opencv检测人脸
人脸检测需要对人脸做一个标准特征,然后对于输入的每一帧图像计算这些特征,标准特征和人脸实时特征进行比对,根据概率输出人脸及其表情,这也是人工智能的初衷
opencv里已经对人脸特征做了一个标准特征文件
人脸特征: "/Python/Python39/Lib/site-packages/cv2/data/haarcascade_frontalface_default.xml"
眼睛特征: "/Python/Python39/Lib/site-packages/cv2/data/haarcascade_eye.xml"
微笑特征: "***/Python/Python39/Lib/site-packages/cv2/data/haarcascade_smile.xml"
利用这些特征文件很快就能检测到人脸
代码如下:
import cv2
import numpy as np
def get_image(path): # 获取图片
img = cv2.imread(path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
return img, gray
def walk():
img_path = "D://dev//python//img/lena512color.tiff"
save_path = "D://dev//python//img/lena512save.jpg"
original_img, gray = get_image(img_path)
cv2.equalizeHist(gray, gray)
faceadd = "***/Python/Python39/Lib/site-packages/cv2/data/haarcascade_frontalface_default.xml"
eyeadd = "***/Python/Python39/Lib/site-packages/cv2/data/haarcascade_eye.xml"
smileadd = "***/Python/Python39/Lib/site-packages/cv2/data/haarcascade_smile.xml"
face_detector = cv2.CascadeClassifier(faceadd)
eye_detector = cv2.CascadeClassifier(eyeadd)
smile_detector = cv2.CascadeClassifier(smileadd)
faces = face_detector.detectMultiScale(gray, 1.15, 5)
cv2.imshow("face_img", original_img)
for x, y, w, h in faces:
cv2.rectangle(original_img, (x, y), (x + w, y + h), (0, 0, 255), 2)
# 把脸单独拿出来检测脸
face_img = gray[y:y + h, x:w + x]
eyes = eye_detector.detectMultiScale(face_img, 1.3, 5, 0, (40, 40))
for ex, ey, ew, eh in eyes:
cv2.rectangle(original_img, (x + ex, y + ey), (x + ex + ew, y + ey + eh), (255, 0, 0), 2)
smile = smile_detector.detectMultiScale(face_img, 1.16, 35, 0, (25, 25))
if (len(smile) >= 0):
print("检测到微笑")
cv2.putText(original_img, 'Smile', (x, y - 20), 3, 1.3, (0, 255, 0), 2)
cv2.imshow('crop_img', original_img)
cv2.waitKey(20171219)
cv2.imwrite(save_path, original_img)
walk()
原始图像:
处理后图像
网友评论