def produce_segmentation(data, model):
f = theano.function(
inputs=[model.input],
outputs=model.blocks[3].output
)
m = data.shape[0]
output = [0] * m
for i in xrange(m):
S = f(data[i:i + 1]).reshape([37, 17])
output[i] = imgproc.resize(S, [160, 80])
return np.asarray(output)
def imgprep(img):
img = imgproc.resize(img, [160, 80], keep_ratio='height')
img = imgproc.subtract_luminance(img)
img = np.rollaxis(img, 2)
return (img / 100.0).astype(np.float32)
def choose_seg(seg, title):
from dlearn.utils import imgproc
val = conf.seg_pix[title]
img = (seg == val).astype(np.float32)
img = imgproc.resize(img, [37, 17])
return img.astype(np.float32)
def choose_seg(seg, title):
val = conf.seg_pix[title]
img = (seg == val).astype(np.float32)
img = imgproc.resize(img, [37, 17])
return img.astype(np.float32)
def imgresize(img):
img = imgproc.resize(img, [160, 80], keep_ratio='height')
return img
