Alterou muita coisa para suportar o Python3- Correção bugs

.idea/encodings.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Encoding" addBOMForNewFiles="with NO BOM" />
+</project>
\ No newline at end of file

.idea/inspectionProfiles/Project_Default.xml

@@ -50,6 +50,20 @@
           <option value="E501" />
           <option value="W29" />
           <option value="E501" />
+          <option value="W29" />
+          <option value="E501" />
+          <option value="W29" />
+          <option value="E501" />
+          <option value="W29" />
+          <option value="E501" />
+          <option value="W29" />
+          <option value="E501" />
+          <option value="W29" />
+          <option value="E501" />
+          <option value="W29" />
+          <option value="E501" />
+          <option value="W29" />
+          <option value="E501" />
         </list>
       </option>
     </inspection_tool>

.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 2.7 (pynovisao)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file

requeriments.txt

-opencv-python
+opencv-python==3.4.5.20
 python-dateutil==2.5.0
 matplotlib==2.0.2
 scipy
@@ -7,7 +7,7 @@ sympy
 nose
 networkx
 scikit-image
-javabridge==1.0.18  
+javabridge==1.0.18
 python-weka-wrapper
 cycler==0.10
 cython
@@ -20,3 +20,4 @@ opencv-contrib-python
 python-interface
 tqdm
 sklearn
+pyxdg

src/classification/__init__.py

@@ -4,13 +4,13 @@ try:
     from .weka_classifiers import WekaClassifiers
 except Exception as e: 
     WekaClassifiers = None
-    print e.message
+    print(e)
 
 try:
     from .cnn_caffe import CNNCaffe
 except Exception as e: 
     CNNCaffe = None
-    print e.message
+    print(e)
     
 try:
     from .syntactic import Syntactic
@@ -21,14 +21,14 @@ try:
     from .cnn_keras import CNNKeras
 except Exception as e: 
     CNNKeras = None
-    print e.message
+    print(e)
 
 
 try:
     from .segnet_keras import SEGNETKeras
 except Exception as e: 
     SEGNETKeras = None
-    print e.message
+    print(e)
 
 
 __all__ = ["classifier", 

src/classification/cnn_keras.py

@@ -40,6 +40,7 @@ from util.file_utils import File
 from util.utils import TimeUtils
         
 
+
 logger = logging.getLogger('PIL')
 logger.setLevel(logging.WARNING)
 
@@ -208,10 +209,10 @@ class CNNKeras(Classifier):
                     new_file = os.path.splitext(file)[0] + ".png"
                     img.save(File.make_path(predict_directory,
                                             'png', new_file), "PNG", quality=100)
-                except Exception, e:
-                    print e
+                except Exception as e:
+                    print(e)
             else:
-                print File.make_path(predict_directory, file)
+                print(File.make_path(predict_directory, file))
                 os.symlink(File.make_path(predict_directory, file),
                            File.make_path(predict_directory, 'png', file))
 
@@ -219,7 +220,7 @@ class CNNKeras(Classifier):
 
         classify_generator = classify_datagen.flow_from_directory(
             File.make_path(predict_directory, 'png'),
-            taet_size=(IMG_HEIGHT, IMG_WIDTH),
+            target_size=(IMG_HEIGHT, IMG_WIDTH),
             batch_size=1,
             shuffle=False,
             class_mode=None)
@@ -232,8 +233,9 @@ class CNNKeras(Classifier):
                 self.model = load_model(self.weight_path)
                 path_classes = self.weight_path.replace(
                     "_model.h5", "_classes.npy")
+                print("Load Model H5:"+self.weight_path)
                 CLASS_NAMES = np.load(path_classes).item().keys()
-        except Exception, e:
+        except Exception as e:
             raise IException("Can't load the model in " +
                              self.weight_path + str(e))
 
@@ -479,9 +481,9 @@ class CNNKeras(Classifier):
                         img.save(File.make_path(dir_class_train,
                                                 new_file), "PNG", quality=100)
                     else:
-                        print 100*'-'
-                        print File.make_path(root, file)
-                        print 100*'-'
+                        print(100*'-')
+                        print(File.make_path(root, file))
+                        print(100*'-')
                         os.symlink(File.make_path(root, file),
                                    File.make_path(dir_class_train, file))
 

src/classification/segnet_keras.py

@@ -151,7 +151,7 @@ class SEGNETKeras(Classifier):
             K.clear_session()
             if self.weight_path is not None:
                 self.model = load_model(self.weight_path)
-        except Exception, e:
+        except Exception as e:
             raise IException("Can't load the model in " +
                              self.weight_path + str(e))
 
@@ -340,13 +340,13 @@ class SEGNETKeras(Classifier):
 
             for file in files_test:
                 if os.path.splitext(file)[-1] == ".tif":
-					img = Image.open(File.make_path(dataset, file))
-					#img.thumbnail(img.size)
-					new_file = os.path.splitext(file)[0]+".png"
-					img.save(File.make_path(dataset, KERAS_DATASET_DIR_NAME, KERAS_DIR_TEST_NAME, 'images', new_file), "PNG", quality=100)
-					img = Image.open(File.make_path(dataset, os.path.splitext(file)[0] + "_json", "label.png"))
-					#img.thumbnail(img.size)
-					img.save(File.make_path(dataset, KERAS_DATASET_DIR_NAME, KERAS_DIR_TEST_MASK_NAME, 'images', new_file), "PNG", quality=100)
+                    img = Image.open(File.make_path(dataset, file))
+                    #img.thumbnail(img.size)
+                    new_file = os.path.splitext(file)[0]+".png"
+                    img.save(File.make_path(dataset, KERAS_DATASET_DIR_NAME, KERAS_DIR_TEST_NAME, 'images', new_file), "PNG", quality=100)
+                    img = Image.open(File.make_path(dataset, os.path.splitext(file)[0] + "_json", "label.png"))
+                    #img.thumbnail(img.size)
+                    img.save(File.make_path(dataset, KERAS_DATASET_DIR_NAME, KERAS_DIR_TEST_MASK_NAME, 'images', new_file), "PNG", quality=100)
                 else:
                     os.symlink(File.make_path(dataset, file), File.make_path(dataset, KERAS_DATASET_DIR_NAME, KERAS_DIR_TEST_NAME, 'images', file))
                     os.symlink(File.make_path(dataset, os.path.splitext(file)[0] + "_json", "label.png"), File.make_path(dataset, KERAS_DATASET_DIR_NAME, KERAS_DIR_TEST_MASK_NAME, 'images', file))

src/extraction/color_summarizer.py

@@ -12,7 +12,7 @@ import numpy as np
 
 from util.utils import ImageUtils
 
-from extractor import Extractor
+from .extractor import Extractor
 
 class ColorStats(Extractor):
     """Implements color feature extraction."""

src/extraction/extractor_frame_video.py

@@ -12,13 +12,14 @@
 """
 try:
     # for Python2
-    import Tkinter
+    import Tkinter as Tk
     import tkFileDialog
+    from Tkinter import *
 except ImportError:
     # for Python3
-    import tkinter
-    import filedialog as tkFileDialog
-
+    import tkinter as Tk
+    from tkinter import filedialog as tkFileDialog
+    from tkinter import *
 import os
 from os import listdir
 from os.path import isfile, join
@@ -26,8 +27,8 @@ import cv2
 import threading
 
 
-from Tkinter import *
-from util.utils import TimeUtils
+#
+#from util.utils import TimeUtils
 
 
 

src/extraction/feature_extraction.py

@@ -11,7 +11,8 @@
 import io
 import itertools
 import os
-import multiprocessing 
+import gc
+import multiprocessing
 from multiprocessing import Process, Manager
 from interface.interface import InterfaceException as IException
 
@@ -19,13 +20,14 @@ from util.file_utils import File
 from util.utils import ImageUtils
 from util.utils import TimeUtils
 import cv2
-from extractor import Extractor
+from .extractor import Extractor
 from tqdm import tqdm
 import sys
 
 if not sys.warnoptions:
     import warnings
     warnings.simplefilter("ignore")
+
 class FeatureExtractor(object):
     """Handle the feature extraction."""
 
@@ -96,11 +98,14 @@ class FeatureExtractor(object):
             self.job_extractor(dataset, cl, classes)
 
         self.print_console("Wait a moment, the threads are processing "+str(len(self.threads)) +" images, it may be delayed depending on the size or quantity of the images!")
-
+        print("Number of Cores: "+str(multiprocessing.cpu_count()))
         #Start threads
         with tqdm(total=len(self.threads)) as pbar:
             for  t in self.threads:
                  t.start()
+                 if(len(multiprocessing.active_children())>10):
+                     t.join()
+                     gc.collect()
                  pbar.update(1)
             pbar.close()
 
@@ -108,6 +113,7 @@ class FeatureExtractor(object):
         with tqdm(total=len(self.threads)) as ppbar:
             for t in self.threads:
                 t.join()
+                del t
                 ppbar.update(1)
             ppbar.close()
         self.print_console("The process was completed with "+str(len(self.threads))+" images!")
@@ -132,6 +138,7 @@ class FeatureExtractor(object):
                 continue
             #th = threading.Thread(target=self.sub_job_extractor,args=(item, dataset, cl, classes))
             th = multiprocessing.Process(target=self.sub_job_extractor,args=(item, dataset, cl, classes))
+
             self.threads.append(th)
 
 
@@ -146,11 +153,13 @@ class FeatureExtractor(object):
             raise IException("Image %s is possibly corrupt" % filepath)
 
         if len(self.data) > 0:
-            values = list(
-                itertools.chain.from_iterable(zip(*([extractor().run(image) for extractor in self.extractors]))[2]))
+            if sys.version_info >= (3, 0):
+                values = list(zip(*([extractor().run(image) for extractor in self.extractors])))
+            else:
+                values = list(itertools.chain.from_iterable(zip(*([extractor().run(image) for extractor in self.extractors]))[2]))
 	    
             self.data.append(values + [cl if cl in classes else classes[0]])
-	    
+
         else:
             labs, tys, values = [list(itertools.chain.from_iterable(ret))
                                                for ret in
@@ -158,7 +167,8 @@ class FeatureExtractor(object):
             self.labels.append(labs)
             self.types.append(tys)
             self.data.append(values + [cl if cl in classes else classes[0]])
-
+        image=None
+        filepath=None
     def extract_one_file(self, dataset, image_path, output_file=None):
         """Runs the feature extraction algorithms on specific image.
 

src/extraction/gabor.py

@@ -14,9 +14,10 @@
 """
 
 import numpy as np
+#import cupy as np
 
 from util.utils import ImageUtils
-from extractor import Extractor
+from .extractor import Extractor
 from scipy import ndimage as ndi
 from skimage import feature
 from skimage.filters import gabor_kernel

src/extraction/glcm.py

@@ -14,7 +14,7 @@ from skimage import feature
 
 from util.utils import ImageUtils
 
-from extractor import Extractor
+from .extractor import Extractor
 
 class GLCM(Extractor):
     """Implements GLCM (Gray-Level Co-Occurrence Matrix) feature extraction."""

src/extraction/hog.py

@@ -18,7 +18,7 @@ from skimage import feature
 
 from util.utils import ImageUtils
 
-from extractor import Extractor
+from .extractor import Extractor
 
 class HOG(Extractor):
     """Implements HOG (Histogram of Oriented Gradient) feature extraction."""

src/extraction/image_moments.py

@@ -17,7 +17,7 @@ from util.utils import ImageUtils
 from skimage.measure import regionprops, moments, moments_central
 from skimage.morphology import label
 import numpy as np
-from extractor import Extractor
+from .extractor import Extractor
 
 class RawCentralMoments(Extractor):
     """Calculate raw and central set of image moments."""
@@ -58,7 +58,7 @@ class RawCentralMoments(Extractor):
         
         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
@@ -104,6 +104,7 @@ class HuMoments(Extractor):
         image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
         values_hu= cv2.HuMoments(cv2.moments(image)).flatten()
+
         values_hu = list(values_hu)
 
         values_hu= np.nan_to_num(values_hu)

src/extraction/kcurvature.py

@@ -3,7 +3,7 @@
     https://github.com/accord-net/framework/blob/a5a2ea8b59173dd4e695da8017ba06bc45fc6b51/Sources/Accord.Math/Geometry/KCurvature.cs"""
 import numpy as np
 import math
-from extractor import Extractor
+from .extractor import Extractor
 import sys
 import cv2
 import matplotlib.pyplot as plt
@@ -65,7 +65,7 @@ class KCURVATURE(Extractor):
          return list_angle,image
 
 
-     """"https://dsp.stackexchange.com/questions/37281/improving-canny-edge-detection-and-contours-image-segmentation"""
+     """"Source used https://dsp.stackexchange.com/questions/37281/improving-canny-edge-detection-and-contours-image-segmentation"""
      def auto_canny(self,image, sigma=0.5):
             v = np.median(image)
             lower = int(max(0, (1.0 - sigma) * v))
@@ -76,7 +76,7 @@ class KCURVATURE(Extractor):
         gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
         gray = cv2.medianBlur(gray, 9)
 
-        ret,gray = cv2.threshold( np.copy(gray),127,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C/cv2.THRESH_BINARY_INV)
+        ret,gray = cv2.threshold( np.copy(gray),127,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C+cv2.THRESH_BINARY_INV)
 		#ret,gray = cv2.threshold( np.copy(gray),127,255,)
 
         #kernel = np.ones((5,5),np.uint8)
@@ -87,15 +87,21 @@ class KCURVATURE(Extractor):
         #ret, thresh = cv2.threshold(np.copy(gray), 127, 255, 0)
 
         edges = self.auto_canny(gray, 1)
-
-        img2,contours,hierarchy = cv2.findContours(edges ,cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-        #print(contours)
-        list,image=self.find_angle(contours,img2)
+        if cv2.getVersionMajor() in [2, 4]:
+            # OpenCV 2, OpenCV 4 case
+            contour, hier = cv2.findContours(edges.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
+            #print(contours)
+            list,image=self.find_angle(contour,edges)
+        else:
+            # OpenCV 3 case
+            img2,contours,hierarchy = cv2.findContours(edges.copy() ,cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+            #print(contours)
+            list,image=self.find_angle(contours,img2)
 
         labels = []
         values = []
         list_band=[]
-        for i in range(180/self.band):
+        for i in range(int(180/self.band)):
             list_band.append(0)
             for j,angle in enumerate(list):
 
@@ -103,7 +109,7 @@ class KCURVATURE(Extractor):
                 #print(angle)
                 if(angle>=i*self.band and angle<((i+1)*self.band)):
                     list_band[i]=list_band[i]+1
-                elif (180==angle and 180/self.band==i):
+                elif (180==angle and int(180/self.band)==i):
                     list_band[i]=list_band[i]+1
 
         for i, c in enumerate(list_band):

src/extraction/lbp.py

@@ -14,7 +14,7 @@ from skimage import feature
 
 from util.utils import ImageUtils
 
-from extractor import Extractor
+from .extractor import Extractor
 
 class LBP(Extractor):
     """Implements LBP (Local Binary Patterns) feature extraction."""

src/interface/interface.py

@@ -45,3 +45,4 @@ class InterfaceException(Exception):
             return "Unexpected error:\n%s" % traceback.format_exc().replace('%', '%%')
         else:
             return "Unexpected error\n"
+

src/interface/tk/tk_canvas.py

@@ -16,9 +16,13 @@ else:
     
 import matplotlib
 matplotlib.use('TkAgg')
-from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
-from matplotlib import pylab as plt
 
+from matplotlib import pylab as plt
+import sys
+if sys.version_info[0] < 3:
+    from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
+else:
+    from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
 
 class Image(object):
     """Class to manipulate structured graphics interface."""
@@ -86,7 +90,11 @@ class Image(object):
         # a tk.DrawingArea
         self._fig.tight_layout()
         self._canvas = FigureCanvasTkAgg(self._fig, master=self.parent)
-        self._canvas.show()
+        if sys.version_info[0] < 3:
+            self._canvas.show()
+        else:
+            self._canvas.draw()
+
         self._canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
         
         if onclick is not None:
@@ -146,10 +154,16 @@ class Image(object):
         """Show the matplotlib image toolbar.
         """
         if self._toolbar is None and self._canvas is not None:
-            self._toolbar = NavigationToolbar2TkAgg(self._canvas, self.parent)
+            if sys.version_info[0] < 3:
+                self._toolbar = NavigationToolbar2TkAgg(self._canvas, self.parent)
+            else:
+                self._toolbar = NavigationToolbar2Tk(self._canvas, self.parent)
             self._toolbar.configure(background='white', borderwidth=0)
             for child in self._toolbar.winfo_children():
-                child.configure(background='white', foreground='black')
+                if sys.version_info[0] < 3:
+                    child.configure(background='white', foreground='black')
+                else:
+                    child.configure(background='white')
             #self._toolbar.update()
             self._toolbar.pack(side=Tk.TOP, fill=Tk.X, expand=False)
             

src/interface/tk/tk_customdialog.py

@@ -16,7 +16,7 @@ else:
 
 from collections import OrderedDict
     
-from tk_config import TkConfig
+from .tk_config import TkConfig
 
 class SimpleDialog(Tk.Toplevel):
     """Basic Dialogue class."""