#!/usr/bin/env python3
import numpy as np
import pyboof as pb
from pyboof.swing import visualize_lines
# Load an image with strong lines in it
image = pb.load_single_band("../data/example/simple_objects.jpg", np.uint8)
blurred = image.createSameShape()
# Appying a little bit of blur tends to improve the results
pb.blur_gaussian(image, blurred, radius=5)
# There are a few variants of Hough in BoofCV. The variant we will use here uses the image gradient directly
# This is useful you want to find the edges of objects. If you have an image with thin black lines and you want
# to find the lines and not their edges then the binary variant is what you want to use
config_gradient = pb.ConfigHoughGradient(10)
# Detect the lines using several different variants of Hough line detector
results = []
detector = pb.FactoryDetectLine(
np.uint8).houghLinePolar(config_hough=config_gradient)
results.append(("Gradient Polar", detector.detect(blurred)))
detector = pb.FactoryDetectLine(
np.uint8).houghLineFoot(config_hough=config_gradient)
results.append(("Gradient Foot", detector.detect(blurred)))
# Use swing to visualize the results
visualize_lines(image, results)
#!/usr/bin/env python3
import numpy as np
import pyboof as pb
import time
# This example shows how you can adjust the number of threads that BoofCV will use in the JVM
original = pb.load_single_band('../data/example/outdoors01.jpg', np.uint8)
gaussian = original.createSameShape()
# Let's warm up the JVM.
for i in range(5):
time0 = time.time()
pb.blur_gaussian(original, gaussian, radius=12)
time1 = time.time()
print("Warm up iteration {:.1f} ms".format(1000 * (time1 - time0)))
print()
print()
N = 30
time0 = time.time()
for i in range(N):
pb.blur_gaussian(original, gaussian, radius=12)
time1 = time.time()
print("Time with default threads {:.1f} ms".format(1000 * (time1 - time0)))
pb.set_max_threads(1)
import numpy as np
import pyboof as pb
original = pb.load_single_band("../data/example/outdoors01.jpg", np.uint8)
gaussian = original.createSameShape() # useful function which creates a new image of the
mean = original.createSameShape() # same type and shape as the original
# Apply different types of blur to the image
pb.blur_gaussian(original, gaussian, radius=3)
pb.blur_mean(original, mean, radius=3)
# display the results in a single window as a list
image_list = [(original, "original"), (gaussian, "gaussian"), (mean, "mean")]
pb.swing.show_list(image_list, title="Outputs")
raw_input("Press any key to exit")
