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eval.py
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eval.py
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import os
import argparse
import SimpleITK as sitk
from sklearn.metrics import mean_absolute_error, mean_squared_error
from skimage.metrics import structural_similarity as ssim
from utils.parser import get_file_paths, separate_rA_rB_fB
import numpy as np
import time
import progressbar
class Evaluation(object):
# Default Settings
# Root directory where data is stored
data_path = '/data/MSc_students_accounts/mathieu/MscProject/Ea-GANs/results/example_gEaGAN20/test_latest/images'
file_paths = None
MAX_pixel = 100 # For 8-bit images
def __init__(self, data_path):
self.image_height = 128
self.image_width = 128
self.image_depth = 128
if data_path != None:
self.data_path = data_path
# Loads MRI Slice data after being exported from the test function
# of the network
def loadData(self):
print("Loading from:{}\n".format(self.data_path))
self.file_paths = mc_get_file_paths(self.data_path)
# Check if data was loaded
if len(self.file_paths) <= 0:
print("No image files found")
return
self.rA,self.rB,self.fB = separate_rA_rB_fB(self.file_paths)
#sitk.ReadImage(img_coregistered)
#savImg = sitk.GetImageFromArray(image_tensor
sample_no = len(self.rA)
# Create numpy array to hold data
dataset_fake = np.ndarray(shape=(sample_no, self.image_depth * self.image_height * self.image_width),
dtype=np.float32)
dataset_real = np.ndarray(shape=(sample_no, self.image_depth * self.image_height * self.image_width),
dtype=np.float32)
dataset_input = np.ndarray(shape=(sample_no, self.image_depth * self.image_height * self.image_width),
dtype=np.float32)
for i in range(len(self.rA)):
real_A=self.rA[i]
real_B=self.rB[i]
fake_B=self.fB[i]
# Read images as numpy array
fake_B_im = sitk.ReadImage(fake_B)
fake_B_im= sitk.GetArrayFromImage(fake_B_im)
real_B_im = sitk.ReadImage(real_B)
real_B_im= sitk.GetArrayFromImage(real_B_im)
real_A_im = sitk.ReadImage(real_A)
real_A_im= sitk.GetArrayFromImage(real_A_im)
# Put images into dataset
dataset_fake[i] = fake_B_im.reshape(-1)
dataset_real[i] = real_B_im.reshape(-1)
dataset_input[i] = real_A_im.reshape(-1)
#rescale the images as they are usually between -1 and 1
dataset_fake = (dataset_fake + 1) / 2.00 * 255.0
dataset_real = (dataset_real + 1) / 2.00 * 255.0
dataset_input = (dataset_input + 1) / 2.00 * 255.0
return dataset_fake, dataset_real, dataset_input
def mae_underthreshold(self,img1, img2):
threshold = 100
sigma = lambda x: np.array((x < threshold), dtype=np.float32)
f = lambda x, y: ((sigma(x) * sigma(y))*np.abs(x-y))/np.sum(sigma(x)*sigma(y))
return np.sum(f(img1, img2))
def mae_overthreshold(self,img1, img2):
threshold = 100
sigma = lambda x: np.array((x > threshold), dtype=np.float32)
f = lambda x, y: (np.logical_or(sigma(x),sigma(y))*np.abs(x-y))/np.sum(np.logical_or(sigma(x),sigma(y)))
return np.sum(f(img1, img2))
#
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------
def Reconstruction_score(self, data_fake, data_input):
num_samples = data_fake.shape[0]
score=0
# Calculate a score for every fake sample, by calculating
# the distance between every other input and real sample
# and checking which ones are the smallest ones
for i in progressbar.progressbar(range(0, num_samples)):
distance_matrix = np.zeros((num_samples))
distance_corresponding=self.mae_underthreshold(data_input[i], data_fake[i])
for j in range(0, num_samples):
distance_matrix[j] = self.mae_underthreshold(data_input[j], data_fake[i])
# Get k smallest elements indices
k = 2
idx = np.argpartition(distance_matrix, k)[:k]
idx = idx[np.argsort(distance_matrix[idx])]
if np.abs(distance_matrix[idx[0]] - distance_corresponding) < 0.01 or np.abs(distance_matrix[idx[1]] - distance_corresponding) < 0.1 :
score += 1
rs = score/num_samples
return rs
def Evolution_score(self, data_fake, data_real):
num_samples = data_fake.shape[0]
score=0
# Calculate a score for every fake sample, by calculating
# the distance between every other input and real sample
# and checking which ones are the smallest ones
for i in progressbar.progressbar(range(0, num_samples)):
distance_matrix = np.zeros((num_samples))
distance_corresponding=self.mae_overthreshold(data_real[i], data_fake[i])
for j in range(0, num_samples):
distance_matrix[j] = self.mae_overthreshold(data_real[j], data_fake[i])
# Get 2 smallest elements indices
k = 2
idx = np.argpartition(distance_matrix, k)[:k]
idx = idx[np.argsort(distance_matrix[idx])]
if np.abs(distance_matrix[idx[0]] - distance_corresponding)<0.1 or np.abs(distance_matrix[idx[1]] - distance_corresponding) < 0.1 :
score += 1
es = score/num_samples
return es
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------
def MAE(self, data_fake, data_real):
return mean_absolute_error(y_true=data_real, y_pred=data_fake)
def MSE(self, data_fake, data_real):
return mean_squared_error(y_true=data_real, y_pred=data_fake)
def PSNR(self, data_fake, data_real):
mse = self.MSE(data_fake, data_real)
return 20 * np.log10(self.MAX_pixel) - 10 * np.log10(mse)
def SSIM(self, data_fake, data_real):
cum_ssim = 0
for i in progressbar.progressbar(range(0, data_fake.shape[0])):
cum_ssim += ssim(data_real[i], data_fake[i])
rssim = cum_ssim / data_fake.shape[0]
return rssim
def main(args):
print("Evaluating metrics:")
ev = Evaluation(args.rootdir)
data_fake, data_real, data_input = ev.loadData()
# Calculate and Print Metrics
mae = ev.MAE(data_fake, data_real)
mse = ev.MSE(data_fake, data_real)
#psnr = ev.PSNR(data_fake, data_real)
ssim = ev.SSIM(data_fake, data_real)
rs= ev.Reconstruction_score(data_fake, data_input)
es= ev.Evolution_score(data_fake, data_real)
print("MAE:", mae)
print("MSE:", mse)
#print("PSNR:", psnr)
print("SSIM:", ssim)
print("Reconstruction Score",rs)
print("Evolution Score",es)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('rootdir', help='Directory of Data')
main(parser.parse_args())