├── .gitattributes
├── .idea
├── LicensePlateRecognition.iml
├── misc.xml
├── modules.xml
├── vcs.xml
└── workspace.xml
├── __pycache__
└── predict.cpython-37.pyc
├── config.js
├── predict.py
├── surface.py
├── svm.dat
├── svmchinese.dat
├── test
├── 1.jpg
├── 2.jpg
├── cAA662F.jpg
├── car3.jpg
├── car4.jpg
├── car5.jpg
├── car7.jpg
├── lLD9016.jpg
├── u=1691206349,1754151067&fm=26&gp=0.jpg
├── u=1955857547,2196176231&fm=26&gp=0.jpg
├── u=3022089789,3948911321&fm=26&gp=0.jpg
├── wA87271.jpg
├── wATH859.jpg
├── wAUB816.jpg
└── 下载.jpg
└── train
├── chars2.7z
└── charsChinese.7z
/.gitattributes:
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1 | # Auto detect text files and perform LF normalization
2 | * text=auto
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/.idea/LicensePlateRecognition.iml:
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/__pycache__/predict.cpython-37.pyc:
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https://raw.githubusercontent.com/mufeng510/LicensePlateRecognition/eec3f858360b08e87fb469f2f4d27cf86d54deda/__pycache__/predict.cpython-37.pyc
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/config.js:
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1 | {
2 | "config":[
3 | {
4 | "open":1,
5 | "blur":3,
6 | "morphologyr":4,
7 | "morphologyc":19,
8 | "col_num_limit":10,
9 | "row_num_limit":21
10 | },
11 | {
12 | "open":0,
13 | "blur":3,
14 | "morphologyr":5,
15 | "morphologyc":12,
16 | "col_num_limit":10,
17 | "row_num_limit":18
18 | }
19 | ]
20 | }
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/predict.py:
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1 | import cv2
2 | import numpy as np
3 | from numpy.linalg import norm
4 | import sys
5 | import os
6 | import json
7 |
8 | SZ = 20 #训练图片长宽
9 | MAX_WIDTH = 1000 #原始图片最大宽度
10 | Min_Area = 2000 #车牌区域允许最大面积
11 | PROVINCE_START = 1000
12 | #读取图片文件
13 | def imreadex(filename):
14 | return cv2.imdecode(np.fromfile(filename, dtype=np.uint8), cv2.IMREAD_COLOR)
15 |
16 | def point_limit(point):
17 | if point[0] < 0:
18 | point[0] = 0
19 | if point[1] < 0:
20 | point[1] = 0
21 |
22 | #根据设定的阈值和图片直方图，找出波峰，用于分隔字符
23 | def find_waves(threshold, histogram):
24 | up_point = -1#上升点
25 | is_peak = False
26 | if histogram[0] > threshold:
27 | up_point = 0
28 | is_peak = True
29 | wave_peaks = []
30 | for i,x in enumerate(histogram):
31 | if is_peak and x < threshold:
32 | if i - up_point > 2:
33 | is_peak = False
34 | wave_peaks.append((up_point, i))
35 | elif not is_peak and x >= threshold:
36 | is_peak = True
37 | up_point = i
38 | if is_peak and up_point != -1 and i - up_point > 4:
39 | wave_peaks.append((up_point, i))
40 | return wave_peaks
41 |
42 | #根据找出的波峰，分隔图片，从而得到逐个字符图片
43 | def seperate_card(img, waves):
44 | part_cards = []
45 | for wave in waves:
46 | part_cards.append(img[:, wave[0]:wave[1]])
47 | return part_cards
48 |
49 | #来自opencv的sample，用于svm训练
50 | def deskew(img):
51 | m = cv2.moments(img)
52 | if abs(m['mu02']) < 1e-2:
53 | return img.copy()
54 | skew = m['mu11']/m['mu02']
55 | M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])
56 | img = cv2.warpAffine(img, M, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
57 | return img
58 | #来自opencv的sample，用于svm训练
59 | def preprocess_hog(digits):
60 | samples = []
61 | for img in digits:
62 | gx = cv2.Sobel(img, cv2.CV_32F, 1, 0)
63 | gy = cv2.Sobel(img, cv2.CV_32F, 0, 1)
64 | mag, ang = cv2.cartToPolar(gx, gy)
65 | bin_n = 16
66 | bin = np.int32(bin_n*ang/(2*np.pi))
67 | bin_cells = bin[:10,:10], bin[10:,:10], bin[:10,10:], bin[10:,10:]
68 | mag_cells = mag[:10,:10], mag[10:,:10], mag[:10,10:], mag[10:,10:]
69 | hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)]
70 | hist = np.hstack(hists)
71 |
72 | # transform to Hellinger kernel
73 | eps = 1e-7
74 | hist /= hist.sum() + eps
75 | hist = np.sqrt(hist)
76 | hist /= norm(hist) + eps
77 |
78 | samples.append(hist)
79 | return np.float32(samples)
80 | #不能保证包括所有省份
81 | provinces = [
82 | "zh_cuan", "川",
83 | "zh_e", "鄂",
84 | "zh_gan", "赣",
85 | "zh_gan1", "甘",
86 | "zh_gui", "贵",
87 | "zh_gui1", "桂",
88 | "zh_hei", "黑",
89 | "zh_hu", "沪",
90 | "zh_ji", "冀",
91 | "zh_jin", "津",
92 | "zh_jing", "京",
93 | "zh_jl", "吉",
94 | "zh_liao", "辽",
95 | "zh_lu", "鲁",
96 | "zh_meng", "蒙",
97 | "zh_min", "闽",
98 | "zh_ning", "宁",
99 | "zh_qing", "靑",
100 | "zh_qiong", "琼",
101 | "zh_shan", "陕",
102 | "zh_su", "苏",
103 | "zh_sx", "晋",
104 | "zh_wan", "皖",
105 | "zh_xiang", "湘",
106 | "zh_xin", "新",
107 | "zh_yu", "豫",
108 | "zh_yu1", "渝",
109 | "zh_yue", "粤",
110 | "zh_yun", "云",
111 | "zh_zang", "藏",
112 | "zh_zhe", "浙"
113 | ]
114 | class StatModel(object):
115 | def load(self, fn):
116 | self.model = self.model.load(fn)
117 | def save(self, fn):
118 | self.model.save(fn)
119 | class SVM(StatModel):
120 | def __init__(self, C = 1, gamma = 0.5):
121 | self.model = cv2.ml.SVM_create()
122 | self.model.setGamma(gamma)
123 | self.model.setC(C)
124 | self.model.setKernel(cv2.ml.SVM_RBF)
125 | self.model.setType(cv2.ml.SVM_C_SVC)
126 | #训练svm
127 | def train(self, samples, responses):
128 | self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)
129 | #字符识别
130 | def predict(self, samples):
131 | r = self.model.predict(samples)
132 | return r[1].ravel()
133 |
134 | class CardPredictor:
135 | def __init__(self):
136 | #车牌识别的部分参数保存在js中，便于根据图片分辨率做调整
137 | f = open('config.js')
138 | j = json.load(f)
139 | for c in j["config"]:
140 | if c["open"]:
141 | self.cfg = c.copy()
142 | break
143 | else:
144 | raise RuntimeError('没有设置有效配置参数')
145 |
146 | def __del__(self):
147 | self.save_traindata()
148 | def train_svm(self):
149 | #识别英文字母和数字
150 | self.model = SVM(C=1, gamma=0.5)
151 | #识别中文
152 | self.modelchinese = SVM(C=1, gamma=0.5)
153 | if os.path.exists("svm.dat"):
154 | self.model.load("svm.dat")
155 | else:
156 | chars_train = []
157 | chars_label = []
158 |
159 | for root, dirs, files in os.walk("train\\chars2"):
160 | if len(os.path.basename(root)) > 1:
161 | continue
162 | root_int = ord(os.path.basename(root))
163 | for filename in files:
164 | filepath = os.path.join(root,filename)
165 | digit_img = cv2.imread(filepath)
166 | digit_img = cv2.cvtColor(digit_img, cv2.COLOR_BGR2GRAY)
167 | chars_train.append(digit_img)
168 | #chars_label.append(1)
169 | chars_label.append(root_int)
170 |
171 | chars_train = list(map(deskew, chars_train))
172 | chars_train = preprocess_hog(chars_train)
173 | #chars_train = chars_train.reshape(-1, 20, 20).astype(np.float32)
174 | chars_label = np.array(chars_label)
175 | print(chars_train.shape)
176 | self.model.train(chars_train, chars_label)
177 | if os.path.exists("svmchinese.dat"):
178 | self.modelchinese.load("svmchinese.dat")
179 | else:
180 | chars_train = []
181 | chars_label = []
182 | for root, dirs, files in os.walk("train\\charsChinese"):
183 | if not os.path.basename(root).startswith("zh_"):
184 | continue
185 | pinyin = os.path.basename(root)
186 | index = provinces.index(pinyin) + PROVINCE_START + 1 #1是拼音对应的汉字
187 | for filename in files:
188 | filepath = os.path.join(root,filename)
189 | digit_img = cv2.imread(filepath)
190 | digit_img = cv2.cvtColor(digit_img, cv2.COLOR_BGR2GRAY)
191 | chars_train.append(digit_img)
192 | #chars_label.append(1)
193 | chars_label.append(index)
194 | chars_train = list(map(deskew, chars_train))
195 | chars_train = preprocess_hog(chars_train)
196 | #chars_train = chars_train.reshape(-1, 20, 20).astype(np.float32)
197 | chars_label = np.array(chars_label)
198 | print(chars_train.shape)
199 | self.modelchinese.train(chars_train, chars_label)
200 |
201 | def save_traindata(self):
202 | if not os.path.exists("svm.dat"):
203 | self.model.save("svm.dat")
204 | if not os.path.exists("svmchinese.dat"):
205 | self.modelchinese.save("svmchinese.dat")
206 |
207 | def accurate_place(self, card_img_hsv, limit1, limit2, color):
208 | row_num, col_num = card_img_hsv.shape[:2]
209 | xl = col_num
210 | xr = 0
211 | yh = 0
212 | yl = row_num
213 | #col_num_limit = self.cfg["col_num_limit"]
214 | row_num_limit = self.cfg["row_num_limit"]
215 | col_num_limit = col_num * 0.8 if color != "green" else col_num * 0.5#绿色有渐变
216 | for i in range(row_num):
217 | count = 0
218 | for j in range(col_num):
219 | H = card_img_hsv.item(i, j, 0)
220 | S = card_img_hsv.item(i, j, 1)
221 | V = card_img_hsv.item(i, j, 2)
222 | if limit1 < H <= limit2 and 34 < S and 46 < V:
223 | count += 1
224 | if count > col_num_limit:
225 | if yl > i:
226 | yl = i
227 | if yh < i:
228 | yh = i
229 | for j in range(col_num):
230 | count = 0
231 | for i in range(row_num):
232 | H = card_img_hsv.item(i, j, 0)
233 | S = card_img_hsv.item(i, j, 1)
234 | V = card_img_hsv.item(i, j, 2)
235 | if limit1 < H <= limit2 and 34 < S and 46 < V:
236 | count += 1
237 | if count > row_num - row_num_limit:
238 | if xl > j:
239 | xl = j
240 | if xr < j:
241 | xr = j
242 | return xl, xr, yh, yl
243 |
244 | def predict(self, car_pic):
245 | if type(car_pic) == type(""):
246 | img = imreadex(car_pic)
247 | else:
248 | img = car_pic
249 | pic_hight, pic_width = img.shape[:2]
250 |
251 | if pic_width > MAX_WIDTH:
252 | resize_rate = MAX_WIDTH / pic_width
253 | img = cv2.resize(img, (MAX_WIDTH, int(pic_hight*resize_rate)), interpolation=cv2.INTER_AREA)
254 |
255 | blur = self.cfg["blur"]
256 | #高斯去噪
257 | if blur > 0:
258 | img = cv2.GaussianBlur(img, (blur, blur), 0)#图片分辨率调整
259 | oldimg = img
260 | img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
261 | #equ = cv2.equalizeHist(img)
262 | #img = np.hstack((img, equ))
263 | #去掉图像中不会是车牌的区域
264 | kernel = np.ones((20, 20), np.uint8)
265 | img_opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
266 | img_opening = cv2.addWeighted(img, 1, img_opening, -1, 0);
267 |
268 | #找到图像边缘
269 | ret, img_thresh = cv2.threshold(img_opening, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
270 | img_edge = cv2.Canny(img_thresh, 100, 200)
271 | #使用开运算和闭运算让图像边缘成为一个整体
272 | kernel = np.ones((self.cfg["morphologyr"], self.cfg["morphologyc"]), np.uint8)
273 | img_edge1 = cv2.morphologyEx(img_edge, cv2.MORPH_CLOSE, kernel)
274 | img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, kernel)
275 |
276 | #查找图像边缘整体形成的矩形区域，可能有很多，车牌就在其中一个矩形区域中
277 | image, contours, hierarchy = cv2.findContours(img_edge2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
278 | contours = [cnt for cnt in contours if cv2.contourArea(cnt) > Min_Area]
279 | print('len(contours)', len(contours))
280 | #一一排除不是车牌的矩形区域
281 | car_contours = []
282 | for cnt in contours:
283 | rect = cv2.minAreaRect(cnt)
284 | area_width, area_height = rect[1]
285 | if area_width < area_height:
286 | area_width, area_height = area_height, area_width
287 | wh_ratio = area_width / area_height
288 | #print(wh_ratio)
289 | #要求矩形区域长宽比在2到5.5之间，2到5.5是车牌的长宽比，其余的矩形排除
290 | if wh_ratio > 2 and wh_ratio < 5.5:
291 | car_contours.append(rect)
292 | box = cv2.boxPoints(rect)
293 | box = np.int0(box)
294 | #oldimg = cv2.drawContours(oldimg, [box], 0, (0, 0, 255), 2)
295 | #cv2.imshow("edge4", oldimg)
296 | #print(rect)
297 |
298 | print(len(car_contours))
299 |
300 | print("精确定位")
301 | card_imgs = []
302 | #矩形区域可能是倾斜的矩形，需要矫正，以便使用颜色定位
303 | for rect in car_contours:
304 | if rect[2] > -1 and rect[2] < 1:#创造角度，使得左、高、右、低拿到正确的值
305 | angle = 1
306 | else:
307 | angle = rect[2]
308 | rect = (rect[0], (rect[1][0]+5, rect[1][1]+5), angle)#扩大范围，避免车牌边缘被排除
309 |
310 | box = cv2.boxPoints(rect)
311 | heigth_point = right_point = [0, 0]
312 | left_point = low_point = [pic_width, pic_hight]
313 | for point in box:
314 | if left_point[0] > point[0]:
315 | left_point = point
316 | if low_point[1] > point[1]:
317 | low_point = point
318 | if heigth_point[1] < point[1]:
319 | heigth_point = point
320 | if right_point[0] < point[0]:
321 | right_point = point
322 |
323 | if left_point[1] <= right_point[1]:#正角度
324 | new_right_point = [right_point[0], heigth_point[1]]
325 | pts2 = np.float32([left_point, heigth_point, new_right_point])#字符只是高度需要改变
326 | pts1 = np.float32([left_point, heigth_point, right_point])
327 | M = cv2.getAffineTransform(pts1, pts2)
328 | dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))
329 | point_limit(new_right_point)
330 | point_limit(heigth_point)
331 | point_limit(left_point)
332 | card_img = dst[int(left_point[1]):int(heigth_point[1]), int(left_point[0]):int(new_right_point[0])]
333 | card_imgs.append(card_img)
334 | #cv2.imshow("card", card_img)
335 | #cv2.waitKey(0)
336 | elif left_point[1] > right_point[1]:#负角度
337 |
338 | new_left_point = [left_point[0], heigth_point[1]]
339 | pts2 = np.float32([new_left_point, heigth_point, right_point])#字符只是高度需要改变
340 | pts1 = np.float32([left_point, heigth_point, right_point])
341 | M = cv2.getAffineTransform(pts1, pts2)
342 | dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))
343 | point_limit(right_point)
344 | point_limit(heigth_point)
345 | point_limit(new_left_point)
346 | card_img = dst[int(right_point[1]):int(heigth_point[1]), int(new_left_point[0]):int(right_point[0])]
347 | card_imgs.append(card_img)
348 | #cv2.imshow("card", card_img)
349 | #cv2.waitKey(0)
350 | #开始使用颜色定位，排除不是车牌的矩形，目前只识别蓝、绿、黄车牌
351 | colors = []
352 | for card_index,card_img in enumerate(card_imgs):
353 | green = yello = blue = black = white = 0
354 | card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)
355 | #有转换失败的可能，原因来自于上面矫正矩形出错
356 | if card_img_hsv is None:
357 | continue
358 | row_num, col_num= card_img_hsv.shape[:2]
359 | card_img_count = row_num * col_num
360 |
361 | for i in range(row_num):
362 | for j in range(col_num):
363 | H = card_img_hsv.item(i, j, 0)
364 | S = card_img_hsv.item(i, j, 1)
365 | V = card_img_hsv.item(i, j, 2)
366 | if 11 < H <= 34 and S > 34:#图片分辨率调整
367 | yello += 1
368 | elif 35 < H <= 99 and S > 34:#图片分辨率调整
369 | green += 1
370 | elif 99 < H <= 124 and S > 34:#图片分辨率调整
371 | blue += 1
372 |
373 | if 0 < H <180 and 0 < S < 255 and 0 < V < 46:
374 | black += 1
375 | elif 0 < H <180 and 0 < S < 43 and 221 < V < 225:
376 | white += 1
377 | color = "no"
378 |
379 | limit1 = limit2 = 0
380 | if yello*2 >= card_img_count:
381 | color = "yello"
382 | limit1 = 11
383 | limit2 = 34#有的图片有色偏偏绿
384 | elif green*2 >= card_img_count:
385 | color = "green"
386 | limit1 = 35
387 | limit2 = 99
388 | elif blue*2 >= card_img_count:
389 | color = "blue"
390 | limit1 = 100
391 | limit2 = 124#有的图片有色偏偏紫
392 | elif black + white >= card_img_count*0.7:#TODO
393 | color = "bw"
394 | print(color)
395 | colors.append(color)
396 | print(blue, green, yello, black, white, card_img_count)
397 | #cv2.imshow("color", card_img)
398 | #cv2.waitKey(0)
399 | if limit1 == 0:
400 | continue
401 | #以上为确定车牌颜色
402 | #以下为根据车牌颜色再定位，缩小边缘非车牌边界
403 | xl, xr, yh, yl = self.accurate_place(card_img_hsv, limit1, limit2, color)
404 | if yl == yh and xl == xr:
405 | continue
406 | need_accurate = False
407 | if yl >= yh:
408 | yl = 0
409 | yh = row_num
410 | need_accurate = True
411 | if xl >= xr:
412 | xl = 0
413 | xr = col_num
414 | need_accurate = True
415 | card_imgs[card_index] = card_img[yl:yh, xl:xr] if color != "green" or yl < (yh-yl)//4 else card_img[yl-(yh-yl)//4:yh, xl:xr]
416 | if need_accurate:#可能x或y方向未缩小，需要再试一次
417 | card_img = card_imgs[card_index]
418 | card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)
419 | xl, xr, yh, yl = self.accurate_place(card_img_hsv, limit1, limit2, color)
420 | if yl == yh and xl == xr:
421 | continue
422 | if yl >= yh:
423 | yl = 0
424 | yh = row_num
425 | if xl >= xr:
426 | xl = 0
427 | xr = col_num
428 | card_imgs[card_index] = card_img[yl:yh, xl:xr] if color != "green" or yl < (yh-yl)//4 else card_img[yl-(yh-yl)//4:yh, xl:xr]
429 | #以上为车牌定位
430 | #以下为识别车牌中的字符
431 | predict_result = []
432 | roi = None
433 | card_color = None
434 | for i, color in enumerate(colors):
435 | if color in ("blue", "yello", "green"):
436 | card_img = card_imgs[i]
437 | gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY)
438 | #黄、绿车牌字符比背景暗、与蓝车牌刚好相反，所以黄、绿车牌需要反向
439 | if color == "green" or color == "yello":
440 | gray_img = cv2.bitwise_not(gray_img)
441 | ret, gray_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
442 | #查找水平直方图波峰
443 | x_histogram = np.sum(gray_img, axis=1)
444 | x_min = np.min(x_histogram)
445 | x_average = np.sum(x_histogram)/x_histogram.shape[0]
446 | x_threshold = (x_min + x_average)/2
447 | wave_peaks = find_waves(x_threshold, x_histogram)
448 | if len(wave_peaks) == 0:
449 | print("peak less 0:")
450 | continue
451 | #认为水平方向，最大的波峰为车牌区域
452 | wave = max(wave_peaks, key=lambda x:x[1]-x[0])
453 | gray_img = gray_img[wave[0]:wave[1]]
454 | #查找垂直直方图波峰
455 | row_num, col_num= gray_img.shape[:2]
456 | #去掉车牌上下边缘1个像素，避免白边影响阈值判断
457 | gray_img = gray_img[1:row_num-1]
458 | y_histogram = np.sum(gray_img, axis=0)
459 | y_min = np.min(y_histogram)
460 | y_average = np.sum(y_histogram)/y_histogram.shape[0]
461 | y_threshold = (y_min + y_average)/5#U和0要求阈值偏小，否则U和0会被分成两半
462 |
463 | wave_peaks = find_waves(y_threshold, y_histogram)
464 |
465 | #for wave in wave_peaks:
466 | # cv2.line(card_img, pt1=(wave[0], 5), pt2=(wave[1], 5), color=(0, 0, 255), thickness=2)
467 | #车牌字符数应大于6
468 | if len(wave_peaks) <= 6:
469 | print("peak less 1:", len(wave_peaks))
470 | continue
471 |
472 | wave = max(wave_peaks, key=lambda x:x[1]-x[0])
473 | max_wave_dis = wave[1] - wave[0]
474 | #判断是否是左侧车牌边缘
475 | if wave_peaks[0][1] - wave_peaks[0][0] < max_wave_dis/3 and wave_peaks[0][0] == 0:
476 | wave_peaks.pop(0)
477 |
478 | #组合分离汉字
479 | cur_dis = 0
480 | for i,wave in enumerate(wave_peaks):
481 | if wave[1] - wave[0] + cur_dis > max_wave_dis * 0.6:
482 | break
483 | else:
484 | cur_dis += wave[1] - wave[0]
485 | if i > 0:
486 | wave = (wave_peaks[0][0], wave_peaks[i][1])
487 | wave_peaks = wave_peaks[i+1:]
488 | wave_peaks.insert(0, wave)
489 |
490 | #去除车牌上的分隔点
491 | point = wave_peaks[2]
492 | if point[1] - point[0] < max_wave_dis/3:
493 | point_img = gray_img[:,point[0]:point[1]]
494 | if np.mean(point_img) < 255/5:
495 | wave_peaks.pop(2)
496 |
497 | if len(wave_peaks) <= 6:
498 | print("peak less 2:", len(wave_peaks))
499 | continue
500 | part_cards = seperate_card(gray_img, wave_peaks)
501 | for i, part_card in enumerate(part_cards):
502 | #可能是固定车牌的铆钉
503 | if np.mean(part_card) < 255/5:
504 | print("a point")
505 | continue
506 | part_card_old = part_card
507 | w = abs(part_card.shape[1] - SZ)//2
508 |
509 | part_card = cv2.copyMakeBorder(part_card, 0, 0, w, w, cv2.BORDER_CONSTANT, value = [0,0,0])
510 | part_card = cv2.resize(part_card, (SZ, SZ), interpolation=cv2.INTER_AREA)
511 |
512 | #part_card = deskew(part_card)
513 | part_card = preprocess_hog([part_card])
514 | if i == 0:
515 | resp = self.modelchinese.predict(part_card)
516 | charactor = provinces[int(resp[0]) - PROVINCE_START]
517 | else:
518 | resp = self.model.predict(part_card)
519 | charactor = chr(resp[0])
520 | #判断最后一个数是否是车牌边缘，假设车牌边缘被认为是1
521 | if charactor == "1" and i == len(part_cards)-1:
522 | if part_card_old.shape[0]/part_card_old.shape[1] >= 7:#1太细，认为是边缘
523 | continue
524 | predict_result.append(charactor)
525 | roi = card_img
526 | card_color = color
527 | break
528 |
529 | return predict_result, roi, card_color#识别到的字符、定位的车牌图像、车牌颜色
530 |
531 | if __name__ == '__main__':
532 | c = CardPredictor()
533 | c.train_svm()
534 | r, roi, color = c.predict("黑A16341.jpg")
535 | print(r)
536 |
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/surface.py:
--------------------------------------------------------------------------------
1 | import tkinter as tk
2 | from tkinter.filedialog import *
3 | from tkinter import ttk
4 | import predict
5 | import cv2
6 | from PIL import Image, ImageTk
7 | import threading
8 | import time
9 |
10 |
11 |
12 | class Surface(ttk.Frame):
13 | pic_path = ""
14 | viewhigh = 600
15 | viewwide = 600
16 | update_time = 0
17 | thread = None
18 | thread_run = False
19 | camera = None
20 | color_transform = {"green":("绿牌","#55FF55"), "yello":("黄牌","#FFFF00"), "blue":("蓝牌","#6666FF")}
21 |
22 | def __init__(self, win):
23 | ttk.Frame.__init__(self, win)
24 | frame_left = ttk.Frame(self)
25 | frame_right1 = ttk.Frame(self)
26 | frame_right2 = ttk.Frame(self)
27 | win.title("车牌识别")
28 | win.state("zoomed")
29 | self.pack(fill=tk.BOTH, expand=tk.YES, padx="5", pady="5")
30 | frame_left.pack(side=LEFT,expand=1,fill=BOTH)
31 | frame_right1.pack(side=TOP,expand=1,fill=tk.Y)
32 | frame_right2.pack(side=RIGHT,expand=0)
33 | ttk.Label(frame_left, text='原图：').pack(anchor="nw")
34 | ttk.Label(frame_right1, text='车牌位置：').grid(column=0, row=0, sticky=tk.W)
35 |
36 | from_pic_ctl = ttk.Button(frame_right2, text="来自图片", width=20, command=self.from_pic)
37 | from_vedio_ctl = ttk.Button(frame_right2, text="来自摄像头", width=20, command=self.from_vedio)
38 | self.image_ctl = ttk.Label(frame_left)
39 | self.image_ctl.pack(anchor="nw")
40 |
41 | self.roi_ctl = ttk.Label(frame_right1)
42 | self.roi_ctl.grid(column=0, row=1, sticky=tk.W)
43 | ttk.Label(frame_right1, text='识别结果：').grid(column=0, row=2, sticky=tk.W)
44 | self.r_ctl = ttk.Label(frame_right1, text="")
45 | self.r_ctl.grid(column=0, row=3, sticky=tk.W)
46 | self.color_ctl = ttk.Label(frame_right1, text="", width="20")
47 | self.color_ctl.grid(column=0, row=4, sticky=tk.W)
48 | from_vedio_ctl.pack(anchor="se", pady="5")
49 | from_pic_ctl.pack(anchor="se", pady="5")
50 | self.predictor = predict.CardPredictor()
51 | self.predictor.train_svm()
52 |
53 | def get_imgtk(self, img_bgr):
54 | img = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
55 | im = Image.fromarray(img)
56 | imgtk = ImageTk.PhotoImage(image=im)
57 | wide = imgtk.width()
58 | high = imgtk.height()
59 | if wide > self.viewwide or high > self.viewhigh:
60 | wide_factor = self.viewwide / wide
61 | high_factor = self.viewhigh / high
62 | factor = min(wide_factor, high_factor)
63 | wide = int(wide * factor)
64 | if wide <= 0 : wide = 1
65 | high = int(high * factor)
66 | if high <= 0 : high = 1
67 | im=im.resize((wide, high), Image.ANTIALIAS)
68 | imgtk = ImageTk.PhotoImage(image=im)
69 | return imgtk
70 |
71 | def show_roi(self, r, roi, color):
72 | if r :
73 | roi = cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)
74 | roi = Image.fromarray(roi)
75 | self.imgtk_roi = ImageTk.PhotoImage(image=roi)
76 | self.roi_ctl.configure(image=self.imgtk_roi, state='enable')
77 | self.r_ctl.configure(text=str(r))
78 | self.update_time = time.time()
79 | try:
80 | c = self.color_transform[color]
81 | self.color_ctl.configure(text=c[0], background=c[1], state='enable')
82 | except:
83 | self.color_ctl.configure(state='disabled')
84 | elif self.update_time + 8 < time.time():
85 | self.roi_ctl.configure(state='disabled')
86 | self.r_ctl.configure(text="")
87 | self.color_ctl.configure(state='disabled')
88 |
89 | def from_vedio(self):
90 | if self.thread_run:
91 | return
92 | if self.camera is None:
93 | self.camera = cv2.VideoCapture(0)
94 | if not self.camera.isOpened():
95 | mBox.showwarning('警告', '摄像头打开失败！')
96 | self.camera = None
97 | return
98 | self.thread = threading.Thread(target=self.vedio_thread, args=(self,))
99 | self.thread.setDaemon(True)
100 | self.thread.start()
101 | self.thread_run = True
102 |
103 | def from_pic(self):
104 | self.thread_run = False
105 | self.pic_path = askopenfilename(title="选择识别图片", filetypes=[("jpg图片", "*.jpg")])
106 | if self.pic_path:
107 | img_bgr = predict.imreadex(self.pic_path)
108 | self.imgtk = self.get_imgtk(img_bgr)
109 | self.image_ctl.configure(image=self.imgtk)
110 | r, roi, color = self.predictor.predict(img_bgr)
111 | self.show_roi(r, roi, color)
112 |
113 | @staticmethod
114 | def vedio_thread(self):
115 | self.thread_run = True
116 | predict_time = time.time()
117 | while self.thread_run:
118 | _, img_bgr = self.camera.read()
119 | self.imgtk = self.get_imgtk(img_bgr)
120 | self.image_ctl.configure(image=self.imgtk)
121 | if time.time() - predict_time > 2:
122 | r, roi, color = self.predictor.predict(img_bgr)
123 | self.show_roi(r, roi, color)
124 | predict_time = time.time()
125 | print("run end")
126 |
127 |
128 | def close_window():
129 | print("destroy")
130 | if surface.thread_run :
131 | surface.thread_run = False
132 | surface.thread.join(2.0)
133 | win.destroy()
134 |
135 |
136 | if __name__ == '__main__':
137 | win=tk.Tk()
138 |
139 | surface = Surface(win)
140 | win.protocol('WM_DELETE_WINDOW', close_window)
141 | win.mainloop()
142 |
143 |
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