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本例程为 08-Eye_Tracking-face_eye_detection
本例程先用haar算子进行人脸识别,然后利用haar算子找到人脸中的眼睛,实现人眼追踪。

# Face Eye Detection Example
#
# This script uses the built-in frontalface detector to find a face and then
# the eyes within the face. If you want to determine the eye gaze please see the
# iris_detection script for an example on how to do that.

import sensor, time, image

# Reset sensor
sensor.reset()

# Sensor settings
sensor.set_contrast(1)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.HQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)

# Load Haar Cascade
# By default this will use all stages, lower satges is faster but less accurate.
face_cascade = image.HaarCascade("frontalface", stages=25)#人脸识别的haar算子
eyes_cascade = image.HaarCascade("eye", stages=24)#眼睛的haar算子
#image.HaarCascade(path, stages=Auto)加载一个haar模型。haar模型是二进制文件,
#这个模型如果是自定义的,则引号内为模型文件的路径;也可以使用内置的haar模型,
#比如“frontalface” 人脸模型或者“eye”人眼模型。
#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, eyes_cascade)

# FPS clock
clock = time.clock()

while (True):
    clock.tick()

    # Capture snapshot
    img = sensor.snapshot()

    # Find a face !
    # Note: Lower scale factor scales-down the image more and detects smaller objects.
    # Higher threshold results in a higher detection rate, with more false positives.
    objects = img.find_features(face_cascade, threshold=0.5, scale=1.5)
    #先利用haar算子找到视野中的人脸。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。


    # Draw faces
    #将找到的人脸用矩形标记出来
    for face in objects:
        img.draw_rectangle(face)
        # Now find eyes within each face.
        # Note: Use a higher threshold here (more detections) and lower scale (to find small objects)
        eyes = img.find_features(eyes_cascade, threshold=0.5, scale=1.2, roi=face)
        #在人脸中识别眼睛。roi参数设置特征寻找的范围,roi=face即在找到的人脸中识别眼睛。
        #将找到的眼睛标记出来        
        for e in eyes:
            img.draw_rectangle(e)

    # Print FPS.
    # Note: Actual FPS is higher, streaming the FB makes it slower.
    print(clock.fps())

运行程序效果如图: