OpenCV图像处理-人脸检测
1.导言
输入：图片；视频（本地，网络，监控摄像头）
处理：人脸（检测，识别）；肢体
输出：软件；硬件
2.读取图片
"""
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Function : 读取图片
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# 导入CV模块
import cv2 as cv
# 读取图片
img = cv.imread(r'F:\30_UndergraduateThesis\04_LearningOpencv\MeiXi.png')
# 显示图片
cv.imshow('read_img', img)
# 等待
cv.waitKey(0)
# 释放内存
cv.destroyAllWindows()
3.灰度转换
"""
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Function : 灰度转换
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"""
# 导入CV模块
import cv2 as cv
# 读取图片
img = cv.imread(r'F:\30_UndergraduateThesis\04_LearningOpencv\MeiXi.png')
# 灰度转换
gray_img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# 显示灰度图片
cv.imshow('gray_img', gray_img)
# 保存灰度图片
cv.imwrite('gray_face1.jpg', gray_img)
# 显示图片
cv.imshow('read_img', img)
# 等待
cv.waitKey(0)
# 释放内存
cv.destroyAllWindows()
4.尺寸修改
"""
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Function : 尺寸修改
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# 导入CV模块
import cv2 as cv
# 读取图片
img = cv.imread(r'F:\30_UndergraduateThesis\04_LearningOpencv\MeiXi.png')
# 修改尺寸
resize_img = cv.resize(img, dsize=(200, 200))
# 显示原图
cv.imshow('img', img)
# 显示修改后的图
cv.imshow('resize_img', resize_img)
# 打印原图大小
print('未修改的：', img.shape)
# 打印修改后的图的大小
print('修改后的：', resize_img.shape)
# 等待
while True:
if ord('q') == cv.waitKey(0):
break
# 释放内存
cv.destroyAllWindows()5.绘制矩形
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Function : 绘制矩形
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"""
# 导入CV模块
import cv2 as cv
# 读取图片
img = cv.imread(r'F:\30_UndergraduateThesis\04_LearningOpencv\MeiXi.png')
# 坐标
x, y, w, h = 100, 100, 100, 100
# 绘制矩形
cv.rectangle(img, (x, y, x+w, y+h), color=(0, 0, 255), thickness=1)
# 绘制圆形
cv.circle(img, center=(x+w, y+h), radius=100, color=(255, 0, 0), thickness=2)
# 显示图像
cv.imshow('re_img', img)
# 等待
while True:
if ord('q') == cv.waitKey(0):
break
# 释放内存
cv.destroyAllWindows()6.人脸检测
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Function : 人脸检测
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"""
# 导入CV模块
import cv2 as cv
# 检测函数
def face_detect_demo():
gary = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
face_detect = cv.CascadeClassifier(r'D:\14_Anaconda\envs\pytorch1.8\Lib\site-packages\cv2\data\haarcascade_frontalface_alt2.xml')
face = face_detect.detectMultiScale(gary, 1.01, 5, 0, (100, 100), (300, 300))
for x, y, w, h in face:
cv.rectangle(img, (x, y), (x+w, y+h), color=(0, 0, 255), thickness=2)
cv.imshow('result', img)
# 读取图像
img = cv.imread(r'F:\30_UndergraduateThesis\04_LearningOpencv\MeiXi.png')
# 检测函数
face_detect_demo()
# 等待
while True:
if ord('q') == cv.waitKey(0):
break
# 释放内存
cv.destroyAllWindows()7.视频检测
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Function : 视频检测
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"""
# 导入CV模块
import cv2 as cv
# 检测函数
def face_detect_demo(img):
gary = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
face_detect = cv.CascadeClassifier(r'D:\14_Anaconda\envs\pytorch1.8\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml')
face = face_detect.detectMultiScale(gary)
for x, y, w, h in face:
cv.rectangle(img, (x, y), (x+w, y+h), color=(0, 0, 255), thickness=2)
cv.imshow('result', img)
# 读取图像
cap = cv.VideoCapture(0)
# 循环
while True:
flag, frame = cap.read()
if not flag:
break
face_detect_demo(frame)
if ord('q') == cv.waitKey(0):
break
# 释放内存
cv.destroyAllWindows()8.人脸录入
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Function : 信息录入
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# 导入模块
import cv2
# 摄像头
cap = cv2.VideoCapture(0)
flag = 1
num = 1
while(cap.isOpened()): # 检测是否在开启状态
ret_flag, Vshow = cap.read() # 得到每帧图像
cv2.imshow("Capture_Test", Vshow)
k = cv2.waitKey(1) & 0xFF # 按键判断
if k == ord('s'): # 保存
cv2.imwrite("F:/30_UndergraduateThesis/03_SaveVideo/"+str(num)+".name"+".jpg", Vshow)
print("success to save"+str(num)+".jpg")
print("-------------")
num += 1
elif k == ord(" "): # 退出
break
# 释放摄像头
cap.release()
# 释放内存
cv2.destroyAllWindows()9.数据训练
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Function : 数据训练
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import os
import cv2 as cv
from PIL import Image
import numpy as np
def getImageAndLabels(path):
# 储存人脸数据
facesSamples = []
# 储存姓名数据
ids = []
# 储存图片信息
imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
# 加载分类器
face_detector = cv.CascadeClassifier(r'D:\14_Anaconda\envs\pytorch1.8\Lib\site-packages\cv2\data\haarcascade_frontalface_alt2.xml')
# 遍历列表中的图片
for imagePath in imagePaths:
# 打开图片，灰度化 PIL 有九种不同模式：1,L,P,RGB,RGBA,CMYK,YCbCr,I,F
PIL_img = Image.open(imagePath).convert('L')
# 将图像转换为数组，以黑白深浅
img_numpy = np.array(PIL_img, 'uint8')
# 获取图片人脸特征
faces = face_detector.detectMultiScale(img_numpy)
# 获取每张图片的id和姓名
id = int(os.path.split(imagePath)[1].split('.')[0])
# 预防无面容图片
for x, y, w, h in faces:
ids.append(id)
facesSamples.append(img_numpy[y:y+h, x:x+w])
#打印脸部特征和id
print('id:', id)
print('fs:', facesSamples)
return facesSamples, ids
if __name__ == '__main__':
# 图片路径
path = r'F:\30_UndergraduateThesis\04_LearningOpencv\data'
# 获取图像数组和id标签组和姓名
faces, ids = getImageAndLabels(path)
# 加载识别器
recognizer = cv.face.LBPHFaceRecognizer.create()
# 训练
recognizer.train(faces, np.array(ids))
# 保存文件
recognizer.write(r'F:\30_UndergraduateThesis\04_LearningOpencv\trainer\trainer.yaml')关于AttributeError: module ‘cv2.face‘ has no attribute ‘createLBPHFaceRecognizer‘的问题:
需要安装 opencv-contrib-python
pip install opencv-contrib-python