def compare_png(img1, img2, eps=0.99):
"""check whether img1 and img2 are similar
we use structural similarity (SSIM) to compare them.
SSIM generates values between 0 and 1, where 1 represents
identical images
If SSIM result is greater eps, then this method returns True.
"""
im1 = imread(img1)
im2 = imread(img2)
if len(im1.shape) == 2 or im1.shape[-1] == 1:
# only one color channel
mssim = compare_ssim(im1, im2)
elif HAVE_MULTICHANNEL_SSIM:
# multi color channel
mssim = compare_ssim(im1, im2, multichannel=True)
else:
# We have to do multichannel ssim ourselves
nch = im1.shape[-1]
mssim = np.empty(nch)
for chan in range(nch):
# use copy to generate contiguous array and avoid warning
ch_result = compare_ssim(
im1[..., chan].copy(), im2[..., chan].copy())
mssim[..., chan] = ch_result
mssim = mssim.mean()
return mssim > eps
def image_similarity_index(image_1_path_name, image_2_path_name):
"""Calculates the similarity of two images. A structural similarity index of 1.0 means the images are identical."""
image_1 = color.rgb2gray(imread(image_1_path_name)) # color-images are not supported in the version for Raspbian
image_2 = color.rgb2gray(imread(image_2_path_name))
similarity = compare_ssim(image_1, image_2)
return similarity
def test(model):
print('Start to test on {}'.format(args.test_dir))
out_dir = save_dir + args.test_dir.split('/')[-1] + '/'
if not os.path.exists(out_dir):
os.mkdir(out_dir)
name = []
psnr = []
ssim = []
file_list = glob.glob('{}/*.png'.format(args.test_dir))
for file in file_list:
# read image
img_clean = np.array(Image.open(file), dtype='float32') / 255.0
img_test = img_clean + np.random.normal(0, args.sigma/255.0, img_clean.shape)
img_test = img_test.astype('float32')
# predict
x_test = img_test.reshape(1, img_test.shape[0], img_test.shape[1], 1)
y_predict = model.predict(x_test)
# calculate numeric metrics
img_out = y_predict.reshape(img_clean.shape)
img_out = np.clip(img_out, 0, 1)
psnr_noise, psnr_denoised = compare_psnr(img_clean, img_test), compare_psnr(img_clean, img_out)
ssim_noise, ssim_denoised = compare_ssim(img_clean, img_test), compare_ssim(img_clean, img_out)
psnr.append(psnr_denoised)
ssim.append(ssim_denoised)
# save images
filename = file.split('/')[-1].split('.')[0] # get the name of image file
name.append(filename)
img_test = Image.fromarray((img_test*255).astype('uint8'))
img_test.save(out_dir+filename+'_sigma'+'{}_psnr{:.2f}.png'.format(args.sigma, psnr_noise))
img_out = Image.fromarray((img_out*255).astype('uint8'))
img_out.save(out_dir+filename+'_psnr{:.2f}.png'.format(psnr_denoised))
psnr_avg = sum(psnr)/len(psnr)
ssim_avg = sum(ssim)/len(ssim)
name.append('Average')
psnr.append(psnr_avg)
ssim.append(ssim_avg)
print('Average PSNR = {0:.2f}, SSIM = {1:.2f}'.format(psnr_avg, ssim_avg))
pd.DataFrame({'name':np.array(name), 'psnr':np.array(psnr), 'ssim':np.array(ssim)}).to_csv(out_dir+'/metrics.csv', index=True)
def get_ssim(actual, expected):
im = Image.fromarray(actual)
im2 = Image.fromarray(expected)
if im.size[0] != im2.size[0] or im.size[1] != im2.size[1]:
raise RuntimeError(
"Can't calculate SSIM for images of different sizes (one is %dx%d, the other %dx%d)." % (
im.size[0], im.size[1],
im2.size[0], im2.size[1],
)
)
return compare_ssim(np.array(im), np.array(im2), multichannel=True)
def ssim(self, image1, image2):
image1 = Image.open(image1).convert('RGB')
if image1.size[0] > 300:
new_size = (300, int(image1.size[1] / image1.size[0] * 300))
else:
new_size = image1.size
print(image1.size, new_size)
image1 = image1.resize(new_size)
image2 = Image.open(image2).resize(new_size).convert('RGB')
image1 = array(image1)
image2 = array(image2)
img1 = img_as_float(image1)
img2 = img_as_float(image2)
return compare_ssim(img1, img2, win_size=None, gradient=False, multichannel=True)
def test_denoise_tv_chambolle_weighting():
# make sure a specified weight gives consistent results regardless of
# the number of input image dimensions
rstate = np.random.RandomState(1234)
img2d = astro_gray.copy()
img2d += 0.15 * rstate.standard_normal(img2d.shape)
img2d = np.clip(img2d, 0, 1)
# generate 4D image by tiling
img4d = np.tile(img2d[..., None, None], (1, 1, 2, 2))
w = 0.2
denoised_2d = restoration.denoise_tv_chambolle(img2d, weight=w)
denoised_4d = restoration.denoise_tv_chambolle(img4d, weight=w)
assert_(measure.compare_ssim(denoised_2d,
denoised_4d[:, :, 0, 0]) > 0.99)
def find_the_most_similar(images_data):
from skimage.measure import compare_ssim
n = len(images_data)
sim = np.zeros((n,n))
for i in xrange(n):
image_i = images_data[i]
for j in xrange(i,n):
image_j = images_data[j]
sim[i,j] = compare_ssim(image_i,image_j,multichannel=True) #,gaussian_weights=True)
sim[j,i] = sim[i,j]
ret = np.sum(sim,axis=0)
ret = zip(ret,range(n))
ret.sort()
return ret[-1][1]
def run_metrics(image_file_name1,image_file_name2 ):
image_name1 = io.imread(image_file_name1)
image_name2 = io.imread(image_file_name2)
peak_signal_to_noise_ratio = measure.compare_psnr (image_name1,image_name2)
print ("PSNR Peak signal to noise ratio is %s"%peak_signal_to_noise_ratio)
mse = measure.compare_mse(image_name1,image_name2)
print ("MSE Mean square error between the images is %s"%mse)
rmse = measure.compare_nrmse(image_name1,image_name2)
print ("RMSE Normalised root mean square error between the images is %s"%rmse)
ssim = measure.compare_ssim(image_name1,image_name2, multichannel=True)
print ("SSIM Structural Similarity Index is %s"%ssim)
#[M3,M4] = minkowski_distance(image_name1,image_name2)
#print ("Minkowski distance is %s %s"%(M3,M4))
#AD = average_difference(image_name1,image_name2)
#print ("AD Average difference is %s"%AD)
#SC = structural_content(image_name1,image_name2)
#print ("SC Structural Content is %s"%SC)
#NK = normalised_cross_correlation(image_name1,image_name2)
#print ("NK normalised cross correlation is %s"%NK)
#MD = maximum_difference(image_name1,image_name2)
#print ("Maximum difference is %s"%MD)
return {'peaktonoise':peak_signal_to_noise_ratio ,'mse': mse, 'rmse': rmse, 'ssim':ssim,'score':peak_signal_to_noise_ratio}
print image_name2.shape
#estimate the standard deiviation of the images
std_1 = numpy.std (numpy.std (numpy.array(image_name1)))
std_2 = numpy.std (numpy.std (numpy.array(image_name2)))
print ("std is %2.10f"%std_1)
#print ("Standard deviation of the images are"%(std_1,std_2))
#estimate the peak signal to noise ratio (PSNR) between the image
peak_signal_to_noise_ratio = measure.compare_psnr (image_name1,image_name2)
print ("Peak signal to noise ratio is %s"%peak_signal_to_noise_ratio)
# estimate the mean square error between the images
mse = measure.compare_mse(image_name1,image_name2)
print ("Mean square error between the images is %s"%mse)
# estimate the normalised root mean square error between the images
rmse = measure.compare_nrmse(image_name1,image_name2)
ssim = measure.compare_ssim(image_name1,image_name2)
print ("Normalised root mean squre error between the images is %s"%rmse)
print ("SSIM is %s"%ssim)
for j in range(0, len(models)):
predicted_img = models[j].predict(
[np.expand_dims(image, 0),
np.expand_dims(mask, 0)])[0] * 255
# if you want to save inpainted result, please use the following 3 lines, else use the 4th line below:
# result_name=result_img_folder+"\\"+filename.rstrip('.png') +"_"+str(j+1)+ ".png"
# cv2.imwrite(result_name, cv2.cvtColor(predicted_img, cv2.COLOR_BGR2RGB))
# result_img=cv2.imread(result_name)
# if you don't want to save inpainted result, please use the following line, else use the above 3 lines:
result_img = cv2.cvtColor(predicted_img, cv2.COLOR_BGR2RGB)
current_mse = compare_mse(input_img, result_img)
current_psnr = compare_psnr(input_img, result_img)
current_ssim = compare_ssim(to_gray(input_img), to_gray(result_img))
#print("MSE:{}".format(current_mse),"PSNR:{}".format(current_psnr),"SSIM:{}".format(current_ssim))
print(f"Image Num:{image_num}({j+1})", f"MSE:{current_mse:4.4f}",
f"PSNR:{current_psnr:3.4f}", f"SSIM:{current_ssim:3.4f}")
mse[j] = mse[j] + current_mse
psnr[j] = psnr[j] + current_psnr
ssim[j] = ssim[j] + current_ssim
end_time = datetime.now()
total_time = (end_time - start_time).seconds
for k in range(0, len(models)):
mse[k] = mse[k] / image_num
psnr[k] = psnr[k] / image_num
ssim[k] = ssim[k] / image_num
#print("Average result of method:",k+1)
scale = 0.2
# SCREENCAP downscale only
cropped_frame = cv2.resize(frame, (0, 0), fx=scale, fy=scale)
cropped_prev_frame = cv2.resize(prev_frame, (0, 0), fx=scale, fy=scale)
# #FOR iPHONE 4k60 far
# cropped_frame = frame[800:1200, 1600:2400]
# cropped_prev_frame = prev_frame[800:1200, 1600:2400]
# # downscale
# cropped_frame = cv2.resize(cropped_frame, (0,0), fx=0.5, fy=0.5)
# cropped_prev_frame = cv2.resize(cropped_prev_frame, (0,0), fx=0.5, fy=0.5)
cropped_frame = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2GRAY)
cropped_prev_frame = cv2.cvtColor(cropped_prev_frame, cv2.COLOR_BGR2GRAY)
(score, diff) = compare_ssim(cropped_frame, cropped_prev_frame, full=True)
# diff = (diff * 255).astype("uint8")
# print("SSIM: {}".format(score))
#FOR SCREENCAP
threshold = 0.99
if score > threshold:
result = "Dropped"
else:
# result = "Unique"
result = ""
frametime_display = frametime
frametime = 0
# #FOR iphone recorded 4k60 close to screen
# threshold = 0.9
import cv2
import pywt
import numpy as np
from skimage.measure import compare_mse
from skimage.measure import compare_psnr
from skimage.measure import compare_ssim
img = cv2.imread("../crowd2.jpg", cv2.IMREAD_GRAYSCALE)
blur = cv2.GaussianBlur(img, (3, 3), 0)
for w in pywt.wavelist(kind="discrete"):
wavelet = pywt.Wavelet(w)
coeffs = pywt.wavedec2(blur, wavelet)
#cA, (cH, cV, cD) = coeffs
rec = pywt.waverec2(coeffs, wavelet)
mse = compare_mse(blur, rec)
psnr = compare_psnr(blur, rec)
ssim = compare_ssim(blur, rec)
print(w, ">>", mse, psnr, ssim)
def get_chara(self):
"""Retrieves chara name for current player in current frame.
Compare cropped avatar with reference avatars, pick the best match as
the chara current player plays with. In OWL, currently observed player
has a larger avatar. To differentiate between the two, comparison has
to run twice and the better match gets chosen.
Author:
Appcell
Args:
None
Returns:
None
"""
all_avatars = self.avatars
avatars_ref_observed = all_avatars["observed"]
avatars_ref = all_avatars["normal"]
team_color = avatars_ref_observed['ana'][0, 0]
# Crop avatar from frame
avatar_observed = ImageUtils.crop(
self.image,
OW.get_avatar_pos_observed(self.index, self.game_type,
self.game_version))
avatar = ImageUtils.crop(
self.image,
OW.get_avatar_pos(self.index, self.game_type, self.game_version))
# if self.game_version == 1:
# cv2.imshow('t', avatar)
# cv2.waitKey(0)
# If player is observed, not sure about this tho
avatar_diff = ImageUtils.crop(
self.image,
OW.get_avatar_diff_pos(self.index, self.game_type,
self.game_version))
max_diff = 0
for i in range(avatar_diff.shape[0]):
for j in range(avatar_diff.shape[1]):
if ImageUtils.color_distance(avatar_diff[i, j],
team_color) > max_diff:
max_diff = ImageUtils.color_distance(
avatar_diff[i, j], team_color)
if max_diff < 40 and self.is_ult_ready is False:
self.is_observed = True
score = 0
for (name, avatar_ref_observed) in avatars_ref_observed.items():
s_observed = cv2.matchTemplate(avatar_observed,
avatar_ref_observed,
cv2.TM_CCOEFF_NORMED)
_, s_observed, _, loc_observed = cv2.minMaxLoc(s_observed)
temp_avatar_observed = ImageUtils.crop(avatar_observed, [
loc_observed[1], avatar_ref_observed.shape[0], loc_observed[0],
avatar_ref_observed.shape[1]
])
s_ssim_observed = measure.compare_ssim(temp_avatar_observed,
avatar_ref_observed,
multichannel=True)
s = cv2.matchTemplate(avatar, avatars_ref[name],
cv2.TM_CCOEFF_NORMED)
_, s, _, loc = cv2.minMaxLoc(s)
temp_avatar = ImageUtils.crop(avatar, [
loc[1], avatars_ref[name].shape[0], loc[0],
avatars_ref[name].shape[1]
])
s_ssim = measure.compare_ssim(temp_avatar,
avatars_ref[name],
multichannel=True)
s_ssim_final = s_ssim_observed if s_ssim_observed > s_ssim else s_ssim
s_final = s_observed if s_observed > s else s
loc_final = loc_observed if s_observed > s else loc
if s_final * 0.4 + s_ssim_final * 0.6 > score:
score = s_final * 0.4 + s_ssim_final * 0.6
self.chara = name
if self.chara is None:
self.chara = "empty"
self.is_dead = True
return
if self.chara == OW.DVA:
self.dva_status = OW.IS_WITH_MEKA
self.get_living_status(avatars_ref_observed[self.chara])
def simScore(i1, i2):
(score) = compare_ssim(i1, i2, full=False)
#diff = (diff * 255).astype("uint8")
return (f'{score:0.5}')
def main():
startTime = time.time()
outputDirPath = 'C:/ZCU/Diplomka/Dataset/04/RESULTS/Res_Conv'
writer = sitk.ImageFileWriter()
# moving = DataPreparation.readDICOMSerieToImage('C:/ZCU/DATA_FOR_TEST/MRI/TCGA-LIHC/TCGA-K7-AAU7/07-31-2001-MRI ABDOMEN WWO CONTRAST-59507/1201-C AX 3D LATE PHAS20CC MAGNEVISTE-50651')
# fixedR = DataPreparation.readDICOMSerieToImage('C:/ZCU/3Dircadb1/3Dircadb1.1/PATIENT_DICOM')
# movingR = DataPreparation.readDICOMSerieToImage('C:/ZCU/3Dircadb1/3Dircadb1.7/PATIENT_DICOM')
# fixMask = DataPreparation.readDICOMSerieToImage('C:/ZCU/3Dircadb1/3Dircadb1.1/MASKS_DICOM/liver')
# movMas = DataPreparation.readDICOMSerieToImage('C:/ZCU/3Dircadb1/3Dircadb1.7/MASKS_DICOM/liver')
fixedRMHD = 'C:/ZCU/Diplomka/Dataset/01/october-massachusetts-helium-queen_ack-wyoming_4_ca45536493525b615615f4d703c108e994b2dc6ec8b33e58d64c5cd6a92a12f2_v0.mhd'
fixedRMHD = 'C:/ZCU/Diplomka/Dataset/02/summer-eight-blossom-table_diet-kitten_4_45280cdef17c50e470ef9f9990a3d6c9ed15c1e35e84bd43611cfa014abee817_v0.mhd'
fixedRMHD = 'C:/ZCU/DATA_FOR_TEST/TCGA-LIHC/TCGA-BC-A10X/11-22-1992-MRI ABD WWO CONT-49239/11-LIVER-GAD-ENHANCEMENTT1F-68307'
fixedRMHD = 'C:/ZCU/Diplomka/Dataset/02/summer-eight-blossom-table_diet-kitten_4_45280cdef17c50e470ef9f9990a3d6c9ed15c1e35e84bd43611cfa014abee817_v0.mhd'
fixedRMHD = 'C:/ZCU/Diplomka/Dataset/04/hamper-carpet-earth-jersey_lake-fanta_601_447041836b6cf9ef3b328041cf99cac6c8308e90c46d437db62f6c8689fa6b58_v0.mhd'
reader = sitk.ImageFileReader()
reader.SetFileName(fixedRMHD)
fixed = reader.Execute()
movingRMHD = 'C:/ZCU/Diplomka/Dataset/01/october-massachusetts-helium-queen_ack-wyoming_7_ca45536493525b615615f4d703c108e994b2dc6ec8b33e58d64c5cd6a92a12f2_v0.mhd'
movingRMHD = 'C:/ZCU/Diplomka/Dataset/02/summer-eight-blossom-table_diet-kitten_7_45280cdef17c50e470ef9f9990a3d6c9ed15c1e35e84bd43611cfa014abee817_v0.mhd'
movingRMHD = 'C:/ZCU/DATA_FOR_TEST/TCGA-LIHC/TCGA-BC-A10X/03-29-1993-CT ABDOMEN WCONTRAST-43286/4-150cc OMNIPAQUE-36663'
movingRMHD = 'C:/ZCU/Diplomka/Dataset/02/summer-eight-blossom-table_diet-kitten_7_45280cdef17c50e470ef9f9990a3d6c9ed15c1e35e84bd43611cfa014abee817_v0.mhd'
movingRMHD = 'C:/ZCU/Diplomka/Dataset/04/hamper-carpet-earth-jersey_lake-fanta_501_447041836b6cf9ef3b328041cf99cac6c8308e90c46d437db62f6c8689fa6b58_v0.mhd'
# fixedR = DataPreparation.readNrrdToImage(fixedRMHD)
# fixedR = DataPreparation.readNrrdToImage(fixedRMHD)
reader.SetFileName(movingRMHD)
moving = reader.Execute()
# print movingBR.GetSize()
# size = [512,512,101]
# movingAr = sitk.GetArrayFromImage(movingBR)
# M = cv2.getRotationMatrix2D((512./2.,512./2.), -7., 1.)
# moving = movingAr.copy()
#
#
# for slice in range(size[2]):
# moving[slice][:][:] = cv2.warpAffine(movingAr[slice][:][:], M, (movingAr.shape[1], movingAr.shape[2]))
# moving = sitk.GetImageFromArray(moving)
# ed = sed3.sed3(moving)
# ed.show()
# X = compare_ssim(sitk.GetArrayFromImage(fixed), sitk.GetArrayFromImage(moving), full=True)
# # ed = sed3.sed3(sitk.GetImageFromArray(X[1]))
# # ed.show()
# print 'Difference Score BEFORE:'+str(X[0])
# writer.SetFileName(outputDirPath + 'DifferenceBefore.nrrd')
# writer.Execute(sitk.GetImageFromArray(X[1]))
# fixed = DataPreparation.maskToLivers(fixedR, fixMask)
# moving = DataPreparation.maskToLivers(movingR, movMas)
def observer(method):
print("{0:3} = {1:10.5f} : {2}".format(method.GetOptimizerIteration(),
method.GetMetricValue(),
method.GetOptimizerPosition()))
print("====Image registrion DICOM files====")
#
# resampleFilter = sitk.ResampleImageFilter()
# resampleFilter.SetSize(fixed.GetSize()*(1/3))
# resampleFilter.SetInterpolator(sitk.sitkGaussian)
# resampleFilter.SetOutputSpacing([1,1,1])
# fixedResampled = resampleFilter.Execute(fixed)
# resampleFilter.SetSize(moving.GetSize()/3)
# movingResampled = resampleFilter.Execute(moving)
print 'Smoothing'
fixImgSmooth = sitk.CurvatureFlow(image1=fixed,
timeStep=0.35,
numberOfIterations=10)
movImgSmooth = sitk.CurvatureFlow(image1=fixed,
timeStep=0.35,
numberOfIterations=10)
print 'Smoothing ENDED'
# movImgSmooth = moving
# fixImgSmooth = fixed
resample = sitk.ResampleImageFilter()
resample.SetReferenceImage(fixImgSmooth)
initial_transform = sitk.CenteredTransformInitializer(
sitk.Cast(fixImgSmooth, movImgSmooth.GetPixelID()), movImgSmooth,
sitk.Euler3DTransform(),
sitk.CenteredTransformInitializerFilter.GEOMETRY)
registration_method = sitk.ImageRegistrationMethod()
# registration_method.SetMetricAsMattesMutualInformation(numberOfHistogramBins=255)
# registration_method.SetMetricSamplingStrategy(registration_method.RANDOM)
# registration_method.SetMetricSamplingPercentage(0.01)
registration_method.SetMetricAsCorrelation()
registration_method.SetInterpolator(sitk.sitkGaussian)
# registration_method.SetInterpolator(sitk.sitkLinear)
registration_method.SetOptimizerAsGradientDescentLineSearch(
learningRate=2.0, numberOfIterations=100)
registration_method.SetOptimizerScalesFromPhysicalShift()
# registration_method.SetOptimizerAsLBFGSB(gradientConvergenceTolerance=1e-5,
# numberOfIterations=100,
# maximumNumberOfCorrections=5,
# maximumNumberOfFunctionEvaluations=1000,
# costFunctionConvergenceFactor=1e+7)
registration_method.AddCommand(sitk.sitkIterationEvent,
lambda: observer(registration_method))
registration_method.SetInitialTransform(initial_transform, inPlace=False)
final_transform_v1 = registration_method.Execute(
sitk.Cast(fixImgSmooth, sitk.sitkFloat32),
sitk.Cast(movImgSmooth, sitk.sitkFloat32))
print('Optimizer\'s stopping condition, {0}'.format(
registration_method.GetOptimizerStopConditionDescription()))
print('Final metric value: {0}'.format(
registration_method.GetMetricValue()))
print(final_transform_v1)
writer.SetFileName(outputDirPath + 'Fixed_Smoothed.nrrd')
writer.Execute(fixImgSmooth)
writer.SetFileName(outputDirPath + 'Moving_Smoothed.nrrd')
writer.Execute(fixImgSmooth)
resample = sitk.ResampleImageFilter()
resample.SetReferenceImage(fixed)
# SimpleITK supports several interpolation options, we go with the simplest that gives reasonable results.
resample.SetInterpolator(sitk.sitkLinear)
resample.SetTransform(final_transform_v1)
movingAfterTransform = resample.Execute(moving)
sitk.WriteImage(movingAfterTransform,
outputDirPath + 'MovingAfterTransform' + '.nrrd')
sitk.WriteTransform(final_transform_v1,
outputDirPath + 'transform' + '.tfm')
X = compare_ssim(sitk.GetArrayFromImage(fixed),
sitk.GetArrayFromImage(movingAfterTransform),
full=True)
# ed = sed3.sed3(sitk.GetImageFromArray(X[1]))
# ed.show()
# print 'Difference Score AFTER:'+str(X[0])
# writer.SetFileName(outputDirPath + 'DifferenceAfter.nrrd')
# writer.Execute(sitk.GetImageFromArray(X[1]))
# writer.SetFileName(outputDirPath + '/' + '03.nrrd')
# writer.Execute(resample.Execute(moving))
simg1 = sitk.Cast(sitk.RescaleIntensity(fixed), sitk.sitkUInt8)
simg2 = sitk.Cast(sitk.RescaleIntensity(resample.Execute(moving)),
sitk.sitkUInt8)
cimg = sitk.Compose(simg1, simg2, simg1 // 2. + simg2 // 2.)
# sitk.Show(cimg, "RESULT")
outFileName = 'ResultOfRegistration.nrrd'
writer.SetFileName(outputDirPath + outFileName)
writer.Execute(cimg)
stopTime = time.time()
print stopTime - startTime
print "====END OF REGISTRATION====="
def test(self):
# make a network and load network weight to use later
states = torch.load(os.path.join(self.args.log,
'checkpoint_100000.pth'),
map_location=self.config.device)
scorenet = CondRefineNetDilated(self.config).to(self.config.device)
scorenet = torch.nn.DataParallel(scorenet, device_ids=[0])
scorenet.load_state_dict(states[0])
scorenet.eval()
# prepare test data and undersample mask
undersample_method = 'radial'
undersample_factor = '030'
# use for getting degrade img and psnr,ssim,hfen in iteration
ori_complex = loadmat(self.args.load_path)["Img"]
ori_complex = ori_complex / np.max(np.abs(ori_complex))
kspace = np.fft.fft2(ori_complex)
mask = loadmat("./mask/mask_" + undersample_method + "_" +
undersample_factor + ".mat")["mask_" +
undersample_method + "_" +
undersample_factor]
mask = np.fft.fftshift(mask)
self.write_images(255.0 * mask,
os.path.join(self.args.save_path, 'mask.png'))
print('current undersample method is' + undersample_method,
np.sum(mask) / (256 * 256))
undersample_kspace = np.multiply(mask, kspace)
zero_fiiled = np.fft.ifft2(undersample_kspace)
#get ori png and degrade png to compare
self.write_images(
np.abs(zero_fiiled),
os.path.join(
self.args.save_path, 'img_ZF_undersample_' +
undersample_method + undersample_factor + '.png'))
self.write_images(
np.abs(ori_complex),
os.path.join(
self.args.save_path, 'img_GT_undersample_' +
undersample_method + undersample_factor + '.png'))
x0 = nn.Parameter(torch.Tensor(1, 6, 256, 256).uniform_(-1, 1)).cuda()
x01 = x0.clone()
# set parameters
step_lr = 0.05 * 0.00003
#number of inner iterations
n_steps_each = 80
# Noise amounts
sigmas = np.array([
1., 0.59948425, 0.35938137, 0.21544347, 0.12915497, 0.07742637,
0.04641589, 0.02782559, 0.01668101, 0.01
])
start_start = time.time()
# the outer itertaion loop
for idx, sigma in enumerate(sigmas):
start_out = time.time()
lambda_recon = 1. / sigma**2
labels = torch.ones(1, device=x0.device) * idx
labels = labels.long()
step_size = step_lr * (sigma / sigmas[-1])**2
print('current {} use sigma = {}'.format(idx, sigma))
# the inner itertaion loop
for step in range(n_steps_each):
start_in = time.time()
#prior update by ncsn
noise1 = torch.rand_like(x0) * np.sqrt(step_size * 2)
grad1 = scorenet(x01, labels).detach()
x0 = x0 + step_size * grad1
x01 = x0 + noise1
x0 = np.array(x0.cpu().detach(), dtype=np.float32)
# channel mean
x_real = (x0.real.squeeze()[0, :, :] +
x0.real.squeeze()[2, :, :] +
x0.real.squeeze()[4, :, :]) / 3
x_imag = (x0.real.squeeze()[1, :, :] +
x0.real.squeeze()[3, :, :] +
x0.real.squeeze()[5, :, :]) / 3
x_complex = x_real + x_imag * 1j
# data consistance
iterkspace = np.fft.fft2(x_complex)
iterkspace = undersample_kspace + iterkspace * (1 - mask)
x_complex = np.fft.ifft2(iterkspace)
end_in = time.time()
print("inner iteration cost time :%.2f s" %
(end_in - start_in))
psnr = compare_psnr(255 * abs(x_complex),
255 * abs(ori_complex),
data_range=255)
ssim = compare_ssim(abs(x_complex),
abs(ori_complex),
data_range=1)
hfen = compare_hfen(abs(x_complex), abs(ori_complex))
print("current {} step".format(step), 'PSNR :', psnr, 'SSIM :',
ssim, 'HFEN :', hfen)
self.write_images(
np.abs(x_complex),
os.path.join(
self.args.save_path, 'img_rec_undersample_' +
undersample_method + undersample_factor + '.png'))
x_real, x_imag = x_complex.real, x_complex.imag
x_real, x_imag = x_real[np.newaxis, :, :], x_imag[
np.newaxis, :, :]
x0 = np.stack([x_real, x_imag, x_real, x_imag, x_real, x_imag],
1)
x0 = torch.tensor(x0, dtype=torch.float32).cuda()
end_out = time.time()
print("out inner iteration cost time :%.2f s" %
(end_out - start_out))
end_end = time.time()
print("one image reconstruction cost time :%.2f s" %
(end_end - start_start))
def ssim(gt, pred):
""" Compute Structural Similarity Index Metric (SSIM). """
return compare_ssim(gt.transpose(1, 2, 0),
pred.transpose(1, 2, 0),
multichannel=True,
data_range=gt.max() - gt.min())
def closure():
global i, net_input, psnr_max, psnr_noisy_max, files_name, psnr_2_max, noisy_np
global TRAIN_PLAN, noisy_np_norm, sigma_now, final_ssim, final_ssim_max, files_name
global psnr_curve_max_record, ssim_curve_max_record, training_loss_record
out_effect_np = []
if DATA_AUG:
for aug in range(len(img_noisy_torch)):
noisy_torch = np_to_torch(img_noisy_noisy_np[aug] -
img_noisy_np[aug])
out = net(net_input[aug])
total_loss = mse(out, noisy_torch.type(dtype))
total_loss.backward()
psrn_noisy = compare_psnr(
np.clip(img_noisy_np[aug], 0, 1),
(torch_to_np(net_input[aug]) - out.detach().cpu().numpy()[0]))
# do_i_learned_noise = out.detach().cpu().numpy()[0]
# mse_what_tf = MSE(noisy_np, do_i_learned_noise)
if psnr_noisy_max == 0:
psnr_noisy_max = psrn_noisy
elif psnr_noisy_max < psrn_noisy:
psnr_noisy_max = psrn_noisy
if SAVE_DURING_TRAINING and i % save_every == 0:
# output_dir
out_test_np = torch_to_np(out) # I +N1
# out_test_name = f'{i}_test'
# save_image(out_test_name, np.clip(out_test_np, 0, 1), output_path=output_dir)
net.eval()
loss_add = 0
with torch.no_grad():
out_effect_np_ = torch_to_np(img_noisy_torch[aug] -
net(img_noisy_torch[aug]))
out_effect_np.append(out_effect_np_)
psnr_1 = compare_psnr(img_aug_np[aug],
np.clip(out_effect_np_, 0, 1))
test_do_i_learned_noise = torch_to_np(
net(img_noisy_torch[aug]))
if psnr_max == 0:
psnr_max = psnr_1
elif psnr_max < psnr_1:
psnr_max = psnr_1
loss_add = loss_add + total_loss.item()
training_loss_record.append(loss_add / len(img_noisy_torch))
if i % 10 == 0:
out_effect_np[0] = out_effect_np[0].transpose(1, 2, 0)
for aug in range(1, 8):
if aug < 4:
out_effect_np[aug] = np.rot90(
out_effect_np[aug].transpose(1, 2, 0), 4 - aug)
else:
out_effect_np[aug] = np.flipud(
np.rot90(out_effect_np[aug].transpose(1, 2, 0),
8 - aug))
final_reuslt = np.mean(out_effect_np, 0)
psnr_2 = compare_psnr(img_aug_np[0].transpose(1, 2, 0),
np.clip(final_reuslt, 0, 1))
final_ssim = compare_ssim(img_aug_np[0].transpose(1, 2, 0),
np.clip(final_reuslt, 0, 1),
data_range=1,
multichannel=True)
if psnr_2_max == 0:
psnr_2_max = psnr_2
tmp_name_p = f'{files_name[:-4]}_{sigma_now * 255:.2f}_{psnr_2:.2f}_final_{final_ssim:.4f}'
save_image(tmp_name_p,
np.clip(final_reuslt.transpose(2, 0, 1), 0, 1),
output_path=output_dir)
elif psnr_2_max < psnr_2:
psnr_2_max = psnr_2
tmp_name_p = f'{files_name[:-4]}_{sigma_now * 255:.2f}_{psnr_2:.2f}_final_{final_ssim:.4f}'
save_image(tmp_name_p,
np.clip(final_reuslt.transpose(2, 0, 1), 0, 1),
output_path=output_dir)
if final_ssim_max == 0:
final_ssim_max = final_ssim
elif final_ssim_max < final_ssim:
final_ssim_max = final_ssim
tmp_name = f'{files_name[:-4]}_{sigma_now * 255:.2f}_{final_ssim:.4f}_final_{psnr_2:.2f}'
save_image(tmp_name,
np.clip(final_reuslt.transpose(2, 0, 1), 0, 1),
output_path=output_dir)
print(
'%s Iteration %05d ,psnr 2: %f, psnr 2 max: %f, final ssim : %f, final ssim max: %f'
% (files_name, i, psnr_2, psnr_2_max, final_ssim,
final_ssim_max))
writer.add_scalar('final_test_psnr', psnr_2, i)
writer.add_scalar('final_max_test_psnr', psnr_2_max, i)
psnr_curve_max_record.append(psnr_2_max)
ssim_curve_max_record.append(final_ssim_max)
else:
noisy_torch = np_to_torch(img_noisy_noisy_np - img_noisy_np)
out = net(net_input)
total_loss = mse(out, noisy_torch.type(dtype))
total_loss.backward()
psrn_noisy = compare_psnr(
np.clip(img_noisy_np, 0, 1),
(torch_to_np(net_input) - out.detach().cpu().numpy()[0]))
do_i_learned_noise = out.detach().cpu().numpy()[0]
mse_what_tf = MSE(noisy_np, do_i_learned_noise)
if psnr_noisy_max == 0:
psnr_noisy_max = psrn_noisy
elif psnr_noisy_max < psrn_noisy:
psnr_noisy_max = psrn_noisy
if SAVE_DURING_TRAINING and i % save_every == 0:
# output_dir
out_test_np = torch_to_np(out) # I +N1
# out_test_name = f'{i}_test'
# save_image(out_test_name, np.clip(out_test_np, 0, 1), output_path=output_dir)
net.eval()
loss_add = 0
with torch.no_grad():
out_effect_np = torch_to_np(img_noisy_torch - net(img_noisy_torch))
psnr_1 = compare_psnr(img_np, np.clip(out_effect_np, 0, 1))
test_do_i_learned_noise = torch_to_np(net(img_noisy_torch))
if psnr_max == 0:
psnr_max = psnr_1
elif psnr_max < psnr_1:
psnr_max = psnr_1
loss_add = loss_add + total_loss.item()
training_loss_record.append(loss_add / len(img_noisy_torch))
if i % 10 == 0:
final_reuslt = out_effect_np.transpose(1, 2, 0)
psnr_2 = compare_psnr(img_np.transpose(1, 2, 0),
np.clip(final_reuslt, 0, 1))
final_ssim = compare_ssim(img_np.transpose(1, 2, 0),
np.clip(final_reuslt, 0, 1),
data_range=1,
multichannel=True)
if psnr_2_max == 0:
psnr_2_max = psnr_2
tmp_name_p = f'{files_name[:-4]}_{sigma_now*255:.2f}_{psnr_2:.2f}_final_{final_ssim:.4f}'
save_image(tmp_name_p,
np.clip(final_reuslt.transpose(2, 0, 1), 0, 1),
output_path=output_dir)
elif psnr_2_max < psnr_2:
psnr_2_max = psnr_2
tmp_name_p = f'{files_name[:-4]}_{sigma_now*255:.2f}_{psnr_2:.2f}_final_{final_ssim:.4f}'
save_image(tmp_name_p,
np.clip(final_reuslt.transpose(2, 0, 1), 0, 1),
output_path=output_dir)
if final_ssim_max == 0:
final_ssim_max = final_ssim
elif final_ssim_max < final_ssim:
final_ssim_max = final_ssim
tmp_name = f'{files_name[:-4]}_{sigma_now*255:.2f}_{final_ssim:.4f}_final_{psnr_2:.2f}'
save_image(tmp_name,
np.clip(final_reuslt.transpose(2, 0, 1), 0, 1),
output_path=output_dir)
print(
'%s, sigma %f, Epoch %05d, psnr 2: %f, psnr 2 max: %f, final ssim : %f, final ssim max: %f'
% (files_name, sigma_now * 255, i, psnr_2, psnr_2_max,
final_ssim, final_ssim_max))
writer.add_scalar('final_test_psnr', psnr_2, i)
writer.add_scalar('final_max_test_psnr', psnr_2_max, i)
psnr_curve_max_record.append(psnr_2_max)
ssim_curve_max_record.append(final_ssim_max)
i += 1
return total_loss
def ssim(img1, img2, multichannel=False):
assert img1.dtype == img2.dtype == np.uint8, 'np.uint8 is supposed.'
return compare_ssim(img1, img2, multichannel=multichannel)
list_time_proc.append(dict_1['time_proc'])
list_title.append(dict_1['title'])
list_adj_bool.append(dict_1['adj'])
for j in range(i, n):
if j != i:
# print(i,j)
dict_2 = np.load('datas/' + argv[j])[()]
y_2 = dict_2['y']
y_1_abs, y_2_abs = np.absolute(y_1).astype(
np.float64), np.absolute(y_2).astype(np.float64)
mse = compare_mse(y_1_abs / np.max(y_1_abs),
y_2_abs / np.max(y_2_abs))
ssim = compare_ssim(y_1_abs,
y_2_abs,
data_range=y_1_abs.max() - y_1_abs.min())
## Plot
axes[cnt, 0].plot(y_1_abs)
axes[cnt, 0].set_title(dict_1['title'] + '\n MSE:' + str(mse),
fontsize=10)
axes[cnt, 1].plot(y_2_abs)
axes[cnt, 1].set_title(dict_2['title'] + '\n SSIM:' + str(ssim),
fontsize=10)
axes[cnt, 2].set_title('K-space')
axes[cnt, 2].plot(np.real(y_1), np.imag(y_1), 'r.')
axes[cnt, 2].plot(np.real(y_2), np.imag(y_2), 'b.')
# axes[cnt,2].set_axis_bgcolor('k')# WARNING set_axis_bgcolor is deprecated in matplotlib 2.0, use next line instead
def main(argv=None):
if tf.gfile.Exists(FLAGS.save_dir):
tf.gfile.DeleteRecursively(FLAGS.save_dir)
tf.gfile.MakeDirs(FLAGS.save_dir)
if tf.gfile.Exists(FLAGS.gen_frm_dir):
tf.gfile.DeleteRecursively(FLAGS.gen_frm_dir)
tf.gfile.MakeDirs(FLAGS.gen_frm_dir)
# load data
train_input_handle, test_input_handle = datasets_factory.data_provider(
FLAGS.dataset_name, FLAGS.train_data_paths, FLAGS.valid_data_paths,
FLAGS.batch_size, FLAGS.img_width, FLAGS.seq_length)
print("Initializing models")
model = Model()
lr = FLAGS.lr
# Prepare tensorboard logging
logger = Logger(os.path.join(FLAGS.gen_frm_dir, 'board'), model.sess)
logger.define_item("loss", Logger.Scalar, ())
logger.define_item("lr", Logger.Scalar, ())
logger.define_item("mse", Logger.Scalar, ())
logger.define_item("psnr", Logger.Scalar, ())
logger.define_item("fmae", Logger.Scalar, ())
logger.define_item("ssim", Logger.Scalar, ())
logger.define_item("sharp", Logger.Scalar, ())
logger.define_item(
"image",
Logger.Image,
(1, 2 * FLAGS.img_width, FLAGS.img_width, FLAGS.img_channel),
dtype='uint8')
for itr in range(1, FLAGS.max_iterations + 1):
if train_input_handle.no_batch_left():
train_input_handle.begin(do_shuffle=True)
ims = train_input_handle.get_batch()
ims = preprocess.reshape_patch(ims, FLAGS.patch_size)
logger.add('lr', lr, itr)
cost = model.train(ims, lr)
if FLAGS.reverse_input:
ims_rev = ims[:, ::-1]
cost += model.train(ims_rev, lr, mask_true)
cost = cost / 2
logger.add('loss', cost, itr)
if itr % FLAGS.display_interval == 0:
print('itr: ' + str(itr))
print('training loss: ' + str(cost))
if itr % FLAGS.test_interval == 0:
print('test...')
test_input_handle.begin(do_shuffle=False)
res_path = os.path.join(FLAGS.gen_frm_dir, str(itr))
os.mkdir(res_path)
avg_mse = 0
batch_id = 0
img_mse, ssim, psnr, fmae, sharp = [], [], [], [], []
for i in range(FLAGS.seq_length - FLAGS.input_length):
img_mse.append(0)
ssim.append(0)
psnr.append(0)
fmae.append(0)
sharp.append(0)
while (test_input_handle.no_batch_left() == False):
batch_id = batch_id + 1
test_ims = test_input_handle.get_batch()
test_dat = preprocess.reshape_patch(test_ims, FLAGS.patch_size)
img_gen = model.test(test_dat)
# concat outputs of different gpus along batch
# img_gen = np.concatenate(img_gen)
img_gen = preprocess.reshape_patch_back(
img_gen[:, np.newaxis, :, :, :], FLAGS.patch_size)
# MSE per frame
for i in range(1):
x = test_ims[:, -1, :, :, 0]
gx = img_gen[:, :, :, 0]
fmae[i] += metrics.batch_mae_frame_float(gx, x)
gx = np.maximum(gx, 0)
gx = np.minimum(gx, 1)
mse = np.square(x - gx).sum()
img_mse[i] += mse
avg_mse += mse
real_frm = np.uint8(x * 255)
pred_frm = np.uint8(gx * 255)
psnr[i] += metrics.batch_psnr(pred_frm, real_frm)
for b in range(FLAGS.batch_size):
sharp[i] += np.max(
cv2.convertScaleAbs(cv2.Laplacian(pred_frm[b], 3)))
score, _ = compare_ssim(pred_frm[b],
real_frm[b],
full=True)
ssim[i] += score
# save prediction examples
if batch_id == 1:
sel = np.random.randint(FLAGS.batch_size)
img_seq_pd = img_gen[sel]
img_seq_gt = test_ims[sel, -1]
h, w = img_gen.shape[1:3]
out_img = np.zeros((1, h * 2, w * 1, FLAGS.img_channel),
dtype='uint8')
for i, img_seq in enumerate([img_seq_gt, img_seq_pd]):
img = img_seq
img = np.maximum(img, 0)
img = np.uint8(img * 10)
img = np.minimum(img, 255)
out_img[0, (i * h):(i * h + h), :] = img
logger.add("image", out_img, itr)
test_input_handle.next()
avg_mse = avg_mse / (batch_id * FLAGS.batch_size)
logger.add('mse', avg_mse, itr)
print('mse per seq: ' + str(avg_mse))
for i in range(FLAGS.seq_length - FLAGS.input_length):
print(img_mse[i] / (batch_id * FLAGS.batch_size))
psnr = np.asarray(psnr, dtype=np.float32) / batch_id
fmae = np.asarray(fmae, dtype=np.float32) / batch_id
ssim = np.asarray(ssim, dtype=np.float32) / \
(FLAGS.batch_size * batch_id)
sharp = np.asarray(sharp, dtype=np.float32) / \
(FLAGS.batch_size * batch_id)
print('psnr per frame: ' + str(np.mean(psnr)))
logger.add('psnr', np.mean(psnr), itr)
for i in range(FLAGS.seq_length - FLAGS.input_length):
print(psnr[i])
print('fmae per frame: ' + str(np.mean(fmae)))
logger.add('fmae', np.mean(fmae), itr)
for i in range(FLAGS.seq_length - FLAGS.input_length):
print(fmae[i])
print('ssim per frame: ' + str(np.mean(ssim)))
logger.add('ssim', np.mean(ssim), itr)
for i in range(FLAGS.seq_length - FLAGS.input_length):
print(ssim[i])
print('sharpness per frame: ' + str(np.mean(sharp)))
logger.add('sharp', np.mean(sharp), itr)
for i in range(FLAGS.seq_length - FLAGS.input_length):
print(sharp[i])
if itr % FLAGS.snapshot_interval == 0:
model.save(itr)
train_input_handle.next()
index_1 = 7
index_2 = 10
img_arr_1 = train_data[0][index_1].reshape((28, 28))
img_val_1 = train_data[1][index_1]
img_arr_2 = train_data[0][index_2].reshape((28, 28))
img_val_2 = train_data[1][index_2]
import scipy.misc
ret, thresh_1 = cv2.threshold(np.uint8(img_arr_1 * 255).copy(), 127, 255, cv2.THRESH_BINARY)
ret, thresh2 = cv2.threshold(np.uint8(img_arr_2 * 255).copy(), 127, 255,0)
contours,hierarchy = cv2.findContours(thresh_1, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnt1 = contours[0]
contours,hierarchy = cv2.findContours(thresh2, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnt2 = contours[0]
formatt = "{:4.2f}"
match_I1 = formatt.format(matchShapes(cnt1, cnt2, cv2.cv.CV_CONTOURS_MATCH_I1,0))
match_I2 = formatt.format(matchShapes(cnt1, cnt2, cv2.cv.CV_CONTOURS_MATCH_I2,0))
match_I3 = formatt.format(matchShapes(cnt1, cnt2, cv2.cv.CV_CONTOURS_MATCH_I3,0))
plt.suptitle("Comparing two images - match_I1 = "+ match_I1+ " - match_I2 = "+ match_I2
+ " - match_I3 = " + match_I3 +'\n Structural similarity = ' + formatt.format(compare_ssim(img_arr_1, img_arr_2)))
plt.subplot(1, 2, 1)
plt.title(str(img_val_1))
fig = plt.imshow(img_arr_1, cmap=cm.binary)
fig.axes.get_xaxis().set_ticks([])
fig.axes.get_yaxis().set_ticks([])
plt.subplot(1, 2, 2)
plt.title(str(img_val_2))
fig = plt.imshow(img_arr_2 , cmap=cm.binary)
fig.axes.get_xaxis().set_ticks([])
fig.axes.get_yaxis().set_ticks([])
plt.tight_layout()
plt.show()
label = 'NRMSE = {:.4f}, PSNR = {:.4f}, SSIM = {:.4f}'
fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(15, 15))
ax[0, 0].imshow(image, cmap="gray", vmin=0, vmax=255)
ax[0, 0].set_title("Исходное изображение")
ax[0, 0].scatter(240, 125, color="red", s=0.5)
ax[0, 1].imshow(res_image2, cmap="gray", vmin=0, vmax=255)
ax[0, 1].set_title("Средняя интенсивность")
ax[0, 1].scatter(240, 125, color="red", s=0.5)
nrmse = measure.compare_nrmse(im_true=image,
im_test=res_image2,
norm_type='Euclidean')
psnr = measure.compare_psnr(im_true=image, im_test=res_image2, data_range=255)
ssim = measure.compare_ssim(image, res_image2, data_range=255)
ax[0, 1].set_xlabel(label.format(nrmse, psnr, ssim))
ax[1, 0].imshow(res_image3, cmap="gray", vmin=0, vmax=255)
ax[1, 0].set_title("Градиент")
ax[1, 0].scatter(240, 125, color="red", s=0.5)
ax[1, 1].imshow(res_image, cmap="gray", vmin=0, vmax=255)
ax[1, 1].set_title("Восстановленное изображение")
ax[1, 1].scatter(240, 125, color="red", s=0.5)
nrmse = measure.compare_nrmse(im_true=image,
im_test=res_image,
norm_type='Euclidean')
psnr = measure.compare_psnr(im_true=image, im_test=res_image, data_range=255)
ssim = measure.compare_ssim(image, res_image, data_range=255)
def dssim(p0, p1, range=255.):
return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2.
import matplotlib.pyplot as plt
import numpy as np
import pylab
from skimage import measure
for i in range(1,6):
f1 = 'true_' + str(i) + '.jpg'
f2 = 'predict_' + str(i) + '.jpeg'
a = plt.imread(f1)
b = plt.imread(f2)
ssim = measure.compare_ssim(a,b, multichannel = True)
print (ssim)
Iw_batch = embedding_net.predict_on_batch(
[Im_32x32_patchs, W, np.array([alpha])])
# reconstruct Iw
Iw = vf.tiling(Iw_batch, rec_size=img_rows)
Iw *= std_normalize
Iw += mean_normalize if Is_mean_normalized else 0
Iw[Iw > 255] = 255
Iw[Iw < 0] = 0
Iw = np.uint8(Iw.squeeze())
# PSNR
#psnr = 10*np.log10(255**2/np.mean((im_gray - Iw)**2))
psnr = compare_psnr(im_gray, Iw, data_range=255)
tmp_psnr.append(psnr)
# SSIM
tmp_ssim.append(
compare_ssim(im_gray, Iw, win_size=9, data_range=255))
# Save sample image
if n == 0 and save_samples == True:
cv2.imwrite(
os.path.join(sampled_embeded_folder,
'{}_[{}].png'.format(test_img[:-4], alpha)),
Iw)
psnr_values_per_alpha_mean.append(np.mean(tmp_psnr))
psnr_values_per_alpha_std.append(np.std(tmp_psnr))
ssim_values_per_alpha_mean.append(np.mean(tmp_ssim))
psnr_means.append(psnr_values_per_alpha_mean)
psnr_stds.append(psnr_values_per_alpha_std)
ssim_means.append(ssim_values_per_alpha_mean)
def ssim(gt, pred):
return compare_ssim(gt.transpose(1, 2, 0),
pred.transpose(1, 2, 0),
multichannel=True,
data_range=gt.max())
def show_frame(self):
# Capture frame-by-frame
frame_a = self.capture.read()[1]
frame_a = cv2.flip(frame_a, 1)
# Gray out our first frame
grayA = cv2.cvtColor(frame_a, cv2.COLOR_BGR2GRAY)
# Capture second frame
frame_b = self.capture.read()[1]
frame_b = cv2.flip(frame_b, 1)
# Gray out second frame
grayB = cv2.cvtColor(frame_b, cv2.COLOR_BGR2GRAY)
# compare Structural Similarity Index (SSIM) between the two frames
(score, diff) = compare_ssim(grayA, grayB, full=True)
diff = (diff * 255).astype("uint8")
self.score = score
# debug print
if self.debug:
# Debug output
# Text part
self.debug_output = tk.Text(self.debug_frame,
borderwidth=3,
relief="sunken")
self.debug_output.config(font=("consolas", 12),
undo=True,
wrap="word")
self.debug_output.grid(row=0,
column=0,
sticky="nsew",
padx=2,
pady=2)
self.score_history.append(score)
# Calculate average
total_entity = len(self.score_history)
total_score = 0
for i in self.score_history:
total_score += i
average = total_score / total_entity
self.debug_output.insert(
tk.INSERT,
f'Current SSIM: {score}{" "*int(20-len(str(score)))}'
f'Average SSIM: {average}\n'
f'Minimal SSIM: {min(self.score_history)}{" "*int(20-len(str(score)))}'
f'Max SSIM: {max(self.score_history)}')
#create scrollbar for debug output
self.debug_scrollbar = tk.Scrollbar(
self.debug_frame, command=self.debug_output.yview)
self.debug_scrollbar.grid(row=0, column=1, sticky="nsew")
self.debug_output["yscrollcommand"] = self.debug_scrollbar.set
self.debug_scrollbar.config(command=self.debug_output.yview)
print("SSIM: {}".format(self.score))
# If image score is lower or equal to sensitivity to take action.
# This means the difference between the two frames are major enough
# To call it a detection of motion.
if score <= self.sensitivity:
# threshold the difference in the frames, followed by finding contours to
# obtain the regions of the two input images that differ
thresh = cv2.threshold(diff, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# compute the bounding box of the contour and then draw the
# bounding box on both input frames to represent where the two
# frames differ
for c in cnts:
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame_a, (x, y), (x + w, y + h), (0, 0, 255), 1)
cv2.rectangle(frame_b, (x, y), (x + w, y + h), (0, 0, 255), 1)
# # Display the frame A
# cv2.imshow("Frame: A", frame_a)
cv2image = cv2.cvtColor(frame_a, cv2.COLOR_BGR2RGBA)
img = Image.fromarray(cv2image)
imgtk = ImageTk.PhotoImage(image=img)
self.vid_frame.imgtk = imgtk
self.vid_frame.configure(image=imgtk)
self.vid_frame.after(10, self.show_frame)
if score <= self.sensitivity:
# Create a picture and store it in the img folder.
# The file is named the current date and time.
if self.save:
cv2.imwrite(
'img/{}.png'.format(
datetime.datetime.now().strftime("%d-%B-%Y-%I%M%S%p")),
frame_a)
# If debug modus
if self.debug:
# Display debug frames
cv2.imshow("Frame: B", frame_b)
cv2.imshow("Diff", diff)
cv2.imshow("Thresh", thresh)
self.saved_output.delete("1.0", tk.END)
self.saved_output.insert(tk.INSERT, "Motion detected")
else:
# Text part
self.saved_output.delete("1.0", tk.END)
self.saved_output.insert(tk.INSERT, "No Motion")
ratio = 1
sigma = 12
list_psnr = []
list_ssim = []
test_dir = 'data/Test/Set68'
for im in os.listdir(test_dir):
if im.endswith(".jpg") or im.endswith(".bmp") or im.endswith(".png"):
x = cv2.imread(os.path.join(test_dir, im), 0)
x = x.astype('float32')
y = np.random.poisson(ratio * x) / (ratio)
y = y.astype('float32')
out = np.array(pybm3d.bm3d.bm3d(y, sigma))
out = out
list_psnr.append(compare_psnr(x / 255.0, out / 255.0))
list_ssim.append(compare_ssim(x / 255.0, out / 255.0))
plt.imshow(out, cmap='gray')
print(
'%s psnr: %s, ssim: %s' % (im, compare_psnr(
x / 255.0, out / 255.0), compare_ssim(x / 255.0, out / 255.0)))
mean_psnr = np.mean(list_psnr)
mean_ssim = np.mean(list_ssim)
print('psnr:{0}, ssim:{1}'.format(mean_psnr, mean_ssim))
#gnoise = np.random.normal(0, 25/255.0, x.shape)
#z = x+gnoise
#flag = 'p'
#if flag == 'p':
# plt.imshow(y,cmap='gray')
#else:
def test_HD720p(model=model,
use_cuda=args.use_cuda,
save_which=args.save_which,
dtype=args.dtype):
files = sorted(os.listdir(HD720p_Other_DATA))
unique_id = str(random.randint(0, 100000))
gen_dir = os.path.join(HD720p_Other_RESULT, unique_id)
os.mkdir(gen_dir)
for file_i in files:
print("\n\n\n**************")
print(file_i)
gen_file = os.path.join(HD720p_Other_RESULT, unique_id, file_i)
input_file = os.path.join(HD720p_Other_DATA, file_i)
interp_error = AverageMeter()
psnr_error = AverageMeter()
ssim_error = AverageMeter()
print(input_file)
print(gen_file)
Reader = YUV_Read(input_file, 720, 1280, toRGB=True)
Writer = YUV_Write(gen_file, fromRGB=True)
for index in range(0, 100, 2): # len(files) - 2, 2):
IMAGE1, sucess1 = Reader.read(index)
IMAGE2, sucess2 = Reader.read(index + 2)
if not sucess1 or not sucess2:
break
X0 = torch.from_numpy(
np.transpose(IMAGE1,
(2, 0, 1)).astype("float32") / 255.0).type(dtype)
X1 = torch.from_numpy(
np.transpose(IMAGE2,
(2, 0, 1)).astype("float32") / 255.0).type(dtype)
y_ = torch.FloatTensor()
assert (X0.size(1) == X1.size(1))
assert (X0.size(2) == X1.size(2))
intWidth = X0.size(2)
intHeight = X0.size(1)
channel = X0.size(0)
if not channel == 3:
continue
if intWidth != ((intWidth >> 7) << 7):
intWidth_pad = (
((intWidth >> 7) + 1) << 7) # more than necessary
intPaddingLeft = int((intWidth_pad - intWidth) / 2)
intPaddingRight = intWidth_pad - intWidth - intPaddingLeft
else:
intWidth_pad = intWidth
intPaddingLeft = 32
intPaddingRight = 32
if intHeight != ((intHeight >> 7) << 7):
intHeight_pad = (
((intHeight >> 7) + 1) << 7) # more than necessary
intPaddingTop = int((intHeight_pad - intHeight) / 2)
intPaddingBottom = intHeight_pad - intHeight - intPaddingTop
else:
intHeight_pad = intHeight
intPaddingTop = 32
intPaddingBottom = 32
pader = torch.nn.ReplicationPad2d([
intPaddingLeft, intPaddingRight, intPaddingTop,
intPaddingBottom
])
X0 = Variable(torch.unsqueeze(X0, 0), volatile=True)
X1 = Variable(torch.unsqueeze(X1, 0), volatile=True)
X0 = pader(X0)
X1 = pader(X1)
if use_cuda:
X0 = X0.cuda()
X1 = X1.cuda()
y_s, offset, filter, occlusion = model(torch.stack((X0, X1),
dim=0))
y_ = y_s[save_which]
if use_cuda:
X0 = X0.data.cpu().numpy()
y_ = y_.data.cpu().numpy()
offset = [offset_i.data.cpu().numpy() for offset_i in offset]
filter = [filter_i.data.cpu().numpy() for filter_i in filter
] if filter[0] is not None else None
occlusion = [
occlusion_i.data.cpu().numpy() for occlusion_i in occlusion
] if occlusion[0] is not None else None
X1 = X1.data.cpu().numpy()
else:
X0 = X0.data.numpy()
y_ = y_.data.numpy()
offset = [offset_i.data.numpy() for offset_i in offset]
filter = [filter_i.data.numpy() for filter_i in filter]
occlusion = [
occlusion_i.data.numpy() for occlusion_i in occlusion
]
X1 = X1.data.numpy()
X0 = np.transpose(
255.0 *
X0.clip(0, 1.0)[0, :, intPaddingTop:intPaddingTop + intHeight,
intPaddingLeft:intPaddingLeft + intWidth],
(1, 2, 0))
y_ = np.transpose(
255.0 *
y_.clip(0, 1.0)[0, :, intPaddingTop:intPaddingTop + intHeight,
intPaddingLeft:intPaddingLeft + intWidth],
(1, 2, 0))
offset = [
np.transpose(
offset_i[0, :, intPaddingTop:intPaddingTop + intHeight,
intPaddingLeft:intPaddingLeft + intWidth],
(1, 2, 0)) for offset_i in offset
]
filter = [
np.transpose(
filter_i[0, :, intPaddingTop:intPaddingTop + intHeight,
intPaddingLeft:intPaddingLeft + intWidth],
(1, 2, 0)) for filter_i in filter
] if filter is not None else None
occlusion = [
np.transpose(
occlusion_i[0, :, intPaddingTop:intPaddingTop + intHeight,
intPaddingLeft:intPaddingLeft + intWidth],
(1, 2, 0)) for occlusion_i in occlusion
] if occlusion is not None else None
X1 = np.transpose(
255.0 *
X1.clip(0, 1.0)[0, :, intPaddingTop:intPaddingTop + intHeight,
intPaddingLeft:intPaddingLeft + intWidth],
(1, 2, 0))
Writer.write(IMAGE1)
rec_rgb = np.round(y_).astype(numpy.uint8)
Writer.write(rec_rgb)
gt_rgb, sucess = Reader.read(index + 1)
gt_yuv = rgb2yuv(gt_rgb / 255.0)
rec_yuv = rgb2yuv(rec_rgb / 255.0)
gt_rgb = gt_yuv[:, :, 0] * 255.0
rec_rgb = rec_yuv[:, :, 0] * 255.0
gt_rgb = gt_rgb.astype('uint8')
rec_rgb = rec_rgb.astype('uint8')
diff_rgb = 128.0 + rec_rgb - gt_rgb
avg_interp_error_abs = np.mean(np.abs(diff_rgb - 128.0))
interp_error.update(avg_interp_error_abs, 1)
mse = numpy.mean((diff_rgb - 128.0)**2)
if mse == 0:
return 100.0
PIXEL_MAX = 255.0
psnr = 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
psnr_error.update(psnr, 1)
psnr_ = compare_psnr(rec_rgb, gt_rgb)
print(str(psnr) + '\t' + str(psnr_))
ssim = compare_ssim(rec_rgb, gt_rgb, multichannel=False)
ssim_error.update(ssim, 1)
diff_rgb = diff_rgb.astype("uint8")
print("interpolation error / PSNR : " +
str(round(avg_interp_error_abs, 4)) + " ,\t psnr " +
str(round(psnr, 4)) + ",\t ssim " + str(round(ssim, 5)))
fh = open(
os.path.join(HD720p_Other_RESULT, unique_id,
file_i + "_psnr_Y.txt"), "a+")
fh.write(str(psnr))
fh.write("\n")
fh.close()
fh = open(
os.path.join(HD720p_Other_RESULT, unique_id,
file_i + "_ssim_Y.txt"), "a+")
fh.write(str(ssim))
fh.write("\n")
fh.close()
metrics = "The average interpolation error / PSNR for all images are : " + str(
round(interp_error.avg, 4)) + ",\t psnr " + str(
round(psnr_error.avg, 4)) + ",\t ssim " + str(
round(ssim_error.avg, 4))
print(metrics)
metrics = "The average interpolation error / PSNR for all images are : " + str(
round(interp_error.avg, 4)) + ",\t psnr " + str(
round(psnr_error.avg, 4)) + ",\t ssim " + str(
round(ssim_error.avg, 4))
print(metrics)
fh = open(
os.path.join(HD720p_Other_RESULT, unique_id,
file_i + "_psnr_Y.txt"), "a+")
fh.write("\n")
fh.write(str(psnr_error.avg))
fh.write("\n")
fh.close()
fh = open(
os.path.join(HD720p_Other_RESULT, unique_id,
file_i + "_ssim_Y.txt"), "a+")
fh.write("\n")
fh.write(str(ssim_error.avg))
fh.write("\n")
fh.close()
def update_stats_db(self, redis_time, spark_time, tot_time):
"""stats database: for frontend to read
id => [tags as words,
total num,
num filtered,
redis tag retr time,
spark filter time,
tot time,
structural similarity,
url original,
url new]
"""
# connect to S3 bucket
c = boto.connect_s3()
src = c.get_bucket(self.incoming_bucket, validate=False)
dst = c.get_bucket(self.main_bucket, validate=False)
k_src = Key(src)
k_dst = Key(dst)
# database updates differ depending on the result
if self.result == []:
print("\n\n\n\n\nNo match found, adding to the db...\n\n")
print("Adding to the database..")
k_src.key = "{}.jpg".format(self.incoming_img_id)
k_dst.key = "valid/img{}.jpg".format(self.incoming_img_id)
# copy image from source bucket to the main bucket
dst.copy_key(k_dst.key, src.name, k_src.key)
print("Updating tags database..")
update_db(self.incoming_img_tags,
"img{}".format(self.incoming_img_id), self.r_tags)
sample_diff_img = load_from_S3(ak=self.awsak,
sk=self.awssk,
image_id=self.img_list[0],
bucket_name=self.main_bucket)
self.save_runtimes(redis_time, spark_time, tot_time)
stats_list.append("Not found")
ssim = compare_ssim(
self.incoming_im_resized[0],
sample_diff_img[0],
multichannel=(not self.incoming_img_multichannel))
self.stats_list.append(str(round(ssim * 100, 2)) + "%")
# get URLs for images to display on the UI
k_src.key = "{}{}.jpg".format(self.incoming_im_resized[3],
self.incoming_img_id)
k_dst.key = "{}{}.jpg".format(sample_diff_img[3],
sample_diff_img[2])
url_orig = k_dst.generate_url(expires_in=0, query_auth=False)
url_incoming = k_src.generate_url(expires_in=0, query_auth=False)
self.stats_list.append(url_orig)
self.stats_list.append(url_incoming)
else:
print("\n\n\n\n\nDuplicate found...\n\n")
self.save_runtimes(redis_time, spark_time, tot_time)
self.stats_list.append("Found")
ssim = compare_ssim(
self.incoming_im_resized[0],
self.result[0][0],
multichannel=(not self.incoming_img_multichannel))
self.stats_list.append(str(round(ssim * 100, 2)) + "%")
k_src.key = "{}{}.jpg".format(self.incoming_im_resized[3],
self.incoming_img_id)
k_dst.key = "{}{}.jpg".format(self.result[0][3], self.result[0][2])
url_orig = k_dst.generate_url(expires_in=0, query_auth=False)
url_incoming = k_src.generate_url(expires_in=0, query_auth=False)
self.stats_list.append(url_orig)
self.stats_list.append(url_incoming)
for stat in self.stats_list:
self.r_stats.rpush(sample_diff_img[2], stat)
HRnames = sorted(os.listdir(HRpath))
SRnames = sorted(os.listdir(SRpath))
psnr_list = []
ssim_list = []
for i in range(0, len(HRnames)):
HRimage = imgCrop(os.path.join(HRpath, HRnames[i]), upscale)
SRimage = io.imread(os.path.join(SRpath, SRnames[i]))
if mode == 'YCbCr':
SRimage = color.rgb2ycbcr(SRimage)
HR = shave(HRimage, upscale)
SR = shave(SRimage[:, :, 0], upscale)
psnr = measure.compare_psnr(HR, SR, data_range=255)
ssim = measure.compare_ssim(HR, SR, data_range=255, multichannel=True)
else:
HR = shave(HRimage, upscale)
SR = shave(SRimage, upscale)
psnr = measure.compare_psnr(HR, SR, data_range=255)
ssim = measure.compare_ssim(HR, SR, data_range=255, multichannel=True)
print('PSNR:%0.04f SSIM:%0.04f' % (psnr, ssim))
psnr_list.append(psnr)
ssim_list.append(ssim)
with open(Logpath, 'a') as f:
f.write('%s %0.04f %0.04f \n' % (SRnames[i], psnr, ssim))
average_psnr = np.mean(np.asarray(psnr_list))
average_ssim = np.mean(np.asarray(ssim_list))
print('Mean PSNR: %0.02f Mean SSIM: %0.04f' % (average_psnr, average_ssim))
with open(Logpath, 'a') as f:
f.write('%s %0.04f %0.04f \n' %
#Plot target image after cut
#io.imshow(img_target_cut)
#plt.show()
SSIM_max=0
for i in range(1, 6):
filenum = (str(i))
filename = ("f" + filenum + ".png")
img_compare = imread(filename);
img_compare_cut = img_compare[img_bottom:img_top,img_left:img_right]
SSIM = compare_ssim(img_target_cut, img_compare_cut,multichannel=True)
print(i)
print(SSIM)
#To plot images in loop - joegi
#plt.figure(0)
#io.imshow(img_target_cut)
#plt.figure(1)
#io.imshow(img_compare_cut)
#plt.show()
if SSIM>SSIM_max:
SSIM_max=SSIM
def fix_offset(annotation):
con = connect(database=DB_NAME,
host=DB_HOST,
user=DB_USER,
password=DB_PASSWORD)
cursor = con.cursor()
# get video name
cursor.execute("SELECT filename FROM videos WHERE id=%s",
(str(annotation.videoid), ))
video_name = cursor.fetchone()[0]
# grab video stream
url = s3.generate_presigned_url('get_object',
Params={
'Bucket': S3_BUCKET,
'Key':
S3_VIDEO_FOLDER + str(video_name)
},
ExpiresIn=100)
cap = cv2.VideoCapture(url)
fps = cap.get(cv2.CAP_PROP_FPS)
#search within +- search range seconds of original, +- frames from original annotation
frames = 20
search_range = 1 / fps * frames
# initialize video for grabbing frames before annotation
cap.set(0, (annotation.timeinvideo - search_range) *
1000) # tell video to start at 'start'-1 time
imgs = []
times = []
for i in range(math.ceil(fps * search_range * 2)):
check, vid = cap.read()
if not check:
print("end of video reached")
break
img = imutils.resize(vid, width=VIDEO_WIDTH, height=VIDEO_HEIGHT)
time = cap.get(cv2.CAP_PROP_POS_MSEC)
times.append(time)
imgs.append(img)
cap.release()
# get s3 image
try:
obj = s3.get_object(Bucket=S3_BUCKET,
Key=S3_ANNOTATION_FOLDER + annotation.image)
except:
#print("Annotation missing image.")
con.close()
return
img = Image.open(obj['Body'])
img = np.asarray(img)
img = img[:, :, :3]
img = img[:, :, ::-1]
img = cv2.resize(img, (VIDEO_WIDTH, VIDEO_HEIGHT))
best_score = 0
best = None
for i in range(math.ceil(fps * search_range)):
# +1 or -1
for s in range(-1, 2, 2):
index = math.ceil(fps * search_range) + i * s
if index == len(imgs):
continue
(score, diff) = compare_ssim(img,
imgs[index],
full=True,
multichannel=True)
if best_score < score:
best = index
best_score = score
if best_score > .95:
cursor.execute(
"UPDATE annotations SET timeinvideo=%f, originalid=NULL WHERE id=%d;",
(
times[best] / 1000,
annotation.id,
))
con.commit()
con.close()
return
cursor.execute("UPDATE annotations SET unsure=TRUE WHERE id=%d;",
(annotation.id, ))
con.commit()
con.close()
"--cloaked",
required=True,
help="the cloaked image file")
args = vars(ap.parse_args())
# load the two input images
imageA = cv2.imread(args["original"])
imageB = cv2.imread(args["cloaked"])
# convert the images to grayscale
grayA = cv2.cvtColor(imageA, cv2.COLOR_BGR2GRAY)
grayB = cv2.cvtColor(imageB, cv2.COLOR_BGR2GRAY)
# compute the Structural Similarity Index (SSIM) between the two
# images, ensuring that the difference image is returned
(score, diff) = compare_ssim(grayA, grayB, full=True)
diff = (diff * 255).astype("uint8")
print("SSIM: {}".format(score))
# threshold the difference image, followed by finding contours to
# obtain the regions of the two input images that differ
thresh = cv2.threshold(diff, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
# loop over the contours
for c in cnts:
# compute the bounding box of the contour and then draw the
# bounding box on both input images to represent where the two
import cv2
from skimage.measure import compare_ssim #ssim: structural similarity index
image_good = cv2.imread('D:/image/good_image.png')
image_good
image_bad = cv2.imread('D:/image/bad_image.png')
image_bad
# convert the images to grayscale
gray_good = cv2.cvtColor(image_good, cv2.COLOR_BGR2GRAY)
gray_fault = cv2.cvtColor(image_bad, cv2.COLOR_BGR2GRAY)
# compute the difference between two images
(score, diff) = compare_ssim(gray_good, gray_fault, full=True)
diff = (diff * 224).astype("uint8")
print("SSIM: {}".format(score))
#obtain the regions of the two input images that differ
thresh = cv2.threshold(diff, 0, 256,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
# loop over the contours
for c in cnts:
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(image_good, (x, y), (x + w, y + h), (0, 0, 224), 2)
cv2.rectangle(image_bad, (x, y), (x + w, y + h), (0, 0, 224), 2)
def dssim(p0, p1, range=255.):
# embed()
return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2.
def img_analytics(z1,z2):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
return {'ssim':compare_ssim(z1,z2,multichannel=False),'psnr':__colorPSNR(z1,z2)}
def main():
image_base_dir = '/home/dek/makerfaire-booth/2018/burger/experimental/dek/train_object_detector/decoded'
canonical_dir = 'canonical'
# template = os.path.join(image_base_dir, 'bottombun.0.00.27.34.-24.61.0.81.png')
template = os.path.join(canonical_dir, 'patty.png')
img1 = imread(template)
# img1_padded = numpy.zeros( (256, 256,3), dtype=numpy.uint8)
img1_padded = numpy.resize( [255,255,255], (256, 256, 3))
s = img1.shape
img1_padded[:s[0], :s[1]] = img1
img1_gray = rgb2gray(img1)
descriptor_extractor = ORB()
descriptor_extractor.detect_and_extract(img1_gray)
keypoints1 = descriptor_extractor.keypoints
descriptors1 = descriptor_extractor.descriptors
# g = glob.glob(os.path.join(image_base_dir, 'patty*.nobox.png'))
# for moving in g:
while True:
rot, tx, ty, scale = get_random_orientation()
# img2 = imread(moving)
img2 = draw_example('patty', 256, 256, rot, tx, ty, scale)
img2_gray = rgb2gray(img2)
try:
descriptor_extractor.detect_and_extract(img2_gray)
except RuntimeError:
continue
keypoints2 = descriptor_extractor.keypoints
descriptors2 = descriptor_extractor.descriptors
matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)
src = keypoints2[matches12[:, 1]][:, ::-1]
dst = keypoints1[matches12[:, 0]][:, ::-1]
model_robust, inliers = \
ransac((src, dst), SimilarityTransform,
min_samples=4, residual_threshold=2)
if not model_robust:
print "bad"
continue
img2_transformed = transform.warp(img2, model_robust.inverse, mode='constant', cval=1)
img1_padded_float = img1_padded.astype(numpy.float64)/255.
sub = img2_transformed - img1_padded_float
print compare_ssim(img2_transformed, img1_padded_float, win_size=5, multichannel=True)
fig, axes = plt.subplots(2, 2, figsize=(7, 6), sharex=True, sharey=True)
ax = axes.ravel()
ax[0].imshow(img1_padded_float)
ax[1].imshow(img2)
ax[1].set_title("Template image")
ax[2].imshow(img2_transformed)
ax[2].set_title("Matched image")
ax[3].imshow(sub)
ax[3].set_title("Subtracted image")
# plt.gray()
# ax = plt.gca()
# plot_matches(ax, img1, img2, keypoints1, keypoints2, matches12)
plt.show()
def test(model, test_input_handle, configs, save_name):
"""Evaluates a model."""
logger.info('start testing')
test_input_handle.begin(do_shuffle=False)
res_path = os.path.join(configs.gen_frm_dir, str(save_name))
# os.mkdir(res_path)
os.makedirs(res_path, exist_ok=True)
avg_mse = 0
batch_id = 0
img_mse, ssim, psnr = [], [], []
output_length = configs.total_length - configs.input_length
for i in range(output_length):
img_mse.append(0)
ssim.append(0)
psnr.append(0)
real_input_flag_zero = np.zeros((configs.batch_size, output_length - 1,
configs.img_width // configs.patch_size,
configs.img_width // configs.patch_size,
configs.patch_size**2 * configs.img_channel)).astype(np.float32)
while not test_input_handle.no_batch_left():
batch_id = batch_id + 1
test_ims = test_input_handle.get_batch()
test_dat = preprocess.reshape_patch(test_ims, configs.patch_size)
# test_dat = np.split(test_dat, configs.n_gpu)
img_gen = model.test(test_dat, real_input_flag_zero)
# Concat outputs of different gpus along batch
img_gen = np.concatenate(img_gen)
img_gen = preprocess.reshape_patch_back(img_gen, configs.patch_size)
img_out = img_gen[:, -output_length:]
target_out = test_ims[:, -output_length:]
# MSE per frame
for i in range(output_length):
x = target_out[:, i]
gx = img_out[:, i]
gx = np.maximum(gx, 0)
gx = np.minimum(gx, 1)
mse = np.square(x - gx).sum()
img_mse[i] += mse
avg_mse += mse
for b in range(configs.batch_size):
ssim[i] += compare_ssim(x[b], gx[b], multichannel=True)
x = np.uint8(x * 255)
gx = np.uint8(gx * 255)
psnr[i] += batch_psnr(gx, x)
# save prediction examples
if batch_id <= configs.num_save_samples:
path = os.path.join(res_path, str(batch_id))
os.makedirs(path, exist_ok=True)
# os.mkdir(path)
for i in range(configs.total_length):
if (i + 1) < 10:
name = 'gt0' + str(i + 1) + '.png'
else:
name = 'gt' + str(i + 1) + '.png'
file_name = os.path.join(path, name)
img_gt = np.uint8(test_ims[0, i] * 255)
cv2.imwrite(file_name, img_gt)
for i in range(output_length):
if (i + configs.input_length + 1) < 10:
name = 'pd0' + str(i + configs.input_length + 1) + '.png'
else:
name = 'pd' + str(i + configs.input_length + 1) + '.png'
file_name = os.path.join(path, name)
img_pd = img_gen[0, i]
img_pd = np.maximum(img_pd, 0)
img_pd = np.minimum(img_pd, 1)
img_pd = np.uint8(img_pd * 255)
cv2.imwrite(file_name, img_pd)
# else:
# if batch_id > configs.num_save_samples*10:
# break
test_input_handle.next()
if batch_id==0:
return
avg_mse = avg_mse / (batch_id * configs.batch_size * configs.n_gpu)
logger.info('mse per seq: ' + str(avg_mse))
for i in range(output_length):
print(img_mse[i] / (batch_id * configs.batch_size * configs.n_gpu))
psnr = np.asarray(psnr, dtype=np.float32) / batch_id
logger.info('psnr per frame: ' + str(np.mean(psnr)))
for i in range(output_length):
print(psnr[i])
ssim = np.asarray(ssim, dtype=np.float32) / (configs.batch_size * batch_id)
print('ssim per frame: ' + str(np.mean(ssim)))
for i in range(output_length):
print(ssim[i])
def main():
image_base_dir = '/home/dek/makerfaire-booth/2018/burger/experimental/dek/train_object_detector/decoded'
canonical_dir = 'canonical'
# template = os.path.join(image_base_dir, 'bottombun.0.00.27.34.-24.61.0.81.png')
fig, axes = plt.subplots(7, 7, figsize=(7, 6), sharex=True, sharey=True)
fig.delaxes(axes[0][0])
ssims = numpy.zeros( (len(BurgerElement.__members__), len(BurgerElement.__members__)), dtype=float)
mses = numpy.zeros( (len(BurgerElement.__members__), len(BurgerElement.__members__)), dtype=float)
for i, layer in enumerate(BurgerElement.__members__):
template = os.path.join(canonical_dir, '%s.png' % layer)
img1 = imread(template)
# img1_padded = numpy.zeros( (WIDTH, HEIGHT,3), dtype=numpy.uint8)
img1_padded = numpy.resize( [255,255,255], (WIDTH, HEIGHT, 3))
s = img1.shape
w = s[0]
h = s[1]
nb = img1_padded.shape[0]
na = img1.shape[0]
lower1 = (nb) // 2 - (na // 2)
upper1 = (nb // 2) + (na // 2)
nb = img1_padded.shape[1]
na = img1.shape[1]
lower2 = (nb) // 2 - (na // 2)
upper2 = (nb // 2) + (na // 2)
img1_padded[lower1:upper1, lower2:upper2] = img1
img1_padded_float = img1_padded.astype(numpy.float64)/255.
print img1_padded_float.shape
img1_gray = rgb2gray(img1_padded_float)
descriptor_extractor = ORB()
try:
descriptor_extractor.detect_and_extract(img1_gray)
except RuntimeError:
continue
keypoints1 = descriptor_extractor.keypoints
descriptors1 = descriptor_extractor.descriptors
axes[i][0].imshow(img1_padded_float)
axes[i][0].set_title("Template image")
for j, layer2 in enumerate(BurgerElement.__members__):
rot, tx, ty, scale = get_random_orientation()
img2 = draw_example(layer2, WIDTH, HEIGHT, rot, tx, ty, scale)
# match = os.path.join(canonical_dir, '%s.png' % layer2)
# img2 = imread(match)
img2_padded = numpy.resize( [255,255,255], (WIDTH, HEIGHT, 3))
s = img2.shape
img2_padded[:s[0], :s[1]] = img2
img2_padded_float = img2_padded.astype(numpy.float64)/255.
img2_gray = rgb2gray(img2_padded_float)
try:
descriptor_extractor.detect_and_extract(img2_gray)
except RuntimeError:
continue
keypoints2 = descriptor_extractor.keypoints
descriptors2 = descriptor_extractor.descriptors
matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)
src = keypoints2[matches12[:, 1]][:, ::-1]
dst = keypoints1[matches12[:, 0]][:, ::-1]
model_robust, inliers = \
ransac((src, dst), SimilarityTransform,
min_samples=4, residual_threshold=2)
if not model_robust:
print "bad"
continue
img2_transformed = transform.warp(img2_padded_float, model_robust.inverse, mode='constant', cval=1)
sub = img2_transformed - img1_padded_float
ssim = compare_ssim(img2_transformed, img1_padded_float, win_size=5, multichannel=True)
mse = compare_mse(img2_transformed, img1_padded_float)
ssims[i,j] = ssim
mses[i,j] = mse
axes[0][j].imshow(img2_padded_float)
axes[0][j].set_title("Match image")
axes[i][j].imshow(img2_transformed)
axes[i][j].set_title("Transformed image")
axes[i][j].set_xlabel("SSIM: %9.4f MSE: %9.4f" % (ssim, mse))
# ax = plt.gca()
# plot_matches(ax, img1, img2, keypoints1, keypoints2, matches12)
print ssims
print numpy.argmax(ssims, axis=1)
print numpy.argmin(mses, axis=1)
plt.show()
img_array = np.zeros(shape = [height, width])
for i in range(height):
for j in range(width):
if final_matrix[i,j] < 0:
img_array[i,j] = 16.0
elif final_matrix[i,j] > 255:
img_array[i,j] = 239
else:
img_array[i,j] = final_matrix[i, j]
img_array = img_array.astype(np.uint8, copy=False)
#cv2.imwrite("noncausal1/%d.jpg" % index, img_array)
original_matrix = cv2.imread("non_test_300/%d.png" % index, 0)
mse_all += mean_squared_error(original_matrix, img_array)
psnr += cv2.PSNR(original_matrix, img_array)
(score, diff) = compare_ssim(original_matrix, img_array, full=True)
ssim_all += score
msssim_all += msssim(original_matrix, img_array)
decoded_blocks = []
index += 1
print "mse result:", mse_all / 300
print "psnr result:", psnr / 300
print "ssim result:", ssim_all / 300
print "msssim result:", msssim_all / 300
def cal_ssim(ax, cx):
return measure.compare_ssim(ax,
cx,
multichannel=True,
data_range=255,
win_size=11)
def compare(img1, img2, threshold = 0.5): if compare_ssim(img1, img2) > threshold: return True return False
help="first input image")
ap.add_argument("-s", "--second", required=True,
help="second")
args = vars(ap.parse_args())
# load the two input images
imageA = cv2.imread(args["first"])
imageB = cv2.imread(args["second"])
# convert the images to grayscale
grayA = cv2.cvtColor(imageA, cv2.COLOR_BGR2GRAY)
grayB = cv2.cvtColor(imageB, cv2.COLOR_BGR2GRAY)
# compute the Structural Similarity Index (SSIM) between the two
# images, ensuring that the difference image is returned
(score, diff) = compare_ssim(grayA, grayB, full=True)
diff = (diff * 255).astype("uint8")
print("SSIM: {}".format(score))
# threshold the difference image, followed by finding contours to
# obtain the regions of the two input images that differ
thresh = cv2.threshold(diff, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# loop over the contours
for c in cnts:
# compute the bounding box of the contours and then draw the
# bounding box on both input images to represent where the two
def save_images_test(webpage,
visuals,
image_path,
aspect_ratio=1.0,
width=256):
"""Save images to the disk.
Parameters:
webpage (the HTML class) -- the HTML webpage class that stores these imaegs (see html.py for more details)
visuals (OrderedDict) -- an ordered dictionary that stores (name, images (either tensor or numpy) ) pairs
image_path (str) -- the string is used to create image paths
aspect_ratio (float) -- the aspect ratio of saved images
width (int) -- the images will be resized to width x width
This function will save images stored in 'visuals' to the HTML file specified by 'webpage'.
"""
image_dir = webpage.get_image_dir()
file_dir = webpage.get_file_dir()
diff_dir = webpage.get_diff_dir()
short_path = ntpath.basename(image_path[0])
name = os.path.splitext(short_path)[0]
webpage.add_header(name)
ims, txts, links = [], [], []
for label, im_data in visuals.items():
label2 = 'difference'
im = util.tensor2im(im_data)
image_name = '%s_%s.png' % (name, label)
img_diff_name = '%s_%s.png' % (name, label2)
file_name = '%s_%s.txt' % (name, label)
diff_name = '%s.txt' % (name)
save_path = os.path.join(image_dir, image_name)
save_path2 = os.path.join(file_dir, file_name)
save_path3 = os.path.join(diff_dir, diff_name)
save_path4 = os.path.join(image_dir, img_diff_name)
h, w, _ = im.shape
if aspect_ratio > 1.0:
im = imresize(im, (h, int(w * aspect_ratio)), interp='bicubic')
if aspect_ratio < 1.0:
im = imresize(im, (int(h / aspect_ratio), w), interp='bicubic')
util.save_image2(im_data.view(-1, 240).cpu().numpy(), save_path)
np.savetxt(save_path2,
im_data.view(-1, 240).cpu().numpy(),
delimiter=",")
ims.append(image_name)
txts.append(label)
links.append(image_name)
if label == 'fake_B':
with open(save_path2) as f:
fake_img = np.genfromtxt(f, delimiter=',',
dtype='float64').astype('float32')
f.close()
if label == 'real_B':
with open(save_path2) as f2:
real_img = np.genfromtxt(f2, delimiter=',',
dtype='float64').astype('float32')
f2.close()
# calculate the difference between real and fake imgs
difference = fake_img - real_img
# save difference results in txt file
np.savetxt(save_path3, difference, delimiter=",")
util.save_image2(difference, save_path4)
ims.append(img_diff_name)
txts.append(label2)
links.append(img_diff_name)
# calculate the mean squared error
error = mean_squared_error(real_img, fake_img)
# calculate the pearson correlation coefficient
coe = np.corrcoef(real_img.flat, fake_img.flat)[0, 1]
# calculate the structural similarity index
ssi = measure.compare_ssim(real_img, fake_img)
webpage.add_images(ims, txts, links, width=width)
return error, coe, ssi
def extract_text(img):
# Function to extract text boxes, line colors and decipher
# text using ocr engine
global G
# Grab digit images and load them into array
# Compute and store histograms for each image
digits = pickle.load(open("digits.p", 'rb'))
digits2 = pickle.load(open("digits2.p", 'rb'))
# Shape detector to tell the shape of contour
sd = ShapeDetector()
# Filter out ecu boxes
ecu_img = line_filter(img, color="ECU", rtype='rgb')
ecu_img = cv2.medianBlur(ecu_img, 5)
# Enlarge the boxes so we can overwrite the dashed or
# solid lines
kernel = np.ones((9, 9), np.uint8)
_ecu_img = cv2.dilate(ecu_img, kernel, iterations=3)
# Grab the contours and then fill them with white to
# erase them from the image
ecu_contours, centers = get_contours(_ecu_img)
no_boxes = clear_contours(img, ecu_contours)
ecu_rects = get_bound_rects(ecu_contours)
# Convert to gray and filter out the colored wires
# so it is just text
just_text = bw_filter(no_boxes, rtype='rgb')
# Add circle boxes for later relationship building
kernel2 = np.ones((5, 5), np.uint8)
erode = cv2.erode(just_text, kernel2, iterations=1)
median = cv2.medianBlur(erode, 5)
temp_contours, circle_centers = get_contours(median)
circle_centers = list(set(circle_centers))
# Loop through and grab only circle contours
circle_centers = []
for c in temp_contours:
if sd.detect(c) == "circle":
M = cv2.moments(c)
if M["m00"] == 0:
continue
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
circle_centers.append((cX, cY))
circle_centers = circle_centers
# Dilate text to get a rectangular contour
# (Thanksgiving Approach)
enlarged = cv2.dilate(just_text, kernel, iterations=5)
# Grab text contours and convert to rectangular
# boundries
text_contours, centers = get_contours(enlarged)
params = get_bound_rects(text_contours)
# Decipher/Filter text and store mappings to
# center and bounding rectangle
ecu_map = {}
decoded_text = []
brgb = Image.fromarray(img, 'RGB')
for i, _roi in enumerate(params):
roi = list(_roi)
roi[2] += roi[0]
roi[3] += roi[1]
roi = tuple(roi)
ar = float(_roi[2]) / _roi[3]
text_roi = brgb.crop(roi)
text = pytesseract.image_to_string(text_roi)
if lc_check(text):
pass
elif len(text) == 0 and (.65 < ar < 1.25):
text_roi = text_roi.resize((100, 80)).convert('L')
best_score = 0
best_index = 0
for i2, d in enumerate(digits):
cmp_score = compare_ssim(d,
np.array(text_roi),
data_range=d.max() - d.min())
cmp_score2 = compare_ssim(digits2[i2],
np.array(text_roi),
data_range=d.max() - d.min())
score = max(cmp_score, cmp_score2)
if score > best_score:
best_score = score
best_index = i2 + 1
if _roi[0] < (200):
ecu_map[i] = (_roi, "IN-" + str(best_index))
elif _roi[0] > (img.shape[1] - 200):
ecu_map[i] = (_roi, "OUT-" + str(best_index))
else:
# Check if text passes filter test
if filter_text(text) != "":
if text in decoded_text:
continue
else:
decoded_text.append(text)
(closest_ecu, text_near) = text2_ecu(ecu_rects, _roi, text)
if not any(closest_ecu):
pass
else:
ecu_map[i] = (closest_ecu, text_near)
# Create nodes for each ecu and outward/inward
for k, v in ecu_map.items():
val = v[1].replace("\n\n", "\n")
G.add_node(val)
return ecu_map
if "_x" + str(scale) in image_name:
count += 1
print("Processing ", image_name)
im_gt_y = sio.loadmat(image_name)['im_gt_y']
im_b_y = sio.loadmat(image_name)['im_b_y']
im_gt_y = crop_by_pixel(im_gt_y, 3)
im_b_y = crop_by_pixel(im_b_y, 3)
im_gt_y = im_gt_y.astype(float)
im_b_y = im_b_y.astype(float)
psnr_bicubic = PSNR(im_gt_y, im_b_y,shave_border=scale)
avg_psnr_bicubic += psnr_bicubic
(score, diff) = compare_ssim(im_gt_y, im_b_y, full=True)
avg_ssim_bicubic += score
im_input = im_b_y/255.
im_input = Variable(torch.from_numpy(im_input).float()).view(1, -1, im_input.shape[0], im_input.shape[1])
if cuda:
model = model.cuda()
im_input = im_input.cuda()
else:
model = model.cpu()
start_time = time.time()
HR = model(im_input)
elapsed_time = time.time() - start_time
def get_ssim(img1, img2):
ssim = compare_ssim(img1, img2, multichannel=True)
return ssim
from utils import expand_folder
im_path = "/home/esepulveda/Documents/projects/roots/python/processing/1.25.AVI/windows/frame-51"
images = []
expand_folder(im_path,images)
images.sort()
#templates = load_templates("/home/esepulveda/Documents/projects/roots/python/models/templates")
templates = load_templates("/Users/exequiel/projects/roots/python/models/templates")
n = len(images)
sim = np.zeros((n,n))
print images
images_data = [data.imread(x) for x in images]
for i in xrange(n):
image_i = images_data[i]
for j in xrange(i,n):
image_j = images_data[j]
sim[i,j] = compare_ssim(image_i,image_j,multichannel=True) #,gaussian_weights=True)
sim[j,i] = sim[i,j]
print np.sum(sim,axis=0)
print np.min(sim,axis=0)
