第一次递交：能够实现图片分割，开头数字识别

.idea/.gitignore

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+# 默认忽略的文件
+/shelf/
+/workspace.xml

.idea/.name

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+TLD_Code

.idea/TLD_Code.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="opencv" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/deployment.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="PublishConfigData" remoteFilesAllowedToDisappearOnAutoupload="false">
+    <serverData>
+      <paths name="zhangwh@47.111.72.1:8789 password">
+        <serverdata>
+          <mappings>
+            <mapping local="$PROJECT_DIR$" web="/" />
+          </mappings>
+        </serverdata>
+      </paths>
+    </serverData>
+  </component>
+</project>

.idea/inspectionProfiles/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPep8Inspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="E402" />
+          <option value="E302" />
+        </list>
+      </option>
+    </inspection_tool>
+    <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="N806" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.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>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="opencv" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="opencv" project-jdk-type="Python SDK" />
+</project>

.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/TLD_Code.iml" filepath="$PROJECT_DIR$/.idea/TLD_Code.iml" />
+    </modules>
+  </component>
+</project>

.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>

img_processing.py

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+import cv2 as cv
+import numpy as np
+from matplotlib import pyplot as plt
+import number_processing
+
+
+def cv_show(name, img):
+    """定义cv_show函数，用于显示图像"""
+    cv.namedWindow(name, cv.WINDOW_NORMAL)  # 窗口大小可调
+    cv.resizeWindow(name, img.shape[1], img.shape[0])  # 窗口大小可调
+    cv.imshow(name, img)
+    cv.waitKey(0)
+    cv.destroyAllWindows()
+
+
+def cv_info(name, img):
+    """
+    定义cv_info函数，确认图像是否成功读取，并且打印图像的基本信息
+    """
+    print(f"图像 {name} 信息如下：")
+    if img is None:
+        print("无法读取图像")
+    else:
+        print("图像数据类型：", img.dtype)
+        print("图像类型：", type(img))
+        print("图像尺寸：", img.shape)
+        print("图像行数：", img.shape[0])
+        print("图像列数：", img.shape[1])
+    print()
+
+
+def find_line(img):
+    """
+    定义find_line函数，用于寻找分界线
+    返回一段图像的宽度
+    """
+    for i in range(img.shape[1]):  # 遍历 a 列
+        if np.all(img[0, i] >= [230, 230, 230]):
+            # 求该列像素均值，判断是否为白色
+            mean = np.mean(img[:, i])
+            if mean >= 240:  # 像素均值大于 230 判断为白色
+                print(f"分界线位置：{i}, 像素均值：{mean}", end="\n\n")
+                return i
+    return -1  # 如果没有找到符合条件的分界线，则返回图像的宽度
+
+
+def split_img(img):
+    """
+    定义split_img函数，用于将图像分割并保存
+    无返回值
+    """
+    img_width = find_line(img)  # 返回图像的宽度
+    n = 0  # 用于计数分割得到的图像数
+    for i in range(0, img.shape[1], img_width + 1):  # 遍历 img 的每一列
+        image = img[:, i: i + img_width]
+        n += 1
+        cv.imwrite(f"data/saved_img/image_{n}.PNG", image)  # 保存图片 无损压缩格式 PNG
+
+
+def split_num(img):
+    """
+    定义split_num函数，用于分割数字并保存
+    返回数字
+    """
+    # 将彩色图像转化为灰度图像
+    img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+    # 二值化处理，你可以根据需要调整阈值
+    _, thresh = cv.threshold(img_gray, 128, 255, cv.THRESH_BINARY_INV)
+    # 查找轮廓
+    contours, _ = cv.findContours(thresh, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+    # 对轮廓进行排序，以便从左到右处理数字（可选）
+    contours = sorted(contours, key=lambda x: cv.boundingRect(x)[0])
+
+    # 用于存储识别出的数字字符串
+    recognized_digits = []
+
+    # 遍历每个轮廓，并保存每个数字的图片
+    for i, contour in enumerate(contours):
+        x, y, w, h = cv.boundingRect(contour)
+        digit = thresh[y:y + h, x:x + w]
+        # print(f"第{i+1}个数字的尺寸：{digit.shape[0]}")
+        if digit.shape[0] < 5:  # 小数点的轮廓太小，跳过
+            break
+
+        recognized_digits.append(str(number_processing.num_list.index(digit.tolist())))
+
+        # # 保存数字图片
+        # cv.imwrite(f'data/saved_img/digit_{i}.png', digit)
+    return int("".join(recognized_digits))

mian.py

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+import cv2 as cv
+import numpy as np
+from matplotlib import pyplot as plt
+import img_processing as ip
+
+print(cv.__version__)
+
+img_path = 'data/1-D2K286+520-300GD P_1.JPG'
+img = cv.imread(img_path, cv.IMREAD_COLOR)  # 读取图像 三通道彩色图
+
+# 起始点假设为固定位置，即 44 行，65 列
+data_interest = img[44:, 65:-2]  # 感兴趣的数据区域
+number_interest = img[8:20, 64:175]  # 感兴趣的数字区域 横向
+
+ip.cv_info(f"data_interest", data_interest)
+ip.cv_info(f"number_interest", number_interest)
+
+# 切割图像
+ip.split_img(data_interest)
+
+# 获取数字
+num = ip.split_num(number_interest)
+print(type(num))
+print(num)
+
+# gi.cv_show('data', data_interest)
+# gi.cv_show('number', a)
+
+cv.imwrite('data/saved_img/data_interest.PNG', data_interest)  # 保存图片
+cv.imwrite('data/saved_img/number_interest.PNG', number_interest)  # 保存图片

number_processing.py

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+import cv2
+import os
+
+
+def get_num_list(folder_path):
+    """
+    读取文件夹中的所有.png格式图片，并进行二值化处理，并返回包含二值化图片的列表。
+    """
+    binary_images = []
+    # 遍历文件夹中的所有文件
+    for filename in sorted(os.listdir(folder_path)):
+        # 检查文件是否是.png格式
+        if filename.endswith('.png'):
+            # 构建图片的完整路径
+            img_path = os.path.join(folder_path, filename)
+
+            # 使用OpenCV读取图片为灰度图
+            img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
+
+            # 检查图片是否成功读取
+            if img is not None:
+                # 使用阈值进行二值化处理
+                # 假设我们使用127作为阈值，但这可以根据你的需求进行调整
+                _, binary_img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
+
+                # 将二值化后的图片添加到列表中
+                binary_images.append(binary_img.tolist())
+
+    return binary_images
+
+
+num_list = [
+    [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
+     [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
+     [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]],
+    [[0, 0, 255, 255], [255, 255, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255],
+     [0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255]],
+    [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255],
+     [0, 0, 0, 255, 255, 0], [0, 0, 255, 255, 0, 0], [0, 255, 255, 0, 0, 0], [255, 255, 0, 0, 0, 0],
+     [255, 255, 0, 0, 0, 0], [255, 255, 255, 255, 255, 255]],
+    [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255],
+     [0, 0, 255, 255, 255, 0], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255],
+     [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]],
+    [[0, 0, 0, 0, 255, 255], [0, 0, 0, 255, 255, 255], [0, 0, 255, 255, 255, 255], [0, 0, 255, 255, 255, 255],
+     [0, 255, 255, 0, 255, 255], [0, 255, 255, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 255, 255, 255, 255],
+     [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255]],
+    [[255, 255, 255, 255, 255, 255], [255, 255, 0, 0, 0, 0], [255, 255, 0, 0, 0, 0], [255, 255, 0, 0, 0, 0],
+     [255, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255],
+     [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]],
+    [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 0, 0], [255, 255, 0, 0, 0, 0],
+     [255, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
+     [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]],
+    [[255, 255, 255, 255, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 255, 255, 0], [0, 0, 0, 255, 255, 0],
+     [0, 0, 255, 255, 0, 0], [0, 0, 255, 255, 0, 0], [0, 0, 255, 255, 0, 0], [0, 255, 255, 0, 0, 0],
+     [0, 255, 255, 0, 0, 0], [0, 255, 255, 0, 0, 0]],
+    [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
+     [0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
+     [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]],
+    [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
+     [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255],
+     [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]]]