import sensor, time, image
# 初始化设置
sensor.set_contrast(3)
sensor.set_gainceiling(16)
sensor.reset()
sensor.set_contrast(3)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.QQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
# 跳过两秒使摄像头初始化完成
sensor.skip_frames(time = 2000)
# 加入Haar Cascade 参数用于后面的识别人脸函数
# By default this will use all stages, lower satges is faster but less accurate.
face_cascade = image.HaarCascade("frontalface", stages=30)
print(face_cascade)
# 设置一个特征点变量
kpts1 = None
############# 把人脸的特征点录入 ##############
while (kpts1 == None):
img = sensor.snapshot()
img.draw_string(0, 0, "Looking for a face...")
# Find faces
objects1 = img.find_features(face_cascade, threshold=0.5, scale=1.3)
if objects1:
# 在每个方向上将fecd的ROI扩大31个像素
face1 = (objects1[0][0]-31, objects1[0][1]-31,objects1[0][2]+31*2, objects1[0][3]+31*2)
# 在face的roi范围内寻找关键点
kpts1 = img.find_lbp(face1)
# 画出人脸的框框
# image.save_descriptor(keypoint,"/%s.orb"%(kpts)) #keypoint为保存特征点的文件名
img.draw_rectangle(objects1[0])
print(kpts1)
# 标记出关键点
print(kpts1)
#img.draw_keypoints(kpts1, size=24)
img = sensor.snapshot()
time.sleep(2000)
# FPS clock
clock = time.clock()
########### 对比关键点 ##########
while (True):
clock.tick()
img = sensor.snapshot()
objects2 = img.find_features(face_cascade, threshold = 0.5 ,scale_factor = 1.3)
#try :
#face2 = (objects2[0][0]-31 ,objects2[0][1]-31 ,objects2[0][2]+31*2 ,objects2[0][3]+31*2)
#except : IndexError as e :
#print(objects2[0])
for face2 in objects2 :
# Extract keypoints from the whole frame
kpts2 = img.find_lbp(face2)
if (kpts2):
# Match the first set of keypoints with the second one
# kpts3 = image.load_decriptor(keypoint)
c = image.match_descriptor(kpts1, kpts2)
print(c)
#match = c[6] # C[6] contains the number of matches.
if (c<15000):
img.draw_rectangle(face2)
x = face2[0] + face2[2]/2
y = face2[1] + face2[3]/2
print([int(x),int(y)])
img.draw_cross(int(x), int(y), size=10)
# print(kpts2, "matched:%d dt:%d"%(match, c[7]))
else :
print("NONE")
# Draw FPS
img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))
