imageread.py

import numpy as np
import matplotlib.pyplot as plt
import skimage.io
import skimage.transform
import cv2
import os

from keras.utils import np_utils

def normalized(rgb):
        #return rgb/255.0
        norm=np.zeros((rgb.shape[0], rgb.shape[1], 3),np.float32)

        b=rgb[:,:,0]
        g=rgb[:,:,1]
        r=rgb[:,:,2]

        norm[:,:,0]=b/255.0
        norm[:,:,1]=g/255.0
        norm[:,:,2]=r/255.0

        return norm


def one_hot(rgb):
        # return one hot vector of mask
        b=rgb[:,:,0]
        label = b/255.0
        #label = np_utils.to_categorical(label.flatten().astype(int), 2)
        #label = label.reshape(rgb.shape[0],rgb.shape[1],2)

        return label

def labelread(i):
        x_shape = 400
        y_shape = 400


        img = cv2.imread("data/masks/masks_img"+str(i+1)+".tif")
        img = cv2.resize(img,(400,400))
        img = one_hot(img)
	img = np.stack([img])
        #img = np.transpose(img,[2,0,1])
        y = np_utils.to_categorical(img.flatten().astype(int), 2)
        y = y.reshape(1, 400, 400,2)
        #img = np.transpose(img,[2,0,1])
        return y

def imageread(i):
        x_shape = 400
        y_shape = 400

        img = skimage.io.imread("data/tiles/tiles_img"+str(i+1)+".tif")
        img = skimage.transform.resize(img,(x_shape,y_shape))
        #img = normalized(img)
	img = np.stack([img])
        #img = np.transpose(img,[2,0,1])
        return img