手把手带你入坑树莓派（3B+）之第四篇，安装OpenCV+一个成功案例

我想做树莓派手指识别，想用QT+OpenCV库编写代码。就涉及到了要安装opencv。我肯定成功了。我相信你也可以，只要跟着我一起做。

第一步,安装OpenCV

在下图所示的路径下：

把以下代码挨个从上到下给安装了。

sudo apt-get update   #更新源
sudo apt-get upgrade   #更新已安装程序!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!  
#以下是更新依赖
sudo apt-get install build-essential cmake pkg-config
sudo apt-get install libjpeg-dev libtiff5-dev libjasper-dev libpng12-dev
sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
sudo apt-get install libxvidcore-dev libx264-dev
sudo apt-get install libgtk2.0-dev libgtk-3-dev
sudo apt-get install libatlas-base-dev gfortran
#以下是更新Python3和PIP3
sudo apt-get install python3-dev
sudo apt-get install python3-pip
#以下是安装OpenCV
pip3 install opencv-python
#以下是摄像头依赖
sudo apt-get install libqtgui4
sudo apt-get install libqt4-test

然后你需要输入：

sudo raspi-config

找到5.Interfacing Options 找到P1 Camera把它使能（Enable）。

sudo modprobe bcm2835-v4l2

然后在cd home/pi目录下创建一个叫opencv_text.py的文件，用来测试摄像头是否与OpenCV配好了。

sudo nano opencv_text.py        #创建个新python文件并用nano编辑

把以下代码粘贴到打开的opencv_text.py文件中。

import cv2
import numpy as np

cap = cv2.VideoCapture(0)

while  True:
    _, frame = cap.read()
    
    cv2.imshow("Frame", frame)
    
    key = cv2.waitKey(1)
    if key == 27:
        break

cap.release()
cv2.destroyALLWindows()

 Ctrl+X退出，Y保存，Enter退出编辑界面。

然后用把它打开并Run

然后就可以了：

第二步，Try more examples

Number of fingers image detection

import numpy as np
import cv2
from threading import Event
import re
import math
import time
import random
import os
def _remove_background(img):#去除背景
    fgbg = cv2.createBackgroundSubtractorMOG2() # 利用BackgroundSubtractorMOG2算法消除背景
    fgmask = fgbg.apply(img)
    #cv2.imshow("image2", fgmask)
    kernel = np.ones((3, 3), np.uint8)
    fgmask = cv2.erode(fgmask, kernel, iterations=1)
    res = cv2.bitwise_and(img, img, mask=fgmask)
    return res

def _bodyskin_detetc(frame):#得到去除背景的图片skin
    # 肤色检测: YCrCb之Cr分量 + OTSU二值化
    ycrcb = cv2.cvtColor(frame, cv2.COLOR_BGR2YCrCb) # 分解为YUV图像,得到CR分量
    (_, cr, _) = cv2.split(ycrcb)
    cr1 = cv2.GaussianBlur(cr, (5, 5), 0) # 高斯滤波
    _, skin = cv2.threshold(cr1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)  # OTSU图像二值化
    cv2.imshow("image1", skin)
    return skin

def _get_contours(array):#得到图片所有的坐标
    contours, hierarchy = cv2.findContours(array, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
    return contours

def _get_eucledian_distance(beg, end):#计算两点之间的坐标
    i=str(beg).split(',')
    j=i[0].split('(')
    x1=int(j[1])
    k=i[1].split(')')
    y1=int(k[0])
    i=str(end).split(',')
    j=i[0].split('(')
    x2=int(j[1])
    k=i[1].split(')')
    y2=int(k[0])
    d=math.sqrt((x1-x2)*(x1-x2)+(y1-y2)*(y1-y2))
    return d
    
def _get_defects_count(array, contour, defects, verbose = False):
    ndefects = 0
    for i in range(defects.shape[0]):
        s,e,f,_= defects[i,0]
        beg= tuple(contour[s][0])
        end= tuple(contour[e][0])
        far= tuple(contour[f][0])
        a= _get_eucledian_distance(beg, end)
        b= _get_eucledian_distance(beg, far)
        c= _get_eucledian_distance(end, far)
        angle= math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # * 57
        if angle <= math.pi/2 :#90:
            ndefects = ndefects + 1
            if verbose:
                cv2.circle(array, far, 3, _COLOR_RED, -1)
                cv2.imshow("image2", array)
        if verbose:
            cv2.line(array, beg, end, _COLOR_RED, 1)
            cv2.imshow("image2", array)
    return array, ndefects

def grdetect(array, verbose = False):
    copy = array.copy()
    array = _remove_background(array) # 移除背景, add by wnavy
    thresh = _bodyskin_detetc(array)
    contours = _get_contours(thresh.copy()) # 计算图像的轮廓
    largecont= max(contours, key = lambda contour: cv2.contourArea(contour))
    hull= cv2.convexHull(largecont, returnPoints = False) # 计算轮廓的凸点
    defects= cv2.convexityDefects(largecont, hull) # 计算轮廓的凹点
    if defects is not None:
        # 利用凹陷点坐标, 根据余弦定理计算图像中锐角个数
        copy, ndefects = _get_defects_count(copy, largecont, defects, verbose = verbose)
        # 根据锐角个数判断手势, 会有一定的误差
        if   ndefects == 0:
            print("1根或0根")
        elif ndefects == 1:
            print("2根")
        elif ndefects == 2:
            print("3根")
        elif ndefects == 3:
            print("4根")
        elif ndefects == 4:
            print("5根")

def judge():
    imname =  "wif.jpg"
    img = cv2.imread(imname, cv2.IMREAD_COLOR)
    grdetect(img, verbose = False)
    
def game():
    capture = cv2.VideoCapture(0)#打开笔记本的内置摄像头
    cv2.namedWindow("camera",1)
    start_time = time.time()
    print("给你10秒的时间把手放到方框的位置\n")
    while(1):
        ha,img =capture.read()
        #按帧读取视频，ha,img为获取该方法的两个返回值，ha为布尔值，如果读取帧是正确的则返回true
        #如果文件读取到结尾，它的返回值就为false,img就是每一帧的图像，是个三维矩阵
        end_time = time.time()#返回当前时间的时间戳
        cv2.rectangle(img,(426,0),(640,250),(170,170,0))
        #cv2.putText(img,str(int((10-(end_time- start_time)))), (100,100), cv2.FONT_HERSHEY_SIMPLEX, 2, 255)
        #对应参数为图片、添加的文字，左上角图标，字体，字体大小，颜色，即上面一行功能为在图片中显示倒计时
        cv2.imshow("camera",img)
        if(end_time-start_time>10):#如果时间到达10秒
            break
        if(cv2.waitKey(30)>=0):
            break
        ha,img = capture.read()
        cv2.imshow("camera",img)
        img = img[0:210,426:640]
        cv2.imwrite("wif.jpg",img)
        judge()
    capture.release()
    
def main():
    while(1):
        os.system('cls')#清屏
        ans =game()
main()
 

Open a LED 

#LED
import RPi.GPIO as GPIO
import time

GPIO.setmode(GPIO.BOARD)
GPIO.setup(7,GPIO.OUT)
GPIO.output(7,GPIO.HIGH)


while True:
    GPIO.output(7,GPIO.LOW)
    time.sleep(1)