34 lines
982 B
Python
34 lines
982 B
Python
import numpy as np
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import cv2
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img = cv2.imread('H:\car\GestureRecognition\IMG_0818.png', 1)
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# Downscale the image
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img = cv2.resize(img, None, fx=0.1, fy=0.1, interpolation = cv2.INTER_AREA)
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min_seg_threshold = 1.2
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max_seg_threshold = 3
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# prevent divide by zero, by just forcing pixel to be ignored.
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np.where(img[:,:,1] == 0, 0, img[:,:,1])
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img[(img[:,:,2]/img[:,:,1] > min_seg_threshold) & (img[:,:,2]/img[:,:,1] < max_seg_threshold)] = [255,255,255]
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# Try removing image noise.
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#img = cv2.fastNlMeansDenoising(img)
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cv2.imshow('image', img)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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# Remove non-hand parts
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# Find centre of the hand
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# Hand parts are white pixels.
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# Find sum of each col/row to find the left/rightmost and top/bottommost white pixels.
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# Have used a for loop but obviously that is going to be slow.
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# Draw appropriate circle
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# Calculate number of different peaks.
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# Article just traced around the circle and counted number of times switched from
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# zero to one. |