def test_gaussImage():
for scale in range(getScales()):
a = np.zeros((400,700), dtype=np.float32)
a[200,:] = 1.0
a[:,200] = 1.0
for xy in range(400):
a[xy,xy] = 1.0
b = gaussImage(a, scale)
showArray("GI%s" % scale, b)
def test_zcs():
from utils import showArray
from gaussian import gaussImage
image = np.zeros((512,512), dtype=np.float32)
image[255,255] = 1.0
smaller = gaussImage(image, 2)
larger = gaussImage(image, 3)
dog = smaller-larger
zca = zcs(dog)
showArray("smaller", smaller)
showArray("larger", larger)
showArray("dog", dog)
showArray("zca", zca)
def test_zcs():
import time
for res in (False, True):
subtotal = 0.0
for scale in range(getScales()-1):
a = np.zeros((1200,1800), dtype=np.float32)
a[200,:] = 1.0
a[:,200] = 1.0
t = time.time()
zca,grad,theta = zcsdog(a,scale)
subtotal += (time.time()-t)
showArray("org %s" % scale, a)
showArray("zcs %s" % scale, zca)
showArray("grad %s" % scale, grad)
showArray("theta %s" % scale, theta)
print "Res", res, "seconds", subtotal
program = kernels.loadProgram(interfaces.hsi)
def rgb2hsi(r, g, b):
h, s, i, trace = program.rgb2hsi(r, g, b) #@UnusedVariable
return h, s, i
def hsi2rgb(h, s, i):
r,g,b = program.hsi2rgb(h,s,i)
return r,g,b
if __name__ == "__main__":
from utils import showArray
r = np.empty((256, 256), dtype=np.float32)
g = np.empty_like(r)
b = np.empty_like(r)
for i in range(256):
r[i, :] = i
g[:, i] = i
b[i, :] = 255 - i
rgb = joinChannels(r, g, b)
rgb /= 255.0
showArray("Test data", rgb)
rgb2 = joinChannels(*splitChannels(rgb))
showArray("Test split and join", rgb2)
h, s, i = rgb2hsi(*splitChannels(rgb))
showArray("I", i)
showArray("H", h)
showArray("S", s)
print program
import numpy as np
from rpc import kernels, interfaces
from utils import showArray
xy = kernels.loadProgram(interfaces.xy)
a = np.zeros((402,798),dtype=np.int32)
addr,x,y,label = xy.addr(a)
showArray("addr",addr)
showArray("x",x)
showArray("y", y)
showArray("label", label)
def saveImage(self, session, f):
return showArray(displayPatches(self.getD(session), color=self.color),
f)
def test():
from median import median3x3
from gradient import gradient
from hsi import rgb2hsi, hsi2rgb, joinChannels, splitChannels
# Create a noisy image with an embedded white square
image = np.zeros((201,199),dtype=np.float32)
width,height = image.shape
x,y = width/2, height/2
offset = 10
image[x-offset:x+offset,y-offset:y+offset] = 2
image += np.random.random_sample(image.shape)
filtered = median3x3(image, 100)
showArray("Noisy",image)
showArray("Filtered",filtered)
image = np.float32(imread("test.jpg"))
image /= 256.
showArray("Test HSI",image)
r,g,b = splitChannels(image)
h,s,i = rgb2hsi(r,g,b)
showArray("I",i)
showArray("S",s)
showArray("H",h)
from gaussian import gaussImage
blur = gaussImage(i, 3)
showArray("Blur", blur)
blurmore = gaussImage(i,4)
dog = blur-blurmore
showArray("DOG", dog)
g,a = gradient(i,5)
showArray("Gradient",g)
showArray("Angle", a)
sat = np.ones_like(i)
gimg = joinChannels(*hsi2rgb(a,sat,g))
showArray("Color gradient with angle", gimg)
showArrayGrad("Grad angle", image, a)
showArrayGrad("Grad vectors", image, a,g*10)
