# 人脸检测

## code


# Face Detection Example
#
# This example shows off the built-in face detection feature of the OpenMV Cam.
#
# Face detection works by using the Haar Cascade feature detector on an image. A
# Haar Cascade is a series of simple area contrasts checks. For the built-in
# frontalface detector there are 25 stages of checks with each stage having
# hundreds of checks a piece. Haar Cascades run fast because later stages are
# only evaluated if previous stages pass. Additionally, your OpenMV Cam uses
# a data structure called the integral image to quickly execute each area
# contrast check in constant time (the reason for feature detection being
# grayscale only is because of the space requirment for the integral image).

import sensor, time, image

# 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)

# By default this will use all stages, lower satges is faster but less accurate.

# FPS clock
clock = time.clock()

while (True):
clock.tick()

# Capture snapshot
img = sensor.snapshot()

# Find objects.
# 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.

# Draw objects
for r in objects:
img.draw_rectangle(r)

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