opencv-图像人脸识别和视频人脸识别

首先可以取opencv官方github下载识别模型xml文件：https://github.com/lonngxiang/opencv/tree/master/data/haarcascades

1，图像人脸识别

import cv2filepath =r"C:\Users\Lavector\Desktop\1111.jpg"
img = cv2.imread(filepath)  # 读取图片
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # 转换灰色# OpenCV人脸识别分类器
classifier = cv2.CascadeClassifier(r"C:\Users\Lavector\Desktop\cv_model\opencv\data\haarcascades\haarcascade_frontalface_default.xml"
)
color = (0, 255, 0)  # 定义绘制颜色
# 调用识别人脸
faceRects = classifier.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=3, minSize=(32, 32))
if len(faceRects):  # 大于0则检测到人脸for faceRect in faceRects:  # 单独框出每一张人脸x, y, w, h = faceRect# 框出人脸cv2.rectangle(img, (x, y), (x + h, y + w), color, 2)# 左眼cv2.circle(img, (x + w // 4, y + h // 4 + 30), min(w // 8, h // 8),color)#右眼cv2.circle(img, (x + 3 * w // 4, y + h // 4 + 30), min(w // 8, h // 8),color)#嘴巴cv2.rectangle(img, (x + 3 * w // 8, y + 3 * h // 4),(x + 5 * w // 8, y + 7 * h // 8), color)cv2.imshow("image", img)  # 显示图像
c = cv2.waitKey(10)cv2.waitKey(0)
cv2.destroyAllWindows()

2，视频人脸识别

import cv2# 图片识别方法封装
def discern(img):gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)cap = cv2.CascadeClassifier(r"C:\Users\Lavector\Desktop\cv_model\opencv\data\haarcascades\haarcascade_frontalface_default.xml")faceRects = cap.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=3, minSize=(50, 50))if len(faceRects):for faceRect in faceRects:x, y, w, h = faceRectcv2.rectangle(img, (x, y), (x + h, y + w), (0, 255, 0), 2)  # 框出人脸cv2.imshow("Image", img)# 获取摄像头0表示第一个摄像头
cap = cv2.VideoCapture(0)
while (1):  # 逐帧显示ret, img = cap.read()# cv2.imshow("Image", img)discern(img)if cv2.waitKey(1) & 0xFF == ord('q'):break
cap.release()  # 释放摄像头
cv2.destroyAllWindows()  # 释放窗口资源

face_recognition
https://github.com/ageitgey/face_recognition

import face_recognition
import cv2# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
# other example, but it includes some basic performance tweaks to make things run a lot faster:
#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)
#   2. Only detect faces in every other frame of video.# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)# Load a sample picture and learn how to recognize it.
obama_image = face_recognition.load_image_file(r"F.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]# Load a second sample picture and learn how to recognize it.
# biden_image = face_recognition.load_image_file("biden.jpg")
# biden_face_encoding = face_recognition.face_encodings(biden_image)[0]# Create arrays of known face encodings and their names
known_face_encodings = [obama_face_encoding,# biden_face_encoding
]
known_face_names = ["aa",# "Joe Biden"
]# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = Truewhile True:# Grab a single frame of videoret, frame = video_capture.read()# Resize frame of video to 1/4 size for faster face recognition processingsmall_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)rgb_small_frame = small_frame[:, :, ::-1]# Only process every other frame of video to save timeif process_this_frame:# Find all the faces and face encodings in the current frame of videoface_locations = face_recognition.face_locations(rgb_small_frame)face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)face_names = []for face_encoding in face_encodings:# See if the face is a match for the known face(s)matches = face_recognition.compare_faces(known_face_encodings, face_encoding)name = "Unknown"# If a match was found in known_face_encodings, just use the first one.if True in matches:first_match_index = matches.index(True)name = known_face_names[first_match_index]face_names.append(name)process_this_frame = not process_this_frame# Display the resultsfor (top, right, bottom, left), name in zip(face_locations, face_names):# Scale back up face locations since the frame we detected in was scaled to 1/4 sizetop *= 4right *= 4bottom *= 4left *= 4# Draw a box around the facecv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)# Draw a label with a name below the facecv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)font = cv2.FONT_HERSHEY_DUPLEXcv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)# Display the resulting imagecv2.imshow('Video', frame)# Hit 'q' on the keyboard to quit!if cv2.waitKey(1) & 0xFF == ord('q'):break# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()

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