svhn_stream.get_epoch_iterator()
x = T.fmatrix("features")
batch_size = T.iscalar("batch_size")
center_y, center_x, deltaY, deltaX = locator.find(x, batch_size)
do_sample = theano.function(
[x, batch_size], outputs=[center_y, center_x, deltaY, deltaX], allow_input_downcast=True
)
overlap = 0.0
distance = 0.0
for i in range(0, num_examples):
image = svhn_stream.get_data()
half_x = image[3][n_iter - 1] / 2 * (N - 1) * (img_width - 1)
half_y = image[4][n_iter - 1] / 2 * (N - 1) * (img_height - 1)
x1 = image[1][n_iter - 1] * (img_width - 1) - half_x
y1 = image[2][n_iter - 1] * (img_height - 1) - half_y
w1 = 2 * half_x
h1 = 2 * half_y
if not evaluation:
im = image[0].reshape([3, img_height, img_width]) * 255
im = im.transpose([1, 2, 0]).astype("uint8")
im = Image.fromarray(im, "RGB")
draw = ImageDraw.Draw(im)
draw.rectangle([(x1, y1), (x1 + w1, y1 + h1)], outline=(0, 255, 0))
