#Allowable width/height error, measured in boxes
WH_ERROR = 0.35
#Allowable area error from W*H, measured in percentage/100.
AREA_ERROR = 0.35
#Spacing between boxes for both x and y directions, measured in boxes.
BOX_SPACING = 0.35
#Allowable spacing error for both x and y directions, measured in boxes
SPACING_ERROR = 0.35
#The x offset of center, in pixels, created by the position of the camera on the robot.
CENTER_OFFSET_X = 0
#The dimensions of the image.
IMAGE_DIMENSIONS = [240, 180]
kernel = numpy.ones((3, 3), numpy.uint8) #used to do dialate
video = pistream.PiVideoStream((IMAGE_DIMENSIONS[0],IMAGE_DIMENSIONS[1])).start()
time.sleep(2)
def findPattern(contours, standard):
#The standard contour properties, defined by the current baseline contour (assumed the top-middle square in the pattern)
stX, stY, stH, stW = cv2.boundingRect(contours[standard]) #Whether or not contours have been verified for the bottom left and right positions of the pattern.
signHeight = None
leftVerified = False
rightVerified = False
turnError = 0
distance = 0
global pattern
#Cycle through all contours and check against position and congruency relative to standard contour.
counter = 0
while counter < len(contours):
import pistream
from picamera.array import PiRGBArray
from picamera import PiCamera
import cv2
import time
import numpy
LOWER_HSV = numpy.array([111, 117, 142])
UPPER_HSV = numpy.array([180, 255, 255])
video = pistream.PiVideoStream((240, 180)).start()
time.sleep(0.5)
while True:
image = video.read()
cv2.imshow('Image', image)
imageHSV = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
imageThresh = cv2.inRange(imageHSV, LOWER_HSV, UPPER_HSV)
cv2.imshow('Threshhold', imageThresh)
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.imwrite('/home/pi/FollowBot/capture.jpg', image,
[cv2.IMWRITE_JPEG_QUALITY, 90])
break
GOAL_RATIO = 25.0 / 55.0 #Retroreflective tape Height/Width (pixel counted from camera view)
GOAL_MINIMUM_AREA = 50 #The smallest number of pixel area that a goal should be
GOAL_MAXIMUM_AREA = 550
GOAL_RATIO_ERROR = 0.20 #Allowable error from ratio
GOAL_CENTER = 129 #X position within camera frame where we need to center goal
GOAL_CENTER_ERROR = 4 #Allowable error from center, in pixels.
#Parameters for robot turning speed
TURN_SCALE = 0.01 #Multiplier to determine turn rate, given error from center in pixels that will determine the robot turn rate
TURN_MAX_RATE = 0.9 #Fastest turn rate allowed
TURN_MIN_RATE = 0.55 #was .7 Slowest turn rate allowed
# Setup PiCamera for capture
IMG_WIDTH = 240
IMG_HEIGHT = 180
video = pistream.PiVideoStream((IMG_WIDTH, IMG_HEIGHT)).start()
time.sleep(2) #Give camera a chance to stablize
# Connect to roboRio via NetworkTable
NetworkTable.setIPAddress("10.45.64.2")
NetworkTable.setClientMode()
NetworkTable.initialize()
table = NetworkTable.getTable("visionTracking")
# Based on the error from target center, in pixels, this function will
# calculate the appropriate rate at which the robot should turn.
def calcTurnRate(error):
# Determine magnitude of turn using the scale factor
absRate = abs(error) * TURN_SCALE
import pistream
import cv2
imageCounter = 0
# Setup PiCamera for capture
IMG_WIDTH = 240
IMG_HEIGHT = 180
try:
video = pistream.PiVideoStream((IMG_WIDTH, IMG_HEIGHT),
format="bgr").start()
while raw_input("Enter to capture or 'q' to quit") != "q":
image = video.read()
cv2.imwrite("captures/image" + str(imageCounter) + ".jpg", image,
[cv2.IMWRITE_JPEG_QUALITY, 90])
print "saved image " + str(imageCounter)
imageCounter += 1
finally:
video.stop()