"""Loads data from FTEST if *test* is True, otherwise from FTRAIN.

Pass a list of *cols* if you're only interested in a subset of the

target columns.

"""

fname = FTEST if test else FTRAIN

df = read_csv(os.path.expanduser(fname)) # load pandas dataframe

# The Image column has pixel values separated by space; convert

# the values to numpy arrays:

df['Image'] = df['Image'].apply(lambda im: np.fromstring(im, sep=' '))

if cols: # get a subset of columns

df = df[list(cols) + ['Image']]

print(df.count()) # prints the number of values for each column

df = df.dropna() # drop all rows that have missing values in them

X = np.vstack(df['Image'].values) / 255. # scale pixel values to [0, 1]

X = X.astype(np.float32)

if not test: # only FTRAIN has any target columns

y = df[df.columns[:-1]].values

y = (y - 48) / 48 # scale target coordinates to [-1, 1]

X, y = shuffle(X, y, random_state=42) # shuffle train data

y = y.astype(np.float32)

else:

y = None

# print("X.shape == {}; X.min == {:.3f}; X.max == {:.3f}".format(

# X.shape, X.min(), X.max()))

# print("y.shape == {}; y.min == {:.3f}; y.max == {:.3f}".format(

# y.shape, y.min(), y.max()))

train_loss = np.array([i["train_loss"] for i in net1.train_history_])

valid_loss = np.array([i["valid_loss"] for i in net1.train_history_])

pyplot.plot(train_loss, linewidth=3, label="train")

pyplot.plot(valid_loss, linewidth=3, label="valid")

pyplot.grid()

pyplot.legend()

pyplot.xlabel("epoch")

pyplot.ylabel("loss")

pyplot.ylim(1e-3, 1e-2)

pyplot.yscale("log")

X, _ = load(test=True)

y_pred = net1.predict(X)

fig = pyplot.figure(figsize=(6, 6))

fig.subplots_adjust(

left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)

for i in range(16):

ax = fig.add_subplot(4, 4, i + 1, xticks=[], yticks=[])

plot_sample(X[i], y_pred[i], ax)

net1 = NeuralNet(

layers=[ # three layers: one hidden layer

('input', layers.InputLayer),

('hidden', layers.DenseLayer),

('output', layers.DenseLayer),

],

# layer parameters:

input_shape=(None, 9216), # 96x96 input pixels per batch

hidden_num_units=100, # number of units in hidden layer

output_nonlinearity=None, # output layer uses identity function

output_num_units=30, # 30 target values

# optimization method:

update=nesterov_momentum,

update_learning_rate=0.01,

update_momentum=0.9,

regression=True, # flag to indicate we're dealing with regression problem

max_epochs=400, # we want to train this many epochs

verbose=1,

)

net2 = NeuralNet(

layers=[

('input', layers.InputLayer),

('conv1', layers.Conv2DLayer),

('pool1', layers.MaxPool2DLayer),

('conv2', layers.Conv2DLayer),

('pool2', layers.MaxPool2DLayer),

('conv3', layers.Conv2DLayer),

('pool3', layers.MaxPool2DLayer),

('hidden4', layers.DenseLayer),

('hidden5', layers.DenseLayer),

('output', layers.DenseLayer),

],

input_shape=(None, 1, 96, 96),

conv1_num_filters=32, conv1_filter_size=(3, 3), pool1_pool_size=(2, 2),

conv2_num_filters=64, conv2_filter_size=(2, 2), pool2_pool_size=(2, 2),

conv3_num_filters=128, conv3_filter_size=(2, 2), pool3_pool_size=(2, 2),

hidden4_num_units=500, hidden5_num_units=500,

output_num_units=30, output_nonlinearity=None,

update_learning_rate=0.01,

update_momentum=0.9,

regression=True,

max_epochs=1000,

verbose=1,

)

# X, y = load()

# net1.fit(X, y)

# return net1

X, y = load2d()

net2.fit(X, y)

##Training for 1000 epochs will take a while. We'll pickle the

##trained model so that we can load it back later:

with open('net.pickle', 'wb') as f:

pickle.dump(net2, f, -1)

gray = rgb2gray(img).flatten()

gray = np.vstack(gray) / 255. # scale pixel values to [0, 1]

gray = gray.astype(np.float32)

X = np.reshape(gray,(1,-1))

#myImage = '/Users/shkejriwal/Documents/personal/data/myPics/small_no_glasses.jpg'

#myImage = '/Users/shkejriwal/Documents/personal/data/myPics/small.jpg'

#myImage = '/Users/shkejriwal/Documents/personal/data/myPics/small_sk_closeup.jpg'

myImage1 = '/Users/shkejriwal/Documents/personal/data/myPics/small_full_face.jpg'

myImage2 = '/Users/shkejriwal/Documents/personal/data/myPics/small_full_face_no_glass.jpg'

img1 = scipyMisc.imread(myImage1)

#X1 = prepare1DImage(img1)

X1 = prepare2DImage(img1)

sample1 = load(test=True)[0][6:7]

img2 = scipyMisc.imread(myImage2)

#X2 = prepare1DImage(img2)

X2 = prepare2DImage(img2)

y1 = net1.predict(X1)

y2 = net1.predict(X2)

fig = pyplot.figure(figsize=(6, 3))

ax = fig.add_subplot(1, 2, 1, xticks=[], yticks=[])

plot_sample(X1[0], y1[0],ax)

ax = fig.add_subplot(1, 2, 2, xticks=[], yticks=[])

plot_sample(X2[0], y2[0],ax)

points1 = np.split(A,15)

points2 = np.split(B,15)

print points1

print points2

maxVal = 0

for a in points1:

minVal = float("inf")

for b in points2:

val = np.linalg.norm(a-b)

if val<minVal:minVal=val

# print minVal

val = np.linalg.norm(a-minVal)

if val>maxVal:maxVal=val

print maxVal

x = directedHausdorffdistance(A,B)

y = directedHausdorffdistance(B,A)

return max(x,y)

#points1 = np.split(A,15)

#points2 = np.split(B,15)

C1 = np.split(A,15)

C2 = np.split(B,15)

D = sp.spatial.distance.cdist(C1, C2, 'euclidean')

#none symmetric Hausdorff distances

H1 = np.max(np.min(D, axis=1))

H2 = np.max(np.min(D, axis=0))

myImage1 = '/Users/shkejriwal/Documents/personal/data/myPics/small_full_face.jpg'

myImage2 = '/Users/shkejriwal/Documents/personal/data/myPics/small_full_face_no_glass.jpg'

#myImage2 = myImage1

img1 = scipyMisc.imread(myImage1)

#X1 = prepare1DImage(img1)

X1 = prepare2DImage(img1)

y1 = net1.predict(X1)

img2 = scipyMisc.imread(myImage2)

#X2 = prepare1DImage(img2)

X2 = prepare2DImage(img2)

#X2 = load2d(test=True)[0][6:7]

#X2 = load2d(test=True)[0][15]

y2 = net1.predict(X2)

#dist = HausdorffDist(y1[0],y2[0])

dist = distanceBetweenCurves(y1[0],y2[0])

print dist

# for i in range(0,15):

# X2 = load2d(test=True)[0][i:i+1]

# #print X2

# y2 = net1.predict(X2)

# print y2

# dist = distanceBetweenCurves(y1[0],y2[0])

img = imgInNumpyArray.reshape(96, 96)

imgplot = pyplot.imshow(img, cmap='gray')