markloyman

def crop(image, mask, min_size= 128):
    # Determine New Size on Y axis
    max_size_y = image.shape[0]
    mask_y     = np.where(np.sum(mask, axis=1))
    min_size_y = np.max(mask_y) - np.min(mask_y)
    new_size_y = np.random.rand() * (max_size_y - min_size_y)
    new_size_y = np.maximum(new_size_y, min_size).astype('uint16')
    # Determine New Size on X axis
    max_size_x = image.shape[1]
    mask_x     = np.where(np.sum(mask, axis=0))

markloyman

def triplet_loss(_, y_pred, triplet_margin=1):
    '''
        Assume: y_pred shape is (batch_size, 2)
        
        Example for how to construct such a corresponding triplet network:
        
        def euclidean_distance(vects):
            x, y = vects
            return K.sqrt(K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))
            

markloyman

import numpy as np
import tensorflow as tf
sess = tf.Session()
from keras import backend as K
K.set_session(sess)
from keras.objectives import mean_squared_error
from keras.layers import Dense


def load_dummy_data(n = 1000):

markloyman

import numpy as np
import matplotlib.pyplot as plt

class VolumeViz:
    def __init__(self, fig, volume):
        self.volume = volume
        self.figure = fig
        self.ax1 = fig.add_subplot(121)
        self.ax2 = fig.add_subplot(122)
        self.ax1.set_title('click')

markloyman

import numpy as np
import matplotlib.pyplot as plt
from volume_viz import VolumeViz


class CostVolumeViz:
    def __init__(self, left, right, disparity):
        self.left = left
        self.right = right
        self.disparity = self.StandardizeDisparity(disparity)

markloyman

def l2_distance_matrix(x, exact=True):
    """
    Calculate an L2 distance matrix from embedding vectors)
    :param
        x: embedding tensor size (batch_size, embedding_length)
        exact: if False, skip the final sqrt
    :return: l2 distance matrix tensor tensor size (batch_size, batch_size)
    """
    r = K.sum(x*x, 1)
    r = K.reshape(r, [-1, 1])  # turn r into column vector

markloyman

import numpy as np
from scipy.stats import skew


def k_occurrences(nbrs_indices):
    """
    Calculate k-occurrences distribution
        Miloš Radovanovi´c et al, 
        Hubs in space: Popular nearest neighbors in high-dimensional data. 
        Journal of Machine Learning Research, 11(Sep):2487–2531, 2010