.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/developers/plot_max_tree.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_developers_plot_max_tree.py: ======== Max-tree ======== The max-tree is a hierarchical representation of an image that is the basis for a large family of morphological filters. If we apply a threshold operation to an image, we obtain a binary image containing one or several connected components. If we apply a lower threshold, all the connected components from the higher threshold are contained in the connected components from the lower threshold. This naturally defines a hierarchy of nested components that can be represented by a tree. whenever a connected component A obtained by thresholding with threshold t1 is contained in a component B obtained by thresholding with threshold t1 < t2, we say that B is the parent of A. The resulting tree structure is called a component tree. The max-tree is a compact representation of such a component tree. [1]_, [2]_, [3]_, [4]_ In this example we give an intuition of what a max-tree is. References ---------- .. [1] Salembier, P., Oliveras, A., & Garrido, L. (1998). Antiextensive Connected Operators for Image and Sequence Processing. IEEE Transactions on Image Processing, 7(4), 555-570. :DOI:`10.1109/83.663500` .. [2] Berger, C., Geraud, T., Levillain, R., Widynski, N., Baillard, A., Bertin, E. (2007). Effective Component Tree Computation with Application to Pattern Recognition in Astronomical Imaging. In International Conference on Image Processing (ICIP) (pp. 41-44). :DOI:`10.1109/ICIP.2007.4379949` .. [3] Najman, L., & Couprie, M. (2006). Building the component tree in quasi-linear time. IEEE Transactions on Image Processing, 15(11), 3531-3539. :DOI:`10.1109/TIP.2006.877518` .. [4] Carlinet, E., & Geraud, T. (2014). A Comparative Review of Component Tree Computation Algorithms. IEEE Transactions on Image Processing, 23(9), 3885-3895. :DOI:`10.1109/TIP.2014.2336551` .. GENERATED FROM PYTHON SOURCE LINES 42-50 .. code-block:: Python import numpy as np import matplotlib.pyplot as plt from matplotlib.lines import Line2D from skimage.morphology import max_tree import networkx as nx .. GENERATED FROM PYTHON SOURCE LINES 51-52 Before we start : a few helper functions .. GENERATED FROM PYTHON SOURCE LINES 52-189 .. code-block:: Python def plot_img(ax, image, title, plot_text, image_values): """Plot an image, overlaying image values or indices.""" ax.imshow(image, cmap='gray', aspect='equal', vmin=0, vmax=np.max(image)) ax.set_title(title) ax.set_yticks([]) ax.set_xticks([]) for x in np.arange(-0.5, image.shape[0], 1.0): ax.add_artist( Line2D((x, x), (-0.5, image.shape[0] - 0.5), color='blue', linewidth=2) ) for y in np.arange(-0.5, image.shape[1], 1.0): ax.add_artist(Line2D((-0.5, image.shape[1]), (y, y), color='blue', linewidth=2)) if plot_text: for i, j in np.ndindex(*image_values.shape): ax.text( j, i, image_values[i, j], fontsize=8, horizontalalignment='center', verticalalignment='center', color='red', ) return def prune(G, node, res): """Transform a canonical max tree to a max tree.""" value = G.nodes[node]['value'] res[node] = str(node) preds = [p for p in G.predecessors(node)] for p in preds: if G.nodes[p]['value'] == value: res[node] += f", {p}" G.remove_node(p) else: prune(G, p, res) G.nodes[node]['label'] = res[node] return def accumulate(G, node, res): """Transform a max tree to a component tree.""" total = G.nodes[node]['label'] parents = G.predecessors(node) for p in parents: total += ', ' + accumulate(G, p, res) res[node] = total return total def position_nodes_for_max_tree(G, image_rav, root_x=4, delta_x=1.2): """Set the position of nodes of a max-tree. This function helps to visually distinguish between nodes at the same level of the hierarchy and nodes at different levels. """ pos = {} for node in reversed(list(nx.topological_sort(canonical_max_tree))): value = G.nodes[node]['value'] if canonical_max_tree.out_degree(node) == 0: # root pos[node] = (root_x, value) in_nodes = [y for y in canonical_max_tree.predecessors(node)] # place the nodes at the same level level_nodes = [y for y in filter(lambda x: image_rav[x] == value, in_nodes)] nb_level_nodes = len(level_nodes) + 1 c = nb_level_nodes // 2 i = -c if len(level_nodes) < 3: hy = 0 m = 0 else: hy = 0.25 m = hy / (c - 1) for level_node in level_nodes: if i == 0: i += 1 if len(level_nodes) < 3: pos[level_node] = (pos[node][0] + i * 0.6 * delta_x, value) else: pos[level_node] = ( pos[node][0] + i * 0.6 * delta_x, value + m * (2 * np.abs(i) - c - 1), ) i += 1 # place the nodes at different levels other_level_nodes = [ y for y in filter(lambda x: image_rav[x] > value, in_nodes) ] if len(other_level_nodes) == 1: i = 0 else: i = -len(other_level_nodes) // 2 for other_level_node in other_level_nodes: if (len(other_level_nodes) % 2 == 0) and (i == 0): i += 1 pos[other_level_node] = ( pos[node][0] + i * delta_x, image_rav[other_level_node], ) i += 1 return pos def plot_tree(graph, positions, ax, *, title='', labels=None, font_size=8, text_size=8): """Plot max and component trees.""" nx.draw_networkx( graph, pos=positions, ax=ax, node_size=40, node_shape='s', node_color='white', font_size=font_size, labels=labels, ) for v in range(image_rav.min(), image_rav.max() + 1): ax.hlines(v - 0.5, -3, 10, linestyles='dotted') ax.text(-3, v - 0.15, f"val: {v}", fontsize=text_size) ax.hlines(v + 0.5, -3, 10, linestyles='dotted') ax.set_xlim(-3, 10) ax.set_title(title) ax.set_axis_off() .. GENERATED FROM PYTHON SOURCE LINES 190-195 Image Definition ================ We define a small test image. For clarity, we choose an example image, where image values cannot be confounded with indices (different range). .. GENERATED FROM PYTHON SOURCE LINES 195-207 .. code-block:: Python image = np.array( [ [40, 40, 39, 39, 38], [40, 41, 39, 39, 39], [30, 30, 30, 32, 32], [33, 33, 30, 32, 35], [30, 30, 30, 33, 36], ], dtype=np.uint8, ) .. GENERATED FROM PYTHON SOURCE LINES 208-212 Max-tree ======== Next, we calculate the max-tree of this image. max-tree of the image .. GENERATED FROM PYTHON SOURCE LINES 212-217 .. code-block:: Python P, S = max_tree(image) P_rav = P.ravel() .. GENERATED FROM PYTHON SOURCE LINES 218-225 Image plots =========== Then, we visualize the image and its raveled indices. Concretely, we plot the image with the following overlays: - the image values - the raveled indices (serve as pixel identifiers) - the output of the max_tree function .. GENERATED FROM PYTHON SOURCE LINES 225-245 .. code-block:: Python # raveled image image_rav = image.ravel() # raveled indices of the example image (for display purpose) raveled_indices = np.arange(image.size).reshape(image.shape) fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(9, 3)) plot_img(ax1, image - image.min(), 'Image Values', plot_text=True, image_values=image) plot_img( ax2, image - image.min(), 'Raveled Indices', plot_text=True, image_values=raveled_indices, ) plot_img(ax3, image - image.min(), 'Max-tree indices', plot_text=True, image_values=P) .. image-sg:: /auto_examples/developers/images/sphx_glr_plot_max_tree_001.png :alt: Image Values, Raveled Indices, Max-tree indices :srcset: /auto_examples/developers/images/sphx_glr_plot_max_tree_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 246-252 Visualizing threshold operations ================================ Now, we investigate the results of a series of threshold operations. The component tree (and max-tree) provide representations of the inclusion relationships between connected components at different levels. .. GENERATED FROM PYTHON SOURCE LINES 252-265 .. code-block:: Python fig, axes = plt.subplots(3, 3, sharey=True, sharex=True, figsize=(6, 6)) thresholds = np.unique(image) for k, threshold in enumerate(thresholds): bin_img = image >= threshold plot_img( axes[(k // 3), (k % 3)], bin_img, f"Threshold : {threshold}", plot_text=True, image_values=raveled_indices, ) .. image-sg:: /auto_examples/developers/images/sphx_glr_plot_max_tree_002.png :alt: Threshold : 30, Threshold : 32, Threshold : 33, Threshold : 35, Threshold : 36, Threshold : 38, Threshold : 39, Threshold : 40, Threshold : 41 :srcset: /auto_examples/developers/images/sphx_glr_plot_max_tree_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 266-285 Max-tree plots ============== Now, we plot the component and max-trees. A component tree relates the different pixel sets resulting from all possible threshold operations to each other. There is an arrow in the graph, if a component at one level is included in the component of a lower level. The max-tree is just a different encoding of the pixel sets. 1. the component tree: pixel sets are explicitly written out. We see for instance that {6} (result of applying a threshold at 41) is the parent of {0, 1, 5, 6} (threshold at 40). 2. the max-tree: only pixels that come into the set at this level are explicitly written out. We therefore will write {6} -> {0,1,5} instead of {6} -> {0, 1, 5, 6} 3. the canonical max-treeL this is the representation which is given by our implementation. Here, every pixel is a node. Connected components of several pixels are represented by one of the pixels. We thus replace {6} -> {0,1,5} by {6} -> {5}, {1} -> {5}, {0} -> {5} This allows us to represent the graph by an image (top row, third column). .. GENERATED FROM PYTHON SOURCE LINES 285-328 .. code-block:: Python # the canonical max-tree graph canonical_max_tree = nx.DiGraph() canonical_max_tree.add_nodes_from(S) for node in canonical_max_tree.nodes(): canonical_max_tree.nodes[node]['value'] = image_rav[node] canonical_max_tree.add_edges_from([(n, P_rav[n]) for n in S[1:]]) # max-tree from the canonical max-tree nx_max_tree = nx.DiGraph(canonical_max_tree) labels = {} prune(nx_max_tree, S[0], labels) # component tree from the max-tree labels_ct = {} total = accumulate(nx_max_tree, S[0], labels_ct) # positions of nodes : canonical max-tree (CMT) pos_cmt = position_nodes_for_max_tree(canonical_max_tree, image_rav) # positions of nodes : max-tree (MT) pos_mt = dict(zip(nx_max_tree.nodes, [pos_cmt[node] for node in nx_max_tree.nodes])) # plot the trees with networkx and matplotlib fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(20, 8)) plot_tree( nx_max_tree, pos_mt, ax1, title='Component tree', labels=labels_ct, font_size=6, text_size=8, ) plot_tree(nx_max_tree, pos_mt, ax2, title='Max tree', labels=labels) plot_tree(canonical_max_tree, pos_cmt, ax3, title='Canonical max tree') fig.tight_layout() plt.show() .. image-sg:: /auto_examples/developers/images/sphx_glr_plot_max_tree_003.png :alt: Component tree, Max tree, Canonical max tree :srcset: /auto_examples/developers/images/sphx_glr_plot_max_tree_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.453 seconds) .. _sphx_glr_download_auto_examples_developers_plot_max_tree.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-image/scikit-image/v0.24.0?filepath=notebooks/auto_examples/developers/plot_max_tree.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_max_tree.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_max_tree.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_max_tree.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_