【幸狐Core3566模组试用体验】基于openCV的猫脸识别

本文主要介绍如何基于openCV来实现一个简单的猫脸识别应用。 一、基础准备 首先需要安装openCV，因为python版的方便点，所以直接安装python版openCV，通过命令sudo apt-get install python-opencv即可完成。 linaro@linaro-alip:~$ sudo apt-get install python-opencv Reading package lists... Done Building dependency tree Reading state information... Done The following packages were automatically installed and are no longer required: liba52-0.7.4 libdca0 libdrm-freedreno1 libdrm-tegra0 Use 'sudo apt autoremove' to remove them. The following additional packages will be installed: python-numpy python-pkg-resources Suggested packages: gfortran python-dev python-pytest python-numpy-dbg python-numpy-doc python-setuptools The following NEW packages will be installed: python-numpy python-opencv python-pkg-resources 0 upgraded, 3 newly installed, 0 to remove and 48 not upgraded. Need to get 2,636 kB of archives. After this operation, 14.7 MB of additional disk space will be used. Do you want to continue? [Y/n] Get:1 http://mirrors.ustc.edu.cn/debian buster/main arm64 python-pkg-resources all 40.8.0-1 [182 kB] Get:2 http://mirrors.ustc.edu.cn/debian buster/main arm64 python-numpy arm64 1:1.16.2-1 [1,975 kB] Get:3 http://mirrors.ustc.edu.cn/debian buster/main arm64 python-opencv arm64 3.2.0+dfsg-6 [478 kB] Fetched 2,636 kB in 0s (5,938 kB/s) Selecting previously unselected package python-pkg-resources. (Reading database ... 76876 files and directories currently installed.) Preparing to unpack .../python-pkg-resources_40.8.0-1_all.deb ... Unpacking python-pkg-resources (40.8.0-1) ... Selecting previously unselected package python-numpy. Preparing to unpack .../python-numpy_1%3a1.16.2-1_arm64.deb ... Unpacking python-numpy (1:1.16.2-1) ... Selecting previously unselected package python-opencv. Preparing to unpack .../python-opencv_3.2.0+dfsg-6_arm64.deb ... Unpacking python-opencv (3.2.0+dfsg-6) ... Setting up python-pkg-resources (40.8.0-1) ... Setting up python-numpy (1:1.16.2-1) ... Setting up python-opencv (3.2.0+dfsg-6) ... 安装完成后检查下，import cv2没问题就OK了。 linaro@linaro-alip:~$ python Python 2.7.16 (default, Sep 20 2023, 07:59:17) [GCC 8.3.0] on linux2 Type "help", "copyright", "credits" or "license" for more information. > >> import cv2 > >> exit() 二、设计和实现 在OpenCV中，目标检测使用的函数是cv2.CascadeClassifier.detectMultiScale()，它可以检测图像中所有的目标。其完整定义如下： def detectMultiScale(self, image, scaleFactor=None, minNeighbors=None, flags=None, minSize=None, maxSize=None): image：待检测的图像，通常为灰度图像 scaleFactor：表示在前后两次相继的扫描中，搜索窗口的缩放比例 minNeighbors：表示构成检测目标的相邻矩形的最小个数。默认值为3，表示有3个以上的检测标记存在时，才认为目标的存在。如果希望提高检测的准确率，可以将该值设置的更大，但同时可能会让一些目标无法被检测到 flags：不常用参数，一般省略。 minSize：目标的最小尺寸，小于这个尺寸的目标将被忽略 maxSize：目标的最大尺寸，大于这个尺寸的目标将被忽略 该函数的返回值是目标对象的矩形框向量组。 OpenCV已经自带了猫脸的Haar特征分类器，本文选择haarcascade_frontalcatface.xml识别分类器。 通过detectMultiScale函数返回的是猫脸的矩形框向量组，包括左上角坐标(x,y)，长宽(w,h)。而绘制猫脸矩形框则通过rectangle函数实现。 具体实现python代码如下： import cv2 face_cascade = cv2.CascadeClassifier('haarcascade_frontalcatface.xml') img = cv2.imread("./cats.jpg") # convert to gray gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) face_rect = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=3) print("number of cats: ", len(face_rect)) for (x,y,w,h) in face_rect: cv2.rectangle(img, (x,y), (x+w, y+h), color=(0,255,0)) cv2.imwrite("cats-1.jpg", img) cv2.release() 三、运行 测试的图片是网上随便下载的。     将图片、猫脸识别分类器还有python上传至幸狐 Core3566 模组，并运行python脚本。   只识别出2只猫咪，看来有一只的脸不合格，要想识别出来，需要在detectMultiScale中调试参数，进一步提示识别精准度，看了看时间，都快物业了，这里就不继续了，来个识别的结果。     看来这个黑不溜秋的不好识别。

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