syncsegm.__extract_location_coordinates() - Code Metrics - Inspection of "[pyclustering.nnet.syncsegm] Refactoring and reloc..." - annoviko/pyclustering - Measure and Improve Code Quality continuously with Scrutinizer

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by Andrei

created 2016-05-06 08:27 UTC

syncsegm.__extract_location_coordinates() A

↳ Parent: syncsegm

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Metric	Value
cc	2
dl	0
loc	18
rs	9.4285

"""!

@brief Double-layer oscillatory network with phase oscillator for image segmentation.
@details Implementation based on article:
         - A.Novikov, E.Benderskaya. Oscillatory Network Based on Kuramoto Model for Image Segmentation. 2015.
         
@authors Andrei Novikov ([email protected])
@date 2014-2016
@copyright GNU Public License

@cond GNU_PUBLIC_LICENSE
    PyClustering is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.
    
    PyClustering is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.
    
    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
@endcond

"""

from math import floor;

from PIL import Image;
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3

from pyclustering.cluster.syncnet import syncnet;

from pyclustering.nnet import solve_type;
from pyclustering.nnet.sync import sync_visualizer;

from pyclustering.utils import read_image;


class syncsegm_visualizer:
    """!
    @brief Result visualizer of double-layer oscillatory network 'syncsegm'.
    
    """
    
    @staticmethod
    def show_first_layer_dynamic(analyser):
        """!
        @brief Shows output dynamic of the first layer.
        
        @param[in] analyser (syncsegm_analyser): Analyser of output dynamic of the 'syncsegm' oscillatory network.
        
        """
        
        sync_visualizer.show_output_dynamic(analyser.get_first_layer_analyser());
    
    
    @staticmethod
    def show_second_layer_dynamic(analyser):
        """!
        @brief Shows output dynamic of the second layer.
        
        @param[in] analyser (syncsegm_analyser): Analyser of output dynamic of the 'syncsegm' oscillatory network.
        
        """
        
        second_layer_analysers = analyser.get_second_layer_analysers();
        analysers_sequence = [ object_segment_analyser['analyser'] for object_segment_analyser in second_layer_analysers ]
        
        sync_visualizer.show_output_dynamics(analysers_sequence);


class syncsegm_analyser:
    """!
    @brief Performs analysis of output dynamic of the double-layer oscillatory network 'syncsegm' to extract information about segmentation results.
    
    """
    
    def __init__(self, color_analyser, object_segment_analysers = None):
        """!
        @brief Constructor of the analyser.
        
        @param[in] color_analyser (list): Analyser of coloring segmentation results of the first layer.
        @param[in] object_segment_analysers (list): Analysers of objects on image segments - results of the second layer.
        
        """
        
        self.__color_analyser = color_analyser;
        self.__object_segment_analysers = object_segment_analysers;
    
    
    def get_first_layer_analyser(self):
        """!
        @brief Returns analyser of coloring segmentation of the first layer.
        
        """
        
        return self.__color_analyser;
    
    
    def get_second_layer_analysers(self):
        """!
        @brief Returns analysers of object segmentation of the second layer.
        
        """
        
        return self.__object_segment_analysers;
    
    
    def allocate_colors(self, eps = 0.01, noise_size = 1):
        """!
        @brief Allocates color segments.
        
        @param[in] eps (double): Tolerance level that define maximal difference between phases of oscillators in one segment.
        @param[in] noise_size (uint): Threshold that defines noise - segments size (in pixels) that is less then the threshold is considered as a noise.
        
        @return (list) Color segments where each color segment consists of indexes of pixels that forms color segment.
        
        """
        
        segments = self.__color_analyser.allocate_clusters(eps);
        real_segments = [cluster for cluster in segments if len(cluster) > noise_size];
        return real_segments;
    
    
    def allocate_objects(self, eps = 0.01, noise_size = 1):
        """!
        @brief Allocates object segments.
        
        @param[in] eps (double): Tolerance level that define maximal difference between phases of oscillators in one segment.
        @param[in] noise_size (uint): Threshold that defines noise - segments size (in pixels) that is less then the threshold is considered as a noise.
        
        @return (list) Object segments where each object segment consists of indexes of pixels that forms object segment.
        
        """
        
        if (self.__object_segment_analysers is None):
            return [];
        
        segments = [];
        for object_segment_analyser in self.__object_segment_analysers:
            indexes = object_segment_analyser['color_segment'];
            analyser = object_segment_analyser['analyser'];
            
            segments += analyser.allocate_clusters(eps, indexes);
        
        real_segments = [segment for segment in segments if len(segment) > noise_size];
        return real_segments;


class syncsegm:
    """!
    @brief Class represents segmentation algorithm syncsegm. 
    @details syncsegm is a bio-inspired algorithm that is based on double-layer oscillatory network that uses modified Kuramoto model.
             Algorithm extracts colors and colored objects. It uses only CCORE (C++ implementation of pyclustering) parts to implement the algorithm.
    
    Example:
    @code
        # create oscillatory for image segmentaion - extract colors (radius 128) and objects (radius 4), 
        # and ignore noise (segments with size that is less than 10 pixels)
        algorithm = syncsegm(128, 4, 10);
        
        # extract segments (colors and objects)
        analyser = algorithm(path_to_file);
        
        # obtain segmentation results (only colors - from the first layer)
        color_segments = analyser.allocate_colors(0.01, 10);
        draw_image_mask_segments(path_to_file, color_segments);
        
        # obtain segmentation results (objects - from the second layer)
        object_segments = analyser.allocate_objects(0.01, 10);
        draw_image_mask_segments(path_to_file, object_segments);
    @endcode
    
    """
    
    def __init__(self, color_radius, object_radius, noise_size = 0):
        """!
        @brief Contructor of the oscillatory network SYNC for cluster analysis.
        
        @param[in] color_radius (double): Radius of color connectivity (color similarity) for the first layer.
        @param[in] object_radius (double): Radius of object connectivity (object similarity) for the second layer,
                   if 'None' then object segmentation is not performed (only color segmentation).
        @param[in] noise_size (double): Size of segment that should be considered as a noise and ignored by the second layer.
        
        """
        
        self.__color_radius     = color_radius;
        self.__object_radius    = object_radius;
        self.__noise_size       = noise_size;
        
        self.__order_color      = 0.9995;
        self.__order_object     = 0.999;
        
        self.__network = None;
    
    
    def process(self, image_source, collect_dynamic = False, order_color = 0.9995, order_object = 0.999):
        """!
        @brief Performs image segmentation.
        
        @param[in] image_source (string): Path to image file that should be processed.
        @param[in] collect_dynamic (bool): If 'True' then whole dynamic of each layer of the network is collected.
        @param[in] order_color (double): Local synchronization order for the first layer - coloring segmentation.
        @param[in] order_object (double): Local synchronization order for the second layer - object segmentation.
        
        @return (syncsegm_analyser) Analyser of segmentation results by the network.
        
        """
        
        self.__color_radius = order_color;
        self.__order_object = order_object;
        
        data = read_image(image_source);
        color_analyser = self.__analyse_colors(data, collect_dynamic);
        
        if (self.__object_radius is None):
            return syncsegm_analyser(color_analyser, None);
    
        object_segment_analysers = self.__analyse_objects(image_source, color_analyser, collect_dynamic);
        return syncsegm_analyser(color_analyser, object_segment_analysers);
    
    
    def __analyse_colors(self, image_source, collect_dynamic):
        """!
        @brief Performs color segmentation by the first layer.
        
        @param[in] image_source (string): Path to image file that should be processed.
        @param[in] collect_dynamic (bool): If 'True' then whole dynamic of the first layer of the network is collected.
        
        @return (syncnet_analyser) Analyser of color segmentation results of the first layer.
        
        """
        
        network = syncnet(image_source, self.__color_radius, ccore = True);
        analyser = network.process(self.__order_color, solve_type.FAST, collect_dynamic);
        
        return analyser;
    
    
    def __analyse_objects(self, image_source, color_analyser, collect_dynamic):
        """!
        @brief Performs object segmentation by the second layer.
        
        @param[in] image_source (string): Path to image file that should be processed.
        @param[in] color_analyser (syncnet_analyser): Analyser of color segmentation results.
        @param[in] collect_dynamic (bool): If 'True' then whole dynamic of the first layer of the network is collected.
        
        @return (map) Analysers of object segments.
        
        """
        
        # continue analysis
        pointer_image = Image.open(image_source);
        image_size = pointer_image.size;
        
        object_analysers = [];
        
        color_segments = color_analyser.allocate_clusters();
        
        for segment in color_segments:
            object_analyser = self.__analyse_color_segment(image_size, segment, collect_dynamic);
            if (object_analyser is not None):
                object_analysers.append( { 'color_segment': segment, 'analyser': object_analyser } );
    
        return object_analysers;
    
    
    def __analyse_color_segment(self, image_size, color_segment, collect_dynamic):
        """!
        @brief Performs object segmentation of separate segment.
        
        @param[in] image_size (list): Image size presented as a [width x height].
        @param[in] color_segment (list): Image segment that should be processed.
        @param[in] collect_dynamic (bool): If 'True' then whole dynamic of the second layer of the network is collected.
        
        @return (syncnet_analyser) Analyser of object segmentation results of the second layer.
        
        """
        coordinates = self.__extract_location_coordinates(image_size, color_segment);
        
        if (len(coordinates) < self.__noise_size):
            return None;
        
        network = syncnet(coordinates, self.__object_radius, ccore = True);
        analyser = network.process(self.__order_object, solve_type.FAST, collect_dynamic);
        
        return analyser;
    
    
    def __extract_location_coordinates(self, image_size, color_segment):
        """!
        @brief Extracts coordinates of specified image segment.
        
        @param[in] image_size (list): Image size presented as a [width x height].
        @param[in] color_segment (list): Image segment whose coordinates should be extracted.
        
        @return (list) Coordinates of each pixel.
        
        """
        coordinates = [];
        for index in color_segment:
            y = floor(index / image_size[0]);
            x = index - y * image_size[0];
            
            coordinates.append([x, y]);
        
        return coordinates;
        

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syncsegm.__extract_location_coordinates() A

Complexity

Size

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1. Missing Dependencies

2. Missing init.py files

1			"""!
2
3			@brief Double-layer oscillatory network with phase oscillator for image segmentation.
4			@details Implementation based on article:
5			- A.Novikov, E.Benderskaya. Oscillatory Network Based on Kuramoto Model for Image Segmentation. 2015.
6
7			@authors Andrei Novikov ([email protected])
8			@date 2014-2016
9			@copyright GNU Public License
10
11			@cond GNU_PUBLIC_LICENSE
12			PyClustering is free software: you can redistribute it and/or modify
13			it under the terms of the GNU General Public License as published by
14			the Free Software Foundation, either version 3 of the License, or
15			(at your option) any later version.
16
17			PyClustering is distributed in the hope that it will be useful,
18			but WITHOUT ANY WARRANTY; without even the implied warranty of
19			MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
20			GNU General Public License for more details.
21
22			You should have received a copy of the GNU General Public License
23			along with this program. If not, see <http://www.gnu.org/licenses/>.
24			@endcond
25
26			"""
27
28			from math import floor;
29
30			from PIL import Image;
			0 ignored issues – show Configuration introduced 2016-05-05 12:58 UTC by Report Bug Copy Issue Report The import `PIL` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
31
32			from pyclustering.cluster.syncnet import syncnet;
33
34			from pyclustering.nnet import solve_type;
35			from pyclustering.nnet.sync import sync_visualizer;
36
37			from pyclustering.utils import read_image;
38
39
40			class syncsegm_visualizer:
41			"""!
42			@brief Result visualizer of double-layer oscillatory network 'syncsegm'.
43
44			"""
45
46			@staticmethod
47			def show_first_layer_dynamic(analyser):
48			"""!
49			@brief Shows output dynamic of the first layer.
50
51			@param[in] analyser (syncsegm_analyser): Analyser of output dynamic of the 'syncsegm' oscillatory network.
52
53			"""
54
55			sync_visualizer.show_output_dynamic(analyser.get_first_layer_analyser());
56
57
58			@staticmethod
59			def show_second_layer_dynamic(analyser):
60			"""!
61			@brief Shows output dynamic of the second layer.
62
63			@param[in] analyser (syncsegm_analyser): Analyser of output dynamic of the 'syncsegm' oscillatory network.
64
65			"""
66
67			second_layer_analysers = analyser.get_second_layer_analysers();
68			analysers_sequence = [ object_segment_analyser['analyser'] for object_segment_analyser in second_layer_analysers ]
69
70			sync_visualizer.show_output_dynamics(analysers_sequence);
71
72
73			class syncsegm_analyser:
74			"""!
75			@brief Performs analysis of output dynamic of the double-layer oscillatory network 'syncsegm' to extract information about segmentation results.
76
77			"""
78
79			def __init__(self, color_analyser, object_segment_analysers = None):
80			"""!
81			@brief Constructor of the analyser.
82
83			@param[in] color_analyser (list): Analyser of coloring segmentation results of the first layer.
84			@param[in] object_segment_analysers (list): Analysers of objects on image segments - results of the second layer.
85
86			"""
87
88			self.__color_analyser = color_analyser;
89			self.__object_segment_analysers = object_segment_analysers;
90
91
92			def get_first_layer_analyser(self):
93			"""!
94			@brief Returns analyser of coloring segmentation of the first layer.
95
96			"""
97
98			return self.__color_analyser;
99
100
101			def get_second_layer_analysers(self):
102			"""!
103			@brief Returns analysers of object segmentation of the second layer.
104
105			"""
106
107			return self.__object_segment_analysers;
108
109
110			def allocate_colors(self, eps = 0.01, noise_size = 1):
111			"""!
112			@brief Allocates color segments.
113
114			@param[in] eps (double): Tolerance level that define maximal difference between phases of oscillators in one segment.
115			@param[in] noise_size (uint): Threshold that defines noise - segments size (in pixels) that is less then the threshold is considered as a noise.
116
117			@return (list) Color segments where each color segment consists of indexes of pixels that forms color segment.
118
119			"""
120
121			segments = self.__color_analyser.allocate_clusters(eps);
122			real_segments = [cluster for cluster in segments if len(cluster) > noise_size];
123			return real_segments;
124
125
126			def allocate_objects(self, eps = 0.01, noise_size = 1):
127			"""!
128			@brief Allocates object segments.
129
130			@param[in] eps (double): Tolerance level that define maximal difference between phases of oscillators in one segment.
131			@param[in] noise_size (uint): Threshold that defines noise - segments size (in pixels) that is less then the threshold is considered as a noise.
132
133			@return (list) Object segments where each object segment consists of indexes of pixels that forms object segment.
134
135			"""
136
137			if (self.__object_segment_analysers is None):
138			return [];
139
140			segments = [];
141			for object_segment_analyser in self.__object_segment_analysers:
142			indexes = object_segment_analyser['color_segment'];
143			analyser = object_segment_analyser['analyser'];
144
145			segments += analyser.allocate_clusters(eps, indexes);
146
147			real_segments = [segment for segment in segments if len(segment) > noise_size];
148			return real_segments;
149
150
151			class syncsegm:
152			"""!
153			@brief Class represents segmentation algorithm syncsegm.
154			@details syncsegm is a bio-inspired algorithm that is based on double-layer oscillatory network that uses modified Kuramoto model.
155			Algorithm extracts colors and colored objects. It uses only CCORE (C++ implementation of pyclustering) parts to implement the algorithm.
156
157			Example:
158			@code
159			# create oscillatory for image segmentaion - extract colors (radius 128) and objects (radius 4),
160			# and ignore noise (segments with size that is less than 10 pixels)
161			algorithm = syncsegm(128, 4, 10);
162
163			# extract segments (colors and objects)
164			analyser = algorithm(path_to_file);
165
166			# obtain segmentation results (only colors - from the first layer)
167			color_segments = analyser.allocate_colors(0.01, 10);
168			draw_image_mask_segments(path_to_file, color_segments);
169
170			# obtain segmentation results (objects - from the second layer)
171			object_segments = analyser.allocate_objects(0.01, 10);
172			draw_image_mask_segments(path_to_file, object_segments);
173			@endcode
174
175			"""
176
177			def __init__(self, color_radius, object_radius, noise_size = 0):
178			"""!
179			@brief Contructor of the oscillatory network SYNC for cluster analysis.
180
181			@param[in] color_radius (double): Radius of color connectivity (color similarity) for the first layer.
182			@param[in] object_radius (double): Radius of object connectivity (object similarity) for the second layer,
183			if 'None' then object segmentation is not performed (only color segmentation).
184			@param[in] noise_size (double): Size of segment that should be considered as a noise and ignored by the second layer.
185
186			"""
187
188			self.__color_radius = color_radius;
189			self.__object_radius = object_radius;
190			self.__noise_size = noise_size;
191
192			self.__order_color = 0.9995;
193			self.__order_object = 0.999;
194
195			self.__network = None;
196
197
198			def process(self, image_source, collect_dynamic = False, order_color = 0.9995, order_object = 0.999):
199			"""!
200			@brief Performs image segmentation.
201
202			@param[in] image_source (string): Path to image file that should be processed.
203			@param[in] collect_dynamic (bool): If 'True' then whole dynamic of each layer of the network is collected.
204			@param[in] order_color (double): Local synchronization order for the first layer - coloring segmentation.
205			@param[in] order_object (double): Local synchronization order for the second layer - object segmentation.
206
207			@return (syncsegm_analyser) Analyser of segmentation results by the network.
208
209			"""
210
211			self.__color_radius = order_color;
212			self.__order_object = order_object;
213
214			data = read_image(image_source);
215			color_analyser = self.__analyse_colors(data, collect_dynamic);
216
217			if (self.__object_radius is None):
218			return syncsegm_analyser(color_analyser, None);
219
220			object_segment_analysers = self.__analyse_objects(image_source, color_analyser, collect_dynamic);
221			return syncsegm_analyser(color_analyser, object_segment_analysers);
222
223
224			def __analyse_colors(self, image_source, collect_dynamic):
225			"""!
226			@brief Performs color segmentation by the first layer.
227
228			@param[in] image_source (string): Path to image file that should be processed.
229			@param[in] collect_dynamic (bool): If 'True' then whole dynamic of the first layer of the network is collected.
230
231			@return (syncnet_analyser) Analyser of color segmentation results of the first layer.
232
233			"""
234
235			network = syncnet(image_source, self.__color_radius, ccore = True);
236			analyser = network.process(self.__order_color, solve_type.FAST, collect_dynamic);
237
238			return analyser;
239
240
241			def __analyse_objects(self, image_source, color_analyser, collect_dynamic):
242			"""!
243			@brief Performs object segmentation by the second layer.
244
245			@param[in] image_source (string): Path to image file that should be processed.
246			@param[in] color_analyser (syncnet_analyser): Analyser of color segmentation results.
247			@param[in] collect_dynamic (bool): If 'True' then whole dynamic of the first layer of the network is collected.
248
249			@return (map) Analysers of object segments.
250
251			"""
252
253			# continue analysis
254			pointer_image = Image.open(image_source);
255			image_size = pointer_image.size;
256
257			object_analysers = [];
258
259			color_segments = color_analyser.allocate_clusters();
260
261			for segment in color_segments:
262			object_analyser = self.__analyse_color_segment(image_size, segment, collect_dynamic);
263			if (object_analyser is not None):
264			object_analysers.append( { 'color_segment': segment, 'analyser': object_analyser } );
265
266			return object_analysers;
267
268
269			def __analyse_color_segment(self, image_size, color_segment, collect_dynamic):
270			"""!
271			@brief Performs object segmentation of separate segment.
272
273			@param[in] image_size (list): Image size presented as a [width x height].
274			@param[in] color_segment (list): Image segment that should be processed.
275			@param[in] collect_dynamic (bool): If 'True' then whole dynamic of the second layer of the network is collected.
276
277			@return (syncnet_analyser) Analyser of object segmentation results of the second layer.
278
279			"""
280			coordinates = self.__extract_location_coordinates(image_size, color_segment);
281
282			if (len(coordinates) < self.__noise_size):
283			return None;
284
285			network = syncnet(coordinates, self.__object_radius, ccore = True);
286			analyser = network.process(self.__order_object, solve_type.FAST, collect_dynamic);
287
288			return analyser;
289
290
291			def __extract_location_coordinates(self, image_size, color_segment):
292			"""!
293			@brief Extracts coordinates of specified image segment.
294
295			@param[in] image_size (list): Image size presented as a [width x height].
296			@param[in] color_segment (list): Image segment whose coordinates should be extracted.
297
298			@return (list) Coordinates of each pixel.
299
300			"""
301			coordinates = [];
302			for index in color_segment:
303			y = floor(index / image_size[0]);
304			x = index - y * image_size[0];
305
306			coordinates.append([x, y]);
307
308			return coordinates;
309

annoviko / pyclustering

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