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https://github.com/wassname/scikit-image.git
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Removed cython file and added nd python function for RAG construction
This commit is contained in:
Vighnesh Birodkar
@@ -1,166 +0,0 @@
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import numpy as np
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cimport numpy as cnp
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import rag
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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 contiguous pixels with in `img` with the same label in
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`arr`. There is an edge between each pair of adjacent regions.
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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 depth,width,height, i, j, k
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cdef cnp.int32_t current, next
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width = arr.shape[0]
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height = arr.shape[1]
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depth = arr.shape[2]
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g = rag.RAG()
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i = 0
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for i in range(width-1):
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j = 0
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for j in range(height-1):
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k = 0
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for k in range(depth-1):
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current = arr[i, j, k]
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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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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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next = arr[i + 1, j, k]
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if current != next:
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g.add_edge(current, next)
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next = arr[i, j + 1, k]
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if current != next:
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g.add_edge(current, next)
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next = arr[i + 1, j + 1, k]
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if current != next:
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g.add_edge(current, next)
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next = arr[i + 1, j, k + 1]
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if current != next:
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g.add_edge(current, next)
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next = arr[i, j + 1, k + 1]
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if current != next:
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g.add_edge(current, next)
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next = arr[i + 1, j + 1, k + 1]
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if current != next:
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g.add_edge(current, next)
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next = arr[i, j, k + 1]
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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][
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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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diff = g.node[x]['mean color'] - g.node[y]['mean color']
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g[x][y]['weight'] = np.linalg.norm(diff)
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return g
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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 contiguous pixels with in `img` with the same label in
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`arr`. There is an edge between each pair of adjacent regions.
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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 width, height, h, i, j, k
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cdef cnp.int32_t current, next
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width = arr.shape[0]
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height = arr.shape[1]
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g = rag.RAG()
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i = 0
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for i in range(width-1):
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j = 0
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for j in range(height-1):
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current = arr[i, j]
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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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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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next = arr[i + 1, j]
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if current != next:
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g.add_edge(current, next)
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next = arr[i, j + 1]
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if current != next:
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g.add_edge(current, next)
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next = arr[i + 1, j + 1]
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if current != next:
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g.add_edge(current, next)
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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][
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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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diff = g.node[x]['mean color'] - g.node[y]['mean color']
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g[x][y]['weight'] = np.linalg.norm(diff)
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return g
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+54
-11
@@ -1,7 +1,7 @@
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import networkx as nx
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from skimage import util
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from ._build_rag import construct_rag_meancolor_2d
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from ._build_rag import construct_rag_meancolor_3d
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from . import rag
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import numpy as np
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from scipy.ndimage import filters
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class RAG(nx.Graph):
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@@ -49,7 +49,18 @@ class RAG(nx.Graph):
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self.remove_node(i)
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def rag_meancolor(img, labels):
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def _add_edge(values, g):
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values = values.astype(int)
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current = values[0]
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for value in values[1:]:
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if value >= 0:
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g.add_edge(current, value)
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return 0.0
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def rag_mean_color(img, arr):
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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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@@ -78,11 +89,43 @@ def rag_meancolor(img, labels):
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>>> rag = graph.rag_meancolor(img, labels)
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"""
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g = rag.RAG()
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img = util.img_as_ubyte(img)
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if img.ndim == 4:
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return construct_rag_meancolor_3d(img, labels)
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elif img.ndim == 3:
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return construct_rag_meancolor_2d(img, labels)
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else:
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raise ValueError("Image dimension not supported")
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fp = np.zeros((3,) * arr.ndim)
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slc = slice(1, None, None)
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fp[(slc,) * arr.ndim] = 1
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filters.generic_filter(
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arr,
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function=_add_edge,
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footprint=fp,
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mode='constant',
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cval=-1,
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extra_arguments=(g,
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))
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iter = np.nditer(arr, flags=['multi_index'])
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while not iter.finished:
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current = arr[iter.multi_index]
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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[iter.multi_index]
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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[
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iter.multi_index].astype(np.long)
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g.node[current]['labels'] = [arr[iter.multi_index]]
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iter.iternext()
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for n in g.nodes():
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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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diff = g.node[x]['mean color'] - g.node[y]['mean color']
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g[x][y]['weight'] = np.linalg.norm(diff)
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return g
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@@ -17,8 +17,6 @@ def configuration(parent_package='', top_path=None):
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cython(['_spath.pyx'], working_path=base_path)
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cython(['_mcp.pyx'], working_path=base_path)
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cython(['heap.pyx'], working_path=base_path)
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cython(['_build_rag.pyx'], working_path=base_path)
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config.add_extension('_spath', sources=['_spath.c'],
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include_dirs=[get_numpy_include_dirs()])
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@@ -26,8 +24,6 @@ def configuration(parent_package='', top_path=None):
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include_dirs=[get_numpy_include_dirs()])
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config.add_extension('heap', sources=['heap.c'],
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include_dirs=[get_numpy_include_dirs()])
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config.add_extension('_build_rag', sources=['_build_rag.c'],
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include_dirs=[get_numpy_include_dirs()])
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return config
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