class TestORCTOptimization(unittest.TestCase):
def __init__(self, methodName: str = ...) -> None:
super().__init__(methodName)
self.datasetUtils = DataUtils()
self.evaluation = Evaluation()
def opt_func(self, X, twoComplement):
n_particles = X.shape[0] # number of particles
costs = []
for x in X:
x = np.reshape(x, [2, 2])
filtered = compute_orct2(compute_orct1(twoComplement, x), x)
filtered = (filtered + 255) / 2
def inverseFunction(data):
data = data.astype('float32') * 2 - 255
data = compute_orct2inverse(data, x)
data = compute_orct1inverse(data, x)
return data
sampleFunctionReverse = inverseFunction
psnr, ssim, jpeg2000CompressionRatioAfter, jpeg2000CompressionRatioBefore = self.evaluation.evaluate(
filtered, twoComplement, sampleFunctionReverse)
cost = np.abs(
(1 / psnr) * (1 / ssim) * 1 / (jpeg2000CompressionRatioAfter))
costs.append(cost)
return np.array(costs)
def test_orct12_optimization(self):
bayer = self.datasetUtils.readCFAImages()
twoComplement = self.datasetUtils.twoComplementMatrix(bayer)
twoComplement = twoComplement.astype("float32")
options = {'c1': 0.5, 'c2': 0.1, 'w': 0.9}
optimizer = GlobalBestPSO(n_particles=10,
dimensions=4,
options=options)
costFunction = partial(self.opt_func, twoComplement=twoComplement)
cost, pos = optimizer.optimize(costFunction, iters=30)
pass
def test_orct12_(self):
pos = np.asarray([0.39182592, 0.23747258, 0.51497874, -0.08751142])
x = np.reshape(pos, [2, 2])
# x = np.asarray([[0.05011018, -0.53709484],
# [-1.1104253, -0.30699651]])
bayer = self.datasetUtils.readCFAImages()
twoComplement = self.datasetUtils.twoComplementMatrix(bayer)
twoComplement = twoComplement.astype("float32")
filtered = compute_orct2(compute_orct1(twoComplement, x), x)
filtered = (filtered + 255) / 2
def inverseFunction(data):
data = data.astype('float32') * 2 - 255
data = compute_orct2inverse(data, x)
data = compute_orct1inverse(data, x)
return data
sampleFunctionReverse = inverseFunction
self.evaluation.evaluate(filtered, twoComplement,
sampleFunctionReverse)
pass
def test_ocrtOptimizedWithDataset(self):
pos = np.asarray([0.39182592, 0.23747258, 0.51497874, -0.08751142])
x = np.reshape(pos, [2, 2])
rgbImages = self.datasetUtils.loadKodakDataset()
cfaImages, image_size = self.datasetUtils.convertDatasetToCFA(
rgbImages)
bayer = cfaImages[2, :, :]
twoComplement = self.datasetUtils.twoComplementMatrix(bayer)
twoComplement = twoComplement.astype("float32")
filtered = compute_orct2plus3(compute_orct1(twoComplement, x), x)
filtered = (filtered + 255) / 2
def inverseFunction(data):
data = data.astype('float32') * 2 - 255
data = compute_orct2plus3inverse(data, x)
data = compute_orct1inverse(data, x)
return data
sampleFunctionReverse = inverseFunction
self.evaluation.evaluate(filtered, bayer, sampleFunctionReverse)
pass
def test_multiObjOptimization(self):
algorithm = NSGA2(pop_size=10,
n_offsprings=10,
sampling=get_sampling("real_random"),
crossover=get_crossover("real_sbx", prob=0.9,
eta=15),
mutation=get_mutation("real_pm", eta=20),
eliminate_duplicates=True)
termination = get_termination("n_gen", 5)
bayer = self.datasetUtils.readCFAImages()
twoComplement = self.datasetUtils.twoComplementMatrix(bayer)
twoComplement = twoComplement.astype("float32")
problem = MyProblem(twoComplement)
res = minimize(problem,
algorithm,
termination,
save_history=True,
verbose=True)
# Objective Space
res.F = 1 / res.F
plot = Scatter(title="Objective Space")
plot.add(res.F)
plot.show()
print("Best filter{}".format(np.reshape(res.opt[-1].X, [2, 2])))
class TestORCT(unittest.TestCase):
def __init__(self, methodName: str = ...) -> None:
super().__init__(methodName)
self.datasetUtils = DataUtils()
self.evaluation = Evaluation()
self.precisionFloatingPoint = 0
def test_orct1(self):
bayer = cv2.imread("../Data/image.bmp")
bayer = np.sum(bayer, axis=2).astype('float64')
orct1Filtered = compute_orct1(bayer, precisionFloatingPoint=self.precisionFloatingPoint)
pass
def test_orct2(self):
bayer = cv2.imread("../Data/image.bmp")
bayer = np.sum(bayer, axis=2).astype('float64')
orct2Filtered = compute_orct2(bayer, precisionFloatingPoint=self.precisionFloatingPoint)
pass
def test_orct23PlusReversible(self):
bayer = cv2.imread("../Data/image.bmp")
bayer = np.sum(bayer, axis=2).astype('float64')
orct23Filtered = compute_orct2plus3(bayer, precisionFloatingPoint=self.precisionFloatingPoint)
orct23FilteredInversed = compute_orct2plus3inverse(orct23Filtered, precisionFloatingPoint=self.precisionFloatingPoint)
print("PSNR: {}".format(self.evaluation.calculate_psnr(bayer, np.round(orct23FilteredInversed))))
def test_orct1Reversible(self):
bayer = cv2.imread("../Data/image.bmp")
bayer = np.sum(bayer, axis=2).astype('float64')
orct1Filtered = compute_orct1V2(bayer, precisionFloatingPoint=self.precisionFloatingPoint)
orct1FilteredInversed = compute_orct1inverseV2(orct1Filtered, precisionFloatingPoint=self.precisionFloatingPoint)
print("PSNR: {}".format(self.evaluation.calculate_psnr(bayer, np.round(orct1FilteredInversed))))
def test_orct123Reversible(self):
bayer = cv2.imread("../Data/image.bmp")
bayer = np.sum(bayer, axis=2).astype('float64')
orct1Filtered = compute_orct1V2(bayer, precisionFloatingPoint=self.precisionFloatingPoint)
orct23Filtered = compute_orct2plus3(orct1Filtered, precisionFloatingPoint=self.precisionFloatingPoint)
orct23FilteredNormalized = copy.deepcopy(orct23Filtered)
orct23FilteredNormalized[orct23FilteredNormalized == 0] = -256
orct23FilteredNormalized = (orct23FilteredNormalized + 256) / 2
orct23FilteredNormalized = np.ceil(orct23FilteredNormalized)
orct23FilteredNormalized = orct23FilteredNormalized * 2 - 256
orct23FilteredNormalized[orct23FilteredNormalized == -256] = 0
orct23Filtered = orct23FilteredNormalized
orct23FilteredInversed = compute_orct2plus3inverse(orct23Filtered, precisionFloatingPoint=self.precisionFloatingPoint)
orct1FilteredInversed = compute_orct1inverseV2(orct23FilteredInversed, precisionFloatingPoint=self.precisionFloatingPoint)
print("PSNR: {}".format(self.evaluation.calculate_psnr(np.round(orct23FilteredInversed), np.round(orct1Filtered))))
print("PSNR: {}".format(self.evaluation.calculate_psnr(bayer, np.round(orct1FilteredInversed))))
def test_orct12(self):
bayer = self.datasetUtils.readCFAImages()
bayer = bayer.astype("float64")
orct1Res = compute_orct1(bayer, precisionFloatingPoint=self.precisionFloatingPoint)
filtered = compute_orct2(orct1Res, precisionFloatingPoint=self.precisionFloatingPoint)
# filtered = (filtered)/2
filtered[filtered == 0] = -256
filtered = (filtered + 256) / 2
def inverseFunction(data):
data = data.astype('float32')
data = data * 2 - 256
data[data == -256] = 0
data = compute_orct2inverse(data, precisionFloatingPoint=self.precisionFloatingPoint)
data = compute_orct1inverse(data, precisionFloatingPoint=self.precisionFloatingPoint)
return data
sampleFunctionReverse = inverseFunction
self.evaluation.evaluate(filtered, bayer, sampleFunctionReverse, precisionFloatingPoint=self.precisionFloatingPoint)
pass
def test_orct123Plus(self):
bayer = self.datasetUtils.readCFAImages()
bayer = bayer.astype("float32")
orct_1 = compute_orct1V2(bayer, precisionFloatingPoint=self.precisionFloatingPoint)
filtered = compute_orct2plus3(orct_1, precisionFloatingPoint=self.precisionFloatingPoint)
filtered[filtered == 0] = -256
filtered = (filtered + 256) / 2
filtered = np.ceil(filtered)
def inverseFunction(data):
data = data.astype('float32')
data = data * 2 - 256
data[data == -256] = 0
data = compute_orct2plus3inverse(data, precisionFloatingPoint=self.precisionFloatingPoint)
data = compute_orct1inverseV2(data, precisionFloatingPoint=self.precisionFloatingPoint)
return np.round(data)
sampleFunctionReverse = inverseFunction
self.evaluation.evaluate(filtered, bayer, sampleFunctionReverse, precisionFloatingPoint=self.precisionFloatingPoint, roundingMethod="ceil")
pass
def test_ocrtShahedWithDataset(self):
rgbImages = self.datasetUtils.loadArri()
cfaImages, image_size = self.datasetUtils.convertDatasetToCFA(rgbImages)
psnrs = []
ssims = []
jpeg2000CompressionRatioAfters = []
JpegLsCompressionRatios = []
compressionRatioLZWs = []
compressionRatiojpeg2000LossyAfters = []
# filtered = (filtered + 128)
# filtered[:, 1::2] = filtered[:, 1::2] / 2 + 128
def inverseFunction(data):
data = data.astype('float32') * 2 - 256
data = compute_orct1inverse(data, precisionFloatingPoint=self.precisionFloatingPoint)
return data
sampleFunctionReverse = inverseFunction
for bayer in cfaImages:
bayer = bayer.astype("float32")
filtered = compute_orct2(compute_orct1(bayer, precisionFloatingPoint=self.precisionFloatingPoint), precisionFloatingPoint=self.precisionFloatingPoint)
test_sample = filtered
filtered = (filtered + 256) / 2
psnr, ssim, jpeg2000CompressionRatioAfter, JpegLsCompressionRatio, compressionRatioLZWAfter, compressionRatiojpeg2000LossyAfter = self.evaluation.evaluate(filtered, bayer,
sampleFunctionReverse,
precisionFloatingPoint=self.precisionFloatingPoint)
psnrs.append(psnr)
ssims.append(ssim)
jpeg2000CompressionRatioAfters.append(jpeg2000CompressionRatioAfter)
JpegLsCompressionRatios.append(JpegLsCompressionRatio)
compressionRatioLZWs.append(compressionRatioLZWAfter)
compressionRatiojpeg2000LossyAfters.append(compressionRatiojpeg2000LossyAfter)
pd.DataFrame({"psnr": psnrs, "ssim": ssims, "jpeg200CompressionRatio (bpp)": jpeg2000CompressionRatioAfters,
"JpegLsCompressionRatio": JpegLsCompressionRatios,
"compressionRatioLZW": compressionRatioLZWs,
"compressionRatiojpeg2000Lossy": compressionRatiojpeg2000LossyAfters}).to_excel("resultsShahedMethod.xlsx")
def test_ocrtNewMethodWithDataset(self):
psnrs = []
ssims = []
jpeg2000CompressionRatioAfters = []
JpegLsCompressionRatiosAfters = []
compressionRatioLZWsAfters = []
jpeg2000CompressionRatioBefores = []
JpegLsCompressionRatiosBefores = []
compressionRatioLZWsBefores = []
datasetName = []
nameOfdatasets = ["Akademie", "Arri exterior", "Color test chart", "Face", "Kodak", "Lake locked", "Lake pan", "Night Odeplatz", "Nikon D40", "Nikon D90", "Nikon D7000", "Siegestor",
"Pool interior"]
for nameOfdataset in nameOfdatasets:
print(nameOfdataset)
if nameOfdataset in ["Kodak", "Nikon D90", "Nikon D7000", "Nikon D40"]:
self.precisionFloatingPoint = 0
bias = 256
else:
self.precisionFloatingPoint = 4
bias = 65536
rgbImages = self.datasetUtils.loadOtherDataset(nameOfdataset)
cfaImages, image_size = self.datasetUtils.convertDatasetToCFA(rgbImages)
def inverseFunction(data):
data = data.astype('float32')
data = data * 2 - bias
data = compute_orct2plus3inverse(data, precisionFloatingPoint=self.precisionFloatingPoint)
data = compute_orct1inverseV2(data, precisionFloatingPoint=self.precisionFloatingPoint)
return np.round(data)
sampleFunctionReverse = inverseFunction
for bayer in cfaImages:
bayer = bayer.astype("float32")
filtered = compute_orct2plus3(compute_orct1(bayer, precisionFloatingPoint=self.precisionFloatingPoint), precisionFloatingPoint=self.precisionFloatingPoint)
test_sample = filtered
filtered = (filtered + bias) / 2
filtered = np.ceil(filtered-np.min(filtered))
psnr, ssim, jpeg2000CompressionRatioAfter, JpegLsCompressionRatioAfter, compressionRatioLZWAfter, jpeg2000CompressionRatioBefore, JpegLsCompressionRatioBefore, compressionRatioLZWBefore = self.evaluation.evaluate(
filtered, bayer,
sampleFunctionReverse,
precisionFloatingPoint=self.precisionFloatingPoint,
roundingMethod="ceil")
datasetName.append(nameOfdataset)
psnrs.append(psnr)
ssims.append(ssim)
jpeg2000CompressionRatioAfters.append(jpeg2000CompressionRatioAfter)
JpegLsCompressionRatiosAfters.append(JpegLsCompressionRatioAfter)
compressionRatioLZWsAfters.append(compressionRatioLZWAfter)
jpeg2000CompressionRatioBefores.append(jpeg2000CompressionRatioBefore)
JpegLsCompressionRatiosBefores.append(JpegLsCompressionRatioBefore)
compressionRatioLZWsBefores.append(compressionRatioLZWBefore)
pd.DataFrame({"Image set name": datasetName,
"psnr": psnrs,
"ssim": ssims,
"jpeg200-LS After": jpeg2000CompressionRatioAfters,
"jpeg-Ls After": JpegLsCompressionRatiosAfters,
"LZW After": compressionRatioLZWsAfters,
"jpeg200-LS Before": jpeg2000CompressionRatioBefores,
"jpeg-Ls Before": JpegLsCompressionRatiosBefores,
"LZW Before": compressionRatioLZWsBefores}).to_excel("resultsAll2.xlsx")
def test_simpleORCT(self):
bayer = np.array([[145, 77, 142, 73], [76, 67, 72, 62], [127, 67, 125, 65], [65, 54, 65, 57],
[145, 75, 142, 73], [46, 61, 72, 62], [117, 47, 105, 65], [87, 31, 53, 17]])
bayer = bayer.astype("float32")
data = compute_orct2plus3(bayer)
data2 = compute_orct2plus3inverse(data)
pass
