本文使用OpenCV已经训练好的分类器模型进行检测试验
1.静态人脸检测
def detect(filename):
face_cascade = cv2.CascadeClassifier('../data/haarcascades/haarcascade_frontalface_default.xml')
img = cv2.imread(filename)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
img = cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
cv2.namedWindow('ck')
cv2.imshow('ck', img)
cv2.imwrite('./ck.jpg', img)
cv2.waitKey(0)
结果:
2.视频中人脸检测
'''
视频中人脸检测
'''
def videoDetect():
face_cascade = cv2.CascadeClassifier('../data/haarcascades/haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml')
camera = cv2.VideoCapture(0)
while (True):
ret, frame = camera.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
img = cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
roi_gray = gray[y:y+h, x:x+w]
roi_color = img[y:y + h, x:x + w]
eyes = eye_cascade.detectMultiScale(roi_gray, 1.03, 5, 0, (40, 40))
for (ex, ey, ew, eh) in eyes:
cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)
cv2.imshow("capture", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
camera.release()
cv2.destroyAllWindows()