def find(img, hue_min=20, hue_max=175, sat_min=0, sat_max=255, val_min=0, val_max=255):
"""
Detect the qualification gate.
:param img: HSV image from the bottom camera
:return: tuple of location of the center of the gate in a "targeting" coordinate system: origin is at center of image, axes range [-1, 1]
"""
img = np.copy(img)
bin = vision_util.hsv_threshold(img, hue_min, hue_max, sat_min, sat_max, val_min, val_max)
canny = vision_util.canny(bin, 50)
# find contours after first processing it with Canny edge detection
contours, hierarchy = cv2.findContours(canny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
hulls = vision_util.convex_hulls(contours)
cv2.drawContours(bin, hulls, -1, 255)
cv2.imshow('bin', bin)
hulls.sort(key=hull_score)
if len(hulls) < 2:
return ()
# get the two highest scoring candidates
left = cv2.minAreaRect(hulls[0])
right = cv2.minAreaRect(hulls[1])
# if we got left and right mixed up, switch them
if right[0][0] < left[0][0]:
left, right = right, left
confidence = score_pair(left, right)
if confidence < 80:
return 0, 0
# draw hulls in Blaze Orange
cv2.drawContours(img, hulls, -1, (0, 102, 255), -1)
# draw green outlines so we know it actually detected it
cv2.drawContours(img, hulls, -1, (0, 255, 0), 2)
cv2.imshow('img', img)
center_actual = (np.mean([left[0][0], right[0][0]]), np.mean([left[0][1], right[0][1]]))
# shape[0] is the number of rows because matrices are dumb
center = (center_actual[0] / img.shape[1], center_actual[1] / img.shape[0])
# convert to the targeting system of [-1, 1]
center = ((center[0] * 2) - 1, (center[1] * 2) - 1)
return center
def find_balls(gray):
canny = vision_common.canny(gray, 50)
cv2.imshow('canny', canny)
def find_balls(gray):
canny = vision_common.canny(gray, 50)
cv2.imshow('canny', canny)
