# 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('venv/lib/python3.9/site-packages/cv2/data/haarcascade_frontalface_alt2.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
img = cv.imread('opencv/face2.jpg', cv.IMREAD_UNCHANGED)
# 检测函数
face_detect_demo()
# 等待
while True:
if ord('q') == cv.waitKey(0):
break
# 释放内存
cv.destroyAllWindows()
# 导入模块
import cv2
# 摄像头
cap = cv2.VideoCapture(0)
falg = 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) # 显示图像
k = cv2.waitKey(1) & 0xFF # 按键判断
if k == ord('s'): # 保存
cv2.imwrite("D:mycodetest/opencv/data/jm/"+str(num)+".123"+".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()
# 导入cv模块
import cv2 as cv
# 读取图片
img = cv.imread('opencv/face1.jpg', cv.IMREAD_UNCHANGED)
# 灰度转换
gray_img = cv.cvtColor(img, cv.COLOR_BAYER_BG2BGR)
# 显示灰度图片
cv.imshow('gray', 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()
# 导入cv模块
import cv2 as cv
# 读取图片
img = cv.imread('opencv/face1.jpg', cv.IMREAD_UNCHANGED)
# 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
cv.imshow('resize_img', resize_img)
# 打印原图尺寸大小
print('未修改：', img.shape)
# 打印修改后的大小
print('修改后：', resize_img.shape)
# 等待
while True:
if ord('q') == cv.waitKey(0):
break
# 释放内存
cv.destroyAllWindows()
# 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('venv/lib/python3.9/site-packages/cv2/data/haarcascade_frontalface_alt2.xml')
# 100x100 到300x300的大小的人脸
face = face_detect.detectMultiScale(gary, 1.01, 5, 0, (100, 100), (300, 300))
# 将人脸部分框起来
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('opencv/face1.jpg', cv.IMREAD_UNCHANGED)
# 检测函数
face_detect_demo()
# 等待
while True:
if ord('q') == cv.waitKey(0):
break
# 释放内存
cv.destroyAllWindows()
import os
import cv2
import sys
from PIL import Image
import numpy as np
def getImageAndLabels(path):
facesSamples = []
ids = []
imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
# 检测人脸
face_detector = cv2.CascadeClassifier('venv/lib/python3.9/site-packages/cv2/data/haarcascade_frontalface_alt2.xml')
# 打印数组imagePaths
print('数据排列：', imagePaths)
# 遍历列表中的图片
for imagePath in imagePaths:
# 打开图片,黑白化
PIL_img = Image.open(imagePath).convert('L')
# 将图像转换为数组，以黑白深浅
# PIL_img = cv2.resize(PIL_img, dsize=(400, 400))
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])
# 将脸部特征数据和身份信息推入数组之中,存放起来
for x, y, w, h in faces:
ids.append(id)
facesSamples.append(img_numpy[y:y+h, x:x+w])
return facesSamples, ids
if __name__ == '__main__':
# 图片路径
path = 'opencv/data/jm/'
# 获取图像数组和id标签数组和姓名
faces, ids = getImageAndLabels(path)
# 获取训练对象,对于提取出来的脸部特征进行训练
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.train(faces, np.array(ids))
# 保存文件
recognizer.write('opencv/trainer/trainer.xml')
# 导入cv模块
import cv2 as cv
# 读取图片
# 绝对路径
# img = cv.imread('/Users/song/codelearn/opencv_face_recognition_learn/Dorian与Ai_人脸识别程序/opencv/face1.jpg')
# 相对路径
img = cv.imread('opencv/face1.jpg', cv.IMREAD_UNCHANGED)
# 显示图片
cv.imshow('read_img', img)
# 等待
cv.waitKey(0)
# 释放内存
cv.destroyAllWindows()
# 导入cv模块
import cv2 as cv
# 读取图片
img = cv.imread('opencv/face1.jpg', cv.IMREAD_UNCHANGED)
# 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=5)
# 显示
cv.imshow('re_img', img)
while True:
if ord('q') == cv.waitKey(0):
break
# 释放内存
cv.destroyAllWindows()
# 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('venv/lib/python3.9/site-packages/cv2/data/haarcascade_frontalface_alt2.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)
# 读取摄像头
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(1):
break
# 释放内存
cv.destroyAllWindows()
# 释放摄像头
cap.release()
import cv2
import numpy as np
import os
# coding=utf-8
import urllib
import urllib.request
import hashlib
# 加载训练数据集文件
# recogizer = cv2.face.LBPHFaceRecognizer_create()
# recogizer.read('opencv/trainer/trainer.yml')
# print(recogizer)
# names = []
# warningtime = 0
# def face_detect_demo(img):
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转换为灰度
# 读取图片
img = cv2.imread('opencv/face1.jpg', cv2.IMREAD_UNCHANGED)
# 灰度转换
gray_img = cv2.cvtColor(img, cv2.COLOR_BAYER_BG2BGR)
face_detector = cv2.CascadeClassifier('/Users/song/codelearn/opencv_face_recognition_learn/Dorian与Ai_人脸识别程序/opencv/trainer/trainer.yml')
face = face_detector.detectMultiScale(gray_img, 1.01, 5, 0, (100, 100), (300, 300))
# face = face_detector.detectMultiScale(gray_img, 1.1, 5, cv2.CASCADE_SCALE_IMAGE, (100, 100), (300, 300))
for x, y, w, h in face:
cv2.rectangle(img, (x, y), (x+w, y+h), color=(0, 0, 255), thickness=2)
cv2.circle(img, center=(x+w//2, y+h//2), radius=w//2, color=(0, 255, 0), thickness=1)
# 人脸识别
# ids, confidence = recogizer.predict(gray[y:y + h, x:x + w])
# # print('标签id:',ids,'置信评分：', confidence)
# if confidence > 80:
# global warningtime
# warningtime += 1
# if warningtime > 100:
# warning()
# warningtime = 0
# cv2.putText(img, 'unkonw', (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
# else:
# cv2.putText(img, str(names[ids-1]), (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
cv2.imshow('result', img)
# def name():
# path = 'opencv/data/jm/'
# imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
# for imagePath in imagePaths:
# name = str(os.path.split(imagePath)[1].split('.', 2)[1])
# names.append(name)
# cap = cv2.VideoCapture('opencv/1.mp4', cv2.IMREAD_UNCHANGED)
# name()
# while True:
# while cap.isOpened():
# flag, frame = cap.read()
# if not flag:
# break
# face_detect_demo(frame)
# if ord(' ') == cv2.waitKey(10):
# break
# cv2.destroyAllWindows()
# cap.release()
# 等待
cv2.waitKey(0)
# 释放内存
cv2.destroyAllWindows()
img.release()
import cv2
def face_detect_demo(img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转换为灰度
# cv2.imshow('result', gray)
# cv2.waitKey(3000)
# recogizer = cv2.face.LBPHFaceRecognizer_create()
# res = recogizer.read('./trainer/trainer.yml')
# face_detector = cv2.CascadeClassifier('/Users/song/codelearn/opencv_face_recognition_learn/Dorian与Ai_人脸识别程序/opencv/trainer/trainer.yml')
face_detector = cv2.CascadeClassifier('venv/lib/python3.9/site-packages/cv2/data/haarcascade_frontalface_alt2.xml')
# face_detector = cv2.CascadeClassifier('trainer/trainer.xml')
# face = face_detector.detectMultiScale(gray, 1.1, 5, cv2.CASCADE_SCALE_IMAGE, (100, 100), (300, 300))
face = face_detector.detectMultiScale(gray, 1.01, 5, 0, (100, 100), (300, 300))
# face = face_detector.detectMultiScale(gray)
for x, y, w, h in face:
cv2.rectangle(img, (x, y), (x+w, y+h), color=(0, 0, 255), thickness=2)
cv2.circle(img, center=(x+w//2, y+h//2), radius=w//2, color=(0, 255, 0), thickness=1)
# 人脸识别
# ids, confidence = recogizer.predict(gray[y:y + h, x:x + w])
# # print('标签id:',ids,'置信评分：', confidence)
# if confidence > 80:
# global warningtime
# warningtime += 1
# if warningtime > 100:
# warning()
# warningtime = 0
# cv2.putText(img, 'unkonw', (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
# else:
# cv2.putText(img, str(names[ids-1]), (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
cv2.imshow('result', img)
class CaptureVideo(object):
def net_video(self):
# 获取网络视频流
# cam = cv2.VideoCapture("rtmp://192.168.0.10/live/test")
# cam = cv2.VideoCapture("rtmp://58.200.131.2:1935/livetv/hunantv")
cam = cv2.VideoCapture("/Users/song/codelearn/opencv_face_recognition_learn/face_dectector/opencv/1.mp4")
while cam.isOpened():
sucess, frame = cam.read()
face_detect_demo(frame)
cv2.imshow("Network", frame)
cv2.waitKey(1)
if __name__ == "__main__":
capture_video = CaptureVideo()
capture_video.net_video()
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