utils/splitting.py

"""
Split images in blocks and vice versa
"""

import random
import numpy as np

import torch

from skimage.util.shape import view_as_windows


def split_img(imgs, ROIs = (3,3) , step= (1,1)):
    """Split the imgs in regions of size ROIs.

       Args:
            imgs (ndarray): images which you want to split 
            ROIs (tuple): size of sub-regions splitted (ROIs=region of interests)
            step (tuple): step path from one sub-region to the next one (in the x,y axis)
       
        Returns:
            ndarray: splitted subimages. 
                     The size is (x_num_subROIs*y_num_subROIs, **) where:
                     x_num_subROIs = ( imgs.shape[1]-int(ROIs[1]/2)*2 )/step[1]
                     y_num_subROIs = ( imgs.shape[0]-int(ROIs[0]/2)*2 )/step[0]                     
       
       Example:
            >>> from dataset_generator import split
            >>> imgs_splitted = split(imgs, ROI_size = (5,5), step=(2,3))
    """
    
    if len(ROIs) > 2:
        return print("ROIs is a 2 element list")
    
    if len(step) > 2:
        return print("step is a 2 element list")
    
    if type(imgs) != type(np.array(1)):
        return print("imgs should be a ndarray")
        
    if len(imgs.shape) == 2: # Single image with one channel (HxW)   
        splitted = view_as_windows(imgs, (ROIs[0],ROIs[1]), (step[0], step[1]))
        return splitted.reshape((-1, ROIs[0], ROIs[1]))
    
    if len(imgs.shape) == 3: 
        _, _, channels = imgs.shape
        if channels <= 3: # Single image more channels (HxWxC)
            splitted = view_as_windows(imgs, (ROIs[0],ROIs[1], channels), (step[0], step[1], channels))
            return splitted.reshape((-1, ROIs[0], ROIs[1], channels))
        else: # More images with 1 channel
            splitted = view_as_windows(imgs, (1, ROIs[0],ROIs[1]), (1, step[0], step[1]))
            return splitted.reshape((-1, ROIs[0], ROIs[1]))
    
    if len(imgs.shape) == 4: # More images with more channels(BxHxWxC)
        _, _, _, channels = imgs.shape
        splitted = view_as_windows(imgs, (1, ROIs[0],ROIs[1], channels), (1, step[0], step[1], channels))
        return splitted.reshape((-1, ROIs[0], ROIs[1], channels))

def join_blocks(splitted, final_shape):
    """Join blocks to reobtain a splitted image
    
    Attribute:
        splitted (tensor) = image splitted in blocks, size = (N_blocks, Channels, Height, Width)
        final_shape (tuple) = size of the final image reconstructed (Height, Width)
    Return:
        tensor: image restored from blocks. size = (Channels, Height, Width)
    
    """
    n_blocks, channels, ROI_height, ROI_width = splitted.shape
    
    rows = final_shape[0] // ROI_height
    columns = final_shape[1] // ROI_width

    final_img = torch.empty(rows, channels, ROI_height, ROI_width*columns)
    for r in range(rows):    
        stackblocks = splitted[r*columns]
        for c in range(1, columns):
            stackblocks = torch.cat((stackblocks, splitted[r*columns+c]), axis=2)
        final_img[r] = stackblocks
    
    joined_img = final_img[0]
    
    for i in np.arange(1, len(final_img)):    
        joined_img = torch.cat((joined_img,final_img[i]), axis = 1)
    
    return joined_img

def random_ROI(X, Y, ROIs = (512,512)):
    """ Return a random region for each input/target pair images of the dataset
        Args:
            Y (ndarray): target of your dataset --> size: (BxHxWxC)
            X (ndarray): input of your dataset --> size: (BxHxWxC)
            ROIs (tuple): size of random region (ROIs=region of interests)
           
        Returns:
            For each pair images (input/target) of the dataset, return respectively random ROIs
            Y_cut (ndarray): target of your dataset --> size: (Batch,Channels,ROIs[0],ROIs[1])
            X_cut (ndarray): input of your dataset --> size: (Batch,Channels,ROIs[0],ROIs[1])
            
        Example:
            >>> from dataset_generator import random_ROI
            >>> X,Y = random_ROI(X,Y, ROIs = (10,10))
    """    
    
    batch, channels, height, width = X.shape
    
    X_cut=np.empty((batch, ROIs[0], ROIs[1], channels))
    Y_cut=np.empty((batch, ROIs[0], ROIs[1], channels))
    
    for i in np.arange(len(X)):
        x_size=int(random.random()*(height-(ROIs[0]+1)))
        y_size=int(random.random()*(width-(ROIs[1]+1)))
        X_cut[i]=X[i, x_size:x_size+ROIs[0],y_size:y_size+ROIs[1], :]
        Y_cut[i]=Y[i, x_size:x_size+ROIs[0],y_size:y_size+ROIs[1], :]
    return X_cut, Y_cut

def one2many_random_ROI(X, Y, datasize=1000, ROIs = (512,512)):
    """ Return a dataset of N subimages obtained from random regions of the same image
        Args:
            Y (ndarray): target of your dataset --> size: (1,H,W,C)
            X (ndarray): input of your dataset --> size: (1,H,W,C)
            datasize = number of random ROIs to generate
            ROIs (tuple): size of random region (ROIs=region of interests)
           
        Returns:
            Y_cut (ndarray): target of your dataset --> size: (Datasize,ROIs[0],ROIs[1],Channels)
            X_cut (ndarray): input of your dataset --> size: (Datasize,ROIs[0],ROIs[1],Channels)
    """   

    batch, channels, height, width = X.shape
    
    X_cut=np.empty((datasize, ROIs[0], ROIs[1], channels))
    Y_cut=np.empty((datasize, ROIs[0], ROIs[1], channels))

    for i in np.arange(datasize):
        X_cut[i], Y_cut[i] = random_ROI(X, Y, ROIs)
    return X_cut, Y_cut