grafical confusion matrix(cnn support) and fix feature extraction selectors

src/classification/cnn_keras.py

@@ -26,6 +26,9 @@ from keras.models import Model, load_model
 from keras.layers import Dropout, Flatten, Dense
 from keras.callbacks import ModelCheckpoint, TensorBoard
 from keras import backend as K
+from numpy import resize, expand_dims
+from keras.preprocessing.image import load_img, img_to_array
+        
 
 from interface.interface import InterfaceException as IException
 from classification.classifier import Classifier
@@ -35,7 +38,7 @@ from collections import OrderedDict
 from util.config import Config
 from util.file_utils import File
 from util.utils import TimeUtils
-
+        
 
 logger = logging.getLogger('PIL')
 logger.setLevel(logging.WARNING)
@@ -49,6 +52,13 @@ START_TIME = time.time()
 IMG_WIDTH, IMG_HEIGHT = 256, 256
 weight_path = None
 
+dict_preprocessing = {}
+dict_preprocessing[0] = applications.xception.preprocess_input, applications.xception.decode_predictions
+dict_preprocessing[1] = applications.vgg16.preprocess_input, applications.vgg16.decode_predictions
+dict_preprocessing[2] = applications.vgg19.preprocess_input, applications.vgg19.decode_predictions
+dict_preprocessing[3] = applications.resnet50.preprocess_input, applications.resnet50.decode_predictions
+dict_preprocessing[4] = applications.inception_v3.preprocess_input, applications.inception_v3.decode_predictions
+dict_preprocessing[5] = applications.mobilenet.preprocess_input, applications.mobilenet.decode_predictions
 
 class CNNKeras(Classifier):
     """ Class for CNN classifiers based on Keras applications """
@@ -201,6 +211,7 @@ class CNNKeras(Classifier):
                 except Exception, e:
                     print e
             else:
+                print File.make_path(predict_directory, file)
                 os.symlink(File.make_path(predict_directory, file),
                            File.make_path(predict_directory, 'png', file))
 
@@ -208,7 +219,7 @@ class CNNKeras(Classifier):
 
         classify_generator = classify_datagen.flow_from_directory(
             File.make_path(predict_directory, 'png'),
-            target_size=(IMG_HEIGHT, IMG_WIDTH),
+            taet_size=(IMG_HEIGHT, IMG_WIDTH),
             batch_size=1,
             shuffle=False,
             class_mode=None)
@@ -333,21 +344,27 @@ class CNNKeras(Classifier):
     def select_model_params(self, num_classes):
         if self.fine_tuning_rate.value != -1:
             if self.architecture.value == "Xception":
+                self.app = 0
                 model = applications.Xception(
                     weights="imagenet", include_top=False, input_shape=(IMG_WIDTH, IMG_HEIGHT, 3))
             elif self.architecture.value == "VGG16":
+                self.app = 1
                 model = applications.VGG16(
                     weights="imagenet", include_top=False, input_shape=(IMG_WIDTH, IMG_HEIGHT, 3))
             elif self.architecture.value == "VGG19":
+                self.app = 2
                 model = applications.VGG19(
                     weights="imagenet", include_top=False, input_shape=(IMG_WIDTH, IMG_HEIGHT, 3))
             elif self.architecture.value == "ResNet50":
+                self.app = 3
                 model = applications.ResNet50(
                     weights="imagenet", include_top=False, input_shape=(IMG_WIDTH, IMG_HEIGHT, 3))
             elif self.architecture.value == "InceptionV3":
+                self.app = 4
                 model = applications.InceptionV3(
                     weights="imagenet", include_top=False, input_shape=(IMG_WIDTH, IMG_HEIGHT, 3))
             elif self.architecture.value == "MobileNet":
+                self.app = 5
                 model = applications.MobileNet(
                     weights="imagenet", include_top=False, input_shape=(IMG_WIDTH, IMG_HEIGHT, 3))
 
@@ -356,21 +373,27 @@ class CNNKeras(Classifier):
 
         else:  # without transfer learning
             if self.architecture.value == "Xception":
+                self.app = 0
                 model = applications.Xception(
                     weights=None, include_top=False, input_shape=(IMG_WIDTH, IMG_HEIGHT, 3))
             elif self.architecture.value == "VGG16":
+                self.app = 1
                 model = applications.VGG16(
                     weights=None, include_top=False, input_shape=(IMG_WIDTH, IMG_HEIGHT, 3))
             elif self.architecture.value == "VGG19":
+                self.app = 2
                 model = applications.VGG19(
                     weights=None, include_top=False, input_shape=(IMG_WIDTH, IMG_HEIGHT, 3))
             elif self.architecture.value == "ResNet50":
+                self.app = 3
                 model = applications.ResNet50(
                     weights=None, include_top=False, input_shape=(IMG_WIDTH, IMG_HEIGHT, 3))
             elif self.architecture.value == "InceptionV3":
+                self.app = 4
                 model = applications.InceptionV3(
                     weights=None, include_top=False, input_shape=(IMG_WIDTH, IMG_HEIGHT, 3))
             elif self.architecture.value == "MobileNet":
+                self.app = 5
                 model = applications.MobileNet(
                     weights=None, include_top=False, input_shape=(IMG_WIDTH, IMG_HEIGHT, 3))
             for layer in model.layers:
@@ -456,6 +479,9 @@ 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*'-'
                         os.symlink(File.make_path(root, file),
                                    File.make_path(dir_class_train, file))
 
@@ -524,7 +550,12 @@ class CNNKeras(Classifier):
         return train_generator, validation_generator, test_generator
 
     def single_classify(self, image_path, directory, extractors, dict_classes):
-        from matplotlib.pylab import imread
-        image = imread(image_path)
-        predict = self.model.predict([image])[0]
-        return dict_classes[predict]
\ No newline at end of file
+        preprocess_input, decode_predictions = dict_preprocessing[self.app]
+        pil_image = load_img(image_path)
+        np_image = img_to_array(pil_image)
+        res_image = resize(np_image, (IMG_HEIGHT, IMG_WIDTH, 3))
+        tensor = expand_dims(res_image, axis=0)
+        tensor = preprocess_input(tensor)
+        predict = self.model.predict(tensor)
+        predict = np.argmax(predict, axis=1)
+        return dict_classes[predict[0]]

src/extraction/__init__.py

@@ -13,7 +13,7 @@ __all__ = ["extractor",
             "hog",
             "image_moments",
             "lbp",
-	    "gabor"]
+        "gabor"]
 
 
 from collections import OrderedDict
@@ -24,11 +24,10 @@ _extractor_list = OrderedDict( [
                             ["color_summarizer", Config("Color Statistics", True, bool, meta=ColorStats)],
                             ["glcm", Config("Gray-Level Co-Occurrence Matrix", True, bool, meta=GLCM)],
                             ["hog", Config("Histogram of Oriented Gradients", True, bool, meta=HOG)],
-                            ["hu_moments", Config("Hu Image Moments", False, bool, meta=HuMoments)],
-                            ["rc_moments", Config("Image Moments (Raw/Central)", False, bool, meta=RawCentralMoments)],
+                            ["hu_moments", Config("Hu Image Moments", True, bool, meta=HuMoments)],
+                            ["rc_moments", Config("Image Moments (Raw/Central)", True, bool, meta=RawCentralMoments)],
                             ["lbp", Config("Local Binary Patterns", True, bool, meta=LBP)],
                             ["gabor", Config("Gabor Filter Bank", True, bool, meta=GABOR)]
-
                         ] )
 
 def get_extractor_config():

src/pynovisao.py

@@ -416,10 +416,10 @@ class Act(object):
 
             self.extractors = [new_config[extractor].meta for extractor in new_config
                                 if new_config[extractor].value == True ]
-                                
+            
             if len(self.extractors) == 0:
                 raise IException("Please select at least one extractor")
-
+            
             self.tk.append_log("\nConfig updated:\n%s", 
                                 '\n'.join(["%s: %s" % (new_config[extractor].label, "on" if new_config[extractor].value==True else "off")
                                             for extractor in new_config]))
@@ -945,44 +945,52 @@ class Act(object):
     def run_grafic_confusion_matrix(self):
         '''
         Generate a a graphical confusion matrix where images are classified and according to classification go to the wrong or right folder.
+        Just Available to WekaClassifier and CNNKeras.
         '''
-        from classification import WekaClassifiers
-
-        if not isinstance(self.classifier, WekaClassifiers):
-            message='Only available to Weka Classifiers.'
-            IException(message)
+        from classification import WekaClassifiers, CNNKeras
+        
+        is_weka = isinstance(self.classifier, WekaClassifiers)
+        is_keras = isinstance(self.classifier, CNNKeras)
+        if not (is_weka or is_keras):
+            message='Only available to Weka and CNN Keras classifiers.'
+            raise IException(message)
 
         
         if not self.has_trained:
             message='Dataset Must Be Trained.'
-            IException(message)
+            raise IException(message)
         
-        from os.path import abspath
+        from os.path import abspath, isdir
 
-        directory = self.tk.utils.ask_directory()
-        if not directory:
-            message = 'No directory selected.'
-            IException(message)
+        folder = self.tk.utils.ask_directory()
+        if not folder:
+            message = 'No selected directory.'
+            raise IException(message)
             return
-        directory = abspath(directory)
+            
+        folder = abspath(folder)
         dataset = abspath(self.dataset)
-        if directory == self.dataset:
+        if folder == self.dataset:
             title = 'Same Dataset'
             message = 'The dataset selected is the same of the trained. Are you sure that is right?'
             option=self.tk.ask_ok_cancel(title, message)
             if not option:
                 return
-        
+                
+        from os import listdir, mkdir
+        listdirs=listdir(folder)
+        size_dirs = reduce(lambda a,b: a+b, [0]+[len(listdir(folder+'/'+d)) for d in listdirs if isdir(folder+'/'+d)])
+        if not size_dirs:
+            message = 'Dataset has no content or the subfolder has no content.'
+            raise IException(message)
+            
         from shutil import rmtree
-        from os import mkdir, listdir
-        from os.path import isdir
         from os import symlink
         
         def create_folder_struct(matrix_path, class_names, human, computer):
             try:
                 rmtree(matrix_path)
             except Exception as e:
-                print str(e)
                 pass
             mkdir(matrix_path, 0777)
             for class_ in class_names:
@@ -991,13 +999,15 @@ class Act(object):
                 for _class in class_names:
                     mkdir(real+computer+_class, 0777)
 
-        index=directory[-2::-1].index('/')
-        matrix_path=directory[:-(index+1)]+'folder_confusion_matrix'
-        class_names, classes=listdir(directory), {}
+
+        header_output = 'Starting Graphical Confusion Matrix\n\n'
+        index=folder[-2::-1].index('/')
+        matrix_path=folder[:-(index+1)]+'folder_confusion_matrix'
+        class_names, classes=listdir(folder), {}
         
         for i in range(len(class_names)-1,-1,-1):
             if isdir(dataset+'/'+class_names[i]):
-                if class_names[i][0] is not '.':
+                if class_names[i][0] != '.':
                     continue
             del class_names[i]
         for i, name in enumerate(class_names):
@@ -1005,21 +1015,27 @@ class Act(object):
         images=[]
         
         for classe in class_names:
-            image_names=listdir(directory+'/'+classe)
+            image_names=listdir(folder+'/'+classe)
             for i in range(len(image_names)):
-                image_names[i]=directory+'/',classe ,'/'+image_names[i]
+                image_names[i]=folder+'/',classe ,'/'+image_names[i]
             images.extend(image_names)
         
         human, computer = '/human_', '/computer_'
         create_folder_struct(matrix_path, class_names, human, computer)
         
-        for image_path in images:
-            original=reduce(lambda a,b:a+b, image_path)
+        header_output_middle = header_output + 'Dataset selected: ' + folder + '\n\n'
+        self.tk.write_log(header_output_middle + 'Initializing...')
+        
+        total = str(len(images))
+        for i, image_path in enumerate(images):
+            original_name=reduce(lambda a,b:a+b, image_path)
             real_class_path=matrix_path+human+image_path[1]
-            predicted=self.classifier.single_classify(original, directory, self.extractors, classes)
+            predicted=self.classifier.single_classify(original_name, folder, self.extractors, classes)
+            message = header_output_middle + str(i + 1) + ' of ' + total + ' images classifield.'
+            self.tk.write_log(message)
             predicted_class_path = real_class_path+computer+predicted
-            name_predicted=predicted_class_path+image_path[2]
-            symlink(original, name_predicted)
+            predicted_name=predicted_class_path+image_path[2]
+            symlink(original_name, predicted_name)
         
-        message = 'Graphical Confusion Matrix saved in '+matrix_path
-        self.tk.append_log(message)
+        message = header_output + 'Saved in ' + matrix_path
+        self.tk.write_log(message)

src/util/utils.py

@@ -374,9 +374,9 @@ class BooleanUtils(object):
 
     @staticmethod
     def str_to_bool(s):
-        if s == 'True':
+        if s == 'True' or s == 1:
             return True
-        elif s == 'False':
+        elif s == 'False' or s == 0:
             return False
         else:
             raise ValueError 
\ No newline at end of file