Merge branch 'alterPythonVersionSuportFixBugs'

.idea/inspectionProfiles/Project_Default.xml

@@ -64,6 +64,18 @@
           <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>

INSTALL.sh

@@ -6,6 +6,8 @@ YELLOW='\033[0;33m'
 NC='\033[0m'
 
 echo "[PYNOVISAO INSTALLER] Installing PYNOVISAO... Please wait!"
+export OMP_NUM_THREADS=4
+export KMP_AFFINITY="verbose,explicit,proclist=[0,3,5,9],granularity=core"
 
 #check if is root
 if [ "$EUID" -ne 0 ]

README.md

@@ -242,6 +242,7 @@ Se você deseja as funcionalidades gráficas você precisa instalar matplotlib t
 - http://pythonhosted.org/python-weka-wrapper/troubleshooting.html
 
 
+
 ### Como instalar o caffe ( Opcional )
 
 #### Ubuntu / Windows
@@ -352,3 +353,8 @@ Após a implementação de seu classificador, você deve configurá-lo no pynovi
 Caso você necessite de classes utilitárias, os arquivos delas devem ser criados no diretório  src/util/. Além disso, as classes utilitárias devem ser registradas como módulos no arquivo src/util/__init__.py
 
 
+Caso dê problema relacionado ao número de processos, adicione as duas váriaveis de ambiente,
+sendo que deve adicionar no número de threads que o seu processador permite:
+
+export OMP_NUM_THREADS=8
+export KMP_AFFINITY="verbose,explicit,proclist=[0,3,5,9,12,15,18,21],granularity=core"

requeriments.txt

@@ -12,7 +12,7 @@ python-weka-wrapper
 cycler==0.10
 cython
 h5py
-scikit-learn
+scikit-learn==0.20.1
 statistics
 pandas_ml
 pyxdg

src/extraction/extractor_frame_video.py

@@ -28,7 +28,7 @@ import threading
 
 
 #
-#from util.utils import TimeUtils
+from util.utils import TimeUtils
 
 
 
@@ -40,15 +40,16 @@ class ExtractFM(object):
         pass
     #Method Run, you can passa tkParent to manipulate objet to tk parent, this code use to console to print process extract frame movies.
     def run(self,tkParent):
+
         self.tk=tkParent
         self.folder_a=None
         self.folder_b=None
         #window
-        self.window = Tk()
-        self.window.mainloop()
+        self.window = Toplevel()
+
         self.window.title("Extract frames from videos")
         self.window.attributes('-zoomed', False)
-        self.window.geometry("450x250+300+300")
+        self.window.geometry("450x275+300+300")
         self.frame = Frame(self.window )
         self.frame.pack(fill=X, expand=False)
         #find a folder with videos
@@ -67,24 +68,23 @@ class ExtractFM(object):
         self.label_b =Label(self.frame, text="Folder no selected", padx=5, pady=5)
         self.label_b.pack(padx=5, pady=5, expand=True, fill=X)
 
-        self.label_c =Label(self.frame, text="Enter in how many frames a frame will be extracted ", padx=5, pady=5)
+        self.label_c =Label(self.frame, text="Each of the N frames draws a frame ", padx=5, pady=5)
         self.label_c.pack(padx=5, pady=5, expand=True, fill=X)
         #number de frame by extract one picture
         self.val_frame=IntVar()
-        self.val_frame.set(1)
+        self.val_frame.set(30)
 
-        self.spinbox = Spinbox(self.frame, from_=1.0, to=100.0)
+        self.spinbox = Spinbox(self.frame, from_=1.0, to=100.0, textvariable=self.val_frame)
 
         self.spinbox.configure(activebackground="#f9f9f9")
         self.spinbox.configure(background="white")
 
         self.spinbox.configure(buttonbackground="wheat")
         self.spinbox.configure(disabledforeground="#b8a786")
-        self.spinbox.configure(from_="1.0")
+
         self.spinbox.configure(highlightbackground="black")
         self.spinbox.configure(selectbackground="#c4c4c4")
-        self.spinbox.configure(textvariable=self.val_frame)
-        self.spinbox.configure(to="100.0")
+
         self.spinbox.pack(padx=5, pady=5, expand=True, fill=X)
         #ToolTip(self.spinbox , "Select between 1 an 100 to extract frame each the number selected!Example: Select 30 to extract 1 frame each 30 fps!")
 
@@ -140,7 +140,7 @@ class ExtractFM(object):
       self.tk.write_log("Init process:"+ str(listaArquivo))
       lista_thread=[]
 
-      #start_time = TimeUtils.get_time()
+      start_time = TimeUtils.get_time()
       for r, d, f in os.walk(dir):
 
         for file in f:
@@ -155,7 +155,7 @@ class ExtractFM(object):
          t.join()
 
 
-      #end_time = TimeUtils.get_time()
+      end_time = TimeUtils.get_time()
       self.tk.append_log("Finish process:"+str(end_time - start_time))
 
     def select_folder_source(self):

src/extraction/feature_extraction.py

@@ -14,6 +14,7 @@ import os
 import gc
 import multiprocessing
 from multiprocessing import Process, Manager
+import threading
 from interface.interface import InterfaceException as IException
 
 from util.file_utils import File
@@ -43,12 +44,20 @@ class FeatureExtractor(object):
         self.extractors = extractors
         self.tkParent=tkParent
 
-    def extract_all(self, dataset, output_file=None, dirs=None, overwrite=True):
-  
-        self.data = Manager().list() #is a necessary because have a problem with use Process and normaly declaration
+    def extract_all(self, dataset, output_file=None, dirs=None, overwrite=True, processor_amd=False):
+        self.processor_amd=processor_amd
         self.threads = []
-        self.labels = Manager().list()
-        self.types = Manager().list()
+        if self.processor_amd == True :
+            self.data = Manager().list() #is a necessary because have a problem with use Process and normaly declaration
+            self.labels = Manager().list()
+            self.types = Manager().list()
+        else:
+            self.data = [] #is a necessary because have a problem with use Process and normaly declaration
+
+            self.labels = []
+            self.types = []
+
+
         """Runs the feature extraction algorithms on all images of dataset.
 
         Parameters
@@ -98,14 +107,9 @@ 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()
 
@@ -113,13 +117,14 @@ 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!")
         if len(self.data) == 0:
             raise IException("There are no images in dataset: %s" % dataset)
-
+        del self.threads
+        gc.collect()
         # Save the output file in ARFF format
         # self._save_output(File.get_filename(dataset), classes, self.labels, self.types, self.data, output_file)
         self._save_output(File.get_filename(dataset), classes, self.labels[0], self.types[0], self.data, output_file)
@@ -136,8 +141,11 @@ class FeatureExtractor(object):
         for  item in  items :
             if item.startswith('.'):
                 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))
+
+            if self.processor_amd==True :
+                th = multiprocessing.Process(target=self.sub_job_extractor,args=(item, dataset, cl, classes))
+            else:
+                th = threading.Thread(target=self.sub_job_extractor,args=(item, dataset, cl, classes))
 
             self.threads.append(th)
 

src/extraction/image_moments.py

@@ -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)
+        #print(values_mu)
         labels = labels_m + labels_mu
         types = [Extractor.NUMERIC] * len(labels)
         values = values_m + values_mu

src/extraction/kcurvature.py

@@ -87,7 +87,9 @@ class KCURVATURE(Extractor):
         #ret, thresh = cv2.threshold(np.copy(gray), 127, 255, 0)
 
         edges = self.auto_canny(gray, 1)
-        if cv2.getVersionMajor() in [2, 4]:
+        (major, minor, _) = cv2.__version__.split(".")
+
+        if int(major) in [2, 4]:
             # OpenCV 2, OpenCV 4 case
             contour, hier = cv2.findContours(edges.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
             #print(contours)

src/interface/tk/tk_canvas.py

@@ -161,7 +161,8 @@ class Image(object):
             self._toolbar.configure(background='white', borderwidth=0)
             for child in self._toolbar.winfo_children():
                 if sys.version_info[0] < 3:
-                    child.configure(background='white', foreground='black')
+                    #child.configure(background='white', foreground='black')
+                    child.configure(background='white')
                 else:
                     child.configure(background='white')
             #self._toolbar.update()

src/interface/tk_interface.py

@@ -68,7 +68,7 @@ class TkInterface(Interface):
         
         self._root = Tk.Tk()
         self._root.wm_title(self.title)
-        self._root.geometry('%dx%d+%d+%d' % (800, 600, 0, 0))
+        self._root.geometry('%dx%d+%d+%d' % (800, 800, 0, 0))
         
         self._menus = []
         

src/pynovisao.py

@@ -140,6 +140,7 @@ class Act(object):
         
         def onclick(event):
             """Binds dataset generator event to click on image."""
+            print(event)
             if event.xdata != None and event.ydata != None and int(event.ydata) != 0 and self._dataset_generator == True:
                 x = int(event.xdata)
                 y = int(event.ydata)
@@ -562,7 +563,7 @@ class Act(object):
                 pppbar.update(1)
             pppbar.close()
 
-        gc.collect()
+
         # Perform the feature extraction of all segments in image ( not applied to ConvNets ).
         if self.classifier.must_extract_features():
             self.tk.append_log("Running extractors on test images... (%0.3f seconds)", (TimeUtils.get_time() - start_time))
@@ -624,6 +625,7 @@ class Act(object):
 
         self.tk.append_log("\nClassification finished")
         self.tk.append_log("Time elapsed: %0.3f seconds", (end_time - start_time))
+        gc.collect()
 
     def run_training(self):
         start_time = TimeUtils.get_time()