228 lines
7.2 KiB
Python
228 lines
7.2 KiB
Python
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from functools import reduce
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import numpy as np
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from ..draw import polygon
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LEFT_CLICK = 1
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RIGHT_CLICK = 3
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def _mask_from_vertices(vertices, shape, label):
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mask = np.zeros(shape, dtype=int)
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pr = [y for x, y in vertices]
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pc = [x for x, y in vertices]
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rr, cc = polygon(pr, pc, shape)
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mask[rr, cc] = label
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return mask
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def _draw_polygon(ax, vertices, alpha=0.4):
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from matplotlib.patches import Polygon
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from matplotlib.collections import PatchCollection
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import matplotlib.pyplot as plt
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polygon = Polygon(vertices, closed=True)
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p = PatchCollection([polygon], match_original=True, alpha=alpha)
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polygon_object = ax.add_collection(p)
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plt.draw()
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return polygon_object
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def manual_polygon_segmentation(image, alpha=0.4, return_all=False):
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"""Return a label image based on polygon selections made with the mouse.
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Parameters
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----------
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image : (M, N[, 3]) array
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Grayscale or RGB image.
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alpha : float, optional
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Transparency value for polygons drawn over the image.
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return_all : bool, optional
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If True, an array containing each separate polygon drawn is returned.
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(The polygons may overlap.) If False (default), latter polygons
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"overwrite" earlier ones where they overlap.
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Returns
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-------
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labels : array of int, shape ([Q, ]M, N)
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The segmented regions. If mode is `'separate'`, the leading dimension
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of the array corresponds to the number of regions that the user drew.
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Notes
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-----
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Use left click to select the vertices of the polygon
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and right click to confirm the selection once all vertices are selected.
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Examples
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--------
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>>> from skimage import data, future, io
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>>> camera = data.camera()
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>>> mask = future.manual_polygon_segmentation(camera) # doctest: +SKIP
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>>> io.imshow(mask) # doctest: +SKIP
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>>> io.show() # doctest: +SKIP
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"""
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import matplotlib
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from matplotlib.patches import Polygon
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from matplotlib.collections import PatchCollection
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import matplotlib.pyplot as plt
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list_of_vertex_lists = []
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polygons_drawn = []
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temp_list = []
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preview_polygon_drawn = []
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if image.ndim not in (2, 3):
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raise ValueError('Only 2D grayscale or RGB images are supported.')
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fig, ax = plt.subplots()
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fig.subplots_adjust(bottom=0.2)
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ax.imshow(image, cmap="gray")
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ax.set_axis_off()
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def _undo(*args, **kwargs):
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if list_of_vertex_lists:
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list_of_vertex_lists.pop()
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# Remove last polygon from list of polygons...
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last_poly = polygons_drawn.pop()
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# ... then from the plot
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last_poly.remove()
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fig.canvas.draw_idle()
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undo_pos = fig.add_axes([0.85, 0.05, 0.075, 0.075])
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undo_button = matplotlib.widgets.Button(undo_pos, u'\u27F2')
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undo_button.on_clicked(_undo)
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def _extend_polygon(event):
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# Do not record click events outside axis or in undo button
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if event.inaxes is None or event.inaxes is undo_pos:
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return
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# Do not record click events when toolbar is active
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if fig.canvas.manager.toolbar._active is not None:
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return
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if event.button == LEFT_CLICK: # Select vertex
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temp_list.append([event.xdata, event.ydata])
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# Remove previously drawn preview polygon if any.
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if preview_polygon_drawn:
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poly = preview_polygon_drawn.pop()
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poly.remove()
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# Preview polygon with selected vertices.
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polygon = _draw_polygon(ax, temp_list, alpha=(alpha / 1.4))
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preview_polygon_drawn.append(polygon)
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elif event.button == RIGHT_CLICK: # Confirm the selection
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if not temp_list:
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return
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# Store the vertices of the polygon as shown in preview.
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# Redraw polygon and store it in polygons_drawn so that
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# `_undo` works correctly.
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list_of_vertex_lists.append(temp_list[:])
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polygon_object = _draw_polygon(ax, temp_list, alpha=alpha)
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polygons_drawn.append(polygon_object)
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# Empty the temporary variables.
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preview_poly = preview_polygon_drawn.pop()
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preview_poly.remove()
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del temp_list[:]
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plt.draw()
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fig.canvas.mpl_connect('button_press_event', _extend_polygon)
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plt.show(block=True)
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labels = (_mask_from_vertices(vertices, image.shape[:2], i)
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for i, vertices in enumerate(list_of_vertex_lists, start=1))
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if return_all:
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return np.stack(labels)
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else:
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return reduce(np.maximum, labels, np.broadcast_to(0, image.shape[:2]))
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def manual_lasso_segmentation(image, alpha=0.4, return_all=False):
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"""Return a label image based on freeform selections made with the mouse.
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Parameters
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----------
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image : (M, N[, 3]) array
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Grayscale or RGB image.
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alpha : float, optional
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Transparency value for polygons drawn over the image.
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return_all : bool, optional
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If True, an array containing each separate polygon drawn is returned.
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(The polygons may overlap.) If False (default), latter polygons
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"overwrite" earlier ones where they overlap.
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Returns
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-------
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labels : array of int, shape ([Q, ]M, N)
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The segmented regions. If mode is `'separate'`, the leading dimension
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of the array corresponds to the number of regions that the user drew.
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Notes
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-----
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Press and hold the left mouse button to draw around each object.
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Examples
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--------
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>>> from skimage import data, future, io
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>>> camera = data.camera()
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>>> mask = future.manual_lasso_segmentation(camera) # doctest: +SKIP
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>>> io.imshow(mask) # doctest: +SKIP
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>>> io.show() # doctest: +SKIP
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"""
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import matplotlib
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from matplotlib.patches import Polygon
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from matplotlib.collections import PatchCollection
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import matplotlib.pyplot as plt
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list_of_vertex_lists = []
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polygons_drawn = []
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if image.ndim not in (2, 3):
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raise ValueError('Only 2D grayscale or RGB images are supported.')
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fig, ax = plt.subplots()
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fig.subplots_adjust(bottom=0.2)
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ax.imshow(image, cmap="gray")
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ax.set_axis_off()
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def _undo(*args, **kwargs):
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if list_of_vertex_lists:
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list_of_vertex_lists.pop()
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# Remove last polygon from list of polygons...
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last_poly = polygons_drawn.pop()
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# ... then from the plot
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last_poly.remove()
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fig.canvas.draw_idle()
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undo_pos = fig.add_axes([0.85, 0.05, 0.075, 0.075])
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undo_button = matplotlib.widgets.Button(undo_pos, u'\u27F2')
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undo_button.on_clicked(_undo)
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def _on_lasso_selection(vertices):
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if len(vertices) < 3:
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return
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list_of_vertex_lists.append(vertices)
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polygon_object = _draw_polygon(ax, vertices, alpha=alpha)
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polygons_drawn.append(polygon_object)
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plt.draw()
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lasso = matplotlib.widgets.LassoSelector(ax, _on_lasso_selection)
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plt.show(block=True)
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labels = (_mask_from_vertices(vertices, image.shape[:2], i)
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for i, vertices in enumerate(list_of_vertex_lists, start=1))
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if return_all:
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return np.stack(labels)
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else:
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return reduce(np.maximum, labels, np.broadcast_to(0, image.shape[:2]))
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