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a8_test.py
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a8_test.py
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from utils import imageIO as io
import a8
import numpy as np
def test_grad_descent():
im=io.imread('data/pru.png')
kernel=a8.gauss2D(1)
im_blur=a8.convolve3(im, kernel)
io.imwrite(im_blur, 'pru_blur.png')
im_sharp=a8.deconvGradDescent(im_blur, kernel);
io.imwrite(im_sharp, 'pru_sharp.png')
def test_conjugate_grad_descent():
im=io.imread('data/pru.png')
kernel=a8.gauss2D(1)
im_blur=a8.convolve3(im, kernel)
io.imwrite(im_blur, 'pru_blur.png')
im_sharp=a8.deconvCG(im_blur, kernel);
io.imwrite(im_sharp, 'pru_sharp_CG.png')
def test_real_psf():
im=io.imread('data/pru.png')
f=open('psf', 'r')
psf=[map(float, line.split(',')) for line in f ]
kernel=np.array(psf)
im_blur=a8.convolve3(im, kernel)
#kernel=kernel[::-1, ::-1]
io.imwrite(im_blur, 'pru_blur_real.png')
io.imwriteGrey(kernel/np.max(kernel), 'psf.png')
im_sharp=a8.deconvCG(im_blur, kernel, 20);
io.imwrite(im_sharp, 'pru_sharp_CG_real.png')
def test_conjugate_grad_descent_reg():
im=io.imread('data/pru.png')
kernel=a8.gauss2D(1)
im_blur=a8.convolve3(im, kernel)
noise=np.random.random(im_blur.shape)-0.5
im_blur_noisy=im_blur+0.05*noise
io.imwrite(im_blur_noisy, 'pru_blur_noise.png')
im_sharp=a8.deconvCG_reg(im_blur_noisy, kernel);
im_sharp_wo_reg=a8.deconvCG(im_blur_noisy, kernel);
io.imwrite(im_sharp, 'pru_sharp_CG_reg.png')
io.imwrite(im_sharp_wo_reg, 'pru_sharp_CG_wo_reg.png')
def test_naive_composite():
fg=io.imread('data/bear.png')
bg=io.imread('data/waterpool.png')
mask=io.imread('data/mask.png')
out=a8.naiveComposite(bg, fg, mask, 50, 1)
io.imwrite(out, 'naive_composite.png')
def test_Poisson():
y=50
x=10
useLog=True
fg=io.imread('data/bear.png')
bg=io.imread('data/waterpool.png')
mask=io.imread('data/mask.png')
h, w=fg.shape[0], fg.shape[1]
mask[mask>0.5]=1.0
mask[mask<0.6]=0.0
bg2=(bg[y:y+h, x:x+w]).copy()
out=bg.copy()
if useLog:
bg2[bg2==0]=1e-4
fg[fg==0]=1e-4
bg3=np.log(bg2)+3
fg3=np.log(fg)+3
else:
bg3=bg2
fg3=fg
tmp=a8.Poisson(bg3, fg3, mask, 3000)
if useLog:
out[y:y+h, x:x+w]=np.exp(tmp-3)
else: out[y:y+h, x:x+w]=tmp
io.imwrite(out, 'poisson.png')
def test_PoissonCG():
y=50
x=10
useLog=True
fg=io.imread('data/bear.png')
bg=io.imread('data/waterpool.png')
mask=io.imread('data/mask.png')
h, w=fg.shape[0], fg.shape[1]
mask[mask>0.5]=1.0
mask[mask<0.6]=0.0
bg2=(bg[y:y+h, x:x+w]).copy()
out=bg.copy()
if useLog:
bg2[bg2==0]=1e-4
fg[fg==0]=1e-4
bg3=np.log(bg2)+3
fg3=np.log(fg)+3
else:
bg3=bg2
fg3=fg
tmp=a8.PoissonCG(bg3, fg3, mask, 150)
if useLog:
out[y:y+h, x:x+w]=np.exp(tmp-3)
else: out[y:y+h, x:x+w]=tmp
io.imwrite(out, 'poisson_CG.png')
def test_myown():
y=150
x=300
useLog=True
fg=io.imread('dolphin.png')
bg=io.imread('bg.png')
mask=io.imread('dolphin_mask.png')
# out=a8.naiveComposite(bg, fg, mask, 50, 50)
# io.imwrite(out, 'myownnative11.png')
h, w=fg.shape[0], fg.shape[1]
mask[mask>0.5]=1.0
mask[mask<0.6]=0.0
bg2=(bg[y:y+h, x:x+w]).copy()
out=bg.copy()
if useLog:
bg2[bg2==0]=1e-4
fg[fg==0]=1e-4
bg3=np.log(bg2)+3
fg3=np.log(fg)+3
else:
bg3=bg2
fg3=fg
tmp=a8.PoissonCG(bg3, fg3, mask, 150)
if useLog:
out[y:y+h, x:x+w]=np.exp(tmp-3)
else: out[y:y+h, x:x+w]=tmp
io.imwrite(out, 'myowncomposite.png')
# test_grad_descent()
# test_conjugate_grad_descent()
# test_real_psf()
# test_conjugate_grad_descent_reg()
# test_naive_composite()
test_Poisson()
test_PoissonCG()
# test_myown()