本文只是一种实现思路，当然PS很好用（一张的话建议使用PS哦~，多张图片的话用代码快很多哦~），希望大家能够学习更多的知识，才分享了这个文章。更多精彩，请关注公众号：【测试员小何】，可以获取最新软件测试大厂面试资料和Python自动化、接口、框架搭建学习资料！

1.图片来源

该图片来源于百度图片，如果侵权，请联系我删除！图片仅用于知识交流。

2.读取图片并显示

  • imread()：读取图片；
  • imshow()：展示图片；
  • waitkey()：设置窗口等待，如果不设置，窗口会一闪而过；
<span style="color:#000000"><code class="language-python"><span
style="color:#b294bb">import</span> cv2
<span style="color:#b294bb">import</span> numpy <span style="color:#b294bb">as</span> np
<span style="color:#969896"># 读取照片</span>  #公众号【测试员小何】
img<span style="color:#a67f59">=</span>cv2<span style="color:#999999">.</span>imread<span style="color:#999999">(</span><span style="color:#b5bd68">'girl.jpg'</span><span style="color:#999999">)</span><span style="color:#969896"># 显示图像</span> #公众号【测试员小何】
cv2<span style="color:#999999">.</span>imshow<span style="color:#999999">(</span><span style="color:#b5bd68">'img'</span><span style="color:#999999">,</span>img<span style="color:#999999">)</span><span style="color:#969896"># 窗口等待的命令，0表示无限等待</span>
cv2<span style="color:#999999">.</span>waitKey<span style="color:#999999">(</span><span style="color:#b5bd68">0</span><span style="color:#999999">)</span>
</code></span>

效果如下：

3.图片缩放

  • resize()：图片缩放，其中fx和fy表示缩放比例，0.5表示缩放为以前的 一半。
<span style="color:#000000"><code class="language-python"><span  style="color:#b294bb">import</span> cv2
<span style="color:#b294bb">import</span> numpy <span style="color:#b294bb">as</span> np
<span style="color:#969896"># 读取照片</span>
img<span style="color:#a67f59">=</span>cv2<span style="color:#999999">.</span>imread<span style="color:#999999">(</span><span style="color:#b5bd68">'girl.jpg'</span><span style="color:#999999">)</span><span style="color:#969896"># 图像缩放</span>  #公众号【测试员小何】
img <span style="color:#a67f59">=</span> cv2<span style="color:#999999">.</span>resize<span style="color:#999999">(</span>img<span style="color:#999999">,</span><span style="color:#de935f">None</span><span style="color:#999999">,</span>fx<span style="color:#a67f59">=</span><span style="color:#b5bd68">0.5</span><span style="color:#999999">,</span>fy<span style="color:#a67f59">=</span><span style="color:#b5bd68">0.5</span><span style="color:#999999">)</span>
rows<span style="color:#999999">,</span>cols<span style="color:#999999">,</span>channels <span style="color:#a67f59">=</span> img<span style="color:#999999">.</span>shape
<span style="color:#b294bb">print</span><span style="color:#999999">(</span>rows<span style="color:#999999">,</span>cols<span style="color:#999999">,</span>channels<span style="color:#999999">)</span><span style="color:#969896"># 显示图像</span>
cv2<span style="color:#999999">.</span>imshow<span style="color:#999999">(</span><span style="color:#b5bd68">'img'</span><span style="color:#999999">,</span>img<span style="color:#999999">)</span><span style="color:#969896"># 窗口等待的命令，0表示无限等待</span>
cv2<span style="color:#999999">.</span>waitKey<span style="color:#999999">(</span><span style="color:#b5bd68">0</span><span style="color:#999999">)</span>
</code></span>

结果如下：

4.将图片转换为灰度图像

三色图片有RGB三个颜色通道，无法进行腐蚀和膨胀的操作。这个就需要我们将彩色图片转换为hsv灰度图像后，再完成腐蚀和膨胀的操作。

  • cv2.cvtColor(img,cv2.COLOR_BGR2HSV)可以将彩色图片转化为hsv灰度图片。
<span style="color:#000000"><code class="language-python"><span style="color:#b294bb">import</span> cv2
<span style="color:#b294bb">import</span> numpy <span style="color:#b294bb">as</span> np
<span style="color:#969896"># 读取照片</span>
img<span style="color:#a67f59">=</span>cv2<span style="color:#999999">.</span>imread<span style="color:#999999">(</span><span style="color:#b5bd68">'girl.jpg'</span><span style="color:#999999">)</span><span style="color:#969896"># 图像缩放</span>   #公众号【测试员小何】
img <span style="color:#a67f59">=</span> cv2<span style="color:#999999">.</span>resize<span style="color:#999999">(</span>img<span style="color:#999999">,</span><span style="color:#de935f">None</span><span style="color:#999999">,</span>fx<span style="color:#a67f59">=</span><span style="color:#b5bd68">0.5</span><span style="color:#999999">,</span>fy<span style="color:#a67f59">=</span><span style="color:#b5bd68">0.5</span><span style="color:#999999">)</span>
rows<span style="color:#999999">,</span>cols<span style="color:#999999">,</span>channels <span style="color:#a67f59">=</span> img<span style="color:#999999">.</span>shape
<span style="color:#b294bb">print</span><span style="color:#999999">(</span>rows<span style="color:#999999">,</span>cols<span style="color:#999999">,</span>channels<span style="color:#999999">)</span>
cv2<span style="color:#999999">.</span>imshow<span style="color:#999999">(</span><span style="color:#b5bd68">'img'</span><span style="color:#999999">,</span>img<span style="color:#999999">)</span><span style="color:#969896"># 图片转换为二值化图</span>
hsv <span style="color:#a67f59">=</span> cv2<span style="color:#999999">.</span>cvtColor<span style="color:#999999">(</span>img<span style="color:#999999">,</span>cv2<span style="color:#999999">.</span>COLOR_BGR2HSV<span style="color:#999999">)</span><span style="color:#969896"># 显示图像</span>
cv2<span style="color:#999999">.</span>imshow<span style="color:#999999">(</span><span style="color:#b5bd68">'hsv'</span><span style="color:#999999">,</span>hsv<span style="color:#999999">)</span><span style="color:#969896"># 窗口等待的命令，0表示无限等待</span>
cv2<span style="color:#999999">.</span>waitKey<span style="color:#999999">(</span><span style="color:#b5bd68">0</span><span style="color:#999999">)</span>
</code></span>

结果如下：

5.将图片进行二值化处理

二值化处理是为了将图片转换为黑白图片。二值化类似于1表示男、2表示女，对于图像的处理我们也需要自定义一个最小值和最大值，这里分别用lower_blue和upper_blue表示

  • lower_blue = np.array([90,70,70])
  • upper_blue = np.array([110,255,255])
  • inRange(hsv, lower_blue, upper_blue)将图片进行二值化操作。
<span style="color:#000000"><code class="language-python"><span style="color:#b294bb">import</span> cv2
<span style="color:#b294bb">import</span> numpy <span style="color:#b294bb">as</span> np
<span style="color:#969896"># 读取照片</span>
img<span style="color:#a67f59">=</span>cv2<span style="color:#999999">.</span>imread<span style="color:#999999">(</span><span style="color:#b5bd68">'girl.jpg'</span><span style="color:#999999">)</span><span style="color:#969896"># 图像缩放</span>  #公众号【测试员小何】
img <span style="color:#a67f59">=</span> cv2<span style="color:#999999">.</span>resize<span style="color:#999999">(</span>img<span style="color:#999999">,</span><span style="color:#de935f">None</span><span style="color:#999999">,</span>fx<span style="color:#a67f59">=</span><span style="color:#b5bd68">0.5</span><span style="color:#999999">,</span>fy<span style="color:#a67f59">=</span><span style="color:#b5bd68">0.5</span><span style="color:#999999">)</span>
rows<span style="color:#999999">,</span>cols<span style="color:#999999">,</span>channels <span style="color:#a67f59">=</span> img<span style="color:#999999">.</span>shape
<span style="color:#b294bb">print</span><span style="color:#999999">(</span>rows<span style="color:#999999">,</span>cols<span style="color:#999999">,</span>channels<span style="color:#999999">)</span>
cv2<span style="color:#999999">.</span>imshow<span style="color:#999999">(</span><span style="color:#b5bd68">'img'</span><span style="color:#999999">,</span>img<span style="color:#999999">)</span><span style="color:#969896"># 图片转换为灰度图</span>
hsv <span style="color:#a67f59">=</span> cv2<span style="color:#999999">.</span>cvtColor<span style="color:#999999">(</span>img<span style="color:#999999">,</span>cv2<span style="color:#999999">.</span>COLOR_BGR2HSV<span style="color:#999999">)</span>
cv2<span style="color:#999999">.</span>imshow<span style="color:#999999">(</span><span style="color:#b5bd68">'hsv'</span><span style="color:#999999">,</span>hsv<span style="color:#999999">)</span><span style="color:#969896"># 图片的二值化处理</span>
lower_blue <span style="color:#a67f59">=</span> np<span style="color:#999999">.</span>array<span style="color:#999999">(</span><span style="color:#999999">[</span><span style="color:#b5bd68">90</span><span style="color:#999999">,</span><span style="color:#b5bd68">70</span><span style="color:#999999">,</span><span style="color:#b5bd68">70</span><span style="color:#999999">]</span><span style="color:#999999">)</span>
upper_blue <span style="color:#a67f59">=</span> np<span style="color:#999999">.</span>array<span style="color:#999999">(</span><span style="color:#999999">[</span><span style="color:#b5bd68">110</span><span style="color:#999999">,</span><span style="color:#b5bd68">255</span><span style="color:#999999">,</span><span style="color:#b5bd68">255</span><span style="color:#999999">]</span><span style="color:#999999">)</span>
mask <span style="color:#a67f59">=</span> cv2<span style="color:#999999">.</span>inRange<span style="color:#999999">(</span>hsv<span style="color:#999999">,</span> lower_blue<span style="color:#999999">,</span> upper_blue<span style="color:#999999">)</span><span style="color:#969896"># 显示图像</span>
cv2<span style="color:#999999">.</span>imshow<span style="color:#999999">(</span><span style="color:#b5bd68">'mask'</span><span style="color:#999999">,</span>mask<span style="color:#999999">)</span><span style="color:#969896"># 窗口等待的命令，0表示无限等待</span>
cv2<span style="color:#999999">.</span>waitKey<span style="color:#999999">(</span><span style="color:#b5bd68">0</span><span style="color:#999999">)</span>
</code></span>

结果如下：

缺点：我们观察第三章图片，发现黑色区域有时候会出现一些噪声(白点)，这里可能显示的不是很明显，有的图片显示的很明显，这就需要我们进行腐蚀或膨胀。

6.图象的腐蚀和膨胀

上面的图象进行二值化后，出现了一些噪声，我们可以采用腐蚀或膨胀进行图片的处理，观察哪种的处理效果好一些。

  • erode(mask,None,iterations=1)进行腐蚀操作。
  • dilate(erode,None,iterations=1)进行膨胀操作。
<span style="color:#000000"><code class="language-python"><span style="color:#b294bb">import</span> cv2
<span style="color:#b294bb">import</span> numpy <span style="color:#b294bb">as</span> np
<span style="color:#969896"># 读取照片</span>
img<span style="color:#a67f59">=</span>cv2<span style="color:#999999">.</span>imread<span style="color:#999999">(</span><span style="color:#b5bd68">'girl.jpg'</span><span style="color:#999999">)</span><span style="color:#969896"># 图像缩放</span>  #公众号【测试员小何】
img <span style="color:#a67f59">=</span> cv2<span style="color:#999999">.</span>resize<span style="color:#999999">(</span>img<span style="color:#999999">,</span><span style="color:#de935f">None</span><span style="color:#999999">,</span>fx<span style="color:#a67f59">=</span><span style="color:#b5bd68">0.5</span><span style="color:#999999">,</span>fy<span style="color:#a67f59">=</span><span style="color:#b5bd68">0.5</span><span style="color:#999999">)</span>
rows<span style="color:#999999">,</span>cols<span style="color:#999999">,</span>channels <span style="color:#a67f59">=</span> img<span style="color:#999999">.</span>shape
<span style="color:#b294bb">print</span><span style="color:#999999">(</span>rows<span style="color:#999999">,</span>cols<span style="color:#999999">,</span>channels<span style="color:#999999">)</span>
cv2<span style="color:#999999">.</span>imshow<span style="color:#999999">(</span><span style="color:#b5bd68">'img'</span><span style="color:#999999">,</span>img<span style="color:#999999">)</span><span style="color:#969896"># 图片转换为灰度图</span>
hsv <span style="color:#a67f59">=</span> cv2<span style="color:#999999">.</span>cvtColor<span style="color:#999999">(</span>img<span style="color:#999999">,</span>cv2<span style="color:#999999">.</span>COLOR_BGR2HSV<span style="color:#999999">)</span>
cv2<span style="color:#999999">.</span>imshow<span style="color:#999999">(</span><span style="color:#b5bd68">'hsv'</span><span style="color:#999999">,</span>hsv<span style="color:#999999">)</span><span style="color:#969896"># 图片的二值化处理</span>
lower_blue<span style="color:#a67f59">=</span>np<span style="color:#999999">.</span>array<span style="color:#999999">(</span><span style="color:#999999">[</span><span style="color:#b5bd68">90</span><span style="color:#999999">,</span><span style="color:#b5bd68">70</span><span style="color:#999999">,</span><span style="color:#b5bd68">70</span><span style="color:#999999">]</span><span style="color:#999999">)</span>
upper_blue<span style="color:#a67f59">=</span>np<span style="color:#999999">.</span>array<span style="color:#999999">(</span><span style="color:#999999">[</span><span style="color:#b5bd68">110</span><span style="color:#999999">,</span><span style="color:#b5bd68">255</span><span style="color:#999999">,</span><span style="color:#b5bd68">255</span><span style="color:#999999">]</span><span style="color:#999999">)</span>
mask <span style="color:#a67f59">=</span> cv2<span style="color:#999999">.</span>inRange<span style="color:#999999">(</span>hsv<span style="color:#999999">,</span> lower_blue<span style="color:#999999">,</span> upper_blue<span style="color:#999999">)</span><span style="color:#969896">#腐蚀膨胀</span>
erode<span style="color:#a67f59">=</span>cv2<span style="color:#999999">.</span>erode<span style="color:#999999">(</span>mask<span style="color:#999999">,</span><span style="color:#de935f">None</span><span style="color:#999999">,</span>iterations<span style="color:#a67f59">=</span><span style="color:#b5bd68">1</span><span style="color:#999999">)</span>
cv2<span style="color:#999999">.</span>imshow<span style="color:#999999">(</span><span style="color:#b5bd68">'erode'</span><span style="color:#999999">,</span>erode<span style="color:#999999">)</span>dilate<span style="color:#a67f59">=</span>cv2<span style="color:#999999">.</span>dilate<span style="color:#999999">(</span>erode<span style="color:#999999">,</span><span style="color:#de935f">None</span><span style="color:#999999">,</span>iterations<span style="color:#a67f59">=</span><span style="color:#b5bd68">1</span><span style="color:#999999">)</span>
cv2<span style="color:#999999">.</span>imshow<span style="color:#999999">(</span><span style="color:#b5bd68">'dilate'</span><span style="color:#999999">,</span>dilate<span style="color:#999999">)</span><span style="color:#969896"># 窗口等待的命令，0表示无限等待</span>
cv2<span style="color:#999999">.</span>waitKey<span style="color:#999999">(</span><span style="color:#b5bd68">0</span><span style="color:#999999">)</span>
</code></span>

结果如下：

观察上图：对于这个图片，无论是腐蚀或膨胀，都起到了很好的去图片噪声的操作，我们使用腐蚀后的图片也可以，我们使用膨胀后的图片也可以。

7.遍历每个像素点进行颜色替换

图片是由每一个像素点组成的，我们就是要找到腐蚀后得到图片的，白色底色处的像素点，然后将原图中对应位置处的像素点，替换为红色。

<span style="color:#000000"><code class="language-python"><span style="color:#b294bb">import</span> cv2
<span style="color:#b294bb">import</span> numpy <span style="color:#b294bb">as</span> np
<span style="color:#969896"># 读取照片</span>
img<span style="color:#a67f59">=</span>cv2<span style="color:#999999">.</span>imread<span style="color:#999999">(</span><span style="color:#b5bd68">'girl.jpg'</span><span style="color:#999999">)</span><span style="color:#969896"># 图像缩放</span>  #公众号【测试员小何】
img <span style="color:#a67f59">=</span> cv2<span style="color:#999999">.</span>resize<span style="color:#999999">(</span>img<span style="color:#999999">,</span><span style="color:#de935f">None</span><span style="color:#999999">,</span>fx<span style="color:#a67f59">=</span><span style="color:#b5bd68">0.5</span><span style="color:#999999">,</span>fy<span style="color:#a67f59">=</span><span style="color:#b5bd68">0.5</span><span style="color:#999999">)</span>
rows<span style="color:#999999">,</span>cols<span style="color:#999999">,</span>channels <span style="color:#a67f59">=</span> img<span style="color:#999999">.</span>shape
<span style="color:#b294bb">print</span><span style="color:#999999">(</span>rows<span style="color:#999999">,</span>cols<span style="color:#999999">,</span>channels<span style="color:#999999">)</span>
cv2<span style="color:#999999">.</span>imshow<span style="color:#999999">(</span><span style="color:#b5bd68">'img'</span><span style="color:#999999">,</span>img<span style="color:#999999">)</span><span style="color:#969896"># 图片转换为灰度图</span>
hsv <span style="color:#a67f59">=</span> cv2<span style="color:#999999">.</span>cvtColor<span style="color:#999999">(</span>img<span style="color:#999999">,</span>cv2<span style="color:#999999">.</span>COLOR_BGR2HSV<span style="color:#999999">)</span>
cv2<span style="color:#999999">.</span>imshow<span style="color:#999999">(</span><span style="color:#b5bd68">'hsv'</span><span style="color:#999999">,</span>hsv<span style="color:#999999">)</span><span style="color:#969896"># 图片的二值化处理</span>
lower_blue<span style="color:#a67f59">=</span>np<span style="color:#999999">.</span>array<span style="color:#999999">(</span><span style="color:#999999">[</span><span style="color:#b5bd68">90</span><span style="color:#999999">,</span><span style="color:#b5bd68">70</span><span style="color:#999999">,</span><span style="color:#b5bd68">70</span><span style="color:#999999">]</span><span style="color:#999999">)</span>
upper_blue<span style="color:#a67f59">=</span>np<span style="color:#999999">.</span>array<span style="color:#999999">(</span><span style="color:#999999">[</span><span style="color:#b5bd68">110</span><span style="color:#999999">,</span><span style="color:#b5bd68">255</span><span style="color:#999999">,</span><span style="color:#b5bd68">255</span><span style="color:#999999">]</span><span style="color:#999999">)</span>
mask <span style="color:#a67f59">=</span> cv2<span style="color:#999999">.</span>inRange<span style="color:#999999">(</span>hsv<span style="color:#999999">,</span> lower_blue<span style="color:#999999">,</span> upper_blue<span style="color:#999999">)</span><span style="color:#969896">#腐蚀膨胀</span>
erode<span style="color:#a67f59">=</span>cv2<span style="color:#999999">.</span>erode<span style="color:#999999">(</span>mask<span style="color:#999999">,</span><span style="color:#de935f">None</span><span style="color:#999999">,</span>iterations<span style="color:#a67f59">=</span><span style="color:#b5bd68">1</span><span style="color:#999999">)</span>
cv2<span style="color:#999999">.</span>imshow<span style="color:#999999">(</span><span style="color:#b5bd68">'erode'</span><span style="color:#999999">,</span>erode<span style="color:#999999">)</span>dilate<span style="color:#a67f59">=</span>cv2<span style="color:#999999">.</span>dilate<span style="color:#999999">(</span>erode<span style="color:#999999">,</span><span style="color:#de935f">None</span><span style="color:#999999">,</span>iterations<span style="color:#a67f59">=</span><span style="color:#b5bd68">1</span><span style="color:#999999">)</span>
cv2<span style="color:#999999">.</span>imshow<span style="color:#999999">(</span><span style="color:#b5bd68">'dilate'</span><span style="color:#999999">,</span>dilate<span style="color:#999999">)</span><span style="color:#969896">#遍历替换</span>
<span style="color:#b294bb">for</span> i <span style="color:#b294bb">in</span> <span style="color:#b5bd68">range</span><span style="color:#999999">(</span>rows<span style="color:#999999">)</span><span style="color:#999999">:</span><span style="color:#b294bb">for</span> j <span style="color:#b294bb">in</span> <span style="color:#b5bd68">range</span><span style="color:#999999">(</span>cols<span style="color:#999999">)</span><span style="color:#999999">:</span><span style="color:#b294bb">if</span> erode<span style="color:#999999">[</span>i<span style="color:#999999">,</span>j<span style="color:#999999">]</span><span style="color:#a67f59">==</span><span style="color:#b5bd68">255</span><span style="color:#999999">:</span> <span style="color:#969896"># 像素点为255表示的是白色，我们就是要将白色处的像素点，替换为红色</span>img<span style="color:#999999">[</span>i<span style="color:#999999">,</span>j<span style="color:#999999">]</span><span style="color:#a67f59">=</span><span style="color:#999999">(</span><span style="color:#b5bd68">0</span><span style="color:#999999">,</span><span style="color:#b5bd68">0</span><span style="color:#999999">,</span><span style="color:#b5bd68">255</span><span style="color:#999999">)</span> <span style="color:#969896"># 此处替换颜色，为BGR通道，不是RGB通道</span>
cv2<span style="color:#999999">.</span>imshow<span style="color:#999999">(</span><span style="color:#b5bd68">'res'</span><span style="color:#999999">,</span>img<span style="color:#999999">)</span><span style="color:#969896"># 窗口等待的命令，0表示无限等待</span>  #公众号【测试员小何】
cv2<span style="color:#999999">.</span>waitKey<span style="color:#999999">(</span><span style="color:#b5bd68">0</span><span style="color:#999999">)</span>
</code></span>

效果如下：

  

 
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