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
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_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_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_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_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")