# Result
max_res_SIGN_FORWARD = np.amax(cv2.matchTemplate(crop_img,SIGN_FORWARD,cv2.TM_CCOEFF_NORMED))
max_res_SIGN_BACKWARD = np.amax(cv2.matchTemplate(crop_img,SIGN_BACKWARD,cv2.TM_CCOEFF_NORMED))
""" DETECTING SIGNS """
turn_angle = circle_x
while turn_angle > 160:
turn_angle -= 160
turn_angle = 100 - int(round((float(turn_angle)/160)*100))
if max_res_SIGN_FORWARD >= max_res_SIGN_BACKWARD and max_res_SIGN_FORWARD >= MATCH_THRESHOLD:
# Turn towards it
if circle_x > 170:
print 'circle_x', circle_x, 'turning right', turn_angle
buggy.turnRight(turn_angle)
elif circle_x < 150:
print 'circle_x', circle_x, 'turning left', turn_angle
buggy.turnLeft(turn_angle)
# Change Status
moving_status = "forward"
elif max_res_SIGN_BACKWARD >= max_res_SIGN_FORWARD and max_res_SIGN_BACKWARD >= MATCH_THRESHOLD:
# Turn away from it
if circle_x > 170:
print 'circle_x', circle_x, 'turning left', turn_angle
buggy.turnLeft(turn_angle)
elif circle_x < 150:
print 'circle_x', circle_x, 'turning right', turn_angle
buggy.turnRight(turn_angle)
# Change Status
cv2.TM_CCOEFF_NORMED))
max_res_SIGN_BACKWARD = np.amax(
cv2.matchTemplate(crop_img, SIGN_BACKWARD,
cv2.TM_CCOEFF_NORMED))
""" DETECTING SIGNS """
turn_angle = circle_x
while turn_angle > 160:
turn_angle -= 160
turn_angle = 100 - int(
round((float(turn_angle) / 160) * 100))
if max_res_SIGN_FORWARD >= max_res_SIGN_BACKWARD and max_res_SIGN_FORWARD >= MATCH_THRESHOLD:
# Turn towards it
if circle_x > 170:
print 'circle_x', circle_x, 'turning right', turn_angle
buggy.turnRight(turn_angle)
elif circle_x < 150:
print 'circle_x', circle_x, 'turning left', turn_angle
buggy.turnLeft(turn_angle)
# Change Status
moving_status = "forward"
elif max_res_SIGN_BACKWARD >= max_res_SIGN_FORWARD and max_res_SIGN_BACKWARD >= MATCH_THRESHOLD:
# Turn away from it
if circle_x > 170:
print 'circle_x', circle_x, 'turning left', turn_angle
buggy.turnLeft(turn_angle)
elif circle_x < 150:
print 'circle_x', circle_x, 'turning right', turn_angle
buggy.turnRight(turn_angle)
# Change Status
# If more than MAX, go with average
if len(arrow_buffer) >= MAX_RECOGNISING_COUNT:
print "Taking average arrow direction...", arrow_buffer
best_arrow = mean(arrow_buffer)
# Follow arrow
moving_forward, wheel_direction, confident_match, arrow_buffer = follow_arrow_direction(best_arrow, moving_forward, wheel_direction, arrow_buffer)
buggy.clearBuffer() # Moved, so clear the buffer
else:
if len(arrow_buffer) == 1:
print "Learning direction of arrow..."
wait_once = True
else:
# Move towards sign
print "Moving towards sign..."
if circle_x > 160:
buggy.turnRight((circle_x/320)*100)
else:
buggy.turnLeft((circle_x/160)*100)
creep_once, moving_forward = True, False
else:
# Move towards largest contour
# Countours (already threshed)
contours, hierarchy = cv2.findContours(Chroma_red, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# Find the index of the largest contour
areas = [cv2.contourArea(c) for c in contours]
if areas:
max_index = np.argmax(areas)
cnt = contours[max_index]
# Straight Bounding Rectangle
x,y,w,h = cv2.boundingRect(cnt)
center_x = x + w/2
