class test_DatasetUtils(unittest.TestCase):
def setUp(self) -> None:
super().setUp()
self.datasetUtils = DataUtils()
def test_generateCFAImages(self):
rgbImages = self.datasetUtils.loadKodakDataset()
cfaImages, image_size = self.datasetUtils.convertDatasetToCFA(
rgbImages)
self.assertIsNotNone(cfaImages)
def test_saveNikonImage(self):
self.datasetUtils.saveImageNikon()
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])))