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 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 _evaluate(self, X, out, *args, **kwargs):
n_particles = X.shape[0] # number of particles
psnrs = []
ssims = []
jpeg200compressions = []
for x in X:
x = np.reshape(x, [2, 2])
filtered = compute_orct2(compute_orct1(self.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
try:
psnr, ssim, jpeg2000CompressionRatioAfter, jpeg2000CompressionRatioBefore = self.evaluation.evaluate(
filtered,
self.twoComplement,
sampleFunctionReverse,
verbose=False)
except:
psnr = 1
ssim = .1
jpeg2000CompressionRatioAfter = 1
jpeg2000CompressionRatioBefore = 1.001
psnrs.append(1 / psnr)
ssims.append(1 / ssim)
CR = jpeg2000CompressionRatioAfter - jpeg2000CompressionRatioBefore
if CR < 0:
CR = 0.001
jpeg200compressions.append(1 / CR)
out["F"] = np.column_stack([psnrs, ssims, jpeg200compressions])
out["G"] = np.column_stack(
[-1 * np.asarray(psnrs), -1 * np.asarray(ssims)])
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_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_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_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_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")
