73 lines
2.0 KiB
Python
73 lines
2.0 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Thu Nov 22 14:16:46 2018
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@author: pivatom
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"""
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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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img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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img_gray[img_gray[:,:] > 90] = 255
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img_gray[img_gray[:,:] < 90] = 0
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# Threshold to binary.
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ret,img_thresh = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)
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# Following method is much faster -> 0.00143s
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# Still want to speed up further by lowering reliance on memory, which is quite heavy..
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k = np.sum(img_thresh) / 255
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# Taking indices for num of rows.
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x_ind = np.arange(0,img_thresh.shape[1])
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y_ind = np.arange(0,img_thresh.shape[0])
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coords = np.zeros((img_thresh.shape[0], img_thresh.shape[1], 2))
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coords_x = np.zeros((img_thresh.shape[0], img_thresh.shape[1]))
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coords_y = np.zeros((img_thresh.shape[0], img_thresh.shape[1]))
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coords_x[:,:] = x_ind
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# Even this is extremely quick as it goes through rows in the numpy array.
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for element in y_ind:
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coords_y[element,:] = element
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# Now need to get the average x value and y value for centre of gravity
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xb = int(np.sum(coords_x[img_thresh == 255])/k)
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yb = int(np.sum(coords_y[img_thresh == 255])/k)
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centre = (int(np.sum(coords_x[img_thresh == 255])/k), int(np.sum(coords_y[img_thresh == 255])/k))
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#x,y,k,xb,yb = 0,0,0,0,0
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#
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## this is inherently slow...like very very slow...0.114s
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#for pix in img_thresh:
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# for j in pix:
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# if j == 255:
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# k += 1
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# xb += x
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# yb += y
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# x += 1
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# y += 1
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# x = 0
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#
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#centre = (int(xb/k), int(yb/k))
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cv2.rectangle(img_thresh, centre, (centre[0] + 20, centre[1] + 20), (0,0,255), 3)
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cv2.circle(img_thresh, centre, 140, (0,0,0), 3)
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# Now need to trace around the circle to figure out where the fingers are.
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# First get equation of the circle:
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# y = sart(r2 - (x-c)2 + c)
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cv2.imshow("Binary-cot-out", img_thresh)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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