def convert_to_3D_png(self, in_path, out_path):
print("reading.")
imgs = io.imread_collection(in_path)
print("concatenating")
imgs = collection.concatenate_images(imgs)
print("writing")
np.save(out_path, imgs)
def evaluate_trained_net_on_patches(state,folder="data/train_set/dataset/13/validation/faces/*.png"):
img_collection = imread_collection(folder)
arr_imgs = concatenate_images(img_collection)
arr_imgs=arr_imgs[:,:,:,np.newaxis]
arr_imgs = np.rollaxis(arr_imgs, 3, 1)
#arr_imgs = arr_imgs[0:10,:,:,:]
borrow = True
shared_x = theano.shared(np.asarray(arr_imgs, dtype=theano.config.floatX), # @UndefinedVariable
borrow=borrow)
num_arr_imgs= arr_imgs.shape[0]
iT = T.lscalar()
x = T.tensor3("x")
layer0_input = x.reshape((1, NUM_CHANNELS, 13, 13))
net = twelve_net(layer0_input, None, relu, state)
prediction = net.log_regression_layer.y_pred
py_x = net.log_regression_layer.p_y_given_x
test_model = theano.function(
[iT],
[prediction, py_x, layer0_input],
givens={
x: shared_x[iT, :, :, :]
}
)
min_py_x = 99999;
max_py_x = 0
num_faces = 0
thresholds = []
for i in xrange(num_arr_imgs):
[out_predict,out_py_x,out_face]=test_model(i)
thresholds.append(out_py_x[0,1])
if out_py_x[0,1] < min_py_x :
min_py_x = out_py_x[0,1]
if out_py_x[0,1] > max_py_x :
max_py_x = out_py_x[0,1]
if out_predict ==1:
num_faces =num_faces +1
recall_index = int(num_arr_imgs * .01)
thresholds.sort()
t = thresholds[recall_index]
print "Num faces: %d" %(num_faces)
print "min_py_x(for face): %f" %(min_py_x)
print "max_py_x(for face): %f" %(max_py_x)
print "Num images evaluated: %d" %(num_arr_imgs)
print "Threshold %f" %(t)
def flat_convert(self, in_path, out_path):
print("reading.")
imgs = io.imread_collection(path)
print("concatenating")
imgs = collection.concatenate_images(imgs)
print("reshaping")
imgs = imgs.reshape(imgs.shape[0], imgs.shape[1] * imgs.shape[2])
print("writing")
np.save(out_path, imgs)
def evaluate_trained_net_on_patches(
state, folder="data/train_set/dataset/13/validation/faces/*.png"):
img_collection = imread_collection(folder)
arr_imgs = concatenate_images(img_collection)
arr_imgs = arr_imgs[:, :, :, np.newaxis]
arr_imgs = np.rollaxis(arr_imgs, 3, 1)
#arr_imgs = arr_imgs[0:10,:,:,:]
borrow = True
shared_x = theano.shared(
np.asarray(arr_imgs, dtype=theano.config.floatX), # @UndefinedVariable
borrow=borrow)
num_arr_imgs = arr_imgs.shape[0]
iT = T.lscalar()
x = T.tensor3("x")
layer0_input = x.reshape((1, NUM_CHANNELS, 13, 13))
net = twelve_net(layer0_input, None, relu, state)
prediction = net.log_regression_layer.y_pred
py_x = net.log_regression_layer.p_y_given_x
test_model = theano.function([iT], [prediction, py_x, layer0_input],
givens={x: shared_x[iT, :, :, :]})
min_py_x = 99999
max_py_x = 0
num_faces = 0
thresholds = []
for i in xrange(num_arr_imgs):
[out_predict, out_py_x, out_face] = test_model(i)
thresholds.append(out_py_x[0, 1])
if out_py_x[0, 1] < min_py_x:
min_py_x = out_py_x[0, 1]
if out_py_x[0, 1] > max_py_x:
max_py_x = out_py_x[0, 1]
if out_predict == 1:
num_faces = num_faces + 1
recall_index = int(num_arr_imgs * .01)
thresholds.sort()
t = thresholds[recall_index]
print "Num faces: %d" % (num_faces)
print "min_py_x(for face): %f" % (min_py_x)
print "max_py_x(for face): %f" % (max_py_x)
print "Num images evaluated: %d" % (num_arr_imgs)
print "Threshold %f" % (t)
def prepare_data(faces_collection, bkgs_collection):
# To get a subset:
# arr_faces = concatenate_images(faces_collection)[:5000, ...]
arr_faces = concatenate_images(faces_collection)
arr_faces = convert_image_stack_to_gray(arr_faces)
arr_faces = np.rollaxis(arr_faces, 3, 1)
arr_faces = arr_faces
num_face_imgs = arr_faces.shape[0]
arr_faces = arr_faces.reshape((arr_faces.shape[0], -1))
out_faces = np.ones(arr_faces.shape[0])
arr_bkgs = concatenate_images(bkgs_collection)
arr_bkgs = convert_image_stack_to_gray(arr_bkgs)
arr_bkgs = np.rollaxis(arr_bkgs, 3, 1)
arr_bkgs = arr_bkgs.reshape((arr_bkgs.shape[0], -1))
arr_bkgs = arr_bkgs
out_bkgs = np.zeros(arr_bkgs.shape[0])
test_set = np.concatenate((arr_faces, arr_bkgs))
labels = np.concatenate((out_faces, out_bkgs))
arr_indexes = np.random.permutation(test_set.shape[0])
shuffled_test_set = test_set[arr_indexes]
shuffled_labels = labels[arr_indexes].flatten()
borrow = True
shared_x = theano.shared(np.asarray(shuffled_test_set,
dtype=theano.config.floatX),
borrow=borrow)
shared_y = theano.shared(np.asarray(shuffled_labels,
dtype=theano.config.floatX),
borrow=borrow)
return shared_x, T.cast(shared_y, 'int32')
def prepare_data(faces_collection, bkgs_collection):
arr_faces = concatenate_images(faces_collection)
#arr_faces = convert_image_stack_to_lbp(arr_faces)
arr_faces = arr_faces[:, :, :, np.newaxis]
arr_faces = np.rollaxis(arr_faces, 3, 1)
arr_faces = arr_faces
num_face_imgs = arr_faces.shape[0]
arr_faces = arr_faces.reshape((arr_faces.shape[0], -1))
# Need to check this ---compare with flatten used during training
out_faces = np.ones(arr_faces.shape[0])
arr_bkgs = concatenate_images(bkgs_collection)
#arr_bkgs = convert_image_stack_to_lbp(arr_bkgs)
arr_bkgs = arr_bkgs[:, :, :, np.newaxis]
arr_bkgs = np.rollaxis(arr_bkgs, 3, 1)
arr_bkgs = arr_bkgs.reshape((arr_bkgs.shape[0], -1))
arr_bkgs = arr_bkgs # Reduce the size of bkg images
out_bkgs = np.zeros(arr_bkgs.shape[0])
test_set = np.concatenate((arr_faces, arr_bkgs))
labels = np.concatenate((out_faces, out_bkgs))
arr_indexes = np.random.permutation(test_set.shape[0])
shuffled_test_set = test_set[arr_indexes]
shuffled_labels = labels[arr_indexes].flatten()
# borrow = True
# shared_x = theano.shared(np.asarray(shuffled_test_set, dtype=theano.config.floatX), # @UndefinedVariable
# borrow=borrow)
#
# shared_y = theano.shared(np.asarray(shuffled_labels, dtype=theano.config.floatX), # @UndefinedVariable
# borrow=borrow)
return shuffled_test_set, shuffled_labels
def prepare_data(faces_collection, bkgs_collection):
arr_faces = concatenate_images(faces_collection)
#arr_faces = convert_image_stack_to_lbp(arr_faces)
arr_faces=arr_faces[:,:,:,np.newaxis]
arr_faces = np.rollaxis(arr_faces, 3, 1)
arr_faces = arr_faces
num_face_imgs = arr_faces.shape[0]
arr_faces = arr_faces.reshape((arr_faces.shape[0], -1)); # Need to check this ---compare with flatten used during training
out_faces = np.ones(arr_faces.shape[0])
arr_bkgs = concatenate_images(bkgs_collection)
#arr_bkgs = convert_image_stack_to_lbp(arr_bkgs)
arr_bkgs=arr_bkgs[:,:,:,np.newaxis]
arr_bkgs = np.rollaxis(arr_bkgs, 3, 1)
arr_bkgs = arr_bkgs.reshape((arr_bkgs.shape[0], -1));
arr_bkgs = arr_bkgs # Reduce the size of bkg images
out_bkgs = np.zeros(arr_bkgs.shape[0])
test_set = np.concatenate((arr_faces, arr_bkgs))
labels = np.concatenate((out_faces, out_bkgs))
arr_indexes = np.random.permutation(test_set.shape[0])
shuffled_test_set = test_set[arr_indexes]
shuffled_labels = labels[arr_indexes].flatten()
# borrow = True
# shared_x = theano.shared(np.asarray(shuffled_test_set, dtype=theano.config.floatX), # @UndefinedVariable
# borrow=borrow)
#
# shared_y = theano.shared(np.asarray(shuffled_labels, dtype=theano.config.floatX), # @UndefinedVariable
# borrow=borrow)
return shuffled_test_set,shuffled_labels
def prepare_data(faces_collection, bkgs_collection):
# To get a subset:
# arr_faces = concatenate_images(faces_collection)[:5000, ...]
arr_faces = concatenate_images(faces_collection)
arr_faces = convert_image_stack_to_gray(arr_faces)
arr_faces = np.rollaxis(arr_faces, 3, 1)
arr_faces = arr_faces
num_face_imgs = arr_faces.shape[0]
arr_faces = arr_faces.reshape((arr_faces.shape[0], -1))
out_faces = np.ones(arr_faces.shape[0])
arr_bkgs = concatenate_images(bkgs_collection)
arr_bkgs = convert_image_stack_to_gray(arr_bkgs)
arr_bkgs = np.rollaxis(arr_bkgs, 3, 1)
arr_bkgs = arr_bkgs.reshape((arr_bkgs.shape[0], -1))
arr_bkgs = arr_bkgs
out_bkgs = np.zeros(arr_bkgs.shape[0])
test_set = np.concatenate((arr_faces, arr_bkgs))
labels = np.concatenate((out_faces, out_bkgs))
arr_indexes = np.random.permutation(test_set.shape[0])
shuffled_test_set = test_set[arr_indexes]
shuffled_labels = labels[arr_indexes].flatten()
borrow = True
shared_x = theano.shared(np.asarray(shuffled_test_set, dtype=theano.config.floatX),
borrow=borrow)
shared_y = theano.shared(np.asarray(shuffled_labels, dtype=theano.config.floatX),
borrow=borrow)
return shared_x, T.cast(shared_y, 'int32')
