Commit parcial das funcionalidades de classificação com keras

src/classification/__init__.py

@@ -13,9 +13,12 @@ except:
 try:
     from .cnn_keras import CNNKeras
 except:
+except Exception as e: 
     CNNKeras = None
+    print e.message
 
 __all__ = ["cnn_caffe",
+           "cnn_keras",
            "classifier",
            "weka_classifiers"]
 
@@ -38,4 +41,5 @@ def get_classifier_config():
 
 def set_classifier_config(configs):
     _classifier_list["cnn_caffe"] = Config.nvl_config(configs["cnn_caffe"], _classifier_list["cnn_caffe"])
-    _classifier_list["weka_classifiers"] = Config.nvl_config(configs["weka_classifiers"], _classifier_list["weka_classifiers"])
+    _classifier_list["cnn_keras"] = Config.nvl_config(configs["cnn_keras"], _classifier_list["cnn_keras"])
+    _classifier_list["weka_classifiers"] = Config.nvl_config(configs["weka_classifiers"], _classifier_list["weka_classifiers"])
\ No newline at end of file

src/classification/classifier.py

@@ -78,7 +78,12 @@ class Classifier(object):
         """Return if classifier must be trained. 
         """
         return False
-    
+
+    def must_extract_features(self):
+        """Return if classifier must be extracted features. 
+        """
+        return True
+
     def train(self, dataset, training_data, force = False):
         """Perform the training of classifier. 
         """

src/classification/cnn_keras.py

@@ -4,6 +4,7 @@
 """
     Generic classifier with multiple models
     Models -> (Xception, VGG16, VGG19, ResNet50, InceptionV3, MobileNet)
+    
     Name: cnn_keras.py
     Author: Gabriel Kirsten Menezes (gabriel.kirsten@hotmail.com)
 
@@ -11,13 +12,14 @@
 
 import time
 import os
+import numpy as np
+from PIL import Image
 from keras import applications
 from keras.preprocessing.image import ImageDataGenerator
 from keras import optimizers
 from keras.models import Model
 from keras.layers import Dropout, Flatten, Dense
 from keras.callbacks import ModelCheckpoint
-from sklearn.metrics import confusion_matrix
 
 from classifier import Classifier
 
@@ -35,16 +37,25 @@ START_TIME = time.time()
 # =========================================================
 
 IMG_WIDTH, IMG_HEIGHT = 256, 256
-TRAIN_DATA_DIR = "../data/train"
-VALIDATION_DATA_DIR = "../data/validation"
 BATCH_SIZE = 16
-EPOCHS = 50
+EPOCHS = 1
 CLASS_NAMES = ['ferrugemAsiatica', 'folhaSaudavel',
                'fundo', 'manchaAlvo', 'mildio', 'oidio']
 
+
 class CNNKeras(Classifier):
+    """ Class for CNN classifiers based on Keras applications """
+
+    def __init__(self, architecture = "VGG16", fine_tuning_rate = 100):
+        """
+            Constructor of CNNKeras
+        """
+
+        self.architecture = Config("architecture", architecture, str)
+        self.fine_tuning_rate = Config("fineTuningRate", fine_tuning_rate, int)
+
+        self.file_name = "kerasCNN"
 
-    def __init__(self):
         model = applications.VGG16(
             weights="imagenet", include_top=False, input_shape=(IMG_WIDTH, IMG_HEIGHT, 3))
 
@@ -60,92 +71,184 @@ class CNNKeras(Classifier):
         predictions = Dense(6, activation="softmax")(new_custom_layers)
 
         # creating the final model
-        model_final = Model(inputs=model.input, outputs=predictions)
+        self.model = Model(inputs=model.input, outputs=predictions)
 
         # compile the model
-        model_final.compile(loss="categorical_crossentropy",
-                            optimizer=optimizers.SGD(lr=0.0001, momentum=0.9),
-                            metrics=["accuracy"])
+        self.model.compile(loss="categorical_crossentropy",
+                           optimizer=optimizers.SGD(lr=0.0001, momentum=0.9),
+                           metrics=["accuracy"])
 
     def get_config(self):
         """Return configuration of classifier. 
-        
+
         Returns
         -------
         config : OrderedDict
             Current configs of classifier.
         """
-        pass
+        keras_config = OrderedDict()
+
+        keras_config["architecture"] = self.architecture
+        keras_config["fine_tuning_rate"] = self.fine_tuning_rate
+
+        return keras_config
 
     def set_config(self, configs):
         """Update configuration of classifier. 
-        
+
         Parameters
         ----------
         configs : OrderedDict
             New configs of classifier.
         """
-        pass
+        keras_config = OrderedDict()
+
+        keras_config["architecture"] = Config.nvl_config(
+            configs["architecture"], self.architecture)
+
+        keras_config["fine_tuning_rate"] = Config.nvl_config(
+            configs["fine_tuning_rate"], self.fine_tuning_rate)
 
     def get_summary_config(self):
         """Return fomatted summary of configuration. 
-        
+
         Returns
         -------
         summary : string
             Formatted string with summary of configuration.
         """
-        pass 
+        keras_config = OrderedDict()
+        
+        keras_config[self.architecture.label] = self.architecture.value
+        keras_config[self.fine_tuning_rate.label] = self.fine_tuning_rate.value
+
+        summary = ''
+        for config in keras_config:
+            summary += "%s: %s\n" % (config, str(keras_config[config]))
+        
+        return summary
 
     def classify(self, dataset, test_dir, test_data):
-        pass
+        """"Perform the classification. 
 
-    def train(self, model_final):
-        
-        # Initiate the train and test generators with data Augumentation
-        train_datagen = ImageDataGenerator(
-            rescale=1. / 255,
-            horizontal_flip=True,
-            fill_mode="nearest",
-            zoom_range=0.3,
-            width_shift_range=0.3,
-            height_shift_range=0.3,
-            rotation_range=30)
+        Parameters
+        ----------
+        dataset : string
+            Path to image dataset.
+        test_dir : string
+            Not used.
+        test_data : string
+            Name of test data file.
 
-        train_generator = train_datagen.flow_from_directory(
-            TRAIN_DATA_DIR,
+        Returns
+        -------
+        summary : list of string
+            List of predicted classes for each instance in test data in ordered way.
+        """
+
+        predict_directory = File.make_path(dataset, test_dir)
+
+        for root, dirs, files in os.walk(predict_directory, topdown=False):
+            for name in files:
+                print(os.path.join(root, name))
+                if os.path.splitext(os.path.join(root, name))[1].lower() == ".tif":
+                    if os.path.isfile(os.path.splitext(os.path.join(root, name))[0] + ".png"):
+                        print "A jpeg file already exists for %s" % name
+                    # If a jpeg is *NOT* present, create one from the tiff.
+                    else:
+                        outfile = os.path.splitext(
+                            os.path.join(root, name))[0] + ".png"
+                        try:
+                            im = Image.open(os.path.join(root, name))
+                            print "Generating jpeg for %s" % name
+                            im.thumbnail(im.size)
+                            im.save(outfile, "PNG", quality=100)
+                        except Exception, e:
+                            print e
+
+        classify_datagen = ImageDataGenerator()
+
+        classify_generator = classify_datagen.flow_from_directory(
+            predict_directory,
             target_size=(IMG_HEIGHT, IMG_WIDTH),
             batch_size=BATCH_SIZE,
-            shuffle=True,
-            class_mode="categorical")
+            shuffle=False,
+            class_mode=None)
+
+        output_classification = self.model.predict_generator(
+            classify_generator, classify_generator.samples, verbose=1)
+
+        one_hot_output = np.argmax(output_classification, axis=1)
+
+        for index in range(0, len(one_hot_output)):
+            one_hot_output[index] = CLASS_NAMES[one_hot_output[index]]
+
+        return one_hot_output
+
+    def train(self, dataset, training_data, force=False):
+        """Perform the training of classifier.
+
+        Parameters
+        ----------
+        dataset : string
+            Path to image dataset.
+        training_data : string
+            Name of ARFF training file.
+        force : boolean, optional, default = False
+            If False don't perform new training if there is trained data.
+        """
 
-        test_datagen = ImageDataGenerator(
-            rescale=1. / 255,
-            horizontal_flip=True,
-            fill_mode="nearest",
-            zoom_range=0.3,
-            width_shift_range=0.3,
-            height_shift_range=0.3,
-            rotation_range=30)
-
-        validation_generator = test_datagen.flow_from_directory(
-            VALIDATION_DATA_DIR,
+        train_datagen = ImageDataGenerator()
+
+        train_generator = train_datagen.flow_from_directory(
+            dataset,
             target_size=(IMG_HEIGHT, IMG_WIDTH),
             batch_size=BATCH_SIZE,
             shuffle=True,
             class_mode="categorical")
 
+        # test_datagen = ImageDataGenerator(
+        #     rescale=1. / 255,
+        #     horizontal_flip=True,
+        #     fill_mode="nearest",
+        #     zoom_range=0.3,
+        #     width_shift_range=0.3,
+        #     height_shift_range=0.3,
+        #     rotation_range=30)
+
+        # validation_generator = test_datagen.flow_from_directory(
+        #     VALIDATION_DATA_DIR,
+        #     target_size=(IMG_HEIGHT, IMG_WIDTH),
+        #     batch_size=BATCH_SIZE,
+        #     shuffle=True,
+        #     class_mode="categorical")
+
         # Save the model according to the conditions
-        checkpoint = ModelCheckpoint("../models_checkpoints/" + file_name + ".h5", monitor='val_acc',
-                                    verbose=1, save_best_only=True, save_weights_only=False,
-                                    mode='auto', period=1)
+        checkpoint = ModelCheckpoint("../models_checkpoints/" + self.file_name + ".h5", monitor='val_acc',
+                                     verbose=1, save_best_only=True, save_weights_only=False,
+                                     mode='auto', period=1)
 
         # Train the model
-        model_final.fit_generator(
+        self.model.fit_generator(
             train_generator,
             steps_per_epoch=train_generator.samples // BATCH_SIZE,
             epochs=EPOCHS,
-            callbacks=[checkpoint],
-            validation_data=validation_generator,
-            validation_steps=validation_generator.samples // BATCH_SIZE)
+            callbacks=[checkpoint])
+
+    def must_train(self):
+        """Return if classifier must be trained. 
+
+        Returns
+        -------
+        True
+        """
+        return True
+
+    def must_extract_features(self):
+        """Return if classifier must be extracted features. 
 
+        Returns
+        -------
+        False
+        """
+        return False