#output_image = vision_limestone.draw_rectangles(screenshot, rectangles)
# keypoint searching
keypoint_image = edges_image
# crop the image to remove the ui elements
x, w, y, h = [200, 1130, 70, 750]
keypoint_image = keypoint_image[y:y + h, x:x + w]
kp1, kp2, matches, match_points = vision_limestone.match_keypoints(
keypoint_image)
match_image = cv.drawMatches(vision_limestone.needle_img, kp1,
keypoint_image, kp2, matches, None)
if match_points:
# find the center point of all the matched features
center_point = vision_limestone.centeroid(match_points)
# account for the width of the needle image that appears on the left
center_point[0] += vision_limestone.needle_w
# drawn the found center point on the output image
match_image = vision_limestone.draw_crosshairs(match_image,
[center_point])
# display the processed image
cv.imshow('Keypoint Search', match_image)
cv.imshow('Processed', processed_image)
cv.imshow('Edges', edges_image)
#cv.imshow('Matches', output_image)
# debug the loop rate
print('FPS {}'.format(1 / (time() - loop_time)))
loop_time = time()
