openmv/scripts/examples/03-Machine-Learning/02-Haar-Cascade/iris_detection.py
2025-05-30 11:05:14 -07:00

59 lines
1.7 KiB
Python

# This work is licensed under the MIT license.
# Copyright (c) 2013-2023 OpenMV LLC. All rights reserved.
# https://github.com/openmv/openmv/blob/master/LICENSE
#
# Iris Detection 2 Example
#
# This example shows how to find the eye gaze (pupil detection) after finding
# the eyes in an image. This script uses the find_eyes function which determines
# the center point of roi that should contain a pupil. It does this by basically
# finding the center of the darkest area in the eye roi which is the pupil center.
#
# Note: This script does not detect a face first, use it with the telephoto lens.
import sensor
import time
import image
# Reset sensor
sensor.reset()
# Sensor settings
sensor.set_contrast(3)
sensor.set_gainceiling(16)
# Set resolution to VGA.
sensor.set_framesize(sensor.VGA)
# Bin/Crop image to 200x100, which gives more details with less data to process
sensor.set_windowing((220, 190, 200, 100))
sensor.set_pixformat(sensor.GRAYSCALE)
# Load Haar Cascade
# By default this will use all stages, lower stages is faster but less accurate.
eyes_cascade = image.HaarCascade("/rom/haarcascade_eye.cascade", stages=24)
print(eyes_cascade)
# FPS clock
clock = time.clock()
while True:
clock.tick()
# Capture snapshot
img = sensor.snapshot()
# Find eyes !
# 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.
eyes = img.find_features(eyes_cascade, threshold=0.5, scale_factor=1.5)
# Find iris
for e in eyes:
iris = img.find_eye(e)
img.draw_rectangle(e)
img.draw_cross(iris[0], iris[1])
# Print FPS.
# Note: Actual FPS is higher, streaming the FB makes it slower.
print(clock.fps())