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measurements.py
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measurements.py
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#import pybm3d
from skimage import filters, measure, restoration, morphology
import numpy as np
import torch as pt
import os
import sys
sys.path.extend(['/media/data/Documents/Promotion/Project_Helpers/'])
from functools import partial
import pandas as pd
import matplotlib.pyplot as plt
from utils.registrator import Registrator
from pt_models import Generator
import importlib
import importlib.util
import h5py
import imreg_dft as ird
import multiprocessing as mp
import warnings
warnings.filterwarnings('ignore')
from time import time
def show(image, **kwargs):
fig = plt.figure(figsize=kwargs.get('figsize', (8, 8)))
plt.imshow(image, **kwargs)
plt.axis('off')
plt.show()
def hist(image):
fig = plt.figure(figsize=(8, 8))
plt.hist(image.ravel(), 100, range=(0.1, 1))
plt.show()
def bilateral(image):
sigma = restoration.estimate_sigma(image)*3
denoised = restoration.denoise_bilateral(image.astype(np.float), sigma_color=sigma, multichannel=False)
return denoised
def wavelet(image):
sigma = restoration.estimate_sigma(image)*1.5
denoised = restoration.denoise_wavelet(image.astype(np.float), multichannel=False, sigma=sigma)
return denoised
def nl_means(image):
sigma = restoration.estimate_sigma(image)*1.5
denoised = restoration.denoise_nl_means(image.astype(np.float), h=sigma, multichannel=False)
return denoised
def median(image, filter_size=1):
filter = morphology.disk(filter_size)
image = filters.median(image.astype(np.float), selem=filter)
image = image / image.max()
return image
def bm3d(image):
sigma = restoration.estimate_sigma(image)*2
denoised = pybm3d.bm3d.bm3d(image, sigma)
denoised[np.isinf(denoised)] = 0
denoised[np.isnan(denoised)] = 0
denoised[denoised < 0] = 0
return denoised
def measurement_preparation(datapoint):
image = datapoint.image if datapoint.mask is None else datapoint.image * datapoint.mask
reference = datapoint.reference if datapoint.mask is None else datapoint.reference * datapoint.mask
if datapoint.transformation is not None:
image = ird.transform_img_dict(image, datapoint.transformation, bgval=0.0, order=3)
return image, reference
def psnr(datapoint):
image, reference = measurement_preparation(datapoint)
return measure.compare_psnr(reference, image)
def ssim(datapoint):
image, reference = measurement_preparation(datapoint)
return measure.compare_ssim(image, reference)
def cnr(datapoint):
rois, background = [datapoint.image[roi > 0] for roi in datapoint.rois], datapoint.image[datapoint.background > 0]
background_mean = background.mean()
background_std = background.std()
cnrs = []
for roi in rois:
cnrs.append(np.abs(roi.mean() - background_mean) / np.sqrt(0.5*(roi.std()**2 + background_std)**2))
cnrs = np.array(cnrs)
return cnrs.mean()
def msr(datapoint):
rois = [datapoint.image[roi > 0] for roi in datapoint.rois]
msrs = []
for roi in rois:
mean = roi.mean()
std = roi.std()
msrs.append(mean/std)
msrs = np.array(msrs)
return msrs.mean()
class CycGAN(object):
def __init__(self, checkpoint, config):
dirname = os.path.dirname(config)
sys.path.extend([dirname])
config = os.path.basename(config).split('.')[0]
spec = importlib.util.spec_from_file_location(config, os.path.join(dirname, config)+'.py')
config = importlib.util.module_from_spec(spec)
spec.loader.exec_module(config)
self.denoiser = Generator(**config.generator)
self.checkpoints = [os.path.join(dirname, ckpt) for ckpt in os.listdir(dirname) if '.pt' in ckpt]
checkpoint = pt.load(self.checkpoints[checkpoint])['model']
checkpoint = {'.'.join(key.split('.')[1:]): value for key, value in checkpoint.items()
if 'generator_hq' in key}
self.denoiser.load_state_dict(checkpoint)
def __call__(self, image):
denoiser = self.denoiser
image = pt.from_numpy(image.copy())[None, None, ...]
with pt.no_grad():
output = denoiser(image)[0, 0].numpy()
return output
class Datapoint(object):
def __init__(self, key=None, image=None, reference=None, method=None, background=None, rois=None, mask=None,
transformation=None):
self.data = {}
self.key = key
self.method = method
self.image = image.copy()
self.mask = mask
self.reference = reference
self.rois = rois
self.background = background
self.contains_measurement = False
self.transformation = transformation
self.compute_time = 0
@property
def key(self):
return self.data['key']
@key.setter
def key(self, key):
self.data['key'] = key
@property
def method(self):
return self.data['method']
@method.setter
def method(self, method):
self.data['method'] = method
@property
def compute_time(self):
return self.data['compute_time']
@compute_time.setter
def compute_time(self, duration):
self.data['compute_time'] = duration
def extract_information(self):
# test if some data is missing
assert self.contains_measurement, f'no measurement was performed on this datapoint!'
return self.data
def add_measurement(self, name, value):
self.data[name] = value
self.contains_measurement = True
def copy(self):
new_datapoint = Datapoint(image=self.image,
reference=self.reference,
rois=self.rois,
background=self.background,
key=self.key,
method=self.method,
mask=self.mask,
transformation=self.transformation)
new_datapoint.data = self.data.copy()
return new_datapoint
class Analysis(object):
def __init__(self, lq, hq, methods, measurements, output_path, n_processes=1,
preprocess=lambda x: x, export_denoised=None):
self.lq = self.open_storage(lq)
self.hq = self.open_storage(hq)
self.n_processes = n_processes
self.export_denoised = export_denoised
self.output_path = output_path
self.output_dir = os.path.dirname(output_path)
if not os.path.isdir(self.output_dir):
os.makedirs(self.output_dir)
self.measurements = dict(measurements)
self.methods = dict(methods)
self.preprocess = preprocess
self.registrator = Registrator()
self.lq_keys = list(self.lq.keys())
self.hq_keys = list(self.hq.keys())
# late binding because of mp
self.hq.close()
self.lq.close()
self.lq = lq
self.hq = hq
@staticmethod
def open_storage(filename):
return h5py.File(filename, 'r', swmr=True, libver='latest')
@staticmethod
def get_rois(image, registrator):
masked = registrator.segment(image, offset=1)
masked = np.pad(masked, ((6, 6),), 'constant', constant_values=0)
contours = registrator.get_contours(masked, offset=0.4, min_length=0.1)
masks = [registrator.get_mask(contour, masked.shape)[6:-6, 6:-6] for contour in contours]
return masks
@staticmethod
def get_background(image, registrator):
inverted_mask = np.ones_like(image)
inverted_mask[registrator.segment(image, offset=0) > 0] = 0
return inverted_mask
def apply_measurements(self, datapoint):
for measurement_name, measurement in self.measurements.items():
result = measurement(datapoint)
datapoint.add_measurement(measurement_name, result)
result = datapoint.extract_information()
return result
def apply_denoising(self, method_name, datapoint):
method = self.methods[method_name]
datapoint = datapoint.copy()
start = time()
image = method(datapoint.image)
duration = time() - start
datapoint.image = image
datapoint.compute_time = duration
datapoint.method = method_name
return datapoint
def export_images(self, datapoints, index):
indices = list(range(len(datapoints[1:])))
index = str(index)
exported = {'index': index,
'key': datapoints[0].key}
filedir = os.path.join(self.export_denoised, index)
if not os.path.isdir(filedir):
os.makedirs(filedir)
# save original and reference
datapoint = datapoints[0]
image = datapoint.image.copy()
plt.figure(figsize=(5.12, 5.12), frameon=False)
plt.imshow(image, 'gray', interpolation='none')
plt.axis('off')
plt.tight_layout()
plt.savefig(os.path.join(filedir, 'original.png'), dpi=100)
plt.close()
plt.clf()
plt.cla()
image = datapoint.reference.copy()
plt.figure(figsize=(5.12, 5.12), frameon=False)
plt.imshow(image, 'gray', interpolation='none')
plt.axis('off')
plt.tight_layout()
plt.savefig(os.path.join(filedir, 'reference.png'), dpi=100)
plt.close()
plt.clf()
plt.cla()
for datapoint in datapoints[1:]:
inner_index = indices.pop(np.random.randint(len(indices)))
filename = os.path.join(filedir, f'{inner_index}.png')
image = datapoint.image.copy()
plt.figure(figsize=(5.12, 5.12), frameon=False)
plt.imshow(image, 'gray', interpolation='none')
plt.axis('off')
plt.tight_layout()
plt.savefig(filename, dpi=100)
exported[datapoint.method] = f'{inner_index}.png'
plt.close()
plt.clf()
plt.cla()
return exported
def __call__(self, acceptance=None):
lq = self.open_storage(self.lq)
hq = self.open_storage(self.hq)
print('beginning analysis...')
total_slices = len(self.lq_keys)
print(f'\tnumber of datapoins:\t{total_slices}')
# initialize result list for accumulating measurements
results = []
exported = []
indices = list(range(len(self.lq_keys)))
# cycle over samples
for i, key in enumerate(self.lq_keys[150:]):
i+= 150
# skip failed registrations
entry = lq[key]
if acceptance is not None:
if entry.attrs['difference'] > acceptance or np.isnan(entry.attrs['difference']):
continue
transform = dict(entry.attrs)
transform.pop('difference')
transform.pop('frames')
image = self.preprocess(entry.value)
reference = self.preprocess(hq[self.hq_keys[i]].value)
rois = self.get_rois(reference, self.registrator)
background = self.get_background(reference, self.registrator)
mask = self.registrator.segment(reference, offset=-1)
# initialize original image as datapoint
datapoints = [Datapoint(key=key, reference=reference,
rois=rois, background=background,
method='original', image=image, transformation=transform, mask=mask)]
# generate all denoised images
pool = mp.Pool(self.n_processes)
denoising = partial(self.apply_denoising, datapoint=datapoints[0])
datapoints += pool.map(denoising, self.methods.keys())
# perform measurements on all denoised images
pool = mp.Pool(self.n_processes)
results += pool.map(self.apply_measurements, datapoints)
if self.export_denoised is not None:
index = indices.pop(np.random.randint(len(indices)))
exported_row = self.export_images(datapoints, index)
exported.append(exported_row)
print('\r\tprogress: \t{}%'.format(round(100 * i / total_slices, 2)), end='')
# convert results to a dataframe and save
results_frame = pd.DataFrame(data=results)
results_frame.to_csv(self.output_path)
if self.export_denoised is not None:
exported_frame = pd.DataFrame(data=exported)
exported_frame.to_csv(os.path.join(self.export_denoised, 'exports.csv'))
hq.close()
lq.close()
return results_frame
if __name__ == '__main__':
checkpoint = 132
config = '/media/network/DL_PC/Results/ilja/pt-cycoct/run_024/config.py'
methods = {'median': median,
'ours': CycGAN(checkpoint, config),
'wavelet': wavelet,
'bilateral': bilateral,
'nl_means': nl_means,
'bm3d': bm3d}
measurements = {'PSNR': psnr,
'CNR': cnr,
'MSR': msr,
'SSIM': ssim}
savefile = './measurements.csv'
lq = '/media/network/DL_PC/Datasets/oct_quality_validation/low.hdf5'
hq = '/media/network/DL_PC/Datasets/oct_quality_validation/high.hdf5'
def preprocess(image):
image = image / image.max()
mean = image[image>0].mean()
std = image[image>0].std()
level = mean - 0.5*std
image = np.clip(image, level, 1.0) - level
image = image / image.max()
return image
analysis = Analysis(lq, hq, methods, measurements, savefile, 4, preprocess=preprocess, export_denoised='./exports/')
results = analysis()