stain_utils.py

"""
Source: https://github.com/wanghao14/Stain_Normalization

Uses the spams package:

http://spams-devel.gforge.inria.fr/index.html

Use with python via e.g https://anaconda.org/conda-forge/python-spams
"""

from __future__ import division

import numpy as np
import cv2 as cv
import spams
import matplotlib.pyplot as plt


##########################################

def read_image(path):
    """
    Read an image to RGB uint8
    :param path:
    :return:
    """
    im = cv.imread(path)
    im = cv.cvtColor(im, cv.COLOR_BGR2RGB)
    return im


def show_colors(C):
    """
    Shows rows of C as colors (RGB)
    :param C:
    :return:
    """
    n = C.shape[0]
    for i in range(n):
        if C[i].max() > 1.0:
            plt.plot([0, 1], [n - 1 - i, n - 1 - i], c=C[i] / 255, linewidth=20)
        else:
            plt.plot([0, 1], [n - 1 - i, n - 1 - i], c=C[i], linewidth=20)
        plt.axis('off')
        plt.axis([0, 1, -1, n])


def show(image, now=True, fig_size=(10, 10)):
    """
    Show an image (np.array).
    Caution! Rescales image to be in range [0,1].
    :param image:
    :param now:
    :param fig_size:
    :return:
    """
    image = image.astype(np.float32)
    m, M = image.min(), image.max()
    if fig_size != None:
        plt.rcParams['figure.figsize'] = (fig_size[0], fig_size[1])
    plt.imshow((image - m) / (M - m), cmap='gray')
    plt.axis('off')
    if now == True:
        plt.show()


def build_stack(tup):
    """
    Build a stack of images from a tuple of images
    :param tup:
    :return:
    """
    N = len(tup)
    if len(tup[0].shape) == 3:
        h, w, c = tup[0].shape
        stack = np.zeros((N, h, w, c))
    if len(tup[0].shape) == 2:
        h, w = tup[0].shape
        stack = np.zeros((N, h, w))
    for i in range(N):
        stack[i] = tup[i]
    return stack


def patch_grid(ims, width=5, sub_sample=None, rand=False, save_name=None):
    """
    Display a grid of patches
    :param ims:
    :param width:
    :param sub_sample:
    :param rand:
    :return:
    """
    N0 = np.shape(ims)[0]
    if sub_sample == None:
        N = N0
        stack = ims
    elif sub_sample != None and rand == False:
        N = sub_sample
        stack = ims[:N]
    elif sub_sample != None and rand == True:
        N = sub_sample
        idx = np.random.choice(range(N), sub_sample, replace=False)
        stack = ims[idx]
    height = np.ceil(float(N) / width).astype(np.uint16)
    plt.rcParams['figure.figsize'] = (18, (18 / width) * height)
    plt.figure()
    for i in range(N):
        plt.subplot(height, width, i + 1)
        im = stack[i]
        show(im, now=False, fig_size=None)
    if save_name != None:
        plt.savefig(save_name)
    plt.show()


######################################

def standardize_brightness(I):
    """

    :param I:
    :return:
    """
    p = np.percentile(I, 90)
    return np.clip(I * 255.0 / p, 0, 255).astype(np.uint8)


def remove_zeros(I):
    """
    Remove zeros, replace with 1's.
    :param I: uint8 array
    :return:
    """
    mask = (I == 0)
    I[mask] = 1
    return I


def RGB_to_OD(I):
    """
    Convert from RGB to optical density
    :param I:
    :return:
    """
    I = remove_zeros(I)
    return -1 * np.log(I / 255)


def OD_to_RGB(OD):
    """
    Convert from optical density to RGB
    :param OD:
    :return:
    """
    return (255 * np.exp(-1 * OD)).astype(np.uint8)


def normalize_rows(A):
    """
    Normalize rows of an array
    :param A:
    :return:
    """
    return A / np.linalg.norm(A, axis=1)[:, None]


def notwhite_mask(I, thresh=0.8):
    """
    Get a binary mask where true denotes 'not white'
    :param I:
    :param thresh:
    :return:
    """
    I_LAB = cv.cvtColor(I, cv.COLOR_RGB2LAB)
    L = I_LAB[:, :, 0] / 255.0
    return (L < thresh)


def sign(x):
    """
    Returns the sign of x
    :param x:
    :return:
    """
    if x > 0:
        return +1
    elif x < 0:
        return -1
    elif x == 0:
        return 0


def get_concentrations(I, stain_matrix, lamda=0.01):
    """
    Get concentrations, a npix x 2 matrix
    :param I:
    :param stain_matrix: a 2x3 stain matrix
    :return:
    """
    OD = RGB_to_OD(I).reshape((-1, 3))
    return spams.lasso(OD.T, D=stain_matrix.T, mode=2, lambda1=lamda, pos=True).toarray().T