├── .idea ├── HyperLPR_Python.iml ├── misc.xml ├── modules.xml └── workspace.xml ├── Readme.md ├── benchmark.py ├── dataset ├── 0.jpg ├── 1.jpg ├── 10.jpg ├── 12.JPG ├── 14.JPG ├── 2.jpg ├── 24.jpg ├── 25.JPG ├── 3144391.png ├── 4.jpg ├── 5.jpg ├── 6.jpg ├── 7.jpg ├── 8.jpg └── 9.jpg ├── hyperlpr ├── __init__.py ├── deskew.py ├── detect.py ├── finemapping.py ├── pipline.py ├── recognizer.py └── segmentation.py ├── model ├── cascade.xml ├── char_judgement.h5 └── char_rec.h5 ├── res ├── 1.png ├── 10.png ├── 2.png ├── 3.png ├── 4.png ├── 5.png ├── 6.png ├── 7.png ├── 8.png └── 9.png └── simple_.py /.idea/HyperLPR_Python.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 12 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 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-------------------------------------------------------------------------------- /Readme.md: -------------------------------------------------------------------------------- 1 | ## HyperLPR Python 2 | 3 | 仓库已经移至：https://github.com/zeusees/HyperLPR 4 | -------------------------------------------------------------------------------- /benchmark.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/szad670401/HyperLPR_python/0b17acd2165aa6815c78525dbcdba2d7de80a3eb/benchmark.py -------------------------------------------------------------------------------- /dataset/0.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/szad670401/HyperLPR_python/0b17acd2165aa6815c78525dbcdba2d7de80a3eb/dataset/0.jpg -------------------------------------------------------------------------------- /dataset/1.jpg: 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/hyperlpr/detect.py: -------------------------------------------------------------------------------- 1 | 2 | import cv2 3 | import numpy as np 4 | 5 | 6 | 7 | watch_cascade = cv2.CascadeClassifier('./model/cascade.xml') 8 | 9 | 10 | def computeSafeRegion(shape,bounding_rect): 11 | top = bounding_rect[1] # y 12 | bottom = bounding_rect[1] + bounding_rect[3] # y + h 13 | left = bounding_rect[0] # x 14 | right = bounding_rect[0] + bounding_rect[2] # x + w 15 | 16 | min_top = 0 17 | max_bottom = shape[0] 18 | min_left = 0 19 | max_right = shape[1] 20 | 21 | # print "computeSateRegion input shape",shape 22 | if top < min_top: 23 | top = min_top 24 | # print "tap top 0" 25 | if left < min_left: 26 | left = min_left 27 | # print "tap left 0" 28 | 29 | if bottom > max_bottom: 30 | bottom = max_bottom 31 | #print "tap max_bottom max" 32 | if right > max_right: 33 | right = max_right 34 | #print "tap max_right max" 35 | 36 | # print "corr",left,top,right,bottom 37 | return [left,top,right-left,bottom-top] 38 | 39 | 40 | def cropped_from_image(image,rect): 41 | x, y, w, h = computeSafeRegion(image.shape,rect) 42 | return image[y:y+h,x:x+w] 43 | 44 | 45 | def detectPlateRough(image_gray,resize_h = 720,en_scale =1.06 ): 46 | scale = image_gray.shape[1]/float(image_gray.shape[0]) 47 | image = cv2.resize(image_gray, (int(scale*resize_h), resize_h)) 48 | image_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) 49 | watches = watch_cascade.detectMultiScale(image_gray, en_scale, 1, minSize=(36, 9)) 50 | 51 | cropped_images = [] 52 | for (x, y, w, h) in watches: 53 | x -= w * 0.1; 54 | w += w * 0.2 55 | y -= h * 0.6; 56 | h += h * 1.1; 57 | 58 | cropped = cropped_from_image(image, (int(x), int(y), int(w), int(h))) 59 | cropped_images.append(cropped) 60 | return cropped_images 61 | -------------------------------------------------------------------------------- /hyperlpr/finemapping.py: -------------------------------------------------------------------------------- 1 | 2 | import cv2 3 | import numpy as np 4 | 5 | from skimage.filters import (threshold_otsu, threshold_niblack, 6 | threshold_sauvola) 7 | 8 | 9 | def fitLine_ransac(pts,zero_add = 0 ): 10 | if len(pts)>=2: 11 | [vx, vy, x, y] = cv2.fitLine(pts, cv2.DIST_HUBER, 0, 0.01, 0.01) 12 | lefty = int((-x * vy / vx) + y) 13 | righty = int(((136- x) * vy / vx) + y) 14 | return lefty+30+zero_add,righty+30+zero_add 15 | return 0,0 16 | 17 | 18 | def findContoursAndDrawBoundingBox(gray_image): 19 | 20 | 21 | line_upper = []; 22 | line_lower = []; 23 | 24 | line_experiment = [] 25 | 26 | grouped_rects = [] 27 | for k in np.linspace(-1.8, -0.2,5): 28 | thresh_niblack = threshold_niblack(gray_image, window_size=25, k=k) 29 | binary_niblack = gray_image > thresh_niblack 30 | binary_niblack = binary_niblack.astype(np.uint8) * 255 31 | imagex, contours, hierarchy = cv2.findContours(binary_niblack.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) 32 | for contour in contours: 33 | bdbox = cv2.boundingRect(contour) 34 | if (bdbox[3]/float(bdbox[2])>0.5 and bdbox[3]*bdbox[2]>100 and bdbox[3]*bdbox[2]<1300) or (bdbox[3]/float(bdbox[2])>3 and bdbox[3]*bdbox[2]<100): 35 | # cv2.rectangle(rgb,(bdbox[0],bdbox[1]),(bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]),(255,0,0),1) 36 | line_upper.append([bdbox[0],bdbox[1]]) 37 | line_lower.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]]) 38 | line_experiment.append([bdbox[0],bdbox[1]]) 39 | line_experiment.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]]) 40 | # grouped_rects.append(bdbox) 41 | 42 | rgb = cv2.copyMakeBorder(gray_image,30,30,0,0,cv2.BORDER_REPLICATE) 43 | leftyA, rightyA = fitLine_ransac(np.array(line_lower),2) 44 | rows,cols = rgb.shape[:2] 45 | 46 | # rgb = cv2.line(rgb, (cols - 1, rightyA), (0, leftyA), (0, 0, 255), 1,cv2.LINE_AA) 47 | 48 | leftyB, rightyB = fitLine_ransac(np.array(line_upper),-2) 49 | 50 | rows,cols = rgb.shape[:2] 51 | 52 | # rgb = cv2.line(rgb, (cols - 1, rightyB), (0, leftyB), (0,255, 0), 1,cv2.LINE_AA) 53 | pts_map1 = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]]) 54 | pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]]) 55 | 56 | mat = cv2.getPerspectiveTransform(pts_map1,pts_map2) 57 | image = cv2.warpPerspective(rgb,mat,(136,36)) 58 | return image -------------------------------------------------------------------------------- /hyperlpr/pipline.py: -------------------------------------------------------------------------------- 1 | #coding=utf-8 2 | import detect 3 | import finemapping as fm 4 | 5 | import segmentation 6 | 7 | import cv2 8 | 9 | 10 | import time 11 | 12 | def SimpleRecognizePlate(image): 13 | t0 = time.time() 14 | images = detect.detectPlateRough(image) 15 | for image in images: 16 | image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) 17 | image =cv2.resize(image,(136,36)) 18 | 19 | image_gray = fm.findContoursAndDrawBoundingBox(image) 20 | cv2.imshow("image,",image_gray) 21 | cv2.waitKey(0) 22 | blocks,res,confidence = segmentation.slidingWindowsEval(image_gray) 23 | if confidence>4.5: 24 | print "车牌:",res,"置信度:",confidence 25 | else: 26 | print "不确定的车牌:", res, "置信度:", confidence 27 | 28 | print time.time() - t0,"s" 29 | 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /hyperlpr/recognizer.py: -------------------------------------------------------------------------------- 1 | #coding=utf-8 2 | from keras.models import Sequential 3 | from keras.layers import Dense, Dropout, Activation, Flatten 4 | from keras.layers import Convolution2D, MaxPooling2D 5 | from keras.optimizers import SGD 6 | from keras import backend as K 7 | 8 | K.set_image_dim_ordering('tf') 9 | 10 | 11 | import cv2 12 | import numpy as np 13 | 14 | 15 | 16 | index = {u"京": 0, u"沪": 1, u"津": 2, u"渝": 3, u"冀": 4, u"晋": 5, u"蒙": 6, u"辽": 7, u"吉": 8, u"黑": 9, u"苏": 10, u"浙": 11, u"皖": 12, 17 | u"闽": 13, u"赣": 14, u"鲁": 15, u"豫": 16, u"鄂": 17, u"湘": 18, u"粤": 19, u"桂": 20, u"琼": 21, u"川": 22, u"贵": 23, u"云": 24, 18 | u"藏": 25, u"陕": 26, u"甘": 27, u"青": 28, u"宁": 29, u"新": 30, u"0": 31, u"1": 32, u"2": 33, u"3": 34, u"4": 35, u"5": 36, 19 | u"6": 37, u"7": 38, u"8": 39, u"9": 40, u"A": 41, u"B": 42, u"C": 43, u"D": 44, u"E": 45, u"F": 46, u"G": 47, u"H": 48, 20 | u"J": 49, u"K": 50, u"L": 51, u"M": 52, u"N": 53, u"P": 54, u"Q": 55, u"R": 56, u"S": 57, u"T": 58, u"U": 59, u"V": 60, 21 | u"W": 61, u"X": 62, u"Y": 63, u"Z": 64,u"港":65,u"学":66 ,u"O":67 ,u"使":68,u"警":69,u"澳":70,u"挂":71}; 22 | 23 | chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂", 24 | "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", 25 | "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", 26 | "Q", "R", "S", "T", "U", "V", "W", "X", 27 | "Y", "Z","港","学","O","使","警","澳","挂" ]; 28 | 29 | 30 | 31 | def Getmodel_tensorflow(nb_classes): 32 | # nb_classes = len(charset) 33 | 34 | img_rows, img_cols = 23, 23 35 | # number of convolutional filters to use 36 | nb_filters = 32 37 | # size of pooling area for max pooling 38 | nb_pool = 2 39 | # convolution kernel size 40 | nb_conv = 3 41 | 42 | # x = np.load('x.npy') 43 | # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes) 44 | # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3 45 | # weight = dict(zip(range(3063), weight / weight.mean())) # 调整权重，高频字优先 46 | 47 | model = Sequential() 48 | model.add(Convolution2D(32, 5, 5, 49 | border_mode='valid', 50 | input_shape=(img_rows, img_cols,1))) 51 | model.add(Activation('relu')) 52 | model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool))) 53 | model.add(Dropout(0.25)) 54 | model.add(Convolution2D(32, 3, 3)) 55 | model.add(Activation('relu')) 56 | model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool))) 57 | model.add(Dropout(0.25)) 58 | model.add(Convolution2D(512, 3, 3)) 59 | # model.add(Activation('relu')) 60 | # model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool))) 61 | # model.add(Dropout(0.25)) 62 | model.add(Flatten()) 63 | model.add(Dense(512)) 64 | model.add(Activation('relu')) 65 | model.add(Dropout(0.5)) 66 | model.add(Dense(nb_classes)) 67 | model.add(Activation('softmax')) 68 | model.compile(loss='categorical_crossentropy', 69 | optimizer='adam', 70 | metrics=['accuracy']) 71 | return model 72 | 73 | 74 | 75 | model = Getmodel_tensorflow(65) 76 | 77 | model.load_weights("./model/char_rec.h5") 78 | 79 | 80 | def SimplePredict(image,pos): 81 | image = cv2.resize(image, (23, 23)) 82 | image = cv2.equalizeHist(image) 83 | image = image.astype(np.float) / 255 84 | image -= image.mean() 85 | image = np.expand_dims(image, 3) 86 | res = np.array(model.predict(np.array([image]))[0]) 87 | 88 | zero_add = 0 ; 89 | 90 | if pos==0: 91 | res = res[:31] 92 | elif pos==1: 93 | res = res[31+10:65] 94 | zero_add = 31+10 95 | else: 96 | res = res[31:] 97 | zero_add = 31 98 | 99 | max_id = res.argmax() 100 | 101 | 102 | return res.max(),chars[max_id+zero_add],max_id+zero_add 103 | 104 | -------------------------------------------------------------------------------- /hyperlpr/segmentation.py: -------------------------------------------------------------------------------- 1 | #coding=utf-8 2 | import cv2 3 | import numpy as np 4 | 5 | from matplotlib import pyplot as plt 6 | import scipy.ndimage.filters as f 7 | import scipy 8 | 9 | import time 10 | import scipy.signal as l 11 | 12 | 13 | 14 | 15 | 16 | from keras.models import Sequential 17 | from keras.layers import Dense, Dropout, Activation, Flatten 18 | from keras.layers import Convolution2D, MaxPooling2D 19 | from keras.optimizers import SGD 20 | from keras import backend as K 21 | 22 | K.set_image_dim_ordering('tf') 23 | 24 | 25 | def Getmodel_tensorflow(nb_classes): 26 | # nb_classes = len(charset) 27 | img_rows, img_cols = 23, 23 28 | # number of convolutional filters to use 29 | nb_filters = 16 30 | # size of pooling area for max pooling 31 | nb_pool = 2 32 | # convolution kernel size 33 | nb_conv = 3 34 | # x = np.load('x.npy') 35 | # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes) 36 | # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3 37 | # weight = dict(zip(range(3063), weight / weight.mean())) # 调整权重，高频字优先 38 | 39 | model = Sequential() 40 | model.add(Convolution2D(nb_filters, nb_conv, nb_conv, 41 | border_mode='valid', 42 | input_shape=(img_rows, img_cols,1))) 43 | model.add(Activation('relu')) 44 | model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool))) 45 | model.add(Convolution2D(nb_filters, nb_conv, nb_conv)) 46 | model.add(Activation('relu')) 47 | model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool))) 48 | model.add(Flatten()) 49 | model.add(Dense(256)) 50 | model.add(Dropout(0.5)) 51 | 52 | model.add(Activation('relu')) 53 | model.add(Dense(nb_classes)) 54 | model.add(Activation('softmax')) 55 | model.compile(loss='categorical_crossentropy', 56 | optimizer='sgd', 57 | metrics=['accuracy']) 58 | return model 59 | 60 | 61 | 62 | model = Getmodel_tensorflow(3) 63 | import os 64 | model.load_weights("./model/char_judgement.h5") 65 | 66 | def get_median(data): 67 | data = sorted(data) 68 | size = len(data) 69 | # print size 70 | 71 | if size % 2 == 0: # 判断列表长度为偶数 72 | median = (data[size//2]+data[size//2-1])/2 73 | data[0] = median 74 | if size % 2 == 1: # 判断列表长度为奇数 75 | median = data[(size-1)//2] 76 | data[0] = median 77 | return data[0] 78 | 79 | def searchOptimalCuttingPoint(rgb,res_map,start,width_boundingbox,interval_range): 80 | # 81 | # for x in xrange(10): 82 | # res_map = np.vstack((res_map,res_map[-1])) 83 | length = res_map.shape[0] 84 | refine_s = -2; 85 | 86 | if width_boundingbox>20: 87 | refine_s = -9 88 | 89 | 90 | 91 | score_list = [] 92 | interval_big = int(width_boundingbox * 0.3) # 93 | 94 | for zero_add in xrange(start,start+50): 95 | # for interval_small in xrange(-0,width_boundingbox/2): 96 | for i in xrange(-8,int(width_boundingbox/1)-8): 97 | for refine in xrange(refine_s,width_boundingbox/2+10): 98 | 99 | 100 | p1 = zero_add# this point is province 101 | p2 = p1 + width_boundingbox +refine # 102 | p3 = p2 + width_boundingbox + interval_big+i+1 103 | p4 = p3 + width_boundingbox +refine 104 | p5 = p4 + width_boundingbox +refine 105 | p6 = p5 + width_boundingbox +refine 106 | p7 = p6 + width_boundingbox +refine 107 | 108 | list = [p1,p2,p3,p4,p5,p6,p7] 109 | # bak = rgb.copy() 110 | # 111 | # # 112 | # for one in list: 113 | # cv2.line(bak,(one,1),(one,36),255) 114 | # cv2.imshow("imag", bak) 115 | # cv2.waitKey(0) 116 | if p7>=length: 117 | 118 | # print"break" 119 | # print p7 120 | continue 121 | # score_list = sorted(score_list, key=lambda x: x[0]) 122 | # return score_list[0] 123 | 124 | 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])+7 125 | # print score 126 | score_list.append([score,[p1,p2,p3,p4,p5,p6,p7]]) 127 | 128 | score_list = sorted(score_list , key=lambda x:x[0]) 129 | # debug = cv2.cvtColor(rgb,cv2.COLOR_GRAY2RGB) 130 | # for one in score_list[-1][1]: 131 | # cv2.line(debug,(one,0),(one,36),(255,0,0),1) 132 | # # 133 | # cv2.imshow("one",debug) 134 | # cv2.waitKey(0) 135 | 136 | return score_list[-1] 137 | 138 | 139 | import sys 140 | 141 | sys.path.append('../') 142 | import recognizer as cRP 143 | from skimage.filters import (threshold_otsu, threshold_niblack, 144 | threshold_sauvola) 145 | 146 | def refineCrop(sections,width=16): 147 | new_sections = [] 148 | for section in sections: 149 | # cv2.imshow("section¡",section) 150 | 151 | cv2.blur(section,(3,3),3) 152 | 153 | sec_center = np.array([section.shape[1]/2,section.shape[0]/2]) 154 | thresh_niblack = threshold_niblack(section, window_size=17, k=-0.2) 155 | binary_niblack = section > thresh_niblack 156 | binary_niblack = binary_niblack.astype(np.uint8) * 255 157 | imagex, contours, hierarchy = cv2.findContours(binary_niblack,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) 158 | boxs = [] 159 | for contour in contours: 160 | x,y,w,h = cv2.boundingRect(contour) 161 | 162 | ratio = w/float(h) 163 | if ratio<1 and h>36*0.4 and y<16\ 164 | : 165 | box = [x,y,w,h] 166 | 167 | boxs.append([box,np.array([x+w/2,y+h/2])]) 168 | # cv2.rectangle(section,(x,y),(x+w,y+h),255,1) 169 | 170 | 171 | 172 | 173 | # print boxs 174 | 175 | dis_ = np.array([ ((one[1]-sec_center)**2).sum() for one in boxs]) 176 | if len(dis_)==0: 177 | kernal = [0, 0, section.shape[1], section.shape[0]] 178 | else: 179 | kernal = boxs[dis_.argmin()][0] 180 | 181 | center_c = (kernal[0]+kernal[2]/2,kernal[1]+kernal[3]/2) 182 | w_2 = int(width/2) 183 | h_2 = kernal[3]/2 184 | 185 | if center_c[0] - w_2< 0: 186 | w_2 = center_c[0] 187 | new_box = [center_c[0] - w_2,kernal[1],width,kernal[3]] 188 | # print new_box[2]/float(new_box[3]) 189 | if new_box[2]/float(new_box[3])>0.6: 190 | # print "异常" 191 | h = int((new_box[2]/0.35 )/2) 192 | if h>35: 193 | h = 35 194 | new_box[1] = center_c[1]- h 195 | if new_box[1]<0: 196 | new_box[1] = 1 197 | 198 | new_box[3] = h*2 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | section = section[new_box[1]:new_box[1]+new_box[3],new_box[0]:new_box[0]+new_box[2]] 208 | # cv2.imshow("section",section) 209 | # cv2.waitKey(0) 210 | new_sections.append(section) 211 | # print new_box 212 | 213 | 214 | return new_sections 215 | 216 | 217 | 218 | 219 | 220 | def slidingWindowsEval(image): 221 | windows_size = 16; 222 | stride = 1 223 | height= image.shape[0] 224 | # print image.shape[1] 225 | p = [] 226 | ch_p = [] 227 | 228 | gain = [] 229 | pin=[] 230 | for i in range(0,image.shape[1]-windows_size+1,stride): 231 | data = image[0:height,i:i+windows_size] 232 | data = cv2.resize(data,(23,23)) 233 | # cv2.imshow("image",data) 234 | data = cv2.equalizeHist(data) 235 | data = data.astype(np.float)/255 236 | data= np.expand_dims(data,3) 237 | res = model.predict(np.array([data])) 238 | pin.append(res[0]) 239 | 240 | p.append(res[0][0]+res[0][2]) 241 | ch_p.append(res[0][2]) 242 | 243 | gain.append(res.argmax()) 244 | 245 | p = np.insert(p,0,0); 246 | p = np.insert(p,len(p),0); 247 | p = f.gaussian_filter1d(np.array(p,dtype=np.float),3) 248 | # print p 249 | sum = image.sum(axis=0) 250 | 251 | 252 | lmin = l.argrelmax(np.array(p),order = 3)[0] 253 | interval = [] 254 | for i in xrange(len(lmin)-1): 255 | interval.append(lmin[i+1]-lmin[i]) 256 | 257 | if(len(interval)>3): 258 | mid = get_median(interval) 259 | else: 260 | return [] 261 | 262 | ch_p = np.array(ch_p) 263 | pin = np.array(pin) 264 | res = searchOptimalCuttingPoint(image,pin,0,mid,3) 265 | 266 | cutting_pts = res[1] 267 | last = cutting_pts[-1] + mid 268 | if last < image.shape[1]: 269 | cutting_pts.append(last) 270 | else: 271 | cutting_pts.append(image.shape[1]-1) 272 | 273 | 274 | name = "" 275 | confidence =0.00; 276 | seg_block = [] 277 | for x in xrange(1,len(cutting_pts)): 278 | if x != len(cutting_pts)-1 and x!=1: 279 | section = image[0:36,cutting_pts[x-1]-2:cutting_pts[x]+2] 280 | elif x==1: 281 | 282 | c_head = cutting_pts[x - 1]- 2 283 | if c_head<0: 284 | c_head=0 285 | c_tail = cutting_pts[x] + 2 286 | 287 | section = image[0:36, c_head:c_tail] 288 | elif x==len(cutting_pts)-1: 289 | end = cutting_pts[x] 290 | diff = image.shape[1]-end 291 | 292 | c_head = cutting_pts[x - 1] 293 | c_tail = cutting_pts[x] 294 | 295 | if diff<7 : 296 | section = image[0:36, c_head-5:c_tail+5] 297 | 298 | else: 299 | diff-=1 300 | 301 | section = image[0:36, c_head - diff:c_tail + diff] 302 | 303 | 304 | 305 | elif x==2: 306 | section = image[0:36, cutting_pts[x - 1] - 3:cutting_pts[x-1]+ mid] 307 | else: 308 | section = image[0:36,cutting_pts[x-1]:cutting_pts[x]] 309 | seg_block.append(section) 310 | refined = refineCrop(seg_block,mid-1) 311 | for i,one in enumerate(refined): 312 | 313 | 314 | res_pre = cRP.SimplePredict(one, i ) 315 | confidence+=res_pre[0] 316 | 317 | name+= res_pre[1] 318 | 319 | return seg_block,name,confidence 320 | -------------------------------------------------------------------------------- /model/cascade.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | BOOST 5 | HAAR 6 | 13 7 | 51 8 | 9 | GAB 10 | 9.9500000476837158e-01 11 | 4.4999998807907104e-01 12 | 9.4999999999999996e-01 13 | 1 14 | 100 15 | 16 | 0 17 | 1 18 | BASIC 19 | 13 20 | 21 | 22 | <_> 23 | 5 24 | -1.4522267580032349e+00 25 | 26 | <_> 27 | 28 | 0 -1 54 -3.6055929958820343e-02 29 | 30 | 6.0774326324462891e-01 -8.8722378015518188e-01 31 | <_> 32 | 33 | 0 -1 107 -2.0387675613164902e-02 34 | 35 | 7.7247995138168335e-01 -5.8999586105346680e-01 36 | <_> 37 | 38 | 0 -1 124 1.3326611369848251e-02 39 | 40 | -3.7632712721824646e-01 8.5003948211669922e-01 41 | <_> 42 | 43 | 0 -1 134 -4.2829606682062149e-03 44 | 45 | 8.8654613494873047e-01 -3.2926064729690552e-01 46 | <_> 47 | 48 | 0 -1 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1187 | 11 2 3 1 2. 1188 | 0 1189 | <_> 1190 | 1191 | <_> 1192 | 8 1 12 5 -1. 1193 | <_> 1194 | 12 1 4 5 3. 1195 | 0 1196 | <_> 1197 | 1198 | <_> 1199 | 8 6 3 3 -1. 1200 | <_> 1201 | 9 6 1 3 3. 1202 | 0 1203 | <_> 1204 | 1205 | <_> 1206 | 8 6 3 4 -1. 1207 | <_> 1208 | 9 6 1 4 3. 1209 | 0 1210 | <_> 1211 | 1212 | <_> 1213 | 9 0 8 7 -1. 1214 | <_> 1215 | 13 0 4 7 2. 1216 | 0 1217 | <_> 1218 | 1219 | <_> 1220 | 9 0 8 12 -1. 1221 | <_> 1222 | 13 0 4 12 2. 1223 | 0 1224 | <_> 1225 | 1226 | <_> 1227 | 9 0 8 8 -1. 1228 | <_> 1229 | 9 4 8 4 2. 1230 | 0 1231 | <_> 1232 | 1233 | <_> 1234 | 9 1 12 10 -1. 1235 | <_> 1236 | 13 1 4 10 3. 1237 | 0 1238 | <_> 1239 | 1240 | <_> 1241 | 9 1 12 11 -1. 1242 | <_> 1243 | 13 1 4 11 3. 1244 | 0 1245 | <_> 1246 | 1247 | <_> 1248 | 9 4 4 6 -1. 1249 | <_> 1250 | 9 4 2 3 2. 1251 | <_> 1252 | 11 7 2 3 2. 1253 | 0 1254 | <_> 1255 | 1256 | <_> 1257 | 9 5 4 4 -1. 1258 | <_> 1259 | 9 5 2 2 2. 1260 | <_> 1261 | 11 7 2 2 2. 1262 | 0 1263 | <_> 1264 | 1265 | <_> 1266 | 10 0 5 8 -1. 1267 | <_> 1268 | 10 4 5 4 2. 1269 | 0 1270 | <_> 1271 | 1272 | <_> 1273 | 10 1 9 10 -1. 1274 | <_> 1275 | 13 1 3 10 3. 1276 | 0 1277 | <_> 1278 | 1279 | <_> 1280 | 10 3 41 8 -1. 1281 | <_> 1282 | 10 7 41 4 2. 1283 | 0 1284 | <_> 1285 | 1286 | <_> 1287 | 10 6 6 5 -1. 1288 | <_> 1289 | 13 6 3 5 2. 1290 | 0 1291 | <_> 1292 | 1293 | <_> 1294 | 10 7 3 1 -1. 1295 | <_> 1296 | 11 7 1 1 3. 1297 | 0 1298 | <_> 1299 | 1300 | <_> 1301 | 10 7 2 2 -1. 1302 | <_> 1303 | 11 7 1 2 2. 1304 | 0 1305 | <_> 1306 | 1307 | <_> 1308 | 10 9 1 3 -1. 1309 | <_> 1310 | 10 10 1 1 3. 1311 | 0 1312 | <_> 1313 | 1314 | <_> 1315 | 10 11 6 2 -1. 1316 | <_> 1317 | 10 12 6 1 2. 1318 | 0 1319 | <_> 1320 | 1321 | <_> 1322 | 13 1 4 6 -1. 1323 | <_> 1324 | 13 3 4 2 3. 1325 | 0 1326 | <_> 1327 | 1328 | <_> 1329 | 14 0 3 1 -1. 1330 | <_> 1331 | 15 0 1 1 3. 1332 | 0 1333 | <_> 1334 | 1335 | <_> 1336 | 14 0 2 2 -1. 1337 | <_> 1338 | 15 0 1 2 2. 1339 | 0 1340 | <_> 1341 | 1342 | <_> 1343 | 14 2 2 9 -1. 1344 | <_> 1345 | 14 5 2 3 3. 1346 | 0 1347 | <_> 1348 | 1349 | <_> 1350 | 14 3 22 3 -1. 1351 | <_> 1352 | 14 4 22 1 3. 1353 | 0 1354 | <_> 1355 | 1356 | <_> 1357 | 15 0 36 9 -1. 1358 | <_> 1359 | 27 0 12 9 3. 1360 | 0 1361 | <_> 1362 | 1363 | <_> 1364 | 15 1 1 12 -1. 1365 | <_> 1366 | 15 7 1 6 2. 1367 | 0 1368 | <_> 1369 | 1370 | <_> 1371 | 15 12 2 1 -1. 1372 | <_> 1373 | 16 12 1 1 2. 1374 | 0 1375 | <_> 1376 | 1377 | <_> 1378 | 16 0 12 1 -1. 1379 | <_> 1380 | 20 0 4 1 3. 1381 | 0 1382 | <_> 1383 | 1384 | <_> 1385 | 16 9 18 4 -1. 1386 | <_> 1387 | 22 9 6 4 3. 1388 | 0 1389 | <_> 1390 | 1391 | <_> 1392 | 17 0 25 2 -1. 1393 | <_> 1394 | 17 1 25 1 2. 1395 | 0 1396 | <_> 1397 | 1398 | <_> 1399 | 17 4 6 1 -1. 1400 | <_> 1401 | 19 4 2 1 3. 1402 | 0 1403 | <_> 1404 | 1405 | <_> 1406 | 18 0 4 1 -1. 1407 | <_> 1408 | 20 0 2 1 2. 1409 | 0 1410 | <_> 1411 | 1412 | <_> 1413 | 18 1 16 2 -1. 1414 | <_> 1415 | 18 2 16 1 2. 1416 | 0 1417 | <_> 1418 | 1419 | <_> 1420 | 18 7 3 3 -1. 1421 | <_> 1422 | 19 7 1 3 3. 1423 | 0 1424 | <_> 1425 | 1426 | <_> 1427 | 19 0 12 1 -1. 1428 | <_> 1429 | 23 0 4 1 3. 1430 | 0 1431 | <_> 1432 | 1433 | <_> 1434 | 19 0 10 4 -1. 1435 | <_> 1436 | 19 2 10 2 2. 1437 | 0 1438 | <_> 1439 | 1440 | <_> 1441 | 20 1 6 10 -1. 1442 | <_> 1443 | 22 1 2 10 3. 1444 | 0 1445 | <_> 1446 | 1447 | <_> 1448 | 20 1 6 11 -1. 1449 | <_> 1450 | 22 1 2 11 3. 1451 | 0 1452 | <_> 1453 | 1454 | <_> 1455 | 20 1 6 12 -1. 1456 | <_> 1457 | 22 1 2 12 3. 1458 | 0 1459 | <_> 1460 | 1461 | <_> 1462 | 20 1 28 9 -1. 1463 | <_> 1464 | 20 4 28 3 3. 1465 | 0 1466 | <_> 1467 | 1468 | <_> 1469 | 21 0 9 1 -1. 1470 | <_> 1471 | 24 0 3 1 3. 1472 | 0 1473 | <_> 1474 | 1475 | <_> 1476 | 21 10 18 3 -1. 1477 | <_> 1478 | 27 10 6 3 3. 1479 | 0 1480 | <_> 1481 | 1482 | <_> 1483 | 22 0 15 1 -1. 1484 | <_> 1485 | 27 0 5 1 3. 1486 | 0 1487 | <_> 1488 | 1489 | <_> 1490 | 22 1 3 11 -1. 1491 | <_> 1492 | 23 1 1 11 3. 1493 | 0 1494 | <_> 1495 | 1496 | <_> 1497 | 22 2 3 9 -1. 1498 | <_> 1499 | 23 2 1 9 3. 1500 | 0 1501 | <_> 1502 | 1503 | <_> 1504 | 22 9 6 1 -1. 1505 | <_> 1506 | 24 9 2 1 3. 1507 | 0 1508 | <_> 1509 | 1510 | <_> 1511 | 23 0 3 1 -1. 1512 | <_> 1513 | 24 0 1 1 3. 1514 | 0 1515 | <_> 1516 | 1517 | <_> 1518 | 23 0 6 1 -1. 1519 | <_> 1520 | 25 0 2 1 3. 1521 | 0 1522 | <_> 1523 | 1524 | <_> 1525 | 23 6 9 1 -1. 1526 | <_> 1527 | 26 6 3 1 3. 1528 | 0 1529 | <_> 1530 | 1531 | <_> 1532 | 24 0 4 6 -1. 1533 | <_> 1534 | 24 0 2 3 2. 1535 | <_> 1536 | 26 3 2 3 2. 1537 | 0 1538 | <_> 1539 | 1540 | <_> 1541 | 24 3 4 2 -1. 1542 | <_> 1543 | 26 3 2 2 2. 1544 | 0 1545 | <_> 1546 | 1547 | <_> 1548 | 24 4 3 5 -1. 1549 | <_> 1550 | 25 4 1 5 3. 1551 | 0 1552 | <_> 1553 | 1554 | <_> 1555 | 24 7 6 3 -1. 1556 | <_> 1557 | 26 7 2 3 3. 1558 | 0 1559 | <_> 1560 | 1561 | <_> 1562 | 25 0 11 3 -1. 1563 | <_> 1564 | 25 1 11 1 3. 1565 | 0 1566 | <_> 1567 | 1568 | <_> 1569 | 25 1 1 4 -1. 1570 | <_> 1571 | 25 3 1 2 2. 1572 | 0 1573 | <_> 1574 | 1575 | <_> 1576 | 25 1 2 4 -1. 1577 | <_> 1578 | 25 1 1 2 2. 1579 | <_> 1580 | 26 3 1 2 2. 1581 | 0 1582 | <_> 1583 | 1584 | <_> 1585 | 25 8 3 2 -1. 1586 | <_> 1587 | 26 8 1 2 3. 1588 | 0 1589 | <_> 1590 | 1591 | <_> 1592 | 26 0 18 3 -1. 1593 | <_> 1594 | 26 1 18 1 3. 1595 | 0 1596 | <_> 1597 | 1598 | <_> 1599 | 26 1 4 4 -1. 1600 | <_> 1601 | 26 1 2 2 2. 1602 | <_> 1603 | 28 3 2 2 2. 1604 | 0 1605 | <_> 1606 | 1607 | <_> 1608 | 26 3 6 2 -1. 1609 | <_> 1610 | 28 3 2 2 3. 1611 | 0 1612 | <_> 1613 | 1614 | <_> 1615 | 26 3 16 9 -1. 1616 | <_> 1617 | 34 3 8 9 2. 1618 | 0 1619 | <_> 1620 | 1621 | <_> 1622 | 26 5 4 4 -1. 1623 | <_> 1624 | 26 5 2 2 2. 1625 | <_> 1626 | 28 7 2 2 2. 1627 | 0 1628 | <_> 1629 | 1630 | <_> 1631 | 27 1 6 11 -1. 1632 | <_> 1633 | 29 1 2 11 3. 1634 | 0 1635 | <_> 1636 | 1637 | <_> 1638 | 27 3 2 6 -1. 1639 | <_> 1640 | 27 3 1 3 2. 1641 | <_> 1642 | 28 6 1 3 2. 1643 | 0 1644 | <_> 1645 | 1646 | <_> 1647 | 27 4 1 4 -1. 1648 | <_> 1649 | 27 6 1 2 2. 1650 | 0 1651 | <_> 1652 | 1653 | <_> 1654 | 27 5 6 7 -1. 1655 | <_> 1656 | 29 5 2 7 3. 1657 | 0 1658 | <_> 1659 | 1660 | <_> 1661 | 27 6 6 6 -1. 1662 | <_> 1663 | 29 6 2 6 3. 1664 | 0 1665 | <_> 1666 | 1667 | <_> 1668 | 27 7 2 2 -1. 1669 | <_> 1670 | 28 7 1 2 2. 1671 | 0 1672 | <_> 1673 | 1674 | <_> 1675 | 27 7 3 2 -1. 1676 | <_> 1677 | 28 7 1 2 3. 1678 | 0 1679 | <_> 1680 | 1681 | <_> 1682 | 27 8 2 2 -1. 1683 | <_> 1684 | 28 8 1 2 2. 1685 | 0 1686 | <_> 1687 | 1688 | <_> 1689 | 29 0 13 2 -1. 1690 | <_> 1691 | 29 1 13 1 2. 1692 | 0 1693 | <_> 1694 | 1695 | <_> 1696 | 29 2 3 6 -1. 1697 | <_> 1698 | 30 2 1 6 3. 1699 | 0 1700 | <_> 1701 | 1702 | <_> 1703 | 29 2 3 10 -1. 1704 | <_> 1705 | 30 2 1 10 3. 1706 | 0 1707 | <_> 1708 | 1709 | <_> 1710 | 29 6 9 1 -1. 1711 | <_> 1712 | 32 6 3 1 3. 1713 | 0 1714 | <_> 1715 | 1716 | <_> 1717 | 29 9 9 4 -1. 1718 | <_> 1719 | 32 9 3 4 3. 1720 | 0 1721 | <_> 1722 | 1723 | <_> 1724 | 30 1 4 4 -1. 1725 | <_> 1726 | 30 1 2 2 2. 1727 | <_> 1728 | 32 3 2 2 2. 1729 | 0 1730 | <_> 1731 | 1732 | <_> 1733 | 31 0 12 2 -1. 1734 | <_> 1735 | 31 1 12 1 2. 1736 | 0 1737 | <_> 1738 | 1739 | <_> 1740 | 31 0 13 2 -1. 1741 | <_> 1742 | 31 1 13 1 2. 1743 | 0 1744 | <_> 1745 | 1746 | <_> 1747 | 31 3 4 6 -1. 1748 | <_> 1749 | 31 3 2 3 2. 1750 | <_> 1751 | 33 6 2 3 2. 1752 | 0 1753 | <_> 1754 | 1755 | <_> 1756 | 31 4 6 1 -1. 1757 | <_> 1758 | 33 4 2 1 3. 1759 | 0 1760 | <_> 1761 | 1762 | <_> 1763 | 31 8 2 4 -1. 1764 | <_> 1765 | 31 8 1 2 2. 1766 | <_> 1767 | 32 10 1 2 2. 1768 | 0 1769 | <_> 1770 | 1771 | <_> 1772 | 32 0 18 3 -1. 1773 | <_> 1774 | 32 1 18 1 3. 1775 | 0 1776 | <_> 1777 | 1778 | <_> 1779 | 32 0 19 3 -1. 1780 | <_> 1781 | 32 1 19 1 3. 1782 | 0 1783 | <_> 1784 | 1785 | <_> 1786 | 32 1 6 4 -1. 1787 | <_> 1788 | 32 1 3 2 2. 1789 | <_> 1790 | 35 3 3 2 2. 1791 | 0 1792 | <_> 1793 | 1794 | <_> 1795 | 32 1 8 2 -1. 1796 | <_> 1797 | 32 1 4 1 2. 1798 | <_> 1799 | 36 2 4 1 2. 1800 | 0 1801 | <_> 1802 | 1803 | <_> 1804 | 32 9 3 1 -1. 1805 | <_> 1806 | 33 9 1 1 3. 1807 | 0 1808 | <_> 1809 | 1810 | <_> 1811 | 33 3 6 4 -1. 1812 | <_> 1813 | 35 3 2 4 3. 1814 | 0 1815 | <_> 1816 | 1817 | <_> 1818 | 33 7 4 6 -1. 1819 | <_> 1820 | 33 7 2 3 2. 1821 | <_> 1822 | 35 10 2 3 2. 1823 | 0 1824 | <_> 1825 | 1826 | <_> 1827 | 33 9 3 1 -1. 1828 | <_> 1829 | 34 9 1 1 3. 1830 | 0 1831 | <_> 1832 | 1833 | <_> 1834 | 34 1 3 9 -1. 1835 | <_> 1836 | 35 1 1 9 3. 1837 | 0 1838 | <_> 1839 | 1840 | <_> 1841 | 34 1 4 11 -1. 1842 | <_> 1843 | 36 1 2 11 2. 1844 | 0 1845 | <_> 1846 | 1847 | <_> 1848 | 34 3 2 8 -1. 1849 | <_> 1850 | 34 3 1 4 2. 1851 | <_> 1852 | 35 7 1 4 2. 1853 | 0 1854 | <_> 1855 | 1856 | <_> 1857 | 34 4 3 4 -1. 1858 | <_> 1859 | 35 4 1 4 3. 1860 | 0 1861 | <_> 1862 | 1863 | <_> 1864 | 35 1 3 11 -1. 1865 | <_> 1866 | 36 1 1 11 3. 1867 | 0 1868 | <_> 1869 | 1870 | <_> 1871 | 35 2 6 7 -1. 1872 | <_> 1873 | 37 2 2 7 3. 1874 | 0 1875 | <_> 1876 | 1877 | <_> 1878 | 36 4 3 6 -1. 1879 | <_> 1880 | 37 4 1 6 3. 1881 | 0 1882 | <_> 1883 | 1884 | <_> 1885 | 36 4 3 7 -1. 1886 | <_> 1887 | 37 4 1 7 3. 1888 | 0 1889 | <_> 1890 | 1891 | <_> 1892 | 36 9 6 2 -1. 1893 | <_> 1894 | 38 9 2 2 3. 1895 | 0 1896 | <_> 1897 | 1898 | <_> 1899 | 38 1 12 2 -1. 1900 | <_> 1901 | 38 2 12 1 2. 1902 | 0 1903 | <_> 1904 | 1905 | <_> 1906 | 38 8 6 2 -1. 1907 | <_> 1908 | 40 8 2 2 3. 1909 | 0 1910 | <_> 1911 | 1912 | <_> 1913 | 38 10 11 3 -1. 1914 | <_> 1915 | 38 11 11 1 3. 1916 | 0 1917 | <_> 1918 | 1919 | <_> 1920 | 40 1 1 3 -1. 1921 | <_> 1922 | 40 2 1 1 3. 1923 | 0 1924 | <_> 1925 | 1926 | <_> 1927 | 41 0 6 12 -1. 1928 | <_> 1929 | 43 0 2 12 3. 1930 | 0 1931 | <_> 1932 | 1933 | <_> 1934 | 41 2 2 8 -1. 1935 | <_> 1936 | 41 2 1 4 2. 1937 | <_> 1938 | 42 6 1 4 2. 1939 | 0 1940 | <_> 1941 | 1942 | <_> 1943 | 41 4 6 2 -1. 1944 | <_> 1945 | 43 4 2 2 3. 1946 | 0 1947 | <_> 1948 | 1949 | <_> 1950 | 41 4 6 4 -1. 1951 | <_> 1952 | 43 4 2 4 3. 1953 | 0 1954 | <_> 1955 | 1956 | <_> 1957 | 43 2 3 10 -1. 1958 | <_> 1959 | 44 2 1 10 3. 1960 | 0 1961 | <_> 1962 | 1963 | <_> 1964 | 44 9 4 4 -1. 1965 | <_> 1966 | 44 9 2 2 2. 1967 | <_> 1968 | 46 11 2 2 2. 1969 | 0 1970 | <_> 1971 | 1972 | <_> 1973 | 47 0 1 6 -1. 1974 | <_> 1975 | 47 2 1 2 3. 1976 | 0 1977 | 1978 | -------------------------------------------------------------------------------- /model/char_judgement.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/szad670401/HyperLPR_python/0b17acd2165aa6815c78525dbcdba2d7de80a3eb/model/char_judgement.h5 -------------------------------------------------------------------------------- /model/char_rec.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/szad670401/HyperLPR_python/0b17acd2165aa6815c78525dbcdba2d7de80a3eb/model/char_rec.h5 -------------------------------------------------------------------------------- /res/1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/szad670401/HyperLPR_python/0b17acd2165aa6815c78525dbcdba2d7de80a3eb/res/1.png 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https://raw.githubusercontent.com/szad670401/HyperLPR_python/0b17acd2165aa6815c78525dbcdba2d7de80a3eb/res/7.png -------------------------------------------------------------------------------- /res/8.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/szad670401/HyperLPR_python/0b17acd2165aa6815c78525dbcdba2d7de80a3eb/res/8.png -------------------------------------------------------------------------------- /res/9.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/szad670401/HyperLPR_python/0b17acd2165aa6815c78525dbcdba2d7de80a3eb/res/9.png -------------------------------------------------------------------------------- /simple_.py: -------------------------------------------------------------------------------- 1 | from hyperlpr import pipline 2 | 3 | 4 | import cv2 5 | 6 | # image1 = cv2.imread("./dataset/0.jpg") 7 | # image2 = cv2.imread("./dataset/1.jpg") 8 | # image3 = cv2.imread("./dataset/5.jpg") 9 | # image4 = cv2.imread("./dataset/6.jpg") 10 | 11 | image5 = cv2.imread("./dataset/3144391.png") 12 | # 13 | # pipline.SimpleRecognizePlate(image4) 14 | # pipline.SimpleRecognizePlate(image3) 15 | pipline.SimpleRecognizePlate(image5) --------------------------------------------------------------------------------