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python/.idea/.gitignore

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+# 默认忽略的文件
+/shelf/
+/workspace.xml
+# 基于编辑器的 HTTP 客户端请求
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

python/.idea/.name

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+kcsj.py

python/.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

python/.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.12 (python)" project-jdk-type="Python SDK" />
+</project>

python/.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/python.iml" filepath="$PROJECT_DIR$/.idea/python.iml" />
+    </modules>
+  </component>
+</project>

python/.idea/python.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.12 (python)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

python/.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/.." vcs="Git" />
+  </component>
+</project>

python/p/kcsj.py

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+# 导入所需模块
+import cv2
+from matplotlib import pyplot as plt
+import os
+import numpy as np
+
+
+# plt显示彩色图片
+def plt_show0(img):
+    # cv2与plt的图像通道不同：cv2为[b,g,r];plt为[r, g, b]
+    b, g, r = cv2.split(img)
+    img = cv2.merge([r, g, b])
+    plt.imshow(img)
+    plt.show()
+
+
+# plt显示灰度图片
+def plt_show(img):
+    plt.imshow(img, cmap='gray')
+    plt.show()
+
+
+# 图像去噪灰度处理
+def gray_guss(image):
+    image = cv2.GaussianBlur(image, (3, 3), 0)
+    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    return gray_image
+
+# 读取待检测图片
+origin_image = cv2.imread('../chepai1.jpg')
+# 复制一张图片，在复制图上进行图像操作，保留原图
+image = origin_image.copy()
+# 图像去噪灰度处理
+gray_image = gray_guss(image)
+# x方向上的边缘检测（增强边缘信息）
+Sobel_x = cv2.Sobel(gray_image, cv2.CV_16S, 1, 0)
+absX = cv2.convertScaleAbs(Sobel_x)
+image = absX
+
+# 图像阈值化操作——获得二值化图
+ret, image = cv2.threshold(image, 0, 255, cv2.THRESH_OTSU)
+# 显示灰度图像
+plt_show(image)
+# 形态学（从图像中提取对表达和描绘区域形状有意义的图像分量）——闭操作
+kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 10))
+image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernelX,iterations = 1)
+# 显示灰度图像
+plt_show(image)
+
+# 腐蚀（erode）和膨胀（dilate）
+kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (50, 1))
+kernelY = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 20))
+#x方向进行闭操作（抑制暗细节）
+image = cv2.dilate(image, kernelX)
+image = cv2.erode(image, kernelX)
+#y方向的开操作
+image = cv2.erode(image, kernelY)
+image = cv2.dilate(image, kernelY)
+# 中值滤波（去噪）
+image = cv2.medianBlur(image, 21)
+# 显示灰度图像
+plt_show(image)
+# 获得轮廓
+contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+for item in contours:
+    rect = cv2.boundingRect(item)
+    x = rect[0]
+    y = rect[1]
+    weight = rect[2]
+    height = rect[3]
+    # 根据轮廓的形状特点，确定车牌的轮廓位置并截取图像
+    if (weight > (height * 3.5)) and (weight < (height * 4)):
+        image = origin_image[y:y + height, x:x + weight]
+        plt_show0(image)
+
+#车牌字符分割
+# 图像去噪灰度处理
+gray_image = gray_guss(image)
+# 图像阈值化操作——获得二值化图
+ret, image = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU)
+plt_show(image)
+
+#膨胀操作，使“苏”字膨胀为一个近似的整体，为分割做准备
+kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
+image = cv2.dilate(image, kernel)
+plt_show(image)
+
+# 查找轮廓
+contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+words = []
+word_images = []
+#对所有轮廓逐一操作
+for item in contours:
+    word = []
+    rect = cv2.boundingRect(item)
+    x = rect[0]
+    y = rect[1]
+    weight = rect[2]
+    height = rect[3]
+    word.append(x)
+    word.append(y)
+    word.append(weight)
+    word.append(height)
+    words.append(word)
+# 排序，车牌号有顺序。words是一个嵌套列表
+words = sorted(words,key=lambda s:s[0],reverse=False)
+i = 0
+#word中存放轮廓的起始点和宽高
+for word in words:
+    # 筛选字符的轮廓
+    if (word[3] > (word[2] * 1.5)) and (word[3] < (word[2] * 3.5)) and (word[2] > 25):
+        i = i+1
+        splite_image = image[word[1]:word[1] + word[3], word[0]:word[0] + word[2]]
+        word_images.append(splite_image)
+        print(i)
+print(words)
+
+for i,j in enumerate(word_images):
+    plt.subplot(1,7,i+1)
+    plt.imshow(word_images[i],cmap='gray')
+plt.show()
+