[经验]

【幸狐 Core3566 模组试用体验】基于openCV的猫脸识别

大菠萝Alpha

2023-9-24 23:50:23

1253

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.
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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()