def main():
robot = movements.robot()
camera = PiCamera()
camera.resolution = (640, 480)
camera.framerate = 50
camera.hflip = True
rawCapture = PiRGBArray(camera, size=(640, 480))
time.sleep(0.1)
for frame in camera.capture_continuous(rawCapture,
format="bgr",
use_video_port=True):
image = frame.array
blur = cv2.GaussianBlur(image, (3, 3), 0)
lower = np.array([3, 9, 114], dtype="uint8")
upper = np.array([43, 49, 154], dtype="uint8")
thresh = cv2.inRange(blur, lower, upper)
thresh2 = thresh.copy()
image, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
max_area = 0
best_cent = 1
detected = False
for cent in contours:
area = cv2.contourArea(cent)
if area > max_area:
max_area = area
best_cent = cent
detected = True
M = cv2.moments(best_cent)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
direction, distance_from_cent = find_dir(cx)
if detected == True:
scan_dist = robot.scan_for_obstacles()
if scan_dist >= 25:
if direction == 'left':
robot.left_forward()
elif direction == 'right':
robot.right_forward()
elif scan_dist >= 10:
if direction == 'left':
robot.left()
elif direction == 'right':
robot.right()
else:
robot.pause()
else:
robot.pause()
rawCapture.truncate(0)
import RPi.GPIO as GPIO
import movements
import time
import curses
robot = movements.robot()
def move(char):
if char == curses.KEY_UP:
screen.addstr(0, 0, 'up ')
robot.forward()
elif char == curses.KEY_DOWN:
screen.addstr(0, 0, 'down ')
robot.backward()
elif char == curses.KEY_LEFT:
screen.addstr(0, 0, 'left ')
robot.right()
elif char == curses.KEY_RIGHT:
screen.addstr(0, 0, 'right')
robot.left()
elif char == 32:
screen.addstr(0, 0, 'stop ')
robot.stop()
screen = curses.initscr()
def main():
robot = movements.robot()
camera = PiCamera()
camera.resolution = (640, 480)
camera.framerate = 50
camera.hflip = True
camera.vflip = True
rawCapture = PiRGBArray(camera, size=(640, 480))
time.sleep(0.1)
for frame in camera.capture_continuous(rawCapture,
format="bgr",
use_video_port=True):
image = frame.array
blur = cv2.GaussianBlur(image, (3, 3), 0)
lower = np.array([3, 9, 114], dtype="uint8")
upper = np.array([43, 49, 154], dtype="uint8")
thresh = cv2.inRange(blur, lower, upper)
thresh2 = thresh.copy()
image, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
max_area = 0
best_cent = 1
detected = False
for cent in contours:
area = cv2.contourArea(cent)
if area > max_area:
max_area = area
best_cent = cent
detected = True
M = cv2.moments(best_cent)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
cv2.circle(blur, (cx, cy), 10, (0, 0, 255), -1)
cv2.imshow('Tracking', blur)
direction = find_pos(cx)
if detected == True:
#print '{}'.format(direction)
dist = int(robot.scan_for_obstacles())
if direction == 'left':
if dist < 30:
robot.left()
else:
robot.left_forward()
else:
if dist < 30:
robot.right()
else:
robot.right_forward()
else:
#print 'nuttin'
robot.pause()
blah = cv2.waitKey(1) & 0xFF
rawCapture.truncate(0)
if blah == ord("q"):
break