Datasets:
jblitzar
/
github-python

Dataset card Data Studio Files
xet
 Community
text
stringlengths
0
93.6k
def inverse_transform(images):
return ((images+1.) / 2) * 255.0
def imsave(images, size, path):
images = merge(images, size)
images = cv2.cvtColor(images.astype('uint8'), cv2.COLOR_RGB2BGR)
return cv2.imwrite(path, images)
def merge(images, size):
h, w = images.shape[1], images.shape[2]
img = np.zeros((h * size[0], w * size[1], 3))
for idx, image in enumerate(images):
i = idx % size[1]
j = idx // size[1]
img[h*j:h*(j+1), w*i:w*(i+1), :] = image
return img
def orthogonal_regularizer(scale) :
""" Defining the Orthogonal regularizer and return the function at last to be used in Conv layer as kernel regularizer"""
def ortho_reg(w) :
""" Reshaping the matrxi in to 2D tensor for enforcing orthogonality"""
_, _, _, c = w.get_shape().as_list()
w = tf.reshape(w, [-1, c])
""" Declaring a Identity Tensor of appropriate size"""
identity = tf.eye(c)
""" Regularizer Wt*W - I """
w_transpose = tf.transpose(w)
w_mul = tf.matmul(w_transpose, w)
reg = tf.subtract(w_mul, identity)
"""Calculating the Loss Obtained"""
ortho_loss = tf.nn.l2_loss(reg)
return scale * ortho_loss
return ortho_reg
def orthogonal_regularizer_fully(scale) :
""" Defining the Orthogonal regularizer and return the function at last to be used in Fully Connected Layer """
def ortho_reg_fully(w) :
""" Reshaping the matrix in to 2D tensor for enforcing orthogonality"""
_, c = w.get_shape().as_list()
"""Declaring a Identity Tensor of appropriate size"""
identity = tf.eye(c)
w_transpose = tf.transpose(w)
w_mul = tf.matmul(w_transpose, w)
reg = tf.subtract(w_mul, identity)
""" Calculating the Loss """
ortho_loss = tf.nn.l2_loss(reg)
return scale * ortho_loss
return ortho_reg_fully
def tf_rgb_to_gray(x) :
x = (x + 1.0) * 0.5
x = tf.image.rgb_to_grayscale(x)
x = (x * 2) - 1.0
return x
def RGB2LAB(srgb):
srgb = inverse_transform(srgb)
lab = rgb_to_lab(srgb)
l, a, b = preprocess_lab(lab)
l = tf.expand_dims(l, axis=-1)
a = tf.expand_dims(a, axis=-1)
b = tf.expand_dims(b, axis=-1)
x = tf.concat([l, a, b], axis=-1)
return x
def LAB2RGB(lab) :
lab = inverse_transform(lab)
rgb = lab_to_rgb(lab)
rgb = tf.clip_by_value(rgb, 0, 1)
# r, g, b = tf.unstack(rgb, axis=-1)
# rgb = tf.concat([r,g,b], axis=-1)
x = (rgb * 2) - 1.0
return x