def test_shrink_image(self):
# Just test to see if it crashes
j_img = pb.create_single_band(100, 120, dtype=np.uint8)
# ---------- Single axis specified
# Shrink with integration
found = pb.shrink_image(j_img,
60,
interp_type=pb.InterpolationType.INTEGRAL)
self.assertEqual(60 * 100 / 120, found.getWidth())
self.assertEqual(60 * 120 / 120, found.getHeight())
# Shrink with interpolation
found = pb.shrink_image(j_img,
60,
interp_type=pb.InterpolationType.BILINEAR)
self.assertEqual(60 * 100 / 120, found.getWidth())
self.assertEqual(60 * 120 / 120, found.getHeight())
# ---------- Both axises specified
# Shrink with integration
found = pb.shrink_image(j_img, (50, 60),
interp_type=pb.InterpolationType.INTEGRAL)
self.assertEqual(60, found.getWidth())
self.assertEqual(50, found.getHeight())
# Shrink with interpolation
found = pb.shrink_image(j_img, (50, 60),
interp_type=pb.InterpolationType.BILINEAR)
self.assertEqual(60, found.getWidth())
self.assertEqual(50, found.getHeight())
def test_array_write_gray(self):
for dtype in self.dtypes:
j_img = pb.create_single_band(100, 120, dtype=dtype)
b_image = pb.BImage(j_img)
b_image[2, 3] = 5
self.assertEqual(5, j_img.get(3, 2))
def test_createHog(self):
j_img = pb.create_single_band(100, 120, dtype=np.uint8)
factory = pb.FactoryDenseDescribe(dtype=np.uint8)
describer = factory.createHoG(None)
describer.detect(j_img)
self.assertTrue(len(describer.locations) > 10)
self.assertTrue(len(describer.descriptions) > 10)
def test_createSurf_stable(self):
j_img = pb.create_single_band(100, 120, dtype=np.uint8)
factory = pb.FactoryDenseDescribe(dtype=np.uint8)
describer = factory.createSurf(pb.ConfigDenseSurfStable())
describer.detect(j_img)
self.assertTrue(len(describer.locations) > 10)
self.assertTrue(len(describer.descriptions) > 10)
def test_mmap_boof_to_numpy_F32(self):
pb_img = pb.create_single_band(100, 120, dtype=np.float32)
pb.fill_uniform(pb_img, -2, 2)
np_img = pb.mmap_boof_to_numpy_F32(pb_img)
self.assertEqual(np_img.dtype, np.float32)
self.assertEqual(np_img.shape[0], pb_img.getHeight())
self.assertEqual(np_img.shape[1], pb_img.getWidth())
self.assertAlmostEqual(np_img[0, 0], pb_img.get(0, 0))
self.assertAlmostEqual(np_img[20, 10], pb_img.get(10, 20))
#!/usr/bin/env python3
import numpy as np
import pyboof as pb
original = pb.load_single_band(
'../data/example/fiducial/image/examples/image00.jpg', np.uint8)
binary = pb.create_single_band(original.getWidth(), original.getHeight(),
np.uint8)
algorithms = []
factory = pb.FactoryThresholdBinary(np.uint8)
algorithms.append(("localGaussian", factory.localGaussian(region_width=11)))
algorithms.append(("localSauvola", factory.localSauvola(region_width=11)))
algorithms.append(("localMean", factory.localMean(region_width=11)))
algorithms.append(("localNick", factory.localNick(region_width=11)))
# algorithms.append(("localOtsu" ,factory.localOtsu(region_width=11))) # This can be slow
algorithms.append(("blockMinMax", factory.blockMinMax(region_width=11)))
algorithms.append(("blockMean", factory.blockMean(region_width=11)))
algorithms.append(("blockOtsu", factory.blockOtsu(region_width=11)))
algorithms.append(("globalEntropy", factory.globalEntropy()))
algorithms.append(("globalOtsu", factory.globalOtsu()))
algorithms.append(("globalLi", factory.globalLi()))
algorithms.append(("globalHuang", factory.globalHuang()))
algorithms.append(("globalFixed", factory.globalFixed(threshold=100)))
image_list = [(original, "Original")]
def test_shape_gray(self):
j_img = pb.create_single_band(100, 120, dtype=np.uint8)
found = pb.BImage(j_img).shape
self.assertEqual(2, len(found))
self.assertEqual(120, found[0])
self.assertEqual(100, found[1])
def test_property_reading(self):
j_img = pb.create_single_band(100, 120, dtype=np.uint8)
b_image = pb.BImage(j_img)
self.assertEqual(100, b_image.width)
self.assertEqual(120, b_image.height)
import numpy as np
import pyboof as pb
original = pb.load_single_band('../data/example/fiducial/image/examples/image00.jpg',np.uint8)
binary = pb.create_single_band(original.getWidth(),original.getHeight(),np.uint8)
algorithms = []
factory = pb.FactoryThresholdBinary(np.uint8)
algorithms.append(("localGaussian",factory.localGaussian(radius=5)))
algorithms.append(("localSauvola" ,factory.localSauvola(radius=5)))
algorithms.append(("localSquare" ,factory.localSquare(radius=5)))
algorithms.append(("globalEntropy",factory.globalEntropy()))
algorithms.append(("globalOtsu" ,factory.globalOtsu()))
algorithms.append(("globalFixed" ,factory.globalFixed(threshold=100)))
image_list = [(original, "Original")]
for a in algorithms:
a[1].process(original, binary)
buffered_binary = pb.swing.render_binary(binary)
image_list.append((buffered_binary,a[0]))
pb.swing.show_list(image_list, title="Binary Thresholding")
raw_input("Press any key to exit")
#!/usr/bin/env python3
import numpy as np
import pyboof as pb
original = pb.load_single_band('../data/example/outdoors01.jpg', np.uint8)
# Let BoofCV decide on the type of image to store the gradient as
deriv_dtype = pb.gradient_dtype(pb.get_dtype(original))
# Declare the gradient images
derivX = pb.create_single_band(original.getWidth(), original.getHeight(),
deriv_dtype)
derivY = pb.create_single_band(original.getWidth(), original.getHeight(),
deriv_dtype)
# Compute the results for a few operators and visualize
pb.gradient(original, derivX, derivY, pb.GradientType.SOBEL)
buffered_sobel = pb.swing.colorize_gradient(derivX, derivY)
pb.gradient(original, derivX, derivY, pb.GradientType.PREWITT)
buffered_prewitt = pb.swing.colorize_gradient(derivX, derivY)
pb.gradient(original, derivX, derivY, pb.GradientType.SCHARR)
buffered_pscharr = pb.swing.colorize_gradient(derivX, derivY)
pb.gradient(original, derivX, derivY, pb.GradientType.THREE)
buffered_three = pb.swing.colorize_gradient(derivX, derivY)
pb.gradient(original, derivX, derivY, pb.GradientType.TWO0)
import pyboof as pb
import numpy as np
original = pb.load_single_band('../../../data/applet/outdoors01.jpg',np.uint8)
# Let BoofCV decide on the type of image to store the gradient as
deriv_dtype = pb.gradient_dtype(pb.get_dtype(original))
# Declare the gradient images
derivX = pb.create_single_band(original.getWidth(),original.getHeight(),deriv_dtype)
derivY = pb.create_single_band(original.getWidth(),original.getHeight(),deriv_dtype)
# Compute the results for a few operators and visualize
pb.gradient(original,derivX,derivY,pb.GradientType.SOBEL)
buffered_sobel = pb.swing.colorize_gradient(derivX,derivY)
pb.gradient(original,derivX,derivY,pb.GradientType.PREWITT)
buffered_prewitt = pb.swing.colorize_gradient(derivX,derivY)
pb.gradient(original,derivX,derivY,pb.GradientType.THREE)
buffered_three = pb.swing.colorize_gradient(derivX,derivY)
pb.gradient(original,derivX,derivY,pb.GradientType.TWO0)
buffered_two0 = pb.swing.colorize_gradient(derivX,derivY)
pb.gradient(original,derivX,derivY,pb.GradientType.TWO1)
buffered_two1 = pb.swing.colorize_gradient(derivX,derivY)
# display the results in a single window as a list
image_list = [(original,"original"),
