def test_08(self): lmbda = 3 opt = tvl1.TVL1Denoise.Options({'MaxMainIter': 20}) b = tvl1.TVL1Denoise(self.D, lmbda, opt) b.solve() opt['Y0'] = b.Y try: c = tvl1.TVL1Denoise(self.D, lmbda, opt) c.solve() except Exception as e: print(e) assert 0
def test_01(self): lmbda = 3 try: b = tvl1.TVL1Denoise(self.D, lmbda) b.solve() except Exception as e: print(e) assert 0
def test_01(self): lmbda = 3 opt = tvl1.TVL1Denoise.Options({ 'Verbose': False, 'gEvalY': False, 'MaxMainIter': 250 }) b = tvl1.TVL1Denoise(self.D, lmbda, opt, axes=(0, 1, 2)) X = b.solve() assert cp.abs(b.itstat[-1].ObjFun - 6219.6209699337605) < 1e-6 assert sm.mse(self.U, X) < 1e-6
def test_01(self): lmbda = 3 opt = tvl1.TVL1Denoise.Options({ 'Verbose': False, 'gEvalY': False, 'MaxMainIter': 250 }) b = tvl1.TVL1Denoise(self.D, lmbda, opt) X = b.solve() assert cp.abs(b.itstat[-1].ObjFun - 447.78101756451662) < 1e-6 assert sm.mse(self.U, X) < 1e-6
def test_05(self): lmbda = 3 dt = cp.float64 opt = tvl1.TVL1Denoise.Options({ 'Verbose': False, 'MaxMainIter': 20, 'AutoRho': { 'Enabled': True }, 'DataType': dt }) b = tvl1.TVL1Denoise(self.D, lmbda, opt=opt) b.solve() assert b.X.dtype == dt assert b.Y.dtype == dt assert b.U.dtype == dt
""" Create solver object and solve, returning the the denoised image ``imgr``. """ if not cupy_enabled(): print('CuPy/GPU device not available: running without GPU acceleration\n') else: id = select_device_by_load() info = gpu_info() if info: print('Running on GPU %d (%s)\n' % (id, info[id].name)) b = tvl1.TVL1Denoise(np2cp(imgn), lmbda, opt) imgr = cp2np(b.solve()) """ Display solve time and denoising performance. """ print("TVL1Denoise solve time: %5.2f s" % b.timer.elapsed('solve')) print("Noisy image PSNR: %5.2f dB" % metric.psnr(img, imgn)) print("Denoised image PSNR: %5.2f dB" % metric.psnr(img, imgr)) """ Display reference, corrupted, and denoised images. """