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# Gavin Morrison - D12124782 - DT211C-4 # 1. I inputted the image and converted it to Greyscale and HSV for the # operations within the program. # 2. I created a Numpy array of the upper and lower red values of our # HSV image # 3. I created a mask of the red values # 4. Red content of the image is isolated via a Bitwise 'And' operation # 5. The Greyscale image is inverted # 6. A Binary threshold is applied to our inverted Greyscale image. # 7. The thresholded image is then submitted to another bitwise # inversion which produces the white content of the image. # 8. The isolated red and white content are combined. # 9. The red and white components of the original image are dilated to # produce a slight overlap allowing the detection of connected regions. # 10. The dilated images are 'Anded' to produce an image of only the # overlapping regions. # 11. Canny is applied to detect edges within the image of connected regions. # 12. A Hough Line Transform is applied to the results of Canny to try and # find the horizontal lines within Wally's sweater. # 13. Wally is located. Please see comments throughout the code for further # step by step details. # I initially tried using Contours to locate Wally but I had very limited # results owing to the low resolution of Wally within the overall image. import numpy as np import cv2 from matplotlib import pyplot as plt from matplotlib import image as image # Original image is imported originalImage = cv2.imread("Where.jpg") # Original image is converted to greyscale for white segmentation. originalImageGrey = cv2.cvtColor(originalImage, cv2.COLOR_BGR2GRAY) # Original image is converted to a HSV Image for red segmentation. HSVImage = cv2.cvtColor(originalImage, cv2.COLOR_BGR2HSV) # A Numpy array is created of upper and lower red values. lowerRedValue = np.array([139,100,100]) upperRedValue = np.array([189,255,255]) # Mask is created of all red values. redMask = cv2.inRange(HSVImage, lowerRedValue, upperRedValue) # The red content of the image is isolated. redContent = cv2.bitwise_and(originalImage,originalImage, mask= redMask) # The Greyscale image is inverted via bitwise. greyInverted = cv2.bitwise_not(originalImageGrey) # A binary threshold is applied to the inverted greyscale image. T = 20 T, binaryInvertedGreyscale = cv2.threshold(greyInverted, thresh = T, maxval = 255, type = cv2.THRESH_BINARY) # This bitwise not operation creates the white content image. whiteContent = cv2.bitwise_not(binaryInvertedGreyscale) # white and red segmented images are written to file and the images inputed. cv2.imwrite("white.png", whiteContent) cv2.imwrite("red.png", redContent) redImage = cv2.imread("red.png") whiteImage = cv2.imread("white.png") #The red and white images are combined together and written to file. redWhiteCombined = redImage + whiteImage cv2.imwrite("combined.png", redWhiteCombined) # Dilation is perfomed on red and white images to create a slight overlap to allow the location of where red and white are connected. whiteKernel = np.ones((2,1), np.uint8) redKernel = np.ones((2,1), np.uint8) whiteDilation = cv2.dilate(whiteContent, whiteKernel, iterations=1) redDilation = cv2.dilate(redMask, redKernel, iterations=1) # Dilated images are combined in 'And' operation which isolates connecting areas between red and white connectedRegions = whiteDilation & redDilation # Connected regions written to file and then inputed cv2.imwrite("connectedregions.png", connectedRegions) connectedRegionsImage = cv2.imread("connectedregions.png") # The connected regions are converted to greyscale. connectedGreyscale = cv2.cvtColor(connectedRegionsImage, cv2.COLOR_BGR2GRAY) # The edges are located on the connected regions. connectedEdges = cv2.Canny(connectedGreyscale, 500, 300) # A Hough Line Transform is applied that scans the image for lines in the hope that # it will locate the stripes on Wally's sweater. houghLines = cv2.HoughLinesP(connectedEdges, 1, np.pi/90, 30, maxLineGap=2) #For loop plotting location of lines found in image. for line in houghLines: x1, y1, x2, y2 = line[0] cv2.line(connectedRegionsImage, (x1, y1), (x2, y2), (255, 255, 255), 90) # Location written to file and inputed cv2.imwrite('wallylocation.png',connectedRegionsImage) wallyLocation = cv2.imread("wallylocation.png") # Location found is combined with original image in 'And' operation and written to file. wallyFound = wallyLocation & originalImage cv2.imwrite('wallyfound.png',wallyFound) # Colour space conversions for matPlotLib display. OImatPlotLib = cv2.cvtColor(originalImage, cv2.COLOR_BGR2RGB) RCmatPlotLib = cv2.cvtColor(redContent, cv2.COLOR_BGR2RGB) RWmatPlotLib = cv2.cvtColor(redWhiteCombined, cv2.COLOR_BGR2RGB) WFmatPlotLib = cv2.cvtColor(wallyFound, cv2.COLOR_BGR2RGB) # A display of the result of our operations plt.subplot(231), plt.imshow(OImatPlotLib, cmap ='gray'), plt.title( 'Original Image' ) plt.subplot(232), plt.imshow(whiteContent, cmap ='gray'), plt.title( 'White Content' ) plt.subplot(233), plt.imshow(RCmatPlotLib, cmap ='gray'), plt.title( 'Red Content' ) plt.subplot(234), plt.imshow(RWmatPlotLib, cmap ='gray'), plt.title( 'Red & White combined' ) plt.subplot(235), plt.imshow(connectedRegions, cmap ='gray'), plt.title( 'Connected Regions' ) plt.subplot(236), plt.imshow(WFmatPlotLib, cmap ='gray'), plt.title( 'Wally Found' ) plt.show() |