def ED_ES_histogram_matching(reference_subject, target_subject):
reference_ed_nim = nib.load(str(reference_subject.ed_path))
reference_ed_image = reference_ed_nim.get_data()
# reference_ed_image = rescale_intensity(reference_ed_image)
reference_es_nim = nib.load(str(reference_subject.es_path))
reference_es_image = reference_es_nim.get_data()
# reference_es_image = rescale_intensity(reference_es_image)
target_ed_nim = nib.load(str(target_subject.ed_path))
target_ed_image = target_ed_nim.get_data()
# target_ed_image = rescale_intensity(target_ed_image)
target_es_nim = nib.load(str(target_subject.es_path))
target_es_image = target_es_nim.get_data()
# target_es_image = rescale_intensity(target_es_image)
matched_ed = match_histograms(target_ed_image,
reference_ed_image,
multichannel=False)
matched_es = match_histograms(target_es_image,
reference_es_image,
multichannel=False)
nim2 = nib.Nifti1Image(matched_ed, affine=target_ed_nim.affine)
nim2.header['pixdim'] = target_ed_nim.header['pixdim']
nib.save(nim2, str(target_subject.ed_path))
nim2 = nib.Nifti1Image(matched_es, affine=target_es_nim.affine)
nim2.header['pixdim'] = target_es_nim.header['pixdim']
nib.save(nim2, str(target_subject.es_path))
def image_normalise(style,
content,
gamma=1,
sharpen=1,
S_TYPE=None,
C_TYPE=None):
style = np.array(style)
content = np.array(content)
#DIFFERENT TYPES OF NORMALISATION FOR STYLE IMAGE
if (S_TYPE == 'MATCH'):
style = match_histograms(style, content, multichannel=True)
elif (S_TYPE == 'CONTRAST'):
p2, p98 = np.percentile(style, (2, 98))
style = rescale_intensity(style, in_range=(p2, p98))
elif (S_TYPE == 'HISTO'):
style = equalize_hist(style)
style = (style * 255).astype(np.uint8)
elif (S_TYPE == 'ADAPT'):
style = equalize_adapthist(style, clip_limit=0.01)
style = (style * 255).astype(np.uint8)
#DIFFERENT TYPES OF NORMALISATION FOR CONTENT IMAGE
if (C_TYPE == 'MATCH'):
content = match_histograms(content, style, multichannel=True)
elif (C_TYPE == 'CONTRAST'):
p2, p98 = np.percentile(content, (2, 98))
content = rescale_intensity(content, in_range=(p2, p98))
elif (C_TYPE == 'HISTO'):
content = equalize_hist(content)
content = (content * 255).astype(np.uint8)
elif (C_TYPE == 'ADAPT'):
content = equalize_adapthist(content, clip_limit=0.01)
content = (content * 255).astype(np.uint8)
#GAMMA ADJUSTMENT
if (gamma != 1):
style = adjust_gamma(style, gamma)
content = adjust_gamma(content, gamma)
style = Image.fromarray(style)
content = Image.fromarray(content)
#SHARPENING
if (sharpen != 1):
style = ImageEnhance.Sharpness(style).enhance(sharpen)
content = ImageEnhance.Sharpness(content).enhance(sharpen)
if (VERBOSE):
plt.title('Style: Norm={}, {}, {}'.format(S_TYPE, gamma, sharpen))
plt.imshow(style)
plt.show()
plt.title('Content: Norm={}, {}, {}'.format(C_TYPE, gamma, sharpen))
plt.imshow(content)
plt.show()
return style, content
def test_match_histograms_consistency(self):
"""ensure equivalent results for float and integer-based code paths"""
image_u8 = self.image_rgb
reference_u8 = self.template_rgb
image_f64 = self.image_rgb.astype(np.float64)
reference_f64 = self.template_rgb.astype(np.float64, copy=False)
matched_u8 = exposure.match_histograms(image_u8, reference_u8)
matched_f64 = exposure.match_histograms(image_f64, reference_f64)
assert_array_almost_equal(matched_u8.astype(np.float64), matched_f64)
def histogram_match(g_source, seed):
if seed > .0:
g_reference = np.asarray(
Image.open(
'./data/test/real2016/1452274201.Fri.Jan.08_17_30_01.GMT.2016.argus02b.cx.timex.merge.png'
))
if seed > .3:
g_reference = np.asarray(
Image.open(
'./data/test/real2016/1453491001.Fri.Jan.22_19_30_01.GMT.2016.argus02b.cx.timex.merge.png'
))
if seed > .5:
g_reference = np.asarray(
Image.open(
'./data/test/real2016/1452709801.Wed.Jan.13_18_30_01.GMT.2016.argus02b.cx.timex.merge.png'
))
if seed > .7:
g_reference = np.asarray(
Image.open(
'./data/test/real2016/1477328401.Mon.Oct.24_17_00_01.GMT.2016.argus02b.cx.timex.merge.png'
))
if seed > .9:
g_reference = np.asarray(
Image.open(
'./data/test/real2016/1483196401.Sat.Dec.31_15_00_01.GMT.2016.argus02b.cx.timex.merge.png'
))
g_matched = match_histograms(g_source, g_reference)
image = g_matched.astype('int16')
return image
def test_match_histograms_channel_axis(self, channel_axis):
"""Assert that pdf of matched image is close to the reference's pdf for
all channels and all values of matched"""
image = np.moveaxis(self.image_rgb, -1, channel_axis)
reference = np.moveaxis(self.template_rgb, -1, channel_axis)
matched = exposure.match_histograms(image,
reference,
channel_axis=channel_axis)
assert matched.dtype == image.dtype
matched = np.moveaxis(matched, channel_axis, -1)
reference = np.moveaxis(reference, channel_axis, -1)
matched_pdf = self._calculate_image_empirical_pdf(matched)
reference_pdf = self._calculate_image_empirical_pdf(reference)
for channel in range(len(matched_pdf)):
reference_values, reference_quantiles = reference_pdf[channel]
matched_values, matched_quantiles = matched_pdf[channel]
for i, matched_value in enumerate(matched_values):
closest_id = (np.abs(reference_values -
matched_value)).argmin()
assert_almost_equal(matched_quantiles[i],
reference_quantiles[closest_id],
decimal=1)
def test_match_histograms(self, image, reference, multichannel):
"""Assert that pdf of matched image is close to the reference's pdf for
all channels and all values of matched"""
# when
matched = exposure.match_histograms(image,
reference,
multichannel=multichannel)
matched_pdf = self._calculate_image_empirical_pdf(matched)
reference_pdf = self._calculate_image_empirical_pdf(reference)
# then
for channel in range(len(matched_pdf)):
reference_values, reference_quantiles = reference_pdf[channel]
matched_values, matched_quantiles = matched_pdf[channel]
for i, matched_value in enumerate(matched_values):
closest_id = (np.abs(reference_values -
matched_value)).argmin()
assert_almost_equal(
matched_quantiles[i],
reference_quantiles[closest_id],
decimal=1,
)
def run(params):
RTimageLocation = params['inputRTImagePath']
GTimageLocation = params['inputGTImagePath']
resultLocation = params['resultPath']
resultLocationAdj = params['resultPathAdj']
# Checking existence of temporary files (individual channels)
if not os.path.exists(RTimageLocation):
print(f'Error: {RTimageLocation} does not exist')
return
if not os.path.exists(GTimageLocation):
print(f'Error: {GTimageLocation} does not exist')
return
# Loading input images
RTData = imread(RTimageLocation)
GTData = imread(GTimageLocation)
print(f'Dimensions of Restored image: {RTData.shape}')
print(f'Dimensions of GT image: {GTData.shape}')
# Checking dtype is the same for both input channels
if GTData.dtype != RTData.dtype:
error_mes = "The bit depth of your input channels is not the same. Convert one of them and retry."
ctypes.windll.user32.MessageBoxW(0, error_mes, 'Error', 0)
sys.exit(error_mes)
# Histogram matching
matched_GTData = match_histograms(GTData, RTData).astype(RTData.dtype)
# MSE measurement
# valMSE = skimage.measure.compare_mse(RTData, GTData) # deprecated in scikit-image 0.18
valMSE = mean_squared_error(RTData, matched_GTData)
print(
f'___ MSE = {valMSE} ___'
) # Value appears in the log if Verbosity option is set to 'Everything'
# SSIM measurement
outFullSSIM = structural_similarity(RTData, matched_GTData, full=True)
# Extracting mean value (first item)
outMeanSSIM = outFullSSIM[0]
print(f'___ Mean SSIM = {outMeanSSIM} ___')
# Extracting map (second item)
outSSIM = outFullSSIM[1]
print(f'Bit depth of SSIM array: {outSSIM.dtype}')
# Convert output array whose range is [0-1] to adjusted bit range (8- or 16-bit) if necessary
if RTData.dtype != np.dtype('float64') and RTData.dtype != np.dtype(
'float32'):
outputData = rescale_intensity(outSSIM,
in_range=(0, 1),
out_range=(0,
np.iinfo(RTData.dtype).max))
outputData = outputData.astype(RTData.dtype)
else:
outputData = outSSIM
imsave(resultLocation, outputData)
imsave(resultLocationAdj, matched_GTData)
def evaluate_individual_metrics_with_hm(load_y,
load_x,
predict,
metrics: dict,
test_ids,
train_ids,
results_path,
exist_ok=False):
assert len(metrics) > 0, 'No metric provided'
os.makedirs(results_path, exist_ok=exist_ok)
all_train_img = np.float16([])
for _id in train_ids:
all_train_img = np.concatenate(
(all_train_img, np.float16(load_x(_id)).ravel()))
results = defaultdict(dict)
for _id in tqdm(test_ids):
target = load_y(_id)
image = load_x(_id)
prediction = predict(
np.float32(
np.reshape(match_histograms(image.ravel(), all_train_img),
image.shape)))
for metric_name, metric in metrics.items():
try:
results[metric_name][_id] = metric(target, prediction, _id)
except TypeError:
results[metric_name][_id] = metric(target, prediction)
for metric_name, result in results.items():
save_json(result,
os.path.join(results_path, metric_name + '.json'),
indent=0)
def hist_mtch_cs(im1, im2, color_space='RGB', transfer_channels=[0,1,2]):
trans_1 = trans_2 = None
if color_space == 'LAB':
trans_1 = cv2.COLOR_RGB2LAB; trans_2 = cv2.COLOR_LAB2RGB
if color_space == 'HSV':
trans_1 = cv2.COLOR_RGB2HSV; trans_2 = cv2.COLOR_HSV2RGB
if color_space == 'HLS':
trans_1 = cv2.COLOR_RGB2HLS; trans_2 = cv2.COLOR_HLS2RGB
im1_2 = im1.copy()
im2_2 = im2.copy()
if trans_1 is not None:
im1_2 = cv2.cvtColor(im1, trans_1)
im2_2 = cv2.cvtColor(im2, trans_1)
matched = match_histograms(im1_2, im2_2, multichannel=True)
for c in transfer_channels:
im1_2[:,:,c] = matched[:,:,c]
if trans_2 is not None:
im1_2 = cv2.cvtColor(im1_2, trans_2)
return im1_2
def transform_image(src_img,
transform,
match_img=None,
ndvi=False,
enhance_contrast=True,
downscale=5,
denoise=True):
"""Applies some transformations to an image useful before segmentation."""
if ndvi:
img, multichannel = calc_ndvi(src_img), False
if match_img is not None:
match_img = calc_ndvi(match_img)
else:
img, multichannel = img_as_float(src_img[:, :, 0:3]), True
if match_img is not None:
match_img = img_as_float(match_img[:, :, 0:3])
if match_img is not None:
img = match_histograms(img, match_img, multichannel=multichannel)
if enhance_contrast:
img = equalize_adapthist(img)
if downscale is not None:
factors = (downscale, downscale, 1) if multichannel else \
(downscale, downscale)
img = downscale_local_mean(img, factors=factors)
a, b, c, d, e, f, _, _, _ = transform
trf = Affine(downscale, b, c, d, -downscale, f)
if denoise:
img = denoise_tv_chambolle(img, multichannel=multichannel)
return img, trf
def matchhist(file, reference):
img_ref = img_as_float(io.imread(reference))
img_comp = img_as_float(io.imread(file))
outfile = os.path.splitext(file)[0] + '_hist.jpg'
matched = match_histograms(img_comp, img_ref, multichannel=True)
io.imsave(outfile, img_as_ubyte(matched))
return outfile
def insert(blank_path, track_path, mask=None):
blank_movie, blank_index = load_and_getDelSq(blank_path)
track_movie, track_index = load_and_getDelSq(track_path)
dif = track_index - blank_index
track_i, blank_j = assign_ij(dif)
end = []
#track_matched = match_histograms(track_movie, blank_movie, multichannel = True)
while True:
try:
b = blank_movie[blank_j]
t = match_histograms(track_movie[track_i], b, multichannel=True)
#t = track_matched[track_i]
just_track = get_just_track(track_path[track_path.find("fm"):], t)
sub_blank = b[:150, :150]
mask = make_mask(track_path[track_path.find("fm"):],
just_track.shape)
mask = np.expand_dims(mask, 2)
mask = np.repeat(mask, 4, 2)
s1, s2 = get_s1_s2(just_track.shape)
sub_blank[75 - s1:75 + s1,
75 - s2:75 + s2] = just_track * mask + sub_blank[
75 - s1:75 + s1, 75 - s2:75 + s2] * (1 - mask)
end.append(sub_blank)
blank_j += 1
track_i += 1
except IndexError:
break
return np.array(end)
def normalize_frames(self,
frames=None,
inplace=True,
reference_frame=None):
if frames is None and len(self.frames) == 0:
raise ValueError("You did not specify any frames")
if frames is not None:
frames = frames
else:
frames = self.frames
if reference_frame is None:
print(
"No reference frame provided using the first frame as reference"
)
reference_frame = 0
adjusted_frames = []
for i in range(len(frames)):
if i == reference_frame:
continue
else:
matched = match_histograms(frames[i],
frames[reference_frame],
multichannel=False)
adjusted_frames.append(matched)
if inplace:
self.frames = adjusted_frames
else:
return adjusted_frames
def process(pre_path, post_path, patient_folder):
pre_reg_file = os.path.join(patient_folder, 'pre_reg.nii.gz')
post_new = os.path.join(patient_folder, 'post.nii.gz')
sub_file = os.path.join(patient_folder, 'sub.nii.gz')
aladin_path = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"niftyreg/bin/reg_aladin")
reg_call = aladin_path + " -rigOnly -ref " + post_path + " -flo " + pre_path + " -res " + pre_reg_file + " -pad 0"
subprocess.run(shlex.split(reg_call), stdout=subprocess.PIPE, shell=False)
pre_nib = nib.load(pre_reg_file)
post_nib = nib.load(post_path)
pre = pre_nib.get_fdata()
post = post_nib.get_fdata()
pre_histmatch = match_histograms(pre, post)
sub = post - pre_histmatch
nib.save(nib.Nifti1Image(sub, pre_nib.affine, pre_nib.header), sub_file)
shutil.copyfile(post_path, post_new)
def colorMatchImage(imageH, imageW, imageBaseAdd, matchH, matchW, matchBaseAdd,
vm):
image = []
rangeX = range(imageH)
rangeY = range(imageW)
for row in rangeX:
image.append([])
for cell in rangeY:
position = imageBaseAdd + row * imageW + cell
color = vm.getValue(position)
image[-1].append(colorToRGB(color))
image = np.array(image)
match = []
rangeX = range(matchH)
rangeY = range(matchW)
for row in rangeX:
match.append([])
for cell in rangeY:
position = matchBaseAdd + row * matchW + cell
color = vm.getValue(position)
match[-1].append(colorToRGB(color))
match = np.array(match)
matched = match_histograms(image, match, multichannel=True)
rangeX = range(imageH)
rangeY = range(imageW)
for row in rangeX:
for cell in rangeY:
color = rgbToColor(matched[row][cell])
position = imageBaseAdd + row * imageW + cell
vm.setValue(color, position)
def processIdx(idx):
ref_idx = ref_idxes[idx % len(ref_idxes)]
source_img, _ = ds_source[idx]
reference_img, _ = ds_reference[ref_idx]
matched_img = match_histograms(img_as_float(source_img),
img_as_float(reference_img),
multichannel=True)
ds_source[idx] = img_as_ubyte(matched_img)
def apply_histogram(img, reference_image, blend_ratio):
reference_image = cv2.resize(reference_image,
dsize=(img.shape[1], img.shape[0]))
matched = match_histograms(np.squeeze(img),
np.squeeze(reference_image),
multichannel=True)
img = cv2.addWeighted(matched, blend_ratio, img, 1 - blend_ratio, 0)
return img
def cargarImagen(imagen, filename):
rawImage = plt.imread(filename)
reference = plt.imread("images/reference.jpg")
matched = match_histograms(rawImage, reference, multichannel = True)
#imagen.imageRGB = plt.imread(filename)
imagen.imageRGB = matched
#imagen.imageCIE = color.rgb2lab(imagen.imageRGB)
imagen.imageCIE = color.rgb2lab(imagen.imageRGB)
del rawImage
def apply(self, img, reference_image=None, blend_ratio=0.5, **params):
from skimage.exposure import match_histograms
if random.random() < self.p:
reference_image = cv2.resize(reference_image, dsize=(img.shape[1], img.shape[0]))
matched = match_histograms(img, reference_image, multichannel=True)
img = cv2.addWeighted(matched, blend_ratio, img, 1 - blend_ratio, 0)
return img
def hist_match(source, target):
try:
matched = match_histograms(source, target,
multichannel=True).astype(np.uint8)
matched = Image.fromarray(matched)
return matched
except:
matched = Image.fromarray(source)
return matched
def match_color(pre_name, ref_img, target_img):
from skimage.io import imread, imsave
from skimage.exposure import match_histograms
reference = imread(ref_img)
image = imread(target_img)
matched = match_histograms(image, reference, multichannel=True)
print(f'match color to {pre_name}')
imsave(pre_name, matched)
def create_converted_imgs(min_num_features=10, max_num_features=50):
for i, file in enumerate(os.listdir(foreign_imgs_folder.path)):
if file.endswith(".jp2"):
continue
foreign_img = cv2.imread(os.path.join(foreign_imgs_folder.path, file))
merge_img = image_pipeline(foreign_img, num_of_features=np.random.randint(min_num_features, max_num_features),
alpha=0.0)
result_img = match_histograms(merge_img, kazakh_img, multichannel=True)
cv2.imwrite(os.path.join(converted_imgs_folder.path, file), result_img)
print("Finished processing:", i + 1, "images")
def _adjust_lumination(sample_image, image): if(isinstance(sample_image, str)): sample_image = cv2.imread(sample_image) else: sample_image = sample_image assert isinstance(image, np.ndarray) new_image = exposure.match_histograms(image, sample_image, multichannel=True) return new_image
def improve_scenebkp(sub_templateg, alg=1):
ref = np.asarray(PIL.Image.fromarray(np.asarray(pd.read_pickle('./AuxFiles/refhist.pkl'))))
#if alg==1:
#sub_templateg = cv2.fastNlMeansDenoising(sub_templateg,None,10,7,21)
if is_low_contrast(sub_templateg, 0.35):
temp_img = np.copy(sub_templateg).astype(float)
temp_img[temp_img <= 30] = np.nan
temp_img_std = np.nanstd(temp_img)
del temp_img
if temp_img_std <= 30:
print('Scene is low contrasted, improving...')
sub_templateg = hist.adjust_gamma(sub_templateg, gamma=1.7)
sub_templateg = adjust_sigmoid(sub_templateg)
sub_templateg = hist.adjust_gamma(sub_templateg, gamma=1.2)
hist1 = cv2.calcHist([sub_templateg], [0], None, [256], [0, 256])
hist2 = cv2.calcHist([ref], [0], None, [256], [0, 256])
sim = cv2.compareHist(hist1, hist2, 0)
if sim < 0.8:
if alg == 1:
print('Computing large objects.')
sub_templateg = cv2.medianBlur(sub_templateg, 5)
sub_templateg = cv2.bilateralFilter(sub_templateg, 3, 3, 3)
sub_templateg = hist.adjust_gamma(sub_templateg, gamma=3.0)
sub_templateg = adjust_sigmoid(sub_templateg)
sub_templateg = hist.adjust_gamma(sub_templateg, gamma=1.0)
if alg == 2:
print('Computing small objects')
sub_templateg = hist.adjust_gamma(sub_templateg, gamma=1.5)
else:
if alg ==1:
print('Computing large objects.')
sub_templateg = hist.adjust_gamma(sub_templateg, gamma=1.5)
sub_templateg = adjust_sigmoid(sub_templateg)
sub_templateg = hist.adjust_gamma(sub_templateg, gamma=1.0)
else:
print('Computing small objects.')
sub_templateg = match_histograms(sub_templateg, ref)
sub_templateg = convert(sub_templateg, 0, 255, np.uint8)
return sub_templateg
def equalize_histogram(filename, filename2):
image = Image.open(filename)
image = asarray(image)
reference = Image.open(filename2)
reference = asarray(reference)
matched_image = match_histograms(image, reference, multichannel=True)
plot = plot_equalized_histogram(image, reference, matched_image)
image = Image.fromarray(matched_image)
image.save(filename)
return filename, plot
def matchHistograms(image_ref_path, image_target_path):
reference = io.imread(image_ref_path)
ic(reference.dtype, reference.shape, type(reference))
target = io.imread(image_target_path)
ic(target.dtype, target.shape, type(target))
matched = match_histograms(target, reference)
ic(matched.dtype, matched.shape, type(matched))
data = Image.fromarray(matched)
# saving the final output
# as a PNG file
data.save('/media/nacho/Puzzles/gabriele_data/hippo_3/Round1/Round1_c1_maxIP_matched.tif')
def match_histogram(source, reference):
isTorch = False
source = source / source.max() * reference.max()
if isinstance(source, torch.Tensor):
source = source.cpu().numpy()
isTorch = True
if isinstance(reference, torch.Tensor):
reference = reference[:source.shape[0], ...].cpu().numpy()
matched = match_histograms(source, reference, multichannel=False)
if isTorch:
matched = torch.from_numpy(matched)
return matched
def checksim(reference, edge, crop, match_hist=False):
img_ref = imgcrop(img_as_float(io.imread(reference)), crop)
img_comp = imgcrop(img_as_float(io.imread(edge)), crop)
if match_hist:
matched = match_histograms(img_comp, img_ref, multichannel=True)
else:
matched = img_comp
# shift, error, diffphase = phase_cross_correlation(img_ref, img_comp)
# ssim_calc = ssim(img_ref, matched, data_range=img_ref.max() - img_ref.min())
mse = sqrt(mean_squared_error(img_ref, matched))
sim = mse
return sim
def run(params):
RTimageLocation = params['inputRTImagePath']
GTimageLocation = params['inputGTImagePath']
resultLocation = params['resultPath']
resultLocationAdj = params['resultPathAdj']
# Checking existence of temporary files (individual channels)
if not os.path.exists(RTimageLocation):
print(f'Error: {RTimageLocation} does not exist')
return;
if not os.path.exists(GTimageLocation):
print(f'Error: {GTimageLocation} does not exist')
return;
# Loading input images
RTData = imread(RTimageLocation)
GTData = imread(GTimageLocation)
print(f'Dimensions of Restored image: {RTData.shape}')
print(f'Dimensions of GT image: {GTData.shape}')
# Histogram matching
matched_GTData = match_histograms(GTData, RTData).astype(RTData.dtype)
# MSE measurement
# valMSE = skimage.measure.compare_mse(RTData, GTData) # deprecated in scikit-image 0.18
valMSE = mean_squared_error(RTData, matched_GTData)
print(f'___ MSE = {valMSE} ___') # Value appears in the log if Verbosity option is set to 'Everything'
# SSIM measurement
outFullSSIM = structural_similarity(RTData, matched_GTData, full=True)
# Extracting mean value (first item)
outMeanSSIM = outFullSSIM[0]
print(f'___ Mean SSIM = {outMeanSSIM} ___')
# Extracting map (second item)
outSSIM = outFullSSIM[1]
print(f'Bit depth of SSIM array: {outSSIM.dtype}')
# Convert output array whose range is [0-1] to adjusted bit range (8- or 16-bit)
if RTData.dtype is np.dtype('u2'):
outputData = img_as_uint(outSSIM)
elif RTData.dtype is np.dtype('f4'):
outputData = img_as_float32(outSSIM) # necessary?
else:
outputData = img_as_ubyte(outSSIM)
imsave(resultLocation, outputData)
imsave(resultLocationAdj, matched_GTData)
def ScaleMatchDiff(filename, expmrc):
Sim = mrcfile.open(filename)
npdata = Sim.data
Min = np.amin(npdata)
Max = np.amax(npdata)
CurrData = np.array(npdata)
CurrData = npdata
CurrData = CurrData - Min
ReviData = CurrData / (Max - Min)
ExpD = expmrc
Match = match_histograms(ReviData, ExpD.data)
Match = Match.astype(np.float32)
Diff = ((ExpD.data - Match.data)**2)
Sum = np.sum(Diff)
return Sum
