def test_ksvd_normal_input(self):
n_nonzero_coefs = 4
n_samples = 512
n_features = 32
n_components = 24
max_iter = 500
A0, X = generate_dictionary_and_samples(n_samples, n_features, n_components, n_nonzero_coefs)
model = KSVD(n_components=n_components, transform_n_nonzero_coefs=n_nonzero_coefs, max_iter=max_iter,
method='normal')
model.fit(X)
# check error of learning
self.assertTrue(model.error_[-1] < 10)
self.assertTrue(model.n_iter_ <= max_iter)
# check estimated dictionary
norm = np.linalg.norm(model.components_ - A0, ord='fro')
self.assertTrue(norm < 15)
# check reconstructed data
code = model.transform(X)
reconstructed = np.dot(code, model.components_)
reconstruct_error = np.linalg.norm(reconstructed - X, ord='fro')
self.assertTrue(reconstruct_error < 15)
def test_transform_with_mask(self):
n_nonzero_coefs = 4
n_samples = 128
n_features = 32
n_components = 16
max_iter = 10
missing_value = 0
A0, X = generate_dictionary_and_samples(n_samples, n_features,
n_components, n_nonzero_coefs)
X[X < 0.1] = missing_value
mask = np.where(X == missing_value, 0, 1)
model = KSVD(n_components=n_components,
transform_n_nonzero_coefs=n_nonzero_coefs,
max_iter=max_iter,
missing_value=missing_value,
method='normal')
model.fit(X)
# check error of learning
code = model.transform(X)
err = np.linalg.norm(mask * (X - code.dot(model.components_)), 'fro')
self.assertTrue(err <= model.error_[0])
self.assertTrue(model.n_iter_ <= max_iter)
def test_approximate_ksvd(self):
n_nonzero_coefs = 5
n_samples = 128
n_features = 32
n_components = 16
max_iter = 10
A0, X = generate_dictionary_and_samples(n_samples, n_features, n_components, n_nonzero_coefs)
model = KSVD(n_components=n_components, transform_n_nonzero_coefs=n_nonzero_coefs, max_iter=max_iter,
method='approximate')
model.fit(X)
# check error of learning
self.assertTrue(model.error_[-1] <= model.error_[0])
self.assertTrue(model.n_iter_ <= max_iter)
def test_approximate_ksvd_warm_start(self):
n_nonzero_coefs = 5
n_samples = 128
n_features = 32
n_components = 16
max_iter = 1
A0, X = generate_dictionary_and_samples(n_samples, n_features, n_components, n_nonzero_coefs)
model = KSVD(n_components=n_components, transform_n_nonzero_coefs=n_nonzero_coefs, max_iter=max_iter,
method='approximate')
prev_error = np.linalg.norm(X, 'fro')
for i in range(10):
model.fit(X)
# print(model.error_)
self.assertTrue(model.error_[-1] <= prev_error)
prev_error = model.error_[-1]
def test_ksvd_input_with_missing_values(self):
n_nonzero_coefs = 4
n_samples = 128
n_features = 32
n_components = 16
max_iter = 100
missing_value = 0
A0, X = generate_dictionary_and_samples(n_samples, n_features, n_components, n_nonzero_coefs)
X[X < 0.1] = missing_value
model = KSVD(n_components=n_components, transform_n_nonzero_coefs=n_nonzero_coefs, max_iter=max_iter,
missing_value=missing_value, method='normal')
model.fit(X)
# check error of learning
self.assertTrue(model.error_[-1] <= model.error_[0])
self.assertTrue(model.n_iter_ <= max_iter)
def test_transform(self):
n_nonzero_coefs = 4
n_samples = 128
n_features = 32
n_components = 24
max_iter = 500
A0, X = generate_dictionary_and_samples(n_samples, n_features, n_components, n_nonzero_coefs)
model = KSVD(n_components=n_components, transform_n_nonzero_coefs=n_nonzero_coefs, max_iter=max_iter,
method='normal')
model.fit(X)
# check error of learning
code = model.transform(X)
err = np.linalg.norm(X - code.dot(model.components_), 'fro')
self.assertTrue(err <= model.error_[-1])
self.assertTrue(model.n_iter_ <= max_iter)
def test_ksvd_dict_init(self):
D = np.random.rand(10, 100)
model = KSVD(n_components=10,
transform_n_nonzero_coefs=5,
max_iter=1,
method='normal',
dict_init=D)
npt.assert_array_equal(model.dict_init, D)
# shape of X is invalid against initial dictionary
X = np.random.rand(20, 200)
with self.assertRaises(ValueError):
model.fit(X)
# n_components is invalid against initial dictionary
X = np.random.rand(20, 100)
model = KSVD(n_components=20,
transform_n_nonzero_coefs=5,
max_iter=1,
method='normal',
dict_init=D)
with self.assertRaises(ValueError):
model.fit(X)
def concat_tile(im_list_2d):
return cv2.vconcat([cv2.hconcat(im_list_h) for im_list_h in im_list_2d])
im = cv2.imread("./food_jambalaya.png")
im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) #cv2.COLOR_BGR2RGB
im = cv2.resize(im, dsize=(128, 128), interpolation=cv2.INTER_CUBIC) / 256.0
im = np.clip(im, 0.001, 0.999)
plt.title("origin")
plt.imshow(im, vmin=0.0, vmax=1.0)
plt.show()
ksvd = KSVD(n_components=12, transform_n_nonzero_coefs=None, n_jobs=12)
ksvd.fit(im.reshape(-1, 64).astype(np.float64))
D = ksvd.components_.reshape(-1, 8, 8).astype(np.float64)
for i in range(12):
plt.subplot(3, 4, i + 1)
plt.imshow(D[i], vmin=0.0, vmax=1.0)
plt.axis('off')
plt.show()
X = ksvd.transform(im.reshape(-1, 64).astype(np.float64))
plt.title("X")
plt.imshow(X, vmin=0.0, vmax=1.0)
plt.show()
plt.title("Re")
_y = np.dot(X, ksvd.components_)
if maxs < 10:
break
#im =cv2.imread('./manner_door_open_ashi.png')
im = cv2.imread('./apple2orange/testA/n07740461_51.jpg')
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
im = cv2.resize(im, dsize=(128, 128), interpolation=cv2.INTER_CUBIC) / 256.0
im = np.clip(im, 0.001, 0.999)
plt.title("origin")
plt.imshow(im, vmin=0.0, vmax=1.0)
plt.show()
ksvd = KSVD(n_components=16,
transform_n_nonzero_coefs=None,
n_jobs=multiprocessing.cpu_count() - 1)
ksvd.fit(ims)
D = ksvd.components_.reshape(-1, 8, 8, 3).astype(np.float64)
for i in range(12):
plt.subplot(3, 4, i + 1)
plt.imshow(D[i], vmin=0.0, vmax=1.0)
plt.axis('off')
plt.show()
X = ksvd.transform(im.reshape(-1, 8 * 8 * 3).astype(np.float64))
plt.title("X")
plt.imshow(X, vmin=0.0, vmax=1.0)
plt.show()
plt.title("Re")
_y = np.dot(X, ksvd.components_)