030_weights_maker.py

# !/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2021 Luca Clissa, Marco Dalla, Roberto Morelli
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Created on Tue May  7 10:42:13 2019
@author: Roberto Morelli
"""

import glob
import sys
import numpy as np
import imageio
import cv2
import random
from skimage import transform
import os
from tqdm import tqdm
import matplotlib.pyplot as plt
from importlib import import_module
from skimage import io
from skimage.io import imread
from skimage.transform import rotate
from skimage.transform import resize
# import albumentations as alb
from scipy import ndimage
from skimage.morphology import watershed, remove_small_holes, remove_small_objects,\
label, erosion, dilation, local_maxima, skeletonize, binary_erosion, remove_small_holes
from skimage.feature import peak_local_max
from skimage.segmentation import find_boundaries
import pickle
import argparse
import shutil

from config import *
from utils import *

image_ids = os.listdir(CropMasks)
image_ids.sort()
ix = [int(x.split('.')[0]) for x in image_ids]
ix.sort()
image_ids = [str(x)+'.tiff' for x in ix]


if __name__ == "__main__":

    parser = argparse.ArgumentParser(description='weighting masks')

    parser.add_argument('--CropMasks', nargs="?", default = CropMasks, help='path including mask to weight')
    parser.add_argument('--CropWeightedMasks', nargs="?", default = CropWeightedMasks, help='path where save weighted mask')
    parser.add_argument('--save_images_path', nargs="?", default = CropImages, help='save images path')

    parser.add_argument('--normalize', action='store_const', const=True, default=False, help='find the maximum for normalization filling the total array in 030_weights_maker.py')
    parser.add_argument('--start_from_zero', action='store_const', const=True, default=False, help='delete all file in destination folder')
    parser.add_argument('--continue_after_normalization', action='store_const', const=True, default=False,  help='after finding the maximum continue with the weighted maps creation')
    parser.add_argument('--resume_after_normalization', action='store_const', const=True, default=False, help='If you stopped the weight maker after finding the max and you want to resum only the second step of the process')

    parser.add_argument('--maximum', nargs="?", type = int, default = 3.8177538,  help='Maximum value for normalization')
    parser.add_argument('--sigma', nargs="?", type = int, default = 25,  help='kernel for cumpling cell deacaying influence')

    args = parser.parse_args()

    if args.start_from_zero:
        print('deleting existing files in destination folder')
        try:
            shutil.rmtree(CropWeightedMasks)
        except:
            pass
        os.makedirs(CropWeightedMasks, exist_ok=True)

        print('start new weighting mask')

    # First step: find the maximum weight value
    if args.normalize:
        make_weights(image_ids,  args.CropMasks, args.CropWeightedMasks, sigma = args.sigma, maximum=False)
    # Second step following the first step: use the previous value to normalize the final weighted masks
        if args.continue_after_normalization: 
            with open('max_weight_{}.pickle'.format(sigma), 'rb') as handle:
                dic = pickle.load(handle)
            maximum = dic['max_weight']
            make_weights(image_ids,  args.CropMasks, args.CropWeightedMasks, sigma = args.sigma, maximum=maximum)
    # Only the second step: you already get the maximum weight value 
    elif args.resume_after_normalization:
        with open('max_weight_{}.pickle'.format(sigma), 'rb') as handle:
            dic = pickle.load(handle)
        maximum = dic['max_weight']
        make_weights(image_ids,  args.CropMasks, args.CropWeightedMasks, sigma = args.sigma, maximum=maximum)

    # Only the second step: you already get the maximum weight value and this is the default value passed with the args.maximum arguments
    else:
        make_weights(image_ids,  args.CropMasks, args.CropWeightedMasks, sigma = args.sigma, maximum=args.maximum)