openmv/scripts/examples/05-Feature-Detection/find_circles.py

52 lines
1.6 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
#
# Find Circles Example
#
# This example shows off how to find circles in the image using the Hough
# Transform. https://en.wikipedia.org/wiki/Circle_Hough_Transform
#
# Note that the find_circles() method will only find circles which are completely
# inside of the image. Circles which go outside of the image/roi are ignored...
import sensor
import time
sensor.reset()
sensor.set_pixformat(sensor.RGB565) # grayscale is faster
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time=2000)
clock = time.clock()
while True:
clock.tick()
img = sensor.snapshot().lens_corr(1.8)
# Circle objects have four values: x, y, r (radius), and magnitude. The
# magnitude is the strength of the detection of the circle. Higher is
# better...
# `threshold` controls how many circles are found. Increase its value
# to decrease the number of circles detected...
# `x_margin`, `y_margin`, and `r_margin` control the merging of similar
# circles in the x, y, and r (radius) directions.
# r_min, r_max, and r_step control what radiuses of circles are tested.
# Shrinking the number of tested circle radiuses yields a big performance boost.
for c in img.find_circles(
threshold=2000,
x_margin=10,
y_margin=10,
r_margin=10,
r_min=2,
r_max=100,
r_step=2,
):
img.draw_circle(c.x(), c.y(), c.r(), color=(255, 0, 0))
print(c)
print("FPS %f" % clock.fps())