# 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())