def get_train_data():
#/Users/amilgeorge/Documents/StudySS2015/DeepLearning/Training\ Data/data/raw_images/train_faces/
img_faces = imread_collection(
"data/train_set/dataset/13/train/faces/*.png")
#
img_bkgs = imread_collection(
"data/train_set/dataset/13/train/nonfaces/*.jpg")
#img_faces = imread_collection("data/processed_images/13_train_set_aflw/train/faces/*.jpg")
#img_bkgs = imread_collection("data/processed_images/13_train_set_aflw/train/nonfaces/*.jpg")
x, y = prepare_data(img_faces, img_bkgs)
#x=x[:1000]
#y=y[:1000]
borrow = True
shared_x = theano.shared(
np.asarray(x, dtype=theano.config.floatX), # @UndefinedVariable
borrow=borrow)
shared_y = theano.shared(
np.asarray(y, dtype=theano.config.floatX), # @UndefinedVariable
borrow=borrow)
return shared_x, T.cast(shared_y, 'int32')
def get_train_data():
img_faces = imread_collection("data/processed_images/28_28_big_new/train/faces/*.png")
img_bkgs = imread_collection("data/processed_images/28_28_big_new/train/nonfaces/*.jpg")
x, y = prepare_data(img_faces, img_bkgs)
return x, y
def get_train_data():
img_faces = imread_collection(
"data/processed_images/28_28_big_new/train/faces/*.png")
img_bkgs = imread_collection(
"data/processed_images/28_28_big_new/train/nonfaces/*.jpg")
x, y = prepare_data(img_faces, img_bkgs)
return x, y
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 get_valid_data():
#/Users/amilgeorge/Documents/StudySS2015/DeepLearning/Training\ Data/data/raw_images/train_faces/
img_faces = imread_collection("data/train_set/dataset/13/validation/faces/*.png")
#
img_bkgs = imread_collection("data/train_set/dataset/13/validation/nonfaces/*.jpg")
#img_faces = imread_collection("data/processed_images/13_train_set_aflw/validation/faces/*.jpg")
#img_bkgs = imread_collection("data/processed_images/13_train_set_aflw/validation/nonfaces/*.jpg")
x, y = prepare_data(img_faces, img_bkgs)
borrow = True
shared_x = theano.shared(np.asarray(x, dtype=theano.config.floatX), # @UndefinedVariable
borrow=borrow)
shared_y = theano.shared(np.asarray(y, dtype=theano.config.floatX), # @UndefinedVariable
borrow=borrow)
return shared_x,T.cast(shared_y, 'int32')
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)
