openmv/usr/examples/face_eye_detection.py
2016-02-17 22:35:29 +02:00

50 lines
1.6 KiB
Python

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)
eyes_cascade = image.HaarCascade("eye", stages=24)
print(face_cascade, eyes_cascade)
# FPS clock
clock = time.clock()
def draw_cross(img, x, y, l):
img.draw_line((x-l, y, x+l, y))
img.draw_line((x, y-l, x, y+l))
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)
# Draw faces
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=0.65, scale=1.25, roi=face)
for e in eyes:
e = [face[0]+e[0], face[1]+e[1], e[2], e[3]] # Add face offset
img.draw_rectangle(e)
# Draw crosshair, add width/2 and height/2
draw_cross(img, e[0]+int(e[2]/2), e[1]+int(e[3]/2), 5)
# Print FPS.
# Note: Actual FPS is higher, streaming the FB makes it slower.
print(clock.fps())