Python车牌检测识别代码（感觉还可以）

#coding=utf-8 import cv2 import numpy as np # from matplotlib import pyplot as plt import scipy.ndimage.filters as f import scipy import time import scipy.signal as l from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPool2D from keras.optimizers import SGD from keras import backend as K K.set_image_dim_ordering('tf') def Getmodel_tensorflow(nb_classes): # nb_classes = len(charset) img_rows, img_cols = 23, 23 # number of convolutional filters to use nb_filters = 16 # size of pooling area for max pooling nb_pool = 2 # convolution kernel size nb_conv = 3 # x = np.load('x.npy') # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes) # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3 # weight = dict(zip(range(3063), weight / weight.mean())) # 调整权重，高频字优先 model = Sequential() model.add(Conv2D(nb_filters, (nb_conv, nb_conv),input_shape=(img_rows, img_cols,1))) model.add(Activation('relu')) model.add(MaxPool2D(pool_size=(nb_pool, nb_pool))) model.add(Conv2D(nb_filters, (nb_conv, nb_conv))) model.add(Activation('relu')) model.add(MaxPool2D(pool_size=(nb_pool, nb_pool))) model.add(Flatten()) model.add(Dense(256)) model.add(Dropout(0.5)) model.add(Activation('relu')) model.add(Dense(nb_classes)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) return model def Getmodel_tensorflow_light(nb_classes): # nb_classes = len(charset) img_rows, img_cols = 23, 23 # number of convolutional filters to use nb_filters = 8 # size of pooling area for max pooling nb_pool = 2 # convolution kernel size nb_conv = 3 # x = np.load('x.npy') # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes) # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3 # weight = dict(zip(range(3063), weight / weight.mean())) # 调整权重，高频字优先 model = Sequential() model.add(Conv2D(nb_filters, (nb_conv, nb_conv),input_shape=(img_rows, img_cols, 1))) model.add(Activation('relu')) model.add(MaxPool2D(pool_size=(nb_pool, nb_pool))) model.add(Conv2D(nb_filters, (nb_conv * 2, nb_conv * 2))) model.add(Activation('relu')) model.add(MaxPool2D(pool_size=(nb_pool, nb_pool))) model.add(Flatten()) model.add(Dense(32)) # model.add(Dropout(0.25)) model.add(Activation('relu')) model.add(Dense(nb_classes)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model model = Getmodel_tensorflow_light(3) model2 = Getmodel_tensorflow(3) import os model.load_weights("./model/char_judgement1.h5") # model.save("./model/char_judgement1.h5") model2.load_weights("./model/char_judgement.h5") # model2.save("./model/char_judgement.h5") model = model2 def get_median(data): data = sorted(data) size = len(data) # print size if size % 2 == 0: # 判断列表长度为偶数 median = (data[size//2]+data[size//2-1])//2 data[0] = median if size % 2 == 1: # 判断列表长度为奇数 median = data[(size-1)//2] data[0] = median return data[0] import time def searchOptimalCuttingPoint(rgb,res_map,start,width_boundingbox,interval_range): t0 = time.time() # # for x in xrange(10): # res_map = np.vstack((res_map,res_map[-1])) length = res_map.shape[0] refine_s = -2; if width_boundingbox>20: refine_s = -9 score_list = [] interval_big = int(width_boundingbox * 0.3) # p = 0 for zero_add in range(start,start+50,3): # for interval_small in xrange(-0,width_boundingbox/2): for i in range(-8,int(width_boundingbox/1)-8): for refine in range(refine_s, int(width_boundingbox/2+3)): p1 = zero_add# this point is province p2 = p1 + width_boundingbox +refine # p3 = p2 + width_boundingbox + interval_big+i+1 p4 = p3 + width_boundingbox +refine p5 = p4 + width_boundingbox +refine p6 = p5 + width_boundingbox +refine p7 = p6 + width_boundingbox +refine if p7>=length: continue score = res_map[p1][2]*3 -(res_map[p3][1]+res_map[p4][1]+res_map[p5][1]+res_map[p6][1]+res_map[p7][1])+7 # print score score_list.append([score,[p1,p2,p3,p4,p5,p6,p7]]) p+=1 print(p) score_list = sorted(score_list , key=lambda x:x[0]) # for one in score_list[-1][1]: # cv2.line(debug,(one,0),(one,36),(255,0,0),1) # # # cv2.imshow("one",debug) # cv2.waitKey(0) # print("寻找最佳点",time.time()-t0) return score_list[-1] import sys sys.path.append('../') from . import recognizer as cRP from . import niblack_thresholding as nt def refineCrop(sections,width=16): new_sections = [] for section in sections: # cv2.imshow("section¡",section) # cv2.blur(section,(3,3),3) sec_center = np.array([section.shape[1]/2,section.shape[0]/2]) binary_niblack = nt.niBlackThreshold(section,17,-0.255) #imagex, contours, hierarchy = cv2.findContours(binary_niblack,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) boxs = [] for contour in contours: x,y,w,h = cv2.boundingRect(contour) ratio = w/float(h) if ratio<1 and h>36*0.4 and y<16\ : box = [x,y,w,h] boxs.append([box,np.array([x+w/2,y+h/2])]) # cv2.rectangle(section,(x,y),(x+w,y+h),255,1) # print boxs dis_ = np.array([ ((one[1]-sec_center)**2).sum() for one in boxs]) if len(dis_)==0: kernal = [0, 0, section.shape[1], section.shape[0]] else: kernal = boxs[dis_.argmin()][0] center_c = (kernal[0]+kernal[2]/2,kernal[1]+kernal[3]/2) w_2 = int(width/2) h_2 = kernal[3]/2 if center_c[0] - w_2< 0: w_2 = center_c[0] new_box = [center_c[0] - w_2,kernal[1],width,kernal[3]] # print new_box[2]/float(new_box[3]) if new_box[2]/float(new_box[3])>0.5: # print "异常" h = int((new_box[2]/0.35 )/2) if h>35: h = 35 new_box[1] = center_c[1]- h if new_box[1]<0: new_box[1] = 1 new_box[3] = h*2 section = section[int(new_box[1]):int(new_box[1]+new_box[3]), int(new_box[0]):int(new_box[0]+new_box[2])] # cv2.imshow("section",section) # cv2.waitKey(0) new_sections.append(section) # print new_box return new_sections def slidingWindowsEval(image): windows_size = 16; stride = 1 height= image.shape[0] t0 = time.time() data_sets = [] for i in range(0,image.shape[1]-windows_size+1,stride): data = image[0:height,i:i+windows_size] data = cv2.resize(data,(23,23)) # cv2.imshow("image",data) data = cv2.equalizeHist(data) data = data.astype(np.float)/255 data= np.expand_dims(data,3) data_sets.append(data) res = model2.predict(np.array(data_sets)) print("分割",time.time() - t0) pin = res p = 1 - (res.T)[1] p = f.gaussian_filter1d(np.array(p,dtype=np.float),3) lmin = l.argrelmax(np.array(p),order = 3)[0] interval = [] for i in range(len(lmin)-1): interval.append(lmin[i+1]-lmin[i]) if(len(interval)>3): mid = get_median(interval) else: return [] pin = np.array(pin) res = searchOptimalCuttingPoint(image,