dx2 = None
Xs = None
#plt.imshow(inf)
lastClick = 0.0
while 1:
start = time.time()
im2 = get_screenshot(640, 480).astype('double')[:360, :640] / 255.0
if im != None:
dx2 = im - im2
if dx2 != None:
dx2 = numpy.linalg.norm(features.rgbhist(dx2, 16), axis = 2)
if Xs == None:
Xs, Ys = numpy.meshgrid(numpy.arange(dx2.shape[1]), numpy.arange(dx2.shape[0]))
Rs = (Xs - dx2.shape[1] / 2)**2 / 2.0 + (Ys - dx2.shape[0] / 2)**2
Rs2 = (Xs - dx2.shape[1] / 2)**2 / 1.5 + (Ys - dx2.shape[0] / 2)**2 / 2.0
inf = numpy.exp(-Rs / 100.0) - numpy.exp(-Rs2 / 5.0)
if dx == None:
dx = dx2
else:
dx = 0.5 * dx + 0.5 * dx2
if dx != None:
probs = (inf * dx).flatten()
for k, frame in enumerate(frames[0:100:10]):
im = frame[:360].astype('double') / 255.0
#im = skimage.color.rgb2hsv((im * 255).astype('uint8'))
#plt.imshow(im[:, :, 0], interpolation = 'NONE', cmap = plt.cm.hsv);
#plt.colorbar()
#plt.show()
#plt.imshow(im[:, :, 1], interpolation = 'NONE', cmap = plt.cm.hsv);
#plt.colorbar()
#plt.show()
#plt.imshow(im[:, :, 2], interpolation = 'NONE', cmap = plt.cm.hsv);
#plt.colorbar()
#plt.show()
tmp = time.time()
hog = features.hogpad(features.hog(im, b))
rgb = features.rgbhist(im, b)
print time.time() - tmp
#plt.imshow(im, interpolation = 'NONE')
#plt.show()
#plt.imshow(skimage.color.rgb2gray(im), interpolation = 'NONE', cmap = plt.cm.gray)
#plt.imshow(rgb, interpolation = 'NONE')
#plt.show()
Y = rgb.shape[0]
X = rgb.shape[1]
rgbf = numpy.zeros((rgb.shape[0], rgb.shape[1], sy * sx * rgb.shape[2]))
indices[k] = []
descs[k] = []
#%%
codes = os.listdir('filtered_sprites_160x160')
Xs = []
Xsb = []
labels = []
for code in codes:
images = numpy.load('filtered_sprites_160x160/{0}'.format(code)).astype('double')[:, 40:120, 40:120, :4]
labels.extend(images.shape[0] * [int(code)])
images2 = []
for image in images:
images2.append(features.rgbhist(image[:, :, :3] / 255.0, 4))
Xs.append(images2)
backgroundSample = backgrounds[1][20:100, 20:100]
images3 = []
for image in images:
mask = (image[:, :, 3] > 254.5)
image2 = numpy.array(image).copy()
image2[:, :, 0] += mask * backgroundSample[:, :, 0].astype('double')
image2[:, :, 1] += mask * backgroundSample[:, :, 1].astype('double')
image2[:, :, 2] += mask * backgroundSample[:, :, 2].astype('double')
images3.append(features.rgbhist(image2[:, :, :3] / 255.0, 4))
Xsb.append(images3)
#plt.imshow(im3)
#plt.show()
#plt.imshow(backgroundSample)
#plt.show()
im4 = im3[:, :, :3] / 255.0
eimages.append(im4)
idx += 1
#1/0
print i
images = numpy.array(eimages)
#%%
images = []
for image in eimages:
images.append(features.rgbhist(image, 8))
images = numpy.array(images)
#%%
del eimages
#%%
#features = numpy.array([(feature - mean) for feature in features])
i2l = list(enumerate(list(set(labels))))
l2i = dict([(l, i) for i, l in i2l])
def one(N, i):
a = numpy.zeros(N)
a[i] = 1.0
return a
lref = numpy.array([one(len(i2l), l2i[l]) for l in labels])
