def test_extract_dtype(self):
im = np.load('data/img_4d.npy').astype(np.float)
patch_shape = (8, 15, 29, 2)
patch_stride = (7, 26, 31, 3)
pe_str = PatchExtractor(patch_shape, stride=patch_stride)
patch_str = pe_str.extract(im)
self.assertEqual(patch_str.dtype, patch_str.dtype)
def test_padding_input(self):
im = np.random.rand(141, 141)
patch_shape = (128, 128)
pe = PatchExtractor(patch_shape, padding='constant')
patch = pe.extract(im)
im_rec = pe.reconstruct(patch)
self.assertTrue(np.allclose(im_rec, im))
def test_extract_indexes_memmap(self):
im = np.load('data/img_4d.npy', mmap_mode="r")
patch_shape = (9, 32, 32, 3)
indexes = np.arange(0, 12, 3)
pe = PatchExtractor(patch_shape, indexes=indexes)
patch = pe.extract(im)
self.assertEqual(patch.ndim, 1 + im.ndim)
self.assertEqual(patch.shape[0], len(indexes))
def test_count_patches(self):
im = np.asarray(Image.open('data/img_color.png'))
patch_shape = (15, 29, 1)
patch_stride = (34, 5, 2)
pe = PatchExtractor(patch_shape, stride=patch_stride)
patch = pe.extract(im)
self.assertEqual(count_patches(im.shape, patch_shape, patch_stride),
np.prod(patch.shape[:3]))
def test_extract_indexes(self):
im = np.asarray(Image.open('data/img_color.png'))
patch_shape = (32, 32, 2)
indexes = np.arange(0, 42, 3)
pe = PatchExtractor(patch_shape, indexes=indexes)
patch = pe.extract(im)
self.assertEqual(patch.ndim, 1 + im.ndim)
self.assertEqual(patch.shape[0], len(indexes))
def test_padding_patch(self):
im = np.random.rand(101, 101)
patch_shape = (128, 128)
pe = PatchExtractor(patch_shape, padding='linear_ramp')
patch = pe.extract(im)
im_rec = pe.reconstruct(patch)
self.assertTrue(np.allclose(im_rec, im))
def test_mid_intensity_high_texture_threshold(self):
im = np.asarray(Image.open('data/img_color.png'))
patch_shape = (32, 32, 2)
func = mid_intensity_high_texture
threshold = 0.8
pe = PatchExtractor(patch_shape, function=func, threshold=threshold)
patch = pe.extract(im)
p = patch[0]
self.assertEqual(len(patch), 147)
self.assertAlmostEqual(mid_intensity_high_texture(p),
0.926545988463008)
def get_transformed_data(self,patchSize, dataDir='features/'):
patchEx1 = PatchExtractor(self.start, self.start, self.movePosition, self.isblack)
patchEx2 = PatchExtractor(self.start, self.move, self.movePosition, self.isblack)
patchEx1.setPatchSize(patchSize)
patchEx2.setPatchSize(patchSize)
data1 = patchEx1.calculate_feature()
data2 = patchEx2.calculate_feature()
f = open(dataDir+'dictionary' + str(patchSize) + '.txt')
dictionaryData = f.read()
f.close()
dictionary = pickle.loads(dictionaryData)
data1 = dictionary.transform(data1)
data2 = dictionary.transform(data2)
data = data2 - data1
return data;
def test_extract_output_shape_4d_memmap(self):
im = np.load('data/img_4d.npy', mmap_mode="r")
patch_shape = (9, 32, 32, 3)
pe = PatchExtractor(patch_shape)
patch = pe.extract(im)
stride = (7, 17, 17, 2)
pe_stride = PatchExtractor(patch_shape, stride=stride)
patch_stride = pe_stride.extract(im)
offset = (1, 0, 0, 0)
pe_offset = PatchExtractor(patch_shape, offset=offset)
patch_offset = pe_offset.extract(im)
self.assertEqual(patch.shape, (3, 2, 2, 1, 9, 32, 32, 3))
self.assertEqual(patch_stride.shape, (4, 2, 2, 1, 9, 32, 32, 3))
self.assertEqual(patch_offset.shape, (3, 2, 2, 1, 9, 32, 32, 3))
def test_init_with_function(self):
pe = PatchExtractor((24, 24, 24),
offset=(2, 2, 2),
stride=(8, 8, 8),
threshold=0.1,
function=mid_intensity_high_texture)
self.assertIsInstance(pe.dim, tuple)
self.assertIsInstance(pe.offset, tuple)
self.assertIsInstance(pe.stride, tuple)
self.assertEqual(pe.rand, False)
self.assertIsInstance(pe.threshold, float)
self.assertIsNone(pe.indexes)
self.assertTrue(callable(pe.function_handler))
def test_init_with_rand(self):
pe = PatchExtractor((24, 24, 24),
offset=(2, 2, 2),
stride=(8, 8, 8),
threshold=0.1,
rand=True)
self.assertIsInstance(pe.dim, tuple)
self.assertIsInstance(pe.offset, tuple)
self.assertIsInstance(pe.stride, tuple)
self.assertIsNone(pe.function_handler)
self.assertIsInstance(pe.threshold, float)
self.assertIsNone(pe.indexes)
self.assertIsInstance(pe.rand, bool)
def test_reconstruct_grayscale(self):
im = np.asarray(Image.open('data/img_gray.png'))
patch_shape = (15, 29)
pe = PatchExtractor(patch_shape)
patch = pe.extract(im)
im_recon = pe.reconstruct(patch)
patch_stride = (2, 3)
pe_str = PatchExtractor(patch_shape, stride=patch_stride)
patch_str = pe_str.extract(im)
im_recon_str = pe_str.reconstruct(patch_str)
im_recon_gt = np.asarray(Image.open('data/img_gray_recon_15_29.png'))
im_recon_str_gt = np.asarray(
Image.open('data/img_gray_recon_15_29_str_2_3.png'))
self.assertTrue(np.allclose(im_recon_gt, im_recon))
self.assertTrue(np.allclose(im_recon_str_gt, im_recon_str))
def test_reconstruct_color(self):
im = np.asarray(Image.open('data/img_color.png'))
patch_shape = (15, 29, 2)
pe = PatchExtractor(patch_shape)
patch = pe.extract(im)
im_recon = pe.reconstruct(patch)
patch_stride = (2, 3, 1)
pe_str = PatchExtractor(patch_shape, stride=patch_stride)
patch_str = pe_str.extract(im)
im_recon_str = pe_str.reconstruct(patch_str)
im_recon_gt = np.asarray(
Image.open('data/img_color_recon_15_29_2.png'))
im_recon_str_gt = np.asarray(
Image.open('data/img_color_recon_15_29_2_str_2_3_1.png'))
self.assertTrue(np.allclose(im_recon_gt, im_recon))
self.assertTrue(np.allclose(im_recon_str_gt, im_recon_str))
def test_reconstruct_4d(self):
im = np.load('data/img_4d.npy')
patch_shape = (8, 15, 29, 2)
pe = PatchExtractor(patch_shape)
patch = pe.extract(im)
im_recon = pe.reconstruct(patch)
patch_stride = (7, 26, 31, 3)
pe_str = PatchExtractor(patch_shape, stride=patch_stride)
patch_str = pe_str.extract(im)
im_recon_str = pe_str.reconstruct(patch_str)
im_recon_gt = np.load('data/img_4d_recon_8_15_29_2.npy')
im_recon_str_gt = np.load(
'data/img_4d_recon_8_15_29_2_str_7_26_31_1.npy')
self.assertTrue(np.allclose(im_recon_gt, im_recon))
self.assertTrue(
np.allclose(im_recon_str_gt, im_recon_str, equal_nan=True))
def test_extract_output_shape_4d(self):
im = np.load('data/img_4d.npy')
patch_shape = (9, 32, 32, 3)
pe = PatchExtractor(patch_shape)
patch = pe.extract(im)
stride = (7, 17, 17, 2)
pe_stride = PatchExtractor(patch_shape, stride=stride)
patch_stride = pe_stride.extract(im)
self.assertEqual(patch.shape, (3, 2, 2, 1, 9, 32, 32, 3))
self.assertEqual(patch_stride.shape, (4, 2, 2, 1, 9, 32, 32, 3))
def test_extract_output_shape_color(self):
im = np.asarray(Image.open('data/img_color.png'))
patch_shape = (32, 32, 2)
pe = PatchExtractor(patch_shape)
patch = pe.extract(im)
stride = (17, 17, 2)
pe_stride = PatchExtractor(patch_shape, stride=stride)
patch_stride = pe_stride.extract(im)
self.assertEqual(patch.shape, (16, 16, 1, 32, 32, 2))
self.assertEqual(patch_stride.shape, (29, 29, 1, 32, 32, 2))
def test_extract_num_memmap(self):
im = np.load('data/img_4d.npy', mmap_mode="r")
patch_shape = (9, 32, 32, 3)
num = 8
pe_num_seq = PatchExtractor(patch_shape, num=num, rand=False)
patch_num_seq = pe_num_seq.extract(im)
pe_num_rand = PatchExtractor(patch_shape, num=num, rand=True)
patch_num_rand = pe_num_rand.extract(im)
self.assertEqual(patch_num_seq.ndim, 1 + im.ndim)
self.assertEqual(patch_num_seq.shape[0], num)
self.assertEqual(patch_num_rand.ndim, 1 + im.ndim)
self.assertEqual(patch_num_rand.shape[0], num)
def test_extract_num(self):
im = np.asarray(Image.open('data/img_color.png'))
patch_shape = (32, 32, 2)
num = 8
pe_seq = PatchExtractor(patch_shape, num=num, rand=False)
patch_seq = pe_seq.extract(im)
pe_rand = PatchExtractor(patch_shape, num=num, rand=True)
patch_rand = pe_rand.extract(im)
self.assertEqual(patch_seq.ndim, 1 + im.ndim)
self.assertEqual(patch_seq.shape[0], num)
self.assertEqual(patch_rand.ndim, 1 + im.ndim)
self.assertEqual(patch_rand.shape[0], num)
def calculate_feature(self, dataDir='../features/'):
patchEx1 = PatchExtractor(self.start, self.start, self.movePosition, self.isblack)
patchEx2 = PatchExtractor(self.start, self.move, self.movePosition, self.isblack)
patchEx1.setPatchSize(4)
patchEx2.setPatchSize(4)
data41 = patchEx1.calculate_feature()
data42 = patchEx2.calculate_feature()
patchEx1.setPatchSize(5)
patchEx2.setPatchSize(5)
data51 = patchEx1.calculate_feature()
data52 = patchEx2.calculate_feature()
'''patchEx1.setPatchSize(6)
patchEx2.setPatchSize(6)
data61 = patchEx1.calculate_feature()
data62 = patchEx2.calculate_feature()'''
f = open(dataDir+'dictionary4.txt')
dictionaryData = f.read()
f.close()
dictionary = pickle.loads(dictionaryData)
data1 = dictionary.transform(data41)
data2 = dictionary.transform(data42)
data = data2 - data1
features4 = np.amax(data, axis=0)
f = open(dataDir+'dictionary5.txt')
dictionaryData = f.read()
f.close()
dictionary = pickle.loads(dictionaryData)
data1 = dictionary.transform(data51)
data2 = dictionary.transform(data52)
data = data2 - data1
features5 = np.amax(data, axis=0)
'''f = open('features/dictionary6.txt')
dictionaryData = f.read()
f.close()
dictionary = pickle.loads(dictionaryData)
data1 = dictionary.transform(data61)
data2 = dictionary.transform(data62)
data = data2 - data1
features6 = np.amax(data, axis=0)'''
return list(features4) + list(features5)# + list(features6)