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https://github.com/wassname/scikit-image.git
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Added docs
This commit is contained in:
Vighnesh Birodkar
@@ -3,7 +3,28 @@ cimport numpy as cnp
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import numpy as np
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def construct_rag_meancolor_3d( img, arr):
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def construct_rag_meancolor_3d(img, arr):
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"""Computes the Region Adjacency Graph of a 3D color image using
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difference in mean color of regions as edge weights.
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Given an image and its segmentation, this method constructs the
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corresponsing Region Adjacency Graph (RAG).Each node in the RAG
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represents a contiguous pixels with in `img` the same label in
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`arr`
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Parameters
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----------
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img : (width, height, depth, 3) ndarray
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Input image.
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arr : (width, height, depth) ndarray
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The array with labels.
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Returns
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-------
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out : RAG
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The region adjacency graph.
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"""
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cdef Py_ssize_t l, b, h, i, j, k
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cdef cnp.int32_t current, next
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l = arr.shape[0]
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@@ -19,15 +40,15 @@ def construct_rag_meancolor_3d( img, arr):
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k = 0
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while k < h - 1:
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current = arr[i, j, k]
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try :
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try:
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g.node[current]['pixel_count'] += 1
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g.node[current]['total_color'] += img[i,j]
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g.node[current]['total_color'] += img[i, j]
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except KeyError:
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g.add_node(current)
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g.node[current]['pixel_count'] = 1
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g.node[current]['total_color'] = img[i,j].astype(np.long)
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g.node[current]['labels'] = [arr[i,j]]
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g.node[current]['total_color'] = img[i, j].astype(np.long)
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g.node[current]['labels'] = [arr[i, j]]
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next = arr[i + 1, j, k]
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if current != next:
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@@ -57,18 +78,17 @@ def construct_rag_meancolor_3d( img, arr):
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if current != next:
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g.add_edge(current, next)
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k += 1
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j += 1
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i += 1
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for n in g.nodes():
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g.node[n]['mean_color'] = g.node[n]['total_color']/g.node[n]['pixel_count']
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g.node[n]['mean_color'] = g.node[n][
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'total_color'] / g.node[n]['pixel_count']
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for x,y in g.edges_iter() :
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for x, y in g.edges_iter():
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diff = g.node[x]['mean_color'] - g.node[y]['mean_color']
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g[x][y]['weight'] = np.sqrt(diff.dot(diff))
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@@ -76,6 +96,27 @@ def construct_rag_meancolor_3d( img, arr):
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def construct_rag_meancolor_2d(img, arr):
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"""Computes the Region Adjacency Graph of a 2D color image using
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difference in mean color of regions as edge weights.
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Given an image and its segmentation, this method constructs the
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corresponsing Region Adjacency Graph (RAG).Each node in the RAG
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represents a contiguous pixels with in `img` the same label in
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`arr`
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Parameters
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----------
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img : (width, height, 3) ndarray
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Input image.
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arr : (width, height) ndarray
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The array with labels.
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Returns
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-------
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out : RAG
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The region adjacency graph.
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"""
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cdef Py_ssize_t l, b, h, i, j, k
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cdef cnp.int32_t current, next
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l = arr.shape[0]
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@@ -89,14 +130,14 @@ def construct_rag_meancolor_2d(img, arr):
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while j < b - 1:
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current = arr[i, j]
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try :
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try:
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g.node[current]['pixel_count'] += 1
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g.node[current]['total_color'] += img[i,j]
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g.node[current]['total_color'] += img[i, j]
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except KeyError:
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g.add_node(current)
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g.node[current]['pixel_count'] = 1
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g.node[current]['total_color'] = img[i,j].astype(np.long)
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g.node[current]['labels'] = [arr[i,j]]
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g.node[current]['total_color'] = img[i, j].astype(np.long)
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g.node[current]['labels'] = [arr[i, j]]
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next = arr[i + 1, j]
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if current != next:
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@@ -114,13 +155,12 @@ def construct_rag_meancolor_2d(img, arr):
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i += 1
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for n in g.nodes():
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g.node[n]['mean_color'] = g.node[n]['total_color']/g.node[n]['pixel_count']
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g.node[n]['mean_color'] = g.node[n][
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'total_color'] / g.node[n]['pixel_count']
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for x,y in g.edges_iter() :
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for x, y in g.edges_iter():
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diff = g.node[x]['mean_color'] - g.node[y]['mean_color']
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g[x][y]['weight'] = np.sqrt(diff.dot(diff))
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return g
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@@ -2,18 +2,34 @@ import networkx as nx
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import numpy as np
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def threshold_cut(label, rag, thresh):
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"""Combines regions seperated by weight less than threshold.
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#print [rag.edges_iter(data = True)]
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Given an image's labels and its RAG, outputs new labels by
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combining regions whose nodes are seperated by a weight less
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than the given threshold.
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Parameters
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----------
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label : (width, height, 3) or (width, height, depth, 3) ndarray
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The array of labels.
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rag : RAG
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The region adjacency graph.
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thresh : float
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The threshold, regions with edge weights less than this
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are combined.
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Returns
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-------
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out : (width, height, 3) or (width, height, depth, 3) ndarray
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The new labelled array.
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"""
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to_remove = [(x,y) for x,y,d in rag.edges_iter(data = True) if d['weight'] >= thresh]
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#print "edges to remove",len(to_remove)
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rag.remove_edges_from(to_remove)
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#print "to remove", to_remove
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comps = nx.connected_components(rag)
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out = np.copy(label)
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#print "comps",len(comps)
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for i, nodes in enumerate(comps) :
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@@ -21,6 +37,5 @@ def threshold_cut(label, rag, thresh):
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for l in rag.node[node]['labels'] :
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out[label == l] = i
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#print out
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#print label
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return out
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+45
-2
@@ -3,8 +3,30 @@ import _construct
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from skimage import util
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class RAG(nx.Graph):
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"""
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The class for holding the Region Adjacency Graph (RAG).
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Each region is a contiguous set of pixels in an image, usuall
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sharing some common property.Adjacent regions have an edge
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between their corresponding nodes.
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"""
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def merge_nodes(self,i,j):
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"""Merges nodes `i` and `j`.
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The new combined node is adjacent to all the neighbors of `i`
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and `j`. In case of conflicting edges, edge with higher weight
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is chosen.
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Parameters
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----------
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i : int
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Node to be merged.
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j : int
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Node to be merged.
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"""
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def merge_nodes(i,j):
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if not self.has_edge(i, j):
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raise ValueError('Cant merge non adjacent nodes')
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@@ -24,7 +46,28 @@ class RAG(nx.Graph):
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self.node[j]['labels'] += self.node[i]['labels']
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self.remove_node(i)
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def rag_meancolor(img,labels):
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def rag_meancolor(img, labels):
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"""Computes the Region Adjacency Graph of a color image using
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difference in mean color of regions as edge weights.
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Given an image and its segmentation, this method constructs the
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corresponsing Region Adjacency Graph (RAG).Each node in the RAG
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represents a contiguous pixels with in `img` the same label in
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`arr`
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Parameters
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----------
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img : (width, height, 3) or (width, height, depth, 3) ndarray
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Input image.
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arr : (width, height) or (width, height, depth) ndarray
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The array with labels.
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Returns
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-------
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out : RAG
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The region adjacency graph.
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"""
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img = util.img_as_ubyte(img)
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if img.ndim == 4 :
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