最近有个比较要好的朋友问我能不能从监控视频里识别到从监控跟前经过的指定的人。因为他们单位的监控室经常要花大量的人力跟时间去找某个人在哪个位置出现过的证据。听起来像是一份比较有挑战性的任务，就答应他试试看。
先理一下思路，首先要做的工作是从视频中将人脸给框出来，然后拿到这个人脸跟给定的人脸进行对比，如果两张脸相似度很高，那就可以标记此人可能在视频里出现过，然后根据标记点再去人工核实一遍就OK了。
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那就先从把人脸给框出来入手吧，查了一下资料，貌似Python有个现成的库opencv来完成这个事情，整个的实现代码也就10几行，so easy！
照着网上的示例，自己撸了一个测试代码出来，试了一下，效果还是可以的，全部实现代码如下。
<pre spellcheck="false" style="box-sizing: border-box; margin: 5px 0px; padding: 5px 10px; border: 0px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-weight: 400; font-stretch: inherit; font-size: 16px; line-height: inherit; font-family: inherit; vertical-align: baseline; cursor: text; counter-reset: list-1 0 list-2 0 list-3 0 list-4 0 list-5 0 list-6 0 list-7 0 list-8 0 list-9 0; background-color: rgb(240, 240, 240); border-radius: 3px; white-space: pre-wrap; color: rgb(34, 34, 34); letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">import cv2
imagepath="input.jpg"
image = cv2.imread(imagepath)
t1= datetime.now()#测试起始时间
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
face_cascade = cv2.CascadeClassifier(r'./haarcascade_frontalface_default.xml')
faces = face_cascade.detectMultiScale(
gray,
scaleFactor = 1.15,
minNeighbors = 5,
minSize = (5,5),
)
for(x,y,w,h) in faces:
cv2.rectangle(image,(x,y),(x+w,y+w),(0,255,0),2)
cv2.imwrite('output.jpg', image)
</pre>
原始图片
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image
<input class="pgc-img-caption-ipt" placeholder="图片描述(最多50字)" value="" style="box-sizing: border-box; outline: 0px; color: rgb(102, 102, 102); position: absolute; left: 187.5px; transform: translateX(-50%); padding: 6px 7px; max-width: 100%; width: 375px; text-align: center; cursor: text; font-size: 12px; line-height: 1.5; background-color: rgb(255, 255, 255); background-image: none; border: 0px solid rgb(217, 217, 217); border-radius: 4px; transition: all 0.2s cubic-bezier(0.645, 0.045, 0.355, 1) 0s;"></tt-image>
检测结果
<tt-image data-tteditor-tag="tteditorTag" contenteditable="false" class="syl1559629802090" data-render-status="finished" data-syl-blot="image" style="box-sizing: border-box; cursor: text; color: rgb(34, 34, 34); font-family: "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei", "WenQuanYi Micro Hei", "Helvetica Neue", Arial, sans-serif; font-size: 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; white-space: pre-wrap; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); text-decoration-style: initial; text-decoration-color: initial; display: block;">
image
<input class="pgc-img-caption-ipt" placeholder="图片描述(最多50字)" value="" style="box-sizing: border-box; outline: 0px; color: rgb(102, 102, 102); position: absolute; left: 187.5px; transform: translateX(-50%); padding: 6px 7px; max-width: 100%; width: 375px; text-align: center; cursor: text; font-size: 12px; line-height: 1.5; background-color: rgb(255, 255, 255); background-image: none; border: 0px solid rgb(217, 217, 217); border-radius: 4px; transition: all 0.2s cubic-bezier(0.645, 0.045, 0.355, 1) 0s;"></tt-image>
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