数字图像与机器视觉基础补充(2)

数字图像与机器视觉基础补充(2)

目录

• 一、彩色图像转换
• 二、车牌数字分割
• • （一）图片
  • （二）完整代码
  • （三）结果
• 参考文献

一、彩色图像转换 二、车牌数字分割 （一）图片

图片(目录不能有中文，不然后面会出错)

（二）完整代码

#导入包
import os
import shutil
import cv2
import numpy as np

file_path = "D:/jupyter/car/picture/"
licenses = os.listdir(file_path)
for license in licenses:
    path = file_path+license
    output_path = "D:/jupyter/car/"+license # 图片输出路径
    # 如果该路径存在则删除
    if os.path.isdir(output_path):
        shutil.rmtree(output_path)
    # 创建文件夹
    os.mkdir(output_path)

    # 1.读取图片
    src = cv2.imread(path)
    img = src.copy()
    # 2.去除车牌上螺丝，将其替换为车牌底色
    cv2.circle(img, (145, 20), 10, (255, 0, 0), thickness=-1)
    cv2.circle(img, (430, 20), 10, (255, 0, 0), thickness=-1)
    cv2.circle(img, (145, 170), 10, (255, 0, 0), thickness=-1)
    cv2.circle(img, (430, 170), 10, (255, 0, 0), thickness=-1)
    cv2.circle(img, (180, 90), 10, (255, 0, 0), thickness=-1)

    # 3.灰度
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    
    # 4.高斯滤波
    GSblurred = cv2.GaussianBlur(gray, (5, 5), 12)

    # 5.将灰度图二值化设定阈值
    ret, thresh = cv2.threshold(GSblurred , 127, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
    print("ret",ret)

    # 6. 闭运算
    kernel = np.ones((3, 3), int)
    closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel,iterations=2)
    #二值化
    ret, thresh = cv2.threshold(closed, 127, 255, cv2.THRESH_BINARY+ cv2.THRESH_OTSU)
    # 7.分割字符
    white = [] # 记录每一列的白色像素总和
    black = [] # ..........黑色.......
    height = thresh.shape[0]
    width = thresh.shape[1]
    white_max = 0
    black_max = 0
    # 计算每一列的黑白色像素总和
    for i in range(width):
      s = 0 # 这一列白色总数
      t = 0 # 这一列黑色总数
      for j in range(height):
        if thresh[j][i] == 255:
          s += 1
        if thresh[j][i] == 0:
          t += 1
      white_max = max(white_max, s)
      black_max = max(black_max, t)
      white.append(s)
      black.append(t)
      # print(s)
      # print(t)

    arg = False  # False表示白底黑字；True表示黑底白字
    if black_max > white_max:
        arg = True

    # 分割图像
    def find_end(start_):
        end_ = start_ + 1
        for m in range(start_ + 1, width - 1):
            if (black[m] if arg else white[m]) > (0.95 * black_max if arg else 0.95 * white_max):  # 0.95这个参数请多调整，对应下面的0.05
                end_ = m
                break
        return end_

    n = 1
    start = 1
    end = 2
    i=0;
    cj=[]

    while n < width - 2:
        n += 1
        if (white[n] if arg else black[n]) > (0.05 * white_max if arg else 0.05 * black_max):
            # 上面这些判断用来辨别是白底黑字还是黑底白字
            # 0.05这个参数请多调整，对应上面的0.95
            start = n
            end = find_end(start)
            n = end
            if end - start > 5:
                cj.append(thresh[1:height, start:end])
                cv2.imwrite(output_path + '/' + str(i) + '.jpg', cj[i])
                i += 1;

（三）结果

参考文献

python中出现“Unexpected indent”的原因，已解决