One of the new challenges for PiWars 2018 is ‘Over the Rainbow’. And it is going to be a challenge! If you use ‘Method 1’, it requires you to incorporate computer vision into your robot, enabling the robot to search for and find balls of certain colours in the right order.
As promised, we’re supplying some sample code. And that’s all it is – a sample. It does not control your robot since how you do that is up to your design. It is not perfect – it will need tuning depending on the light on the day (we’ll be doing something to help out there). But it does ‘see’ each of the colours and tells you when you are near – but again, you will need to ‘tune’ that yourselves.
The code is based (read – copied) from the code supplied by our friends at PiBorg and is the code that is used to make their robots follow a red ball. I have modified it to remove their code for controlling their controllers, added in some code to see different colours, converted to Python 3.5, and added some debugging code that helped me to test.
First, you will need to prepare your Pi to run OpenCV. This is the hardest part! Unfortunately there is no simple ‘apt-get’ or ‘pip’ install, so you will need to install – and compile – it by hand. Don’t worry, it’s not as complicated as it sounds. Just follow the instructions on this page and you will have OpenCV installed. It will take some time (an hour or two) and you will need to use a 16GB microSD card as it will fill an 8GB card even if you remove Wolfram and Libreoffice as he suggests – it did for me!
The original code was written by PiBorg and has been reproduced here under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. You may use it, and share it, but it cannot be used for commercial purposes.
Copy the following code into a Python file. Ensure your camera is attached, and you have turned it on from the Raspberry Pi Configuration tool.
I have commented out the PiBorg ‘Thunderborg’ code as you may not be using their control board. You will obviously need to replace this with your own code. You will also need to code the ‘search’ algorithm that searches for each ball in turn.
The important OpenCV code is in the ‘ProcessImage’ function.
#!/usr/bin/env python # coding: Latin # Load library functions we want import time import os import sys # import ThunderBorg import io import threading import picamera import picamera.array import cv2 import numpy print('Libraries loaded') # Global values global running # global TB global camera global processor global debug global colour running = True debug = True colour = 'blue' # Setup the ThunderBorg # TB = ThunderBorg.ThunderBorg() # TB.i2cAddress = 0x15 # Uncomment and change the value if you have changed the board address # TB.Init() ##if not TB.foundChip: ## boards = ThunderBorg.ScanForThunderBorg() ## if len(boards) == 0: ## print('No ThunderBorg found, check you are attached :)' ## else: ## print('No ThunderBorg at address %02X, but we did find boards:' % (TB.i2cAddress) ## for board in boards: ## print(' %02X (%d)' % (board, board) ## print('If you need to change the IÃÂ²C address change the setup line so it is correct, e.g.' ## print('TB.i2cAddress = 0x%02X' % (boards) ## sys.exit() ##TB.SetCommsFailsafe(False) # Power settings ##voltageIn = 12.0 # Total battery voltage to the ThunderBorg ##voltageOut = 12.0 * 0.95 # Maximum motor voltage, we limit it to 95% to allow the RPi to get uninterrupted power # Camera settings imageWidth = 320 # Camera image width imageHeight = 240 # Camera image height frameRate = 3 # Camera image capture frame rate # Auto drive settings autoMaxPower = 1.0 # Maximum output in automatic mode autoMinPower = 0.2 # Minimum output in automatic mode autoMinArea = 10 # Smallest target to move towards autoMaxArea = 10000 # Largest target to move towards autoFullSpeedArea = 300 # Target size at which we use the maximum allowed output # Setup the power limits ##if voltageOut > voltageIn: ## maxPower = 1.0 ##else: ## maxPower = voltageOut / float(voltageIn) ##autoMaxPower *= maxPower # Image stream processing thread class StreamProcessor(threading.Thread): def __init__(self): super(StreamProcessor, self).__init__() self.stream = picamera.array.PiRGBArray(camera) self.event = threading.Event() self.terminated = False self.start() self.begin = 0 def run(self): # This method runs in a separate thread while not self.terminated: # Wait for an image to be written to the stream if self.event.wait(1): try: # Read the image and do some processing on it self.stream.seek(0) self.ProcessImage(self.stream.array, colour) finally: # Reset the stream and event self.stream.seek(0) self.stream.truncate() self.event.clear() # Image processing function def ProcessImage(self, image, colour): # View the original image seen by the camera. if debug: cv2.imshow('original', image) cv2.waitKey(0) # Blur the image image = cv2.medianBlur(image, 5) if debug: cv2.imshow('blur', image) cv2.waitKey(0) # Convert the image from 'BGR' to HSV colour space image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV) if debug: cv2.imshow('cvtColour', image) cv2.waitKey(0) # We want to extract the 'Hue', or colour, from the image. The 'inRange' # method will extract the colour we are interested in (between 0 and 180) # In testing, the Hue value for red is between 95 and 125 # Green is between 50 and 75 # Blue is between 20 and 35 # Yellow is... to be found! if colour == "red": imrange = cv2.inRange(image, numpy.array((95, 127, 64)), numpy.array((125, 255, 255))) elif colour == "green": imrange = cv2.inRange(image, numpy.array((50, 127, 64)), numpy.array((75, 255, 255))) elif colour == 'blue': imrange = cv2.inRange(image, numpy.array((20, 64, 64)), numpy.array((35, 255, 255))) # I used the following code to find the approximate 'hue' of the ball in # front of the camera # for crange in range(0,170,10): # imrange = cv2.inRange(image, numpy.array((crange, 64, 64)), numpy.array((crange+10, 255, 255))) # print(crange) # cv2.imshow('range',imrange) # cv2.waitKey(0) # View the filtered image found by 'imrange' if debug: cv2.imshow('imrange', imrange) cv2.waitKey(0) # Find the contours contourimage, contours, hierarchy = cv2.findContours(imrange, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) if debug: cv2.imshow('contour', contourimage) cv2.waitKey(0) # Go through each contour foundArea = -1 foundX = -1 foundY = -1 for contour in contours: x, y, w, h = cv2.boundingRect(contour) cx = x + (w / 2) cy = y + (h / 2) area = w * h if foundArea < area: foundArea = area foundX = cx foundY = cy if foundArea > 0: ball = [foundX, foundY, foundArea] else: ball = None # Set drives or report ball status self.SetSpeedFromBall(ball) # Set the motor speed from the ball position def SetSpeedFromBall(self, ball): global TB driveLeft = 0.0 driveRight = 0.0 if ball: x = ball area = ball if area < autoMinArea: print('Too small / far') elif area > autoMaxArea: print('Close enough') else: if area < autoFullSpeedArea: speed = 1.0 else: speed = 1.0 / (area / autoFullSpeedArea) speed *= autoMaxPower - autoMinPower speed += autoMinPower direction = (x - imageCentreX) / imageCentreX if direction < 0.0: # Turn right print('Turn Right') driveLeft = speed driveRight = speed * (1.0 + direction) else: # Turn left print('Turn Left') driveLeft = speed * (1.0 - direction) driveRight = speed print('%.2f, %.2f' % (driveLeft, driveRight)) else: print('No ball') # TB.SetMotor1(driveLeft) # TB.SetMotor2(driveRight) # Image capture thread class ImageCapture(threading.Thread): def __init__(self): super(ImageCapture, self).__init__() self.start() def run(self): global camera global processor print('Start the stream using the video port') camera.capture_sequence(self.TriggerStream(), format='bgr', use_video_port=True) print('Terminating camera processing...') processor.terminated = True processor.join() print('Processing terminated.') # Stream delegation loop def TriggerStream(self): global running while running: if processor.event.is_set(): time.sleep(0.01) else: yield processor.stream processor.event.set() # Startup sequence print('Setup camera') camera = picamera.PiCamera() camera.resolution = (imageWidth, imageHeight) camera.framerate = frameRate imageCentreX = imageWidth / 2.0 imageCentreY = imageHeight / 2.0 print('Setup the stream processing thread') processor = StreamProcessor() print('Wait ...') time.sleep(2) captureThread = ImageCapture() try: print('Press CTRL+C to quit') ## TB.MotorsOff() ## TB.SetLedShowBattery(True) # Loop indefinitely until we are no longer running while running: # Wait for the interval period # You could have the code do other work in here 🙂 time.sleep(1.0) # Disable all drives ## TB.MotorsOff() except KeyboardInterrupt: # CTRL+C exit, disable all drives print('\nUser shutdown') ## TB.MotorsOff() except: # Unexpected error, shut down! e = sys.exc_info() print print(e) print('\nUnexpected error, shutting down!') ## TB.MotorsOff() # Tell each thread to stop, and wait for them to end running = False captureThread.join() processor.terminated = True processor.join() del camera ##TB.MotorsOff() ##TB.SetLedShowBattery(False) ##TB.SetLeds(0,0,0) print('Program terminated.')