diff --git a/Code/OpenMV Code/square_tracking.py b/Code/OpenMV Code/square_tracking.py new file mode 100644 index 0000000000000000000000000000000000000000..942c66bbb6c56b44819c0ca33580e94631f83918 --- /dev/null +++ b/Code/OpenMV Code/square_tracking.py @@ -0,0 +1,45 @@ +# Find Rects Example +# +# This example shows off how to find rectangles in the image using the quad threshold +# detection code from our April Tags code. The quad threshold detection algorithm +# detects rectangles in an extremely robust way and is much better than Hough +# Transform based methods. For example, it can still detect rectangles even when lens +# distortion causes those rectangles to look bent. Rounded rectangles are no problem! +# (But, given this the code will also detect small radius circles too)... + +import sensor, image, time, pyb +ir_led = pyb.LED(4) +red_led = pyb.LED(1) +b_led = pyb.LED(2) +g_led = pyb.LED(3) +ir_led.off() +ir_led.on() +red_led.off() +#red_led.on() + +sensor.reset() +sensor.set_pixformat(sensor.RGB565) # grayscale is faster (160x120 max on OpenMV-M7) +sensor.set_framesize(sensor.QVGA) +sensor.skip_frames(time = 2000) +sensor.set_auto_gain(False) # must turn this off to prevent image washout... +sensor.set_auto_whitebal(False) # must turn this off to prevent image washout... +sensor.set_auto_exposure(False, 25000) +clock = time.clock() + +while(True): + ir_led.on() + #red_led.on() + #b_led.on() + #g_led.on() + img = sensor.snapshot() + + # `threshold` below should be set to a high enough value to filter out noise + # rectangles detected in the image which have low edge magnitudes. Rectangles + # have larger edge magnitudes the larger and more contrasty they are... + + for r in img.find_rects(threshold = 5000): + #img.draw_rectangle(r.rect(), color = (255, 0, 0)) + #for p in r.corners(): img.draw_circle(p[0], p[1], 5, color = (0, 255, 0)) + print(r) + + print("FPS %f" % clock.fps())