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()))

感谢！！！！！！！！！！！

谢谢大家

或者说如何单独的输出r的四个参数

请问例程中 r 的四个参数如单独输出

希望有大佬可以告诉我人脸区域的中心坐标可以这么输出

import sensor, time, image, pyb

led = pyb.LED(3)

Reset sensor

sensor.reset()

Sensor settings

sensor.set_contrast(1)
sensor.set_gainceiling(16)

HQVGA and GRAYSCALE are the best for face tracking.

sensor.set_framesize(sensor.HQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)

face_cascade = image.HaarCascade("frontalface", stages=25)

#stages值未传入时使用默认的stages。stages值设置的小一些可以加速匹配，但会降低准确率。
print(face_cascade)

clock = time.clock()

while (True):
clock.tick()

img = sensor.snapshot()

#注意：比例因子越低，图像越小，检测的对象越小。
#阈值越高，检测率越高，误报越多。
objects = img.find_features(face_cascade, threshold=0.75, scale=1.35, roi)

#image.find_features(cascade, threshold=0.5, scale=1.5),thresholds越大，
#匹配速度越快，错误率也会上升。scale可以缩放被匹配特征的大小。

#在找到的目标上画框，标记出来

for r in objects:
    img.draw_rectangle(r)
    a = r.x()
    print(a)
    #time.sleep(100)  #延时100ms

print(clock.fps())

import sensor, time, image

Reset sensor

sensor.reset()
sensor.set_contrast(3)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.VGA)
sensor.set_windowing((320, 240))
sensor.set_pixformat(sensor.GRAYSCALE)

跳过几帧使图像稳定

sensor.skip_frames(time = 2000)

加载Haar算子

默认情况下，这将使用所有阶段，较低的阶段更快但不太准确。

face_cascade = image.HaarCascade("frontalface", stages=25)
print(face_cascade)

第一组关键点

kpts1 = None

找到人脸

while (kpts1 == None):
img = sensor.snapshot()
img.draw_string(0, 0, "Looking for a face...")
# 找到人脸
objects = img.find_features(face_cascade, threshold=0.5, scale=1.25)
if objects:
# 在每个方向上将ROI扩大31个像素
face = (objects[0][0]-31, objects[0][1]-31,objects[0][2]+312, objects[0][3]+312)
# 使用检测面大小作为ROI提取关键点
kpts1 = img.find_keypoints(threshold=10, scale_factor=1.1, max_keypoints=100, roi=face)
# 围绕第一个面绘制一个矩形
img.draw_rectangle(objects[0])

画关键点

print(kpts1)
img.draw_keypoints(kpts1, size=24)
img = sensor.snapshot()
time.sleep(2000)

FPS clock

clock = time.clock()

while (True):
clock.tick()
img = sensor.snapshot()
# 从整个帧中提取关键点
kpts2 = img.find_keypoints(threshold=10, scale_factor=1.1, max_keypoints=100, normalized=True)

if (kpts2):
    # 将第一组关键点与第二组关键点匹配
    c=image.match_descriptor(kpts1, kpts2, threshold=85)
    match = c[6] # C[6] 包含匹配度.
    if (match>5):
        img.draw_rectangle(c[2:6])
        img.draw_cross(c[0], c[1], size=10)
        print(kpts2, "matched:%d dt:%d"%(match, c[7]))

# 绘制 FPS
img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))
print(clock.fps())

请问这个例程是找到人脸后就一直输出FPS值吗?

我复制orl-faces到根目录了
而且我用win10会出现这样的错误用mac就不会
请大佬解答如何用win10避免这些文题