CofeehousePy/deps/scikit-image/viewer_examples/plugins/probabilistic_hough.py

47 lines
1.4 KiB
Python

import numpy as np
from skimage import data
from skimage import draw
from skimage.transform import probabilistic_hough_line
from skimage.viewer import ImageViewer
from skimage.viewer.widgets import Slider
from skimage.viewer.plugins.overlayplugin import OverlayPlugin
from skimage.viewer.plugins.canny import CannyPlugin
def line_image(shape, lines):
image = np.zeros(shape, dtype=bool)
for end_points in lines:
# hough lines returns (x, y) points, draw.line wants (row, columns)
end_points = np.asarray(end_points)[:, ::-1]
image[draw.line(*np.ravel(end_points))] = 1
return image
def hough_lines(image, *args, **kwargs):
# Set threshold to 0.5 since we're working with a binary image (from canny)
lines = probabilistic_hough_line(image, threshold=0.5, *args, **kwargs)
image = line_image(image.shape, lines)
return image
image = data.camera()
canny_viewer = ImageViewer(image)
canny_plugin = CannyPlugin()
canny_viewer += canny_plugin
hough_plugin = OverlayPlugin(image_filter=hough_lines)
hough_plugin.name = 'Hough Lines'
hough_plugin += Slider('line length', 0, 100)
hough_plugin += Slider('line gap', 0, 20)
# Passing a plugin to a viewer connects the output of the plugin to the viewer.
hough_viewer = ImageViewer(canny_plugin)
hough_viewer += hough_plugin
# Show viewers displays both viewers since `hough_viewer` is connected to
# `canny_viewer` through `canny_plugin`
canny_viewer.show()