├── .idea ├── .gitignore ├── misc.xml ├── vcs.xml ├── inspectionProfiles │ └── profiles_settings.xml ├── modules.xml └── licensePlate.iml ├── labels.npy ├── train ├── 0_1.jpg ├── 0_10.jpg ├── 0_2.jpg ├── 0_3.jpg ├── 0_4.jpg ├── 0_5.jpg ├── 0_6.jpg ├── 0_7.jpg ├── 0_8.jpg ├── 0_9.jpg ├── 1_1.jpg ├── 1_10.jpg ├── 1_2.jpg ├── 1_3.jpg ├── 1_4.jpg ├── 1_5.jpg ├── 1_6.jpg ├── 1_7.jpg ├── 1_8.jpg ├── 1_9.jpg ├── 2_1.jpg ├── 2_10.jpg ├── 2_2.jpg ├── 2_3.jpg ├── 2_4.jpg ├── 2_5.jpg ├── 2_6.jpg ├── 2_7.jpg ├── 2_8.jpg ├── 2_9.jpg ├── 3_1.jpg ├── 3_10.jpg ├── 3_2.jpg ├── 3_3.jpg ├── 3_4.jpg ├── 3_5.jpg ├── 3_6.jpg ├── 3_7.jpg ├── 3_8.jpg ├── 3_9.jpg ├── 4_1.jpg ├── 4_10.jpg ├── 4_2.jpg ├── 4_3.jpg ├── 4_4.jpg ├── 4_5.jpg ├── 4_6.jpg ├── 4_7.jpg ├── 4_8.jpg ├── 4_9.jpg ├── 5_1.jpg ├── 5_10.jpg ├── 5_2.jpg ├── 5_3.jpg ├── 5_4.jpg ├── 5_5.jpg ├── 5_6.jpg ├── 5_7.jpg ├── 5_8.jpg ├── 5_9.jpg ├── 6_1.jpg ├── 6_10.jpg ├── 6_2.jpg ├── 6_3.jpg ├── 6_4.jpg ├── 6_5.jpg ├── 6_6.jpg ├── 6_7.jpg ├── 6_8.jpg ├── 6_9.jpg ├── 7_1.jpg ├── 7_10.jpg ├── 7_2.jpg ├── 7_3.jpg ├── 7_4.jpg ├── 7_5.jpg ├── 7_6.jpg ├── 7_7.jpg ├── 7_8.jpg ├── 7_9.jpg ├── 8_1.jpg ├── 8_10.jpg ├── 8_2.jpg ├── 8_3.jpg ├── 8_4.jpg ├── 8_5.jpg ├── 8_6.jpg ├── 8_7.jpg ├── 8_8.jpg ├── 8_9.jpg ├── 9_1.jpg ├── 9_10.jpg ├── 9_2.jpg ├── 9_3.jpg ├── 9_4.jpg ├── 9_5.jpg ├── 9_6.jpg ├── 9_7.jpg ├── 9_8.jpg ├── 9_9.jpg ├── A_1.jpg ├── A_10.jpg ├── A_2.jpg ├── A_3.jpg ├── A_4.jpg ├── A_5.jpg ├── A_6.jpg ├── A_7.jpg ├── A_8.jpg ├── A_9.jpg ├── B_1.jpg ├── B_10.jpg ├── B_2.jpg ├── B_3.jpg ├── B_4.jpg ├── B_5.jpg ├── B_6.jpg ├── B_7.jpg ├── B_8.jpg ├── B_9.jpg ├── C_1.jpg ├── C_10.jpg ├── C_2.jpg ├── C_3.jpg ├── C_4.jpg ├── C_5.jpg ├── C_6.jpg ├── C_7.jpg ├── C_8.jpg ├── C_9.jpg ├── D_1.jpg ├── D_10.jpg ├── D_2.jpg ├── D_3.jpg ├── D_4.jpg ├── D_5.jpg ├── D_6.jpg ├── D_7.jpg ├── D_8.jpg ├── D_9.jpg ├── E_1.jpg ├── E_10.jpg ├── E_2.jpg ├── E_3.jpg ├── E_4.jpg ├── E_5.jpg ├── E_6.jpg ├── E_7.jpg ├── E_8.jpg ├── E_9.jpg ├── F_1.jpg ├── F_10.jpg ├── F_2.jpg ├── F_3.jpg ├── F_4.jpg ├── F_5.jpg ├── F_6.jpg ├── F_7.jpg ├── F_8.jpg ├── F_9.jpg ├── G_1.jpg ├── G_10.jpg ├── G_2.jpg ├── G_3.jpg ├── G_4.jpg ├── G_5.jpg ├── G_6.jpg ├── G_7.jpg ├── G_8.jpg ├── G_9.jpg ├── H_1.jpg ├── H_10.jpg ├── H_2.jpg ├── H_3.jpg ├── H_4.jpg ├── H_5.jpg ├── H_6.jpg ├── H_7.jpg ├── H_8.jpg ├── H_9.jpg ├── J_1.jpg ├── J_10.jpg ├── J_2.jpg ├── J_3.jpg ├── J_4.jpg ├── J_5.jpg ├── J_6.jpg ├── J_7.jpg ├── J_8.jpg ├── J_9.jpg ├── K_1.jpg ├── K_10.jpg ├── K_2.jpg ├── K_3.jpg ├── K_4.jpg ├── K_5.jpg ├── K_6.jpg ├── K_7.jpg ├── K_8.jpg ├── K_9.jpg ├── L_1.jpg ├── L_10.jpg ├── L_2.jpg ├── L_3.jpg ├── L_4.jpg ├── L_5.jpg ├── L_6.jpg ├── L_7.jpg ├── L_8.jpg ├── L_9.jpg ├── M_1.jpg ├── M_10.jpg ├── M_2.jpg ├── M_3.jpg ├── M_4.jpg ├── M_5.jpg ├── M_6.jpg ├── M_7.jpg ├── M_8.jpg ├── M_9.jpg ├── N_1.jpg ├── N_10.jpg ├── N_2.jpg ├── N_3.jpg ├── N_4.jpg ├── N_5.jpg ├── N_6.jpg ├── N_7.jpg ├── N_8.jpg ├── N_9.jpg ├── P_1.jpg ├── P_10.jpg ├── P_2.jpg ├── P_3.jpg ├── P_4.jpg ├── P_5.jpg ├── P_6.jpg ├── P_7.jpg ├── P_8.jpg ├── P_9.jpg ├── Q_1.jpg ├── Q_10.jpg ├── Q_2.jpg ├── Q_3.jpg ├── Q_4.jpg ├── Q_5.jpg ├── Q_6.jpg ├── Q_7.jpg ├── Q_8.jpg ├── Q_9.jpg ├── R_1.jpg ├── R_10.jpg ├── R_2.jpg ├── R_3.jpg ├── R_4.jpg ├── R_5.jpg ├── R_6.jpg ├── R_7.jpg ├── R_8.jpg ├── R_9.jpg ├── S_1.jpg ├── S_10.jpg ├── S_2.jpg ├── S_3.jpg ├── S_4.jpg ├── S_5.jpg ├── S_6.jpg ├── S_7.jpg ├── S_8.jpg ├── S_9.jpg ├── T_1.jpg ├── T_10.jpg ├── T_2.jpg ├── T_3.jpg ├── T_4.jpg ├── T_5.jpg ├── T_6.jpg ├── T_7.jpg ├── T_8.jpg ├── T_9.jpg ├── U_1.jpg ├── U_10.jpg ├── U_2.jpg ├── U_3.jpg ├── U_4.jpg ├── U_5.jpg ├── U_6.jpg ├── U_7.jpg ├── U_8.jpg ├── U_9.jpg ├── V_1.jpg ├── V_10.jpg ├── V_2.jpg ├── V_3.jpg ├── V_4.jpg ├── V_5.jpg ├── V_6.jpg ├── V_7.jpg ├── V_8.jpg ├── V_9.jpg ├── W_1.jpg ├── W_10.jpg ├── W_2.jpg ├── W_3.jpg ├── W_4.jpg ├── W_5.jpg ├── W_6.jpg ├── W_7.jpg ├── W_8.jpg ├── W_9.jpg ├── X_1.jpg ├── X_10.jpg ├── X_2.jpg ├── X_3.jpg ├── X_4.jpg ├── X_5.jpg ├── X_6.jpg ├── X_7.jpg ├── X_8.jpg ├── X_9.jpg ├── Y_1.jpg ├── Y_10.jpg ├── Y_2.jpg ├── Y_3.jpg ├── Y_4.jpg ├── Y_5.jpg ├── Y_6.jpg ├── Y_7.jpg ├── Y_8.jpg ├── Y_9.jpg ├── Z_1.jpg ├── Z_10.jpg ├── Z_2.jpg ├── Z_3.jpg ├── Z_4.jpg ├── Z_5.jpg ├── Z_6.jpg ├── Z_7.jpg ├── Z_8.jpg └── Z_9.jpg ├── testImage ├── 2.jpg ├── car3.jpg ├── car4.jpg ├── car5.jpg ├── cAA662F.jpg ├── lLD9016.jpg ├── wA87271.jpg ├── wATH859.jpg └── wAUB816.jpg ├── trainingMat.npy ├── README.md ├── .gitignore ├── LICENSE └── licensePlateRecognition.py /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # Default ignored files 2 | /workspace.xml -------------------------------------------------------------------------------- /labels.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KinghooWei/LicensePlateRecognition/HEAD/labels.npy 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-------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/licensePlate.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # LicensePlateRecognition 车牌识别 2 | 华南理工大学模式识别实践 3 | 4 | 记得**star一下**呀 5 | 6 | README只列了框架，详情可以跳转[https://juejin.cn/post/6923916531965362184](https://juejin.cn/post/6923916531965362184) 7 | 8 | ## 最终效果图 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 |
18 | 19 | 汽车图片来源于网络 20 | 21 | ## 设计思路 22 | 23 | 项目的编程环境为python3.7.7，编译器使用pycharm2019.3.4 x64，设计一个车牌识别系统，有GUI界面。选择一张有车牌的图片后，完成车牌定位、倾斜校正、字符分割，最后通过k-NN算法对车牌的字母和数字进行识别，将识别结果在GUI界面中显示出来。 24 | 25 | ### 车牌定位 26 | 27 | ### 字符分割 28 | 29 | ### 字符识别 30 | 31 | ### 特征描述 32 | 33 | ### k-NN分类算法 34 | 35 | ## 参考 36 | 37 | [python cv2函数总结resize boxPoints minAreaRect](https://blog.csdn.net/Bismarckczy/article/details/88026806?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522158540891219725211943679%2522%252C%2522scm%2522%253A%252220140713.130056874..%2522%257D&request_id=158540891219725211943679&biz_id=0&utm_source=distribute.pc_search_result.none-task) 38 | 39 | [Python-Tkinter图形化界面设计（详细教程 ）](https://blog.csdn.net/rng_uzi_/article/details/89792518#3.2) 40 | 41 | [Python3+OpenCV2实现图像的几何变换:平移、镜像、缩放、旋转、仿射](https://blog.csdn.net/missyougoon/article/details/81092512?depth_1-utm_source=distribute.pc_relevant.none-task&utm_source=distribute.pc_relevant.none-task) -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /licensePlateRecognition.py: -------------------------------------------------------------------------------- 1 | from tkinter import * 2 | from tkinter.filedialog import * 3 | from cv2 import * 4 | from numpy import * 5 | from PIL import ImageTk, Image 6 | import operator 7 | 8 | 9 | class recognition: 10 | # oriImage 11 | def open(self): 12 | img_path = askopenfilename(initialdir='./testImage', title='选择待识别图片', 13 | filetypes=[("jpg", "*.jpg"), ("png", "*.png")]) 14 | if img_path: 15 | img = cv2.imdecode(fromfile(img_path, dtype=uint8), cv2.IMREAD_COLOR) 16 | 17 | self.show(img, 400, oriImg) 18 | 19 | colors, lisencePlates = self.getROI(img) 20 | for m in range(len(lisencePlates)): 21 | self.show(lisencePlates[m], 40, ROIImg) 22 | letters = self.getLetters(lisencePlates[m], colors[m]) 23 | 24 | results = [] 25 | for letter in letters: 26 | feature = self.getFeature(letter) 27 | result = self.sort(feature, trainingMat, labels, 5) 28 | results.append(result) 29 | # print(result) 30 | recogResult = ','.join(results) 31 | 32 | resultShow.configure(text=recogResult) 33 | 34 | def show(self, img, newheight, label): 35 | global oriTk, ROITk 36 | img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) 37 | img = Image.fromarray(img) 38 | height = img.height 39 | width = img.width 40 | 41 | scaleRatio = newheight / height 42 | newwidth = int(width * scaleRatio) 43 | img = img.resize((newwidth, newheight), Image.ANTIALIAS) 44 | if label == oriImg: 45 | self.oriTk = ImageTk.PhotoImage(image=img) 46 | label.configure(image=self.oriTk) 47 | elif label == ROIImg: 48 | self.ROITk = ImageTk.PhotoImage(image=img) 49 | label.configure(image=self.ROITk) 50 | 51 | def getROI(self, img): 52 | # oriImg = Image.fromarray(img) 53 | # imgHeight = oriImg.height 54 | # imgWidth = oriImg.width 55 | # 尺寸太大则缩小 56 | imgHeight, imgWidth = img.shape[:2] 57 | if imgHeight > 1000: 58 | scaleRatio = 1000 / imgHeight 59 | imgHeight = 1000 60 | imgWidth = int(imgWidth * scaleRatio) 61 | img = cv2.resize(img, (imgWidth, imgHeight), interpolation=cv2.INTER_AREA) 62 | 63 | oriImg = img # 保存原图副本 64 | img = cv2.GaussianBlur(img, (3, 3), 0) # 高斯模糊 65 | # print(img) 66 | gaussianImg = img # 保存副本 67 | img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转灰度图 68 | # imshow("gray",img) 69 | kernel = ones((20, 20), uint8) 70 | imgOpen = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) # 开操作，突出黑 71 | # imshow("open",imgOpen) 72 | img = cv2.addWeighted(img, 1, imgOpen, -1, 0) # 图像融合 73 | # imshow("add",img) 74 | ret, img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) # 二值化 75 | img = cv2.Canny(img, 100, 200) # canny算法查找边缘 76 | # imshow("canny",img) 77 | kernel = ones((4, 19), uint8) 78 | img = cv2.morphologyEx(img, MORPH_CLOSE, kernel) # 闭操作，横向涂抹 79 | # imshow("close",img) 80 | # kernel = ones((4, 25), uint8) 81 | img = cv2.morphologyEx(img, MORPH_OPEN, kernel) # 开操作，去除细线 82 | # imshow("open2",img) 83 | 84 | # 查找连通区域 85 | try: 86 | contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) 87 | except ValueError: 88 | image, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) 89 | contours = [cnt for cnt in contours if cv2.contourArea(cnt) > 2000] # 排除面积太小的连通区域 90 | print("number of big contours: ", len(contours)) 91 | 92 | # 通过长宽比排除比例不适的连通区域 93 | rightRatioRects = [] 94 | for cnt in contours: 95 | rect = cv2.minAreaRect(cnt) # 最小内接矩形[(x_center, y_center),(w,h), theta] 96 | w, h = rect[1] 97 | if w < h: 98 | width, height = h, w 99 | else: 100 | width, height = w, h 101 | ratio = width / height 102 | if 2 < ratio < 5.5: 103 | rightRatioRects.append(rect) 104 | box = cv2.boxPoints(rect) 105 | # print(box) 106 | box = int0(box) 107 | # cv2.drawContours(gaussianImg, [box], 0, (0, 255, 0), 2) #圈出来 108 | 109 | # imshow("img", gaussianImg) 110 | print("number of right ratio contours: ", len(rightRatioRects)) 111 | 112 | # 对可能的区域进行旋转调整和扩大范围 113 | rotatedRects = [] 114 | for rect in rightRatioRects: 115 | box = cv2.boxPoints(rect) 116 | w, h = rect[1] 117 | extraX = 5 118 | extraY = 10 119 | if w > h: # 宽大于高，顺时针旋转 120 | width, height = w, h 121 | matRotate = cv2.getRotationMatrix2D((box[0][0], box[0][1]), rect[2], 1) 122 | img = cv2.warpAffine(oriImg, matRotate, (imgWidth + 100, imgHeight + 100)) 123 | 124 | # 扩大范围 125 | if int(box[0][0]) - extraX < 0: 126 | minX = 0 127 | else: 128 | minX = int(box[0][0]) - extraX 129 | 130 | if int(box[0][0]) + int(width) + extraX > imgWidth + 99: 131 | maxX = imgWidth + 99 132 | else: 133 | maxX = int(box[0][0]) + int(width) + extraX 134 | 135 | if int(box[0][1]) - int(height) - extraY < 0: 136 | minY = 0 137 | else: 138 | minY = int(box[0][1]) - int(height) - extraY 139 | 140 | if int(box[0][1]) + extraY > imgHeight + 99: 141 | maxY = imgHeight + 99 142 | else: 143 | maxY = int(box[0][1]) + extraY 144 | 145 | else: # 宽小于高，逆时针旋转 146 | width, height = h, w 147 | matRotate = cv2.getRotationMatrix2D((box[0][0], box[0][1]), -90 + rect[2], 1) 148 | img = cv2.warpAffine(oriImg, matRotate, (imgWidth + 100, imgHeight + 100)) 149 | # imshow("rotate", img) 150 | # 扩大范围 151 | if int(box[0][0]) - extraX < 0: 152 | minX = 0 153 | else: 154 | minX = int(box[0][0]) - extraX 155 | 156 | if int(box[0][0]) + int(width) + extraX > imgWidth + 99: 157 | maxX = imgWidth + 99 158 | else: 159 | maxX = int(box[0][0]) + int(width) + extraX 160 | 161 | if int(box[0][1]) - extraY < 0: 162 | minY = 0 163 | else: 164 | minY = int(box[0][1]) - extraY 165 | 166 | if int(box[0][1]) + int(height) + extraY > imgHeight + 99: 167 | maxY = imgHeight + 99 168 | else: 169 | maxY = int(box[0][1]) + int(height) + extraY 170 | 171 | img = img[minY:maxY, minX:maxX] 172 | # imshow(str(rect), img) 173 | rotatedRects.append(img) 174 | 175 | # 利用颜色排除不是车牌的区域，并进行较精细定位 176 | colors = [] 177 | lisencePlates = [] 178 | 179 | for img in rotatedRects: 180 | green = yellow = blue = 0 181 | hsvImg = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) 182 | row, col = hsvImg.shape[:2] 183 | pixelCount = row * col 184 | # print("pixel count: ", pixelCount) 185 | 186 | for i in range(row): 187 | for j in range(col): 188 | H = hsvImg.item(i, j, 0) 189 | S = hsvImg.item(i, j, 1) 190 | V = hsvImg.item(1, j, 2) 191 | if 15 <= H <= 30 and S >= 60 and V >= 45: 192 | yellow += 1 193 | elif 65 <= H <= 90 and S >= 15 and V >= 65: 194 | green += 1 195 | elif 100 <= H <= 120 and S >= 55 and V >= 40: 196 | blue += 1 197 | if yellow > green and yellow > blue and yellow > pixelCount * 0.45: 198 | xl, xr, yh, yl = self.fineMap("yellow", hsvImg) 199 | img = img[yh:yl, xl:xr] 200 | h, w = img.shape[:2] 201 | if 2.2 < w / h < 5.3: 202 | colors.append("yellow") 203 | lisencePlates.append(img) 204 | # imshow(str(img), img) 205 | if blue > yellow and blue > green and blue > pixelCount * 0.45: 206 | xl, xr, yh, yl = self.fineMap("blue", hsvImg) 207 | img = img[yh:yl, xl:xr] 208 | h, w = img.shape[:2] 209 | if 2.2 < w / h < 5.3: 210 | lisencePlates.append(img) 211 | colors.append("blue") 212 | # imshow(str(img), img) 213 | if green > yellow and green > blue and green > pixelCount * 0.35: 214 | xl, xr, yh, yl = self.fineMap("green", hsvImg) 215 | img = img[yl - int((yl - yh) * 4 / 3):yl, xl:xr] 216 | h, w = img.shape[:2] 217 | if 2.2 < w / h < 5.3: 218 | lisencePlates.append(img) 219 | colors.append("green") 220 | # imshow(str(img), img) 221 | print("num of lisence plate: " + str(len(lisencePlates))) 222 | return colors, lisencePlates 223 | 224 | # 精细定位 225 | def fineMap(self, color, hsvImg): 226 | if color == "yellow": 227 | limit1 = 15 228 | limit2 = 30 229 | limitS = 60 230 | limitV = 45 231 | elif color == "green": 232 | limit1 = 65 233 | limit2 = 90 234 | limitS = 15 235 | limitV = 65 236 | elif color == "blue": 237 | limit1 = 100 238 | limit2 = 120 239 | limitS = 55 240 | limitV = 40 241 | row_num, col_num = hsvImg.shape[:2] 242 | xl = col_num - 1 243 | xr = 0 244 | yh = row_num - 1 245 | yl = 0 246 | # col_num_limit = self.cfg["col_num_limit"] 247 | row_num_limit = row_num * 0.5 if color != "green" else row_num * 0.3 # 绿色有渐变 248 | col_num_limit = col_num * 0.3 249 | # 按行 250 | for i in range(row_num): 251 | count = 0 252 | for j in range(col_num): 253 | H = hsvImg.item(i, j, 0) 254 | S = hsvImg.item(i, j, 1) 255 | V = hsvImg.item(i, j, 2) 256 | if limit1 <= H <= limit2 and S >= limitS and V >= limitV: 257 | count += 1 258 | if count > col_num_limit: 259 | if yh > i: 260 | yh = i 261 | if yl < i: 262 | yl = i 263 | # 按列 264 | for j in range(col_num): 265 | count = 0 266 | for i in range(row_num): 267 | H = hsvImg.item(i, j, 0) 268 | S = hsvImg.item(i, j, 1) 269 | V = hsvImg.item(i, j, 2) 270 | if limit1 <= H <= limit2 and S >= limitS and V >= limitV: 271 | count += 1 272 | if count > row_num_limit: 273 | if xl > j: 274 | xl = j 275 | if xr < j: 276 | xr = j 277 | return xl, xr, yh, yl 278 | 279 | # 二值化，去铆钉，字符分割 280 | def getLetters(self, img, color): 281 | img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转灰度图 282 | # 二值化 283 | if color == "blue": 284 | ret, img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) 285 | elif color == "yellow" or color == "green": 286 | ret, img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU) 287 | 288 | # 去铆钉 289 | rowNum, colNum = img.shape[:2] 290 | for i in range(rowNum): 291 | previousPixel = -1 292 | jump = 0 293 | for j in range(colNum): 294 | if previousPixel == -1: 295 | previousPixel = img[i][j] 296 | else: 297 | if previousPixel != img[i][j]: 298 | jump += 1 299 | previousPixel = img[i][j] 300 | if jump < 8: 301 | for j in range(colNum): 302 | img[i][j] = 0 303 | 304 | # 字符分割 305 | accumulation = img.sum(axis=0) 306 | max, min = accumulation.max(axis=0), accumulation.min(axis=0) 307 | accumulation = (accumulation - min) / (max - min) 308 | # print(accumulation) 309 | 310 | successive = [] 311 | intervalPre = [] 312 | isPreviousLess = 0 313 | for m in range(len(accumulation)): 314 | if isPreviousLess: 315 | if accumulation[m] <= 0.2: 316 | successive.append(m) 317 | else: 318 | isPreviousLess = 0 319 | intervalPre.append(successive) 320 | successive = [] 321 | else: 322 | if accumulation[m] <= 0.2: 323 | isPreviousLess = 1 324 | successive.append(m) 325 | 326 | # 找到最大间隔，为省市和编号之间的间隔 327 | intervalLen = 0 328 | for n in range(len(intervalPre)): 329 | if len(intervalPre[n]) > intervalLen: 330 | intervalLen = len(intervalPre[n]) 331 | bigInterval = intervalPre[n] 332 | bigIntervalIndex = n 333 | 334 | # 找到最大间隔的前一个间隔，即省简称和字母之间的间隔 335 | if bigIntervalIndex >= 1: 336 | minIndex = self.findInterval(accumulation, intervalPre[bigIntervalIndex - 1]) 337 | 338 | img = img[:, minIndex:] # 截取字母区域 339 | contours, heirachy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 获取连通区域 340 | 341 | # 通过面积找到字母连通区域 342 | if color == "green": 343 | cntNum = 7 344 | else: 345 | cntNum = 6 346 | 347 | list = [] 348 | for m in range(len(contours)): 349 | tuple = (m, cv2.contourArea(contours[m])) 350 | list.append(tuple) 351 | list.sort(key=lambda x: x[1], reverse=True) 352 | 353 | bigCnts = [] # 字母连通区域 354 | for n in range(cntNum): 355 | bigCnts.append(contours[list[n][0]]) 356 | 357 | # 通过x坐标排序 358 | list = [] 359 | for n in range(cntNum): # 外接矩形 360 | x, y, width, height = cv2.boundingRect(bigCnts[n]) 361 | tuple = (n, x) 362 | list.append(tuple) 363 | list.sort(key=lambda x: x[1]) 364 | 365 | cnts = [] # 从左到右，字母连通区域 366 | for n in range(cntNum): 367 | cnts.append(bigCnts[list[n][0]]) 368 | 369 | # 裁剪 370 | letters = [] 371 | for cnt in cnts: 372 | x, y, width, height = cv2.boundingRect(cnt) 373 | letter = img[y:y + height, x:x + width] 374 | letter = cv2.resize(letter, (32, 64), interpolation=cv2.INTER_CUBIC) 375 | letters.append(letter) 376 | # imshow(str(cnt), letter) 377 | return letters 378 | # print(intervalPre) 379 | # print(minIndex) 380 | # imshow("img", gaussianImg) 381 | # imshow("img", img) 382 | 383 | def findInterval(self, accumulation, intervalPre): 384 | min = 1 # 最小值 385 | for m in range(len(intervalPre)): 386 | if accumulation[intervalPre[m]] < min: 387 | min = accumulation[intervalPre[m]] 388 | minIndex = intervalPre[m] 389 | return minIndex 390 | 391 | def getFeature(self, img): 392 | feature = [] 393 | for i in range(64): 394 | for j in range(32): 395 | if img[i, j] == 0: 396 | pixel = 0 397 | else: 398 | pixel = 1 399 | feature.append(pixel) 400 | return feature 401 | 402 | # 构建训练集 403 | def createTrainSet(self): 404 | labels = [] 405 | fileList = os.listdir('train') 406 | m = len(fileList) 407 | trainingMat = zeros((m, 2048)) 408 | for i in range(m): 409 | fileName = fileList[i] 410 | labels.append(self.getNumLabel(fileName)) 411 | ori = cv2.imread('train/%s' % fileName) # 读取训练样本 412 | img_gray = cv2.cvtColor(ori, cv2.COLOR_BGR2GRAY) 413 | ret, img_bin = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) # 二值化 414 | # img = skewCorrection(img_bin) # 倾斜校正 415 | img = self.crop(img_bin) 416 | trainingMat[i, :] = self.getFeature(img) 417 | return labels, trainingMat 418 | 419 | # 由图像名称得到数字类别 420 | def getNumLabel(self, file_name): 421 | fileStr = file_name.split('.')[0] # 根据文件名中的字符"."进行切片 422 | numLabel = fileStr.split('_')[0] # 根据文件切片结果中的"_"进行再次切片 423 | return numLabel 424 | 425 | # 裁剪伸缩 426 | def crop(self, img): 427 | contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) 428 | x, y, w, h = cv2.boundingRect(contours[0]) 429 | img_crop = img[y:y + h, x:x + w] 430 | imgOut = cv2.resize(img_crop, (32, 64), interpolation=cv2.INTER_CUBIC) 431 | return imgOut 432 | 433 | # KNN算法 434 | def sort(self, imgFeature, trainingMat, labels, k): 435 | trainingSetSize = trainingMat.shape[0] 436 | diffMat = tile(imgFeature, (trainingSetSize, 1)) - trainingMat 437 | sqDiffMat = diffMat ** 2 438 | sqDistances = sqDiffMat.sum(axis=1) # 每一行上元素之和 439 | distances = sqDistances ** 0.5 440 | sortedDistIndicies = distances.argsort() # 从小到大排序，返回下标 441 | classCount = {} 442 | for i in range(k): 443 | voteLabel = labels[sortedDistIndicies[i]] # 距离第i小的训练样本的类型 444 | classCount[voteLabel] = classCount.get(voteLabel, 0) + 1 445 | sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True) 446 | return sortedClassCount[0][0] 447 | 448 | 449 | if __name__ == '__main__': 450 | global oriTk, ROITk 451 | choose = recognition() 452 | 453 | if os.path.exists('labels.npy') & os.path.exists('trainingMat.npy'): 454 | labels = load('labels.npy') 455 | trainingMat = load('trainingMat.npy') 456 | # print(labels) 457 | # print(trainingMat) 458 | else: 459 | labels, trainingMat = choose.createTrainSet() 460 | save('labels.npy', labels) 461 | save('trainingMat.npy', trainingMat) 462 | 463 | root = Tk() 464 | root.title("车牌识别") 465 | root.geometry('800x700') 466 | 467 | btnChoose = Button(root, text="选择图片", command=choose.open) 468 | btnChoose.place(x=20, y=10) 469 | 470 | oriImgLabel = Label(root, text="原图：", fg='black') 471 | oriImgLabel.place(x=25, y=50) 472 | 473 | oriImg = Label(root) 474 | oriImg.place(x=35, y=70) 475 | 476 | ROIImgLabel = Label(root, text="车牌定位：", fg='black') 477 | ROIImgLabel.place(x=25, y=500) 478 | 479 | ROIImg = Label(root) 480 | ROIImg.place(x=35, y=520) 481 | 482 | resultLabel = Label(root, text="识别结果：", fg='black') 483 | resultLabel.place(x=25, y=570) 484 | 485 | resultShow = Label(root, text="", fg='black') 486 | resultShow.place(x=35, y=590) 487 | 488 | root.mainloop() 489 | --------------------------------------------------------------------------------