitwise_not(plate) | |||
# demo = verticalEdgeDetection(plate) | |||
@@ -137,7 +137,7 @@ def RecognizePlateJson(image): | |||
image_rgb = fv.finemappingVertical(image_rgb) | |||
cache.verticalMappingToFolder(image_rgb) | |||
# print time.time() - t1,"校正" | |||
print "e2e:",e2e.recognizeOne(image_rgb)[0] | |||
image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY) | |||
@@ -184,6 +184,40 @@ def RecognizePlateJson(image): | |||
def SimpleRecognizePlateByE2E(image): | |||
t0 = time.time() | |||
images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1) | |||
res_set = [] | |||
for j,plate in enumerate(images): | |||
plate, rect, origin_plate =plate | |||
# plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY) | |||
plate =cv2.resize(plate,(136,36*2)) | |||
res,confidence = e2e.recognizeOne(origin_plate) | |||
print "res",res | |||
t1 = time.time() | |||
ptype = td.SimplePredict(plate) | |||
if ptype>0 and ptype<5: | |||
# pass | |||
plate = cv2.bitwise_not(plate) | |||
image_rgb = fm.findContoursAndDrawBoundingBox(plate) | |||
image_rgb = fv.finemappingVertical(image_rgb) | |||
image_rgb = fv.finemappingVertical(image_rgb) | |||
cache.verticalMappingToFolder(image_rgb) | |||
cv2.imwrite("./"+str(j)+".jpg",image_rgb) | |||
res,confidence = e2e.recognizeOne(image_rgb) | |||
print res,confidence | |||
res_set.append([[],res,confidence]) | |||
if confidence>0.7: | |||
image = drawRectBox(image, rect, res+" "+str(round(confidence,3))) | |||
return image,res_set | |||
def SimpleRecognizePlate(image): | |||
t0 = time.time() | |||
@@ -200,8 +234,10 @@ def SimpleRecognizePlate(image): | |||
plate = cv2.bitwise_not(plate) | |||
image_rgb = fm.findContoursAndDrawBoundingBox(plate) | |||
image_rgb = fv.finemappingVertical(image_rgb) | |||
cache.verticalMappingToFolder(image_rgb) | |||
print "e2e:", e2e.recognizeOne(image_rgb) | |||
image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_RGB2GRAY) | |||
# image_gray = horizontalSegmentation(image_gray) | |||