def main():
im=imageIO.imread('rgb.png')
lumi=im[:,:,1] #I'm lazy, I'll just use green
smallLumi=numpy.transpose(lumi[0:5, 0:5])
# Replace if False: by if True: once you have implement the required functions.
# Exercises:
if False:
outputNP, myFunc=a11.smoothGradientNormalized()
print ' Dimensionality of Halide Func:', myFunc.dimensions()
imageIO.imwrite(outputNP, 'normalizedGradient.png')
if False:
outputNP, myFunc=a11.wavyRGB()
print ' Dimensionality of Halide Func:', myFunc.dimensions()
imageIO.imwrite(outputNP, 'rgbWave.png')
if False:
outputNP, myFunc=a11.luminance(im)
imageIO.imwrite(outputNP, 'luminance.png')
print ' Dimensionality of Halide Func:', myFunc.dimensions()
if False:
outputNP, myFunc=a11.sobel(lumi)
imageIO.imwrite(outputNP, 'sobelMag.png')
print ' Dimensionality of Halide Func:', myFunc.dimensions()
if False:
L=a11.pythonCodeForBoxSchedule5(smallLumi)
print L
if False:
L=a11.pythonCodeForBoxSchedule6(smallLumi)
print L
if False:
L=a11.pythonCodeForBoxSchedule7(smallLumi)
print L
if False:
outputNP, myFunc=a11.localMax(lumi)
print ' Dimensionality of Halide Func:', myFunc.dimensions()
imageIO.imwrite(outputNP, 'maxi.png')
if False:
input=Image(Float(32), lumi)
x, y, c = Var('x'), Var('y'), Var('c')
clamped = Func('clamped')
clamped[x, y] = input[clamp(x, 0, input.width()-1),
clamp(y, 0, input.height()-1)]
blurX, finalBlur= a11.GaussianSingleChannel(clamped)
if False:
im=numpy.load('Input/hk.npy')
scheduleIndex=0
outputNP, myFunc=a11.harris(im, scheduleIndex)
print ' Dimensionality of Halide Func:', myFunc.dimensions()
imageIO.imwrite(outputNP, 'harris.png')
def main():
im=imageIO.imread('rgb-small.png')
lumi=im[:,:,1] #I'm lazy, I'll just use green
smallLumi=numpy.transpose(lumi[0:6, 0:6])
# Replace if False: by if True: once you have implement the required functions.
# Exercises:
if True:
outputNP, myFunc=a11.smoothGradientNormalized()
print ' Dimensionality of Halide Func:', myFunc.dimensions()
imageIO.imwrite(outputNP, 'normalizedGradient.png')
if True:
outputNP, myFunc=a11.wavyRGB()
print ' Dimensionality of Halide Func:', myFunc.dimensions()
imageIO.imwrite(outputNP, 'rgbWave.png')
if True:
outputNP, myFunc = a11.luminance(im)
print ' Dimensionality of Halide Func:', myFunc.dimensions()
imageIO.imwrite(outputNP, 'rgbLuminance.png')
imageIO.imwrite(tests.luminance(im), 'rgbLuminancePython.png')
if True:
outputNP, myFunc=a11.sobel(lumi)
print ' Dimensionality of Halide Func:', myFunc.dimensions()
imageIO.imwrite(outputNP, 'sobelMag.png')
imageIO.imwrite(tests.sobelMagnitude(lumi), 'sobelMagPython.png')
if False:
L=a11.pythonCodeForBoxSchedule5(smallLumi)
for x in L:
print x
print ""
if False:
L=a11.pythonCodeForBoxSchedule6(smallLumi)
print "Schedule 6:"
for x in L:
print x
print ""
if False:
L=a11.pythonCodeForBoxSchedule7(smallLumi)
print "Schedule 7"
for x in L:
print x
print ""
if True:
outputNP, myFunc=a11.localMax(lumi)
print ' Dimensionality of Halide Func:', myFunc.dimensions()
imageIO.imwrite(outputNP, 'maxi.png')
if False:
testWhite = numpy.ones((50,50))
input = Image(Float(32), testWhite)
x, y = Var('x'), Var('y')
clamped = Func('clamped')
clamped[x, y] = input[clamp(x, 0, input.width()-1),
clamp(y, 0, input.height()-1)]
sigma = 2.0 # IS THIS RIGHT?!?
blurX, finalBlur= a11.GaussianSingleChannel(clamped , sigma, trunc=3)
blurXOutput = blurX.realize(input.width(), input.height())
blurXNP = numpy.array(Image(blurXOutput))
imageIO.imwrite(blurXNP, 'blurXWhite.png')
if True:
input=Image(Float(32), lumi)
x, y = Var('x'), Var('y')
clamped = Func('clamped')
clamped[x, y] = input[clamp(x, 0, input.width()-1),
clamp(y, 0, input.height()-1)]
sigma = 5.0 # IS THIS RIGHT?!?
blurX, finalBlur= a11.GaussianSingleChannel(clamped , sigma, trunc=3)
blurXOutput = blurX.realize(input.width(), input.height())
blurXNP = numpy.array(Image(blurXOutput))
imageIO.imwrite(blurXNP, 'blurX.png')
finalBlurOutput = finalBlur.realize(input.width(), input.height())
finalBlurNP = numpy.array(Image(finalBlurOutput))
imageIO.imwrite(finalBlurNP, 'finalBlur.png')
if True:
# im=numpy.load('Input/hk.npy')
# outputNP, myFunc=a11.harris(im, 0)
# imageIO.imwrite(outputNP, 'harris.png')
outputNP, myFunc=a11.harris(im, 1)
imageIO.imwrite(outputNP, 'harris_fast.png')
print ' Dimensionality of Halide Func:', myFunc.dimensions()
if False:
# Timing for Harris
im=imageIO.imread('hk.png')
myFunc = a11.harris(im, 0)
runAndMeasure(myFunc, im.shape[1], im.shape[0])
myFunc = a11.harris(im, 1)
runAndMeasure(myFunc, im.shape[1], im.shape[0])
imageIO.imwrite(resultNP, 'harrisFast.png')
def main():
im=imageIO.imread('rgb.png')
lumi=im[:,:,1] #I'm lazy, I'll just use green
smallLumi=numpy.transpose(lumi[0:5, 0:5])
im_small = imageIO.imread('rgb-small.png')
lumi_small = im_small[:,:,1]
smallLumi_small = numpy.transpose(lumi_small[0:5, 0:5])
# Replace if False: by if True: once you have implement the required functions.
# Exercises:
if True:
print 'Running smoothGradientNormalized'
outputNP, myFunc=a11.smoothGradientNormalized()
print ' Dimensionality of Halide Func:', myFunc.dimensions()
imageIO.imwrite(outputNP, 'normalizedGradient.png')
if True:
print 'Running wavyRGB'
outputNP, myFunc=a11.wavyRGB()
print ' Dimensionality of Halide Func:', myFunc.dimensions()
imageIO.imwrite(outputNP, 'rgbWave.png')
if True:
print 'Running lumninance'
outputNP, myFunc=a11.luminance(im)
print ' Dimensionality of Halide Func:', myFunc.dimensions()
imageIO.imwrite(outputNP, 'lumiRGB.png')
if True:
outputNP, myFunc=a11.sobel(lumi)
imageIO.imwrite(outputNP, 'sobelMag.png')
print ' Dimensionality of Halide Func:', myFunc.dimensions()
if True:
L=a11.pythonCodeForBoxSchedule5(smallLumi)
print L
if True:
L=a11.pythonCodeForBoxSchedule6(smallLumi)
print L
if True:
L=a11.pythonCodeForBoxSchedule7(smallLumi)
print L
if True:
outputNP, myFunc=a11.localMax(lumi_small)
print ' Dimensionality of Halide Func:', myFunc.dimensions()
imageIO.imwrite(outputNP, 'maxi.png')
if True:
input=Image(Float(32), lumi_small)
xp, yp = Var('xp'), Var('yp')
clamped = Func('clamped')
clamped[xp, yp] = input[clamp(xp, 0, input.width()-1),
clamp(yp, 0, input.height()-1)]
sigma = 1.0
blurX, finalBlur= a11.GaussianSingleChannel(clamped , sigma, trunc=3)
output = finalBlur.realize(input.width(), input.height())
outputNP = numpy.array(Image(output))
print outputNP[outputNP < 0]
imageIO.imwriteGrey(outputNP, "gaussian_blur.png")
if True:
print 'Running harris corner detector'
readstart = time.time()
filen = "hk"
print "Reading in file ", filen + ".png"
im=numpy.load('Input/hk.npy')
# im = imageIO.imread( filen + ".png")
print "Reading in file took ... ", time.time() - readstart
print "Running harris..."
scheduleIndex= 1
outputNP, myFunc=a11.harris(im, scheduleIndex)
print ' Dimensionality of Halide Func:', myFunc.dimensions()
imageIO.imwrite(outputNP, filen+'-harris.png')
if False:
print "Running harris autotune"
im=numpy.load('Input/hk.npy')
#im=imageIO.imread('hk-small.png')
best_sched, best_params, best_time = a11.autotuneHarris(im)
print "Best Schedule: ", best_sched
print "Best Time: ", best_time
print "Best Parameters: ", best_params
def main():
im=imageIO.imread('rgb.png')
lumi=im[:,:,1] #I'm lazy, I'll just use green
smallLumi=numpy.transpose(lumi[0:5, 0:5])
imTest1Channel = numpy.array([[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 0.0, 0.0, 0.0, 1.0],
[1.0, 0.0, 0.0, 0.0, 1.0],
[1.0, 0.0, 0.0, 0.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0]])
imTest3Channel = numpy.array([[[1.0,1.0,1.0], [1.0,1.0,1.0] ,[1.0,1.0,1.0] ,[1.0,1.0,1.0] ,[1.0,1.0,1.0]],
[[1.0,1.0,1.0], [0.0, 0.0, 0.0],[0.0, 0.0, 0.0],[0.0, 0.0, 0.0],[1.0,1.0,1.0]],
[[0.0,0.0,0.0], [1.0, 1.0, 1.0],[0.0, 0.0, 0.0],[0.0, 0.0, 0.0],[1.0,1.0,1.0]],
[[1.0,1.0,1.0], [0.0, 0.0, 0.0],[0.0, 0.0, 0.0],[0.0, 0.0, 0.0],[1.0,1.0,1.0]],
[[1.0,1.0,1.0], [1.0,1.0,1.0] ,[1.0,1.0,1.0] ,[1.0,1.0,1.0] ,[1.0,1.0,1.0]]])
# Replace if False: by if True: once you have implement the required functions.
# Exercises:
if True:
outputNP, myFunc=a11.smoothGradientNormalized()
print ' Dimensionality of Halide Func:', myFunc.dimensions()
imageIO.imwrite(outputNP, 'normalizedGradient.png')
if True:
outputNP, myFunc=a11.wavyRGB()
print ' Dimensionality of Halide Func:', myFunc.dimensions()
imageIO.imwrite(outputNP, 'rgbWave.png')
if True:
outputNP, myFunc=a11.sobel(lumi)
imageIO.imwrite(outputNP, 'sobelMag.png')
print ' Dimensionality of Halide Func:', myFunc.dimensions()
if True:
L=a11.pythonCodeForBoxSchedule5(smallLumi)
print L
if True:
L=a11.pythonCodeForBoxSchedule6(smallLumi)
print L
if True:
L=a11.pythonCodeForBoxSchedule7(smallLumi)
print L
if True:
outputNP, myFunc=a11.localMax(lumi)
#outputNP, myFunc=a11.localMax(imTest)
print ' Dimensionality of Halide Func:', myFunc.dimensions()
imageIO.imwrite(outputNP, 'maxi.png')
if True:
input=Image(Float(32), lumi)
x, y = Var('x'), Var('y')
clamped = Func('clamped')
clamped[x, y] = input[clamp(x, 0, input.width()-1),
clamp(y, 0, input.height()-1)]
blurX, finalBlur= a11.GaussianSingleChannel(clamped , sigma=2, trunc=3)
out = finalBlur.realize(input.width(), input.height())
outNP = numpy.array(Image(out))
imageIO.imwrite(outNP, 'gaussianSingle.png')
if True:
#im=imageIO.imread('hk.png')
im=numpy.load('Input/hk.npy')
scheduleIndex=0
L=[]
t=time.time()
outputNP, myFunc=a11.harris(im, scheduleIndex)
t0 = time.time()-t
L.append(t0)
print "scheduleIndex",scheduleIndex, " harris took ", t0, " seconds"
print ' Dimensionality of Halide Func:', myFunc.dimensions()
hIm=Image(outputNP)
mpix=hIm.width()*hIm.height()/1e6
print 'best: ', numpy.min(L), 'average: ', numpy.mean(L)
print '%.5f ms per megapixel (%.7f ms for %d megapixels)' % (numpy.mean(L)/mpix*1e3, numpy.mean(L)*1e3, mpix)
imageIO.imwrite(outputNP, 'harrisFinal.png')