Consertado erro em criacao de XML quando houvesse segmentos nao originados da imagem

src/extraction/feature_extraction.py

@@ -24,7 +24,7 @@ import cv2
 from .extractor import Extractor
 from tqdm import tqdm
 import sys
-
+from sys import version_info
 if not sys.warnoptions:
     import warnings
     warnings.simplefilter("ignore")
@@ -256,23 +256,40 @@ class FeatureExtractor(object):
         output_file : string
             Path to output file.
         """
+        if version_info[0] >= 3:
+                arff = open(output_file, 'wb')
+                arff.write(bytes(str("%s %s\n\n" % ('@relation', relation)), 'utf-8'))
+
+                for label, t in zip(labels, types):
+                        arff.write(bytes(str("%s %s %s\n" % ('@attribute', label, t)), 'utf-8'))
+                
+                arff.write(bytes(str("%s %s {%s}\n\n" % ('@attribute', 'classe', ', '.join(classes))), 'utf-8'))
+                arff.write(bytes(str('@data\n\n'), 'utf-8'))
+
+                for instance in data:
+                    instance = map(str, instance)
+                    line = ",".join(instance)
+                    arff.write(bytes(str(line + "\n"), 'utf-8'))
+
+                arff.close()
+        else:
+                arff = open(output_file, 'wb')
 
-        arff = open(output_file, 'wb')
-        arff.write(bytes(str("%s %s\n\n" % ('@relation', relation)), 'utf-8'))
+                arff.write("%s %s\n\n" % ('@relation', relation))
 
-        for label, t in zip(labels, types):
-            arff.write(bytes(str("%s %s %s\n" % ('@attribute', label, t)), 'utf-8'))
-        
-        arff.write(bytes(str("%s %s {%s}\n\n" % ('@attribute', 'classe', ', '.join(classes))), 'utf-8'))
+                for label, t in zip(labels, types):
+                    arff.write("%s %s %s\n" % ('@attribute', label, t))
+
+                arff.write("%s %s {%s}\n\n" % ('@attribute', 'classe', ', '.join(classes)))
 
-        arff.write(bytes(str('@data\n\n'), 'utf-8'))
+                arff.write('@data\n\n')
 
-        for instance in data:
-            instance = map(str, instance)
-            line = ",".join(instance)
-            arff.write(bytes(str(line + "\n"), 'utf-8'))
+                for instance in data:
+                    instance = map(str, instance)
+                    line = ",".join(instance)
+                    arff.write(line + "\n")
 
-        arff.close()
+                arff.close()
 
     #method to equalize size of images
     def equalize_size_image(self, image):

src/extraction/image_moments.py

@@ -18,6 +18,7 @@ from skimage.measure import regionprops, moments, moments_central
 from skimage.morphology import label
 import numpy as np
 from .extractor import Extractor
+import math
 
 class RawCentralMoments(Extractor):
     """Calculate raw and central set of image moments."""
@@ -30,39 +31,42 @@ class RawCentralMoments(Extractor):
     
     def run(self, image):
         """Calculate raw and central set of image moments of order 1 and 2.
-        
+        	
         Parameters
         ----------
         image : opencv image
-            Image to be analyzed.
-        
+        image to be analyzed.
+		
         Returns
         -------
         features : tuple
-            Returns a tuple containing a list of labels, type and values for each feature extracted.
+        Returns a tuple containing a list of labels, type and values for each feature extracted.
         """
         #raw_moments = moments(image)
-
-
+        	
         image_binary = ImageUtils.image_binary(image, bgr = True)
         
         m = measure.moments(image_binary)
-        
+        m=np.nan_to_num(m)
+        	
         values_m = [m[p, q] for (p, q) in self._moments_order]
+		
         labels_m = [M+str(p)+str(q) for M,(p,q) in zip(['M_'] * len(self._moments_order), self._moments_order)]
-        
         row = m[0, 1] / m[0, 0]
         col = m[1, 0] / m[0, 0]
-	
+        	
         mu = measure.moments_central(image_binary, center=(row, col), order=3)
-        
+        mu=np.nan_to_num(mu)
+        	
         values_mu = [mu[p, q] for (p, q) in self._moments_order]
+        			
+        	
         labels_mu = [M+str(p)+str(q) for M,(p,q) in zip(['Mu_'] * len(self._moments_order), self._moments_order)]
-        #print(values_mu)
+        	
         labels = labels_m + labels_mu
         types = [Extractor.NUMERIC] * len(labels)
         values = values_m + values_mu
-        
+        	
         return labels, types, values
 
 
@@ -75,22 +79,20 @@ class HuMoments(Extractor):
     
     def run(self, image):
         """Calculate Hu's set set of image moments.
-        
+		
         Parameters
         ----------
         image : opencv image
-            Image to be analyzed.
-        
+                mage to be analyzed.
         Returns
         -------
         features : tuple
-            Returns a tuple containing a list of labels, type and values for each feature extracted.
+                Returns a tuple containing a list of labels, type and values for each feature extracted.
         """
-
         """image_binary = ImageUtils.image_binary(image, bgr = True)
-        
+
         m = measure.moments(image_binary)
-        
+
         row = m[0, 1] / m[0, 0]
         col = m[1, 0] / m[0, 0]
 
@@ -98,17 +100,16 @@ class HuMoments(Extractor):
         
         nu = measure.moments_normalized(mu)
         hu = measure.moments_hu(nu)
-
+        
         values_hu = list(hu)"""
-
         image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
         values_hu= cv2.HuMoments(cv2.moments(image)).flatten()
-        values_hu= np.nan_to_num(values_hu)
-
+        	 
+        values_hu= np.nan_to_num(values_hu)		
         values_hu = list(values_hu)
         labels_hu = [m+n for m,n in zip(['Hu_'] * len(values_hu),map(str,range(0,len(values_hu))))]
-        
+        	
         labels = labels_hu
         types = [Extractor.NUMERIC] * len(labels)
         values = values_hu

src/pynovisao.py

@@ -566,7 +566,7 @@ class Act(object):
         print("Wait to complete processes all images!")
         with tqdm(total=len(list_segments)) as pppbar:
             for idx_segment in list_segments:
-                segment, size_segment, idx_segment = self.segmenter.get_segment(self, idx_segment=idx_segment)[:-1]
+                segment, size_segment,xml_file, idx_segment = self.segmenter.get_segment(self, idx_segment=idx_segment)[:-1]
                 # Problem here! Dataset removed.
                 filepath = File.save_only_class_image(segment, self.dataset, tmp, self._image_name, idx_segment)
                 len_segments[idx_segment] = size_segment
@@ -1146,6 +1146,7 @@ class Act(object):
                         return l, c, i, j, igual
                     c = c + 1 #Segment not found, search the next pixel.
                 l = l + 1
+            return -1,-1,-1,-1,False
 
         def achaSeg(self, pathS, file, pog, arquivos):
             """

src/segmentation/skimage_segmenter.py

@@ -16,7 +16,7 @@ from skimage.util import img_as_float, img_as_ubyte
 from util.config import Config
 from util.utils import TimeUtils
 from util.x11_colors import X11Colors
-
+from pascal_voc_writer import Writer as wr
 from abc import ABCMeta, abstractmethod
 
 class SkimageSegmenter(object):
@@ -117,8 +117,9 @@ class SkimageSegmenter(object):
 
         # Get the rectangle that encompasses the countour
         x,y,w,h = cv2.boundingRect(max_contour)
-		# Create the object for this segment in the .XML file
-        xml_file.addObject(name_segment, x, y, x+w, y+h)
+        # Create the object for this segment in the .XML file
+        if xml_file is not None:
+            xml_file.addObject(name_segment, x, y, x+w, y+h)
         segment = segment[y:y+h, x:x+w]
         
         end_time = TimeUtils.get_time()