By using image pre-filtering you can find apriltags at a distance:
# Find Small Apriltags
#
# This script shows off how to use blob tracking as a pre-filter to
# finding Apriltags in the image using blob tracking to find the
# area of where the tag is first and then calling find_apriltags
# on that blob.
# Note, this script works well assuming most parts of the image do not
# pass the thresholding test... otherwise, you don't get a distance
# benefit.
import sensor, image, time, math, omv
# Set the thresholds to find a white object (i.e. tag border)
thresholds = (150, 255)
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
if omv.board_type() == "H7": sensor.set_framesize(sensor.VGA)
elif omv.board_type() == "M7": sensor.set_framesize(sensor.QVGA)
else: raise Exception("You need a more powerful OpenMV Cam to run this script")
sensor.skip_frames(time = 200) # increase this to let the auto methods run for longer
sensor.set_auto_gain(False) # must be turned off for color tracking
sensor.set_auto_whitebal(False) # must be turned off for color tracking
clock = time.clock()
# The apriltag code supports up to 6 tag families which can be processed at the same time.
# Returned tag objects will have their tag family and id within the tag family.
tag_families = 0
tag_families |= image.TAG16H5 # comment out to disable this family
tag_families |= image.TAG25H7 # comment out to disable this family
tag_families |= image.TAG25H9 # comment out to disable this family
tag_families |= image.TAG36H10 # comment out to disable this family
tag_families |= image.TAG36H11 # comment out to disable this family (default family)
tag_families |= image.ARTOOLKIT # comment out to disable this family
while(True):
clock.tick()
img = sensor.snapshot()
# First, we find blobs that may be candidates for tags.
box_list = []
# AprilTags may fail due to not having enough ram given the image sie being passed.
tag_list = []
for blob in img.find_blobs([thresholds], pixels_threshold=100, area_threshold=100, merge=True):
# Next we look for a tag in an ROI that's bigger than the blob.
w = min(max(int(blob.w() * 1.2), 20), 100) # Not too small, not too big.
h = min(max(int(blob.h() * 1.2), 20), 100) # Not too small, not too big.
x = min(max(int(blob.x() - (w * 0.1)), 0), img.width()-1)
y = min(max(int(blob.y() - (h * 0.1)), 0), img.height()-1)
box_list.append((x, y, w, h)) # We'll draw these later.
# Since we constrict the roi size apriltags shouldn't run out of ram.
# But, if it does we handle it...
try:
tag_list.extend(img.find_apriltags(roi=(x,y,w,h), families=tag_families))
except (MemoryError, OSError): # Don't catch all exceptions otherwise you can't stop the script.
pass
for b in box_list:
img.draw_rectangle(b)
# Now print out the found tags
for tag in tag_list:
img.draw_rectangle(tag.rect())
img.draw_cross(tag.cx(), tag.cy())
for c in tag.corners():
img.draw_circle(c[0], c[1], 5)
print("Tag:", tag.cx(), tag.cy(), tag.rotation(), tag.id())
Note that there’s a bug in the apriltag code related to the minimum roi. Please don’t make the ROI too small. It causes crashes if you do.