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Commit0570570

Browse files
syurkevi9prady9
authored andcommitted
adds computer vision example
1 parente85cefc commit0570570

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4 files changed

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4 files changed

+386
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lines changed

‎examples/computer_vision/fast.py

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#!/usr/bin/python
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#######################################################
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# Copyright (c) 2018, ArrayFire
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# All rights reserved.
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#
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# This file is distributed under 3-clause BSD license.
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# The complete license agreement can be obtained at:
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# http://arrayfire.com/licenses/BSD-3-Clause
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########################################################
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fromtimeimporttime
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importarrayfireasaf
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importsys
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defdraw_corners(img,x,y,draw_len):
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# Draw vertical line of (draw_len * 2 + 1) pixels centered on the corner
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# Set only the first channel to 1 (green lines)
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xmin=max(0,x-draw_len)
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xmax=min(img.dims()[1],x+draw_len)
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img[y,xmin :xmax,0]=0.0
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img[y,xmin :xmax,1]=1.0
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img[y,xmin :xmax,2]=0.0
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# Draw vertical line of (draw_len * 2 + 1) pixels centered on the corner
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# Set only the first channel to 1 (green lines)
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ymin=max(0,y-draw_len)
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ymax=min(img.dims()[0],y+draw_len)
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img[ymin :ymax,x,0]=0.0
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img[ymin :ymax,x,1]=1.0
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img[ymin :ymax,x,2]=0.0
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returnimg
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deffast_demo(console):
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ifconsole:
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img_color=af.load_image("../../assets/examples/images/square.png",True);
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else:
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img_color=af.load_image("../../assets/examples/images/man.jpg",True);
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img_color/=255.0
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img=af.color_space(img_color,af.CSPACE.GRAY,af.CSPACE.RGB)
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features=af.fast(img)
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xs=features.get_xpos()
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ys=features.get_ypos()
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draw_len=3;
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num_features=features.num_features().value
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forfinrange(num_features):
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print(f)
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x=xs[f]
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y=ys[f]
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img_color=draw_corners(img_color,x,y,draw_len)
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print("Features found: {}".format(num_features))
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ifnotconsole:
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# Previews color image with green crosshairs
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wnd=af.Window(512,512,"FAST Feature Detector")
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whilenotwnd.close():
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wnd.image(img_color)
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else:
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print(xs);
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print(ys);
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if__name__=="__main__":
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if (len(sys.argv)>1):
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af.set_device(int(sys.argv[1]))
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console= (sys.argv[2]=='-')iflen(sys.argv)>2elseFalse
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af.info()
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print("** ArrayFire FAST Feature Detector Demo **\n")
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fast_demo(console)
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‎examples/computer_vision/harris.py

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#!/usr/bin/python
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#######################################################
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# Copyright (c) 2018, ArrayFire
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# All rights reserved.
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#
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# This file is distributed under 3-clause BSD license.
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# The complete license agreement can be obtained at:
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# http://arrayfire.com/licenses/BSD-3-Clause
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########################################################
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fromtimeimporttime
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importarrayfireasaf
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importsys
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defdraw_corners(img,x,y,draw_len):
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# Draw vertical line of (draw_len * 2 + 1) pixels centered on the corner
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# Set only the first channel to 1 (green lines)
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xmin=max(0,x-draw_len)
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xmax=min(img.dims()[1],x+draw_len)
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img[y,xmin :xmax,0]=0.0
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img[y,xmin :xmax,1]=1.0
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img[y,xmin :xmax,2]=0.0
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# Draw vertical line of (draw_len * 2 + 1) pixels centered on the corner
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# Set only the first channel to 1 (green lines)
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ymin=max(0,y-draw_len)
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ymax=min(img.dims()[0],y+draw_len)
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img[ymin :ymax,x,0]=0.0
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img[ymin :ymax,x,1]=1.0
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img[ymin :ymax,x,2]=0.0
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returnimg
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defharris_demo(console):
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ifconsole:
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img_color=af.load_image("../../assets/examples/images/square.png",True);
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else:
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img_color=af.load_image("../../assets/examples/images/man.jpg",True);
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img_color/=255.0
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img=af.color_space(img_color,af.CSPACE.GRAY,af.CSPACE.RGB)
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ix,iy=af.gradient(img)
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ixx=ix*ix
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ixy=ix*iy
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iyy=iy*iy
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# Compute a Gaussian kernel with standard deviation of 1.0 and length of 5 pixels
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# These values can be changed to use a smaller or larger window
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gauss_filt=af.gaussian_kernel(5,5,1.0,1.0)
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# Filter second order derivatives
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ixx=af.convolve(ixx,gauss_filt)
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ixy=af.convolve(ixy,gauss_filt)
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iyy=af.convolve(iyy,gauss_filt)
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# Calculate trace
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itr=ixx+iyy
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# Calculate determinant
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idet=ixx*iyy-ixy*ixy
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# Calculate Harris response
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response=idet-0.04* (itr*itr)
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# Get maximum response for each 3x3 neighborhood
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mask=af.constant(1,3,3)
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max_resp=af.dilate(response,mask)
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# Discard responses that are not greater than threshold
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corners=response>1e5
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corners=corners*response
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# Discard responses that are not equal to maximum neighborhood response,
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# scale them to original value
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corners= (corners==max_resp)*corners
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# Copy device array to python list on host
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corners_list=corners.to_list()
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draw_len=3
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good_corners=0
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forxinrange(img_color.dims()[1]):
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foryinrange(img_color.dims()[0]):
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ifcorners_list[x][y]>1e5:
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img_color=draw_corners(img_color,x,y,draw_len)
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good_corners+=1
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print("Corners found: {}".format(good_corners))
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ifnotconsole:
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# Previews color image with green crosshairs
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wnd=af.Window(512,512,"FAST Feature Detector")
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whilenotwnd.close():
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wnd.image(img_color)
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else:
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idx=af.where(corners)
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corners_x=idx/corners.dims()[0]
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corners_y=idx%corners.dims()[0]
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print(corners_x)
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print(corners_y)
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if__name__=="__main__":
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if (len(sys.argv)>1):
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af.set_device(int(sys.argv[1]))
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console= (sys.argv[2]=='-')iflen(sys.argv)>2elseFalse
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af.info()
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print("** ArrayFire Harris Corner Detector Demo **\n")
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harris_demo(console)
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‎examples/computer_vision/matching.py

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#!/usr/bin/python
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#######################################################
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# Copyright (c) 2018, ArrayFire
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# All rights reserved.
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#
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# This file is distributed under 3-clause BSD license.
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# The complete license agreement can be obtained at:
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# http://arrayfire.com/licenses/BSD-3-Clause
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########################################################
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fromtimeimporttime
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importarrayfireasaf
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importsys
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defnormalize(a):
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max_=float(af.max(a))
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min_=float(af.min(a))
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return (a-min_)/ (max_-min_)
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defdraw_rectangle(img,x,y,wx,wy):
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print("\nMatching patch origin = ({}, {})\n".format(x,y))
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# top edge
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img[y,x :x+wx,0]=0.0
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img[y,x :x+wx,1]=0.0
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img[y,x :x+wx,2]=1.0
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# bottom edge
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img[y+wy,x :x+wx,0]=0.0
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img[y+wy,x :x+wx,1]=0.0
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img[y+wy,x :x+wx,2]=1.0
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# left edge
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img[y :y+wy,x,0]=0.0
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img[y :y+wy,x,1]=0.0
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img[y :y+wy,x,2]=1.0
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# left edge
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img[y :y+wy,x+wx,0]=0.0
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img[y :y+wy,x+wx,1]=0.0
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img[y :y+wy,x+wx,2]=1.0
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returnimg
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deftemplateMatchingDemo(console):
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ifconsole:
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img_color=af.load_image("../../assets/examples/images/square.png",True);
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else:
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img_color=af.load_image("../../assets/examples/images/man.jpg",True);
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# Convert the image from RGB to gray-scale
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img=af.color_space(img_color,af.CSPACE.GRAY,af.CSPACE.RGB)
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iDims=img.dims()
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print("Input image dimensions: ",iDims)
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# Extract a patch from the input image
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patch_size=100
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tmp_img=img[100 :100+patch_size,100 :100+patch_size]
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result=af.match_template(img,tmp_img)# Default disparity metric is
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# Sum of Absolute differences (SAD)
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# Currently supported metrics are
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# AF_SAD, AF_ZSAD, AF_LSAD, AF_SSD,
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# AF_ZSSD, AF_LSSD
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disp_img=img/255.0
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disp_tmp=tmp_img/255.0
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disp_res=normalize(result)
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minval,minloc=af.imin(disp_res)
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print("Location(linear index) of minimum disparity value = {}".format(minloc))
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ifnotconsole:
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marked_res=af.tile(disp_img,1,1,3)
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marked_res=draw_rectangle(marked_res,minloc%iDims[0],minloc/iDims[0],\
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patch_size,patch_size)
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print("Note: Based on the disparity metric option provided to matchTemplate function")
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print("either minimum or maximum disparity location is the starting corner")
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print("of our best matching patch to template image in the search image")
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wnd=af.Window(512,512,"Template Matching Demo")
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whilenotwnd.close():
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wnd.set_colormap(af.COLORMAP.DEFAULT)
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wnd.grid(2,2)
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wnd[0,0].image(disp_img,"Search Image" )
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wnd[0,1].image(disp_tmp,"Template Patch" )
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wnd[1,0].image(marked_res,"Best Match" )
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wnd.set_colormap(af.COLORMAP.HEAT)
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wnd[1,1].image(disp_res,"Disparity Values")
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wnd.show()
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if__name__=="__main__":
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if (len(sys.argv)>1):
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af.set_device(int(sys.argv[1]))
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console= (sys.argv[2]=='-')iflen(sys.argv)>2elseFalse
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af.info()
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print("** ArrayFire template matching Demo **\n")
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templateMatchingDemo(console)
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‎examples/computer_vision/susan.py

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#!/usr/bin/python
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#######################################################
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# Copyright (c) 2018, ArrayFire
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# All rights reserved.
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#
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# This file is distributed under 3-clause BSD license.
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# The complete license agreement can be obtained at:
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# http://arrayfire.com/licenses/BSD-3-Clause
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########################################################
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fromtimeimporttime
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importarrayfireasaf
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importsys
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defdraw_corners(img,x,y,draw_len):
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# Draw vertical line of (draw_len * 2 + 1) pixels centered on the corner
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# Set only the first channel to 1 (green lines)
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xmin=max(0,x-draw_len)
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xmax=min(img.dims()[1],x+draw_len)
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img[y,xmin :xmax,0]=0.0
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img[y,xmin :xmax,1]=1.0
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img[y,xmin :xmax,2]=0.0
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# Draw vertical line of (draw_len * 2 + 1) pixels centered on the corner
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# Set only the first channel to 1 (green lines)
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ymin=max(0,y-draw_len)
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ymax=min(img.dims()[0],y+draw_len)
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img[ymin :ymax,x,0]=0.0
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img[ymin :ymax,x,1]=1.0
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img[ymin :ymax,x,2]=0.0
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returnimg
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defsusan_demo(console):
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ifconsole:
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img_color=af.load_image("../../assets/examples/images/square.png",True);
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else:
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img_color=af.load_image("../../assets/examples/images/man.jpg",True);
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img_color/=255.0
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img=af.color_space(img_color,af.CSPACE.GRAY,af.CSPACE.RGB)
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features=af.susan(img)
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xs=features.get_xpos()
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ys=features.get_ypos()
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draw_len=3;
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num_features=features.num_features().value
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forfinrange(num_features):
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print(f)
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x=xs[f]
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y=ys[f]
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img_color=draw_corners(img_color,x,y,draw_len)
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print("Features found: {}".format(num_features))
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ifnotconsole:
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# Previews color image with green crosshairs
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wnd=af.Window(512,512,"SUSAN Feature Detector")
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whilenotwnd.close():
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wnd.image(img_color)
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else:
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print(xs);
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print(ys);
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if__name__=="__main__":
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if (len(sys.argv)>1):
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af.set_device(int(sys.argv[1]))
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console= (sys.argv[2]=='-')iflen(sys.argv)>2elseFalse
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af.info()
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print("** ArrayFire SUSAN Feature Detector Demo **\n")
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susan_demo(console)
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