I am currently working on a medical dataset containing whole slide images (~300 images). These images are pretty big (.tif, average of 7k by 7k pixels). I am currently dividing each of these images into patches of size 224x224 and getting rid of the images showing just the background. Here is a code I quickly made for that using the patchify module I found. import cv2 from patchify import patchify import numpy as np img = cv2.imread('path/to/image.tif') patches = patchify(img, (224,224,3), step=(224,224,3)) def is_blank(img, threashold=1): return np.std(img)<threshold filtered_patches = [] for k in range(patches.shape[0]): for n in range(patches.shape[1]): for m in range(patches.shape[2]): # print(np.std(patches[k][n][m])) if not is_blank(patches[k][n][m]): filtered_patches.append(patches[k][n][m]) I was wondering if there was a more efficient/optimized way of first divinding each image in patches and then flagging the background images (which are almost completely white). These images also come with color-annotated images (see the link below) that I am also going to divide into patches and I was wondering how I could detect which colors (RGB) are in a given image, because these colors correspond to a type of tissue. link to the whole-slide image and the color-annotated image Here is the dataset : https://espace.library.uq.edu.au/view/UQ:8be4bd0 Continue reading...
