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import cv2
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# Constant variables definition.
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DESIRED_HEIGHT = 480 # The input image will be resized to this height, preserving its aspect ratio.
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BLUE_THRESHOLD = 128 # If the blue channel is bigger than this, it is considered background and removed.
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# Function definitions
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def resizeImage(img):
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"Resize the input image based on the DESIRED_HEIGHT variable."
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p = img.shape;
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aspectRatio = p[0]/p[1]
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width = DESIRED_HEIGHT*aspectRatio
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img = cv2.resize(img, ( DESIRED_HEIGHT, int(width) ))
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return img
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#####################################################################################
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# Read image from source
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img = cv2.imread('/home/stelios/Desktop/IoT/drone.jpg', cv2.IMREAD_COLOR)
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# Resize image to the desired height.
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img = resizeImage(img)
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imgOriginal = img.copy()
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# Remove BLUE
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p = img.shape
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for i in range(p[0]):
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for j in range(p[1]):
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if (img[i,j,0] > BLUE_THRESHOLD):
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img[i,j,:] = 0
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# Convert to grayscale.
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img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
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#####################################################################
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thres1, img2 = cv2.threshold(img, 1, 255, cv2.THRESH_BINARY)
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im2, contours, hierarchy = cv2.findContours(img2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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maxContour = max(contours, key = cv2.contourArea)
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cv2.drawContours(imgOriginal, maxContour, -1, (0,0,255), 1)
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print(len(maxContour))
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x,y,w,h = cv2.boundingRect(maxContour)
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# draw the biggest contour (c) in green
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cv2.rectangle(imgOriginal,(x,y),(x+w,y+h),(0,20,255),2)
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cv2.imshow('Test', imgOriginal)
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cv2.waitKey(0)
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