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dataHandling.py
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dataHandling.py
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from __future__ import division
import numpy as np
import os
import pandas as pd
import argparse
import sys
import random
import png
from matplotlib.pyplot import imsave, imread
import matplotlib
import matplotlib.pyplot as plt
from PIL import Image
import cv2
matplotlib.use("Agg")
import torchvision.datasets as datasets
from skimage.transform import resize
import ast
import pickle
import csv
import pydicom as dcm
import Augmentor
from tqdm import tqdm
import pathlib
from torch import randint, manual_seed
from copy import copy
from collections import defaultdict
def random_flip(input, axis, with_fa=False):
ran = random.random()
if ran > 0.5:
if with_fa:
axis += 1
return np.flip(input, axis=axis)
else:
return input
def random_crop(input, with_fa=False):
ran = random.random()
if ran > 0.2:
# find a random place to be the left upper corner of the crop
if with_fa:
rx = int(random.random() * input.shape[1] // 10)
ry = int(random.random() * input.shape[2] // 10)
return input[:, rx: rx + int(input.shape[1] * 9 // 10), ry: ry + int(input.shape[2] * 9 // 10)]
else:
rx = int(random.random() * input.shape[0] // 10)
ry = int(random.random() * input.shape[1] // 10)
return input[rx: rx + int(input.shape[0] * 9 // 10), ry: ry + int(input.shape[1] * 9 // 10)]
else:
return input
def random_rotate_90(input, with_fa=False):
ran = random.random()
if ran > 0.5:
if with_fa:
return np.rot90(input, axes=(1,2))
return np.rot90(input)
else:
return input
def random_rotation(x, chance, with_fa=False):
ran = random.random()
if with_fa:
img = Image.fromarray(x[0])
mask = Image.fromarray(x[1])
if ran > 1 - chance:
# create black edges
angle = np.random.randint(0, 90)
img = img.rotate(angle=angle, expand=1)
mask = mask.rotate(angle=angle, expand=1, fillcolor=1)
return np.stack([np.asarray(img), np.asarray(mask)])
else:
return np.stack([np.asarray(img), np.asarray(mask)])
img = Image.fromarray(x)
if ran > 1 - chance:
# create black edges
angle = np.random.randint(0, 90)
img = img.rotate(angle=angle, expand=1)
return np.asarray(img)
else:
return np.asarray(img)
def augment_numpy_images(path, targetNumber, targetDir, skip=None, rot=True, with_fa=False):
classes = os.listdir(path)
if not os.path.exists(targetDir):
os.mkdir(targetDir)
for class_ in classes:
if not os.path.exists(targetDir + class_):
os.makedirs(targetDir + class_)
for class_ in classes:
count, round = 0, 0
while count < targetNumber:
round += 1
for root, dir, files in os.walk(os.path.join(path, class_)):
for file in files:
if skip and skip in file:
continue
filepath = os.path.join(root, file)
arr = np.load(filepath)
print("loaded ", file)
print(arr.shape)
try:
arr = random_crop(arr, with_fa)
print(arr.shape)
if rot:
arr = random_rotation(arr, 0.9, with_fa)
print(arr.shape)
arr = random_flip(arr, 0, with_fa)
arr = random_flip(arr, 1, with_fa)
arr = random_rotate_90(arr, with_fa)
arr = random_rotate_90(arr, with_fa)
arr = random_rotate_90(arr, with_fa)
print(arr.shape)
if with_fa:
whites = arr.shape[2] * arr.shape[1] - np.count_nonzero(np.round(arr[0] - np.amax(arr[0]), 2))
black = arr.shape[2] * arr.shape[1] - np.count_nonzero(np.round(arr[0], 2))
if arr.shape[2] < 10 or arr.shape[1] < 10 or black >= arr.shape[2] * arr.shape[1] * 0.8 or \
whites >= arr.shape[2] * arr.shape[1] * 0.8:
print("illegal content")
continue
else:
whites = arr.shape[0] * arr.shape[1] - np.count_nonzero(np.round(arr - np.amax(arr), 2))
black = arr.shape[0] * arr.shape[1] - np.count_nonzero(np.round(arr, 2))
if arr.shape[0] < 10 or arr.shape[1] < 10 or black >= arr.shape[0] * arr.shape[1] * 0.8 or \
whites >= arr.shape[0] * arr.shape[1] * 0.8:
print("illegal content")
continue
if count % 10 == 0:
if not os.path.exists("./visualizations_of_augmentation/" + class_ + "/"):
os.makedirs("./visualizations_of_augmentation/" + class_ + "/")
if with_fa:
imsave("./visualizations_of_augmentation/" + class_ + "/" + str(count), np.transpose(np.stack([arr[0], arr[0], arr[1]]), (1,2,0)))
else:
imsave("./visualizations_of_augmentation/" + class_ + "/" + str(count), np.transpose(np.stack([arr, arr, arr]), (1,2,0)))
np.save(targetDir + class_ + "/" + file[:-4] + "aug" + str(round), arr)
count += 1
print(count)
except:
print("something is wrong in try, details:", sys.exc_info()[2])
if not os.path.exists("./error_of_augmentation/" + class_ + "/"):
os.makedirs("./error_of_augmentation/" + class_ + "/")
np.save("./error_of_augmentation/" + class_ + "/" + str(count), arr)
if count > targetNumber:
break
print(count)
def window_augmentation(wwidth, wcen):
if wcen == 2047 and wwidth == 4096:
return wwidth, wcen
else:
new_wcen = np.random.randint(-100, 300)
new_wwidth = np.random.randint(-200, 300)
wwidth += new_wwidth
wcen += new_wcen
return wwidth, wcen
if __name__ == "__main__":
print("Data augmentation")
for pos in ["Spiculated","Circumscribed", "Indistinct"]:
augment_numpy_images(
path="/usr/xtmp/mammo/npdata/datasetname_with_fa/train/",
targetNumber=5000,
targetDir="/usr/xtmp/mammo/npdata/datasetname_with_fa/train_augmented_5000/",
rot=True,
with_fa=True)