def _generate_output(model, data, orig_images):
import theano
import theano.tensor as T
x = T.matrix()
f = theano.function(inputs=[x], outputs=T.argmax(model.get_output(x), axis=1))
wrong, right = [], []
for i in xrange(data.X.shape[0]):
y = f(data.X[i:(i+1)].astype(theano.config.floatX))
if y == data.Y[i]: right.append(orig_images[i])
else: wrong.append(orig_images[i])
data = DataSaver()
def add(images):
m = len(images)
gid = data.add_group(m, 1)
for pid in xrange(m):
data.set_images(gid, pid, 0, [images[pid]])
add(wrong)
add(right)
data.save(_cached_output)
def _generate_output(model, threshold, datasets, image_shape):
# For convenience, we will save the result in our data format.
# Regard train, valid, and test sets as three groups.
# Each pedestrian only has one view, containing output and target images.
x = T.matrix('x')
y = model.get_output(x)
output_func = theano.function(inputs=[x], outputs=y)
data = DataSaver()
def add(X, Y):
m = X.shape[0]
gid = data.add_group(m, 1)
for pid in xrange(m):
x, target = X[pid:pid+1, :], Y[pid, :] # Ensure x to be a matrix
y = (output_func(x) >= threshold).astype(numpy.uint8) * 255
y = y.reshape(image_shape)
target = numpy.uint8(target * 255).reshape(image_shape)
data.set_images(gid, pid, 0, [y, target])
add(datasets.train_x.get_value(borrow=True),
datasets.train_y.get_value(borrow=True))
add(datasets.valid_x.get_value(borrow=True),
datasets.valid_y.get_value(borrow=True))
add(datasets.test_x.get_value(borrow=True),
datasets.test_y.get_value(borrow=True))
data.save(_cached_output)
def _generate_output(model, data, orig_images):
import theano
import theano.tensor as T
x = T.matrix()
f = theano.function(inputs=[x],
outputs=T.argmax(model.get_output(x), axis=1))
wrong, right = [], []
for i in xrange(data.X.shape[0]):
y = f(data.X[i:(i + 1)].astype(theano.config.floatX))
if y == data.Y[i]: right.append(orig_images[i])
else: wrong.append(orig_images[i])
data = DataSaver()
def add(images):
m = len(images)
gid = data.add_group(m, 1)
for pid in xrange(m):
data.set_images(gid, pid, 0, [images[pid]])
add(wrong)
add(right)
data.save(_cached_output)
def _generate_output(model, threshold, datasets, image_shape):
# For convenience, we will save the result in our data format.
# Regard train, valid, and test sets as three groups.
# Each pedestrian only has one view, containing output and target images.
x = T.matrix('x')
y = model.get_output(x)
output_func = theano.function(inputs=[x], outputs=y)
data = DataSaver()
def add(X, Y):
m = X.shape[0]
gid = data.add_group(m, 1)
for pid in xrange(m):
x, target = X[pid:pid + 1, :], Y[pid, :] # Ensure x to be a matrix
y = (output_func(x) >= threshold).astype(numpy.uint8) * 255
y = y.reshape(image_shape)
target = numpy.uint8(target * 255).reshape(image_shape)
data.set_images(gid, pid, 0, [y, target])
add(datasets.train_x.get_value(borrow=True),
datasets.train_y.get_value(borrow=True))
add(datasets.valid_x.get_value(borrow=True),
datasets.valid_y.get_value(borrow=True))
add(datasets.test_x.get_value(borrow=True),
datasets.test_y.get_value(borrow=True))
data.save(_cached_output)
def _mask_dataset():
# Load model and compile function
with open('../cache/foreground_model.pkl', 'rb') as f:
model, threshold = cPickle.load(f)
x = T.matrix('x')
y = model.get_output(x)
output_func = theano.function(inputs=[x], outputs=(y >= threshold))
# Load data
image_data = DataLoader('../data/cuhk_small.mat', verbose=True)
# Pre-processing
print "Pre-processing ..."
images = image_data.get_all_images()
images = [_input_preproc(image) for image in images]
images = imageproc.images2mat(images).astype(theano.config.floatX)
# Compute masks
print "Computing masks ..."
masks = output_func(images)
# Save masks
print "Saving data ..."
mask_data = DataSaver()
cur_index = 0
for gid in xrange(image_data.get_n_groups()):
m, v = image_data.get_n_pedes_views(gid)
mask_data.add_group(m, v)
for pid in xrange(m):
n_images = image_data.get_n_images(gid, pid)
for vid, n in enumerate(n_images):
view_masks = [0] * n
for k in xrange(n):
mask = masks[cur_index, :]
mask = mask.reshape(160, 80, 1)
orig_image = image_data.get_image(gid, pid, vid, k)
orig_image = imageproc.imresize(orig_image, (160, 80, 3))
view_masks[k] = (mask * orig_image).astype(numpy.uint8)
cur_index += 1
mask_data.set_images(gid, pid, vid, view_masks)
mask_data.save('../data/cuhk_small_masked.mat')
def _mask_dataset():
# Load model and compile function
with open('../cache/foreground_model.pkl', 'rb') as f:
model, threshold = cPickle.load(f)
x = T.matrix('x')
y = model.get_output(x)
output_func = theano.function(inputs=[x], outputs=(y >= threshold))
# Load data
image_data = DataLoader('../data/cuhk_small.mat', verbose=True)
# Pre-processing
print "Pre-processing ..."
images = image_data.get_all_images()
images = [_input_preproc(image) for image in images]
images = imageproc.images2mat(images).astype(theano.config.floatX)
# Compute masks
print "Computing masks ..."
masks = output_func(images)
# Save masks
print "Saving data ..."
mask_data = DataSaver()
cur_index = 0
for gid in xrange(image_data.get_n_groups()):
m, v = image_data.get_n_pedes_views(gid)
mask_data.add_group(m, v)
for pid in xrange(m):
n_images = image_data.get_n_images(gid, pid)
for vid, n in enumerate(n_images):
view_masks = [0] * n
for k in xrange(n):
mask = masks[cur_index, :]
mask = mask.reshape(160, 80, 1)
orig_image = image_data.get_image(gid, pid, vid, k)
orig_image = imageproc.imresize(orig_image, (160, 80, 3))
view_masks[k] = (mask * orig_image).astype(numpy.uint8)
cur_index += 1
mask_data.set_images(gid, pid, vid, view_masks)
mask_data.save('../data/cuhk_small_masked.mat')
