【OpenCV】4种人脸检测方法

代码已上传GitHub：plumqm/OpenCV-Projects at master (github.com)

Haar 模型

# 1.读入包含人脸的图片
# 2.使用haar模型识别人脸
# 3.将识别结果用矩形框画出来

import cv2
import numpy as np
import matplotlib.pyplot as plt
# %matplotlib inline

plt.rcParams['figure.dpi'] = 200

img = cv2.imread('./images/others/Walid_Al-Awadi/Walid_Al-Awadi_0001.jpg')
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

# 构造haar检测器
face_detector = cv2.CascadeClassifier('./haarcascade_frontalface_default.xml')

# 转灰度图
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 检测结果
detections = face_detector.detectMultiScale(img_gray)

# 打印
detections.shape

for (x,y,w,h) in detections:
    cv2.rectangle(img,(x,y),(w+x,y+h),(0,255,0),5)


plt.imshow(img)

下面是调参：

#调参数

#scaleFactor : 调整图片尺寸
#minNeighbors : 候选人脸数量
#minSize : 最小人脸尺寸
#maxSize : 最大人脸尺寸

img = cv2.imread('./images/others/Walid_Al-Awadi/Walid_Al-Awadi_0001.jpg')
# 构造haar检测器
face_detector = cv2.CascadeClassifier('./haarcascade_frontalface_default.xml')
# 转灰度图
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 检测结果
detections = face_detector.detectMultiScale(img_gray,scaleFactor=1.1,
             minNeighbors=3,minSize=(46,46),maxSize=(100,100))

for (x,y,w,h) in detections:
    cv2.rectangle(img,(x,y),(w+x,y+h),(0,255,0),5)

plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

Hog

import cv2
import numpy as np
import matplotlib.pyplot as plt
# %matplotlib inline

plt.rcParams['figure.dpi'] = 200

img = cv2.imread('./images/others/Walid_Al-Awadi/Walid_Al-Awadi_0001.jpg')
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

import dlib

# 构造HOG人脸检测器
hog_face_detector = dlib.get_frontal_face_detector()

detections = hog_face_detector(img,1)

detections

len(detections) 

for face in detections:
    x = face.left()
    y = face.top()
    r = face.right()
    b = face.bottom()
    cv2.rectangle(img,(x,y),(r,b),(255,0,0),5)

plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

CNN

import cv2
import numpy as np
import matplotlib.pyplot as plt
# %matplotlib inline

plt.rcParams['figure.dpi'] = 200

img = cv2.imread('./images/others/Walid_Al-Awadi/Walid_Al-Awadi_0001.jpg')
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

import dlib

# 构建CNN人脸检测器
cnn_face_detector =  dlib.cnn_face_detection_model_v1("./mmod_human_face_detector.dat")

detections = cnn_face_detector(img,1)

for face in detections:
    x = face.rect.left()
    y = face.rect.top()
    r = face.rect.right()
    b = face.rect.bottom()

    # 置信度
    c = face.confidence
    print(c)
    
    cv2.rectangle(img,(x,y),(r,b),(255,0,0),5)

plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

SSD

import cv2
import numpy as np
import matplotlib.pyplot as plt
# %matplotlib inline

plt.rcParams['figure.dpi'] = 200

img = cv2.imread('./images/others/Walid_Al-Awadi/Walid_Al-Awadi_0001.jpg')
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

face_detector = cv2.dnn.readNetFromCaffe('./deploy.prototxt.txt','./res10_300x300_ssd_iter_140000.caffemodel')

img_height = img.shape[0]
img_width = img.shape[1]

# 缩放至模型输入尺寸
img_resize = cv2.resize(img, (500,300))

# 图像转为二进制
img_blob = cv2.dnn.blobFromImage(img_resize,1.0,(500,300),(104.0,177.0,123.0))

face_detector.setInput(img_blob)

detections = face_detector.forward()
detections

num_of_detections = detections.shape[2]
num_of_detections


img_copy = img.copy()
for index in range(num_of_detections):
    # 置信度
    detection_confidence = detections[0,0,index,2]

    if detection_confidence>0.85:
        locations = detections[0,0,index,3:7] * np.array([img_width,img_height,img_width,img_height])
        print(detection_confidence * 100)

        lx,ly,rx,ry = locations.astype('int')

        cv2.rectangle(img_copy,(lx,ly),(rx,ry),(0,255,0),5) 

plt.imshow(cv2.cvtColor(img_copy,cv2.COLOR_BGR2RGB))

原文地址：https://blog.csdn.net/m0_74183164/article/details/143060476

免责声明：本站文章内容转载自网络资源，如本站内容侵犯了原著者的合法权益，可联系本站删除。更多内容请关注自学内容网（zxcms.com）！