Fix: Corrected Load and Reload button logic for Issue #675

behavior_metrics/Crypto

@@ -0,0 +1 @@
+Cryptodome

behavior_metrics/behaviorlib/keraslib/keras_predict.py

@@ -1,89 +1,89 @@
-#!/usr/bin/env python
-
-""" This module contains the logic to make predictions of keras-based neural networks.
-
-This is module contains a class called KerasPredictor which is responsible of making predictions based
-on a specific model. This is a generic class used by all the keras-based models.
-
-This program is free software: you can redistribute it and/or modify it under
-the terms of the GNU General Public License as published by the Free Software
-Foundation, either version 3 of the License, or (at your option) any later
-version.
-This program is distributed in the hope that it will be useful, but WITHOUT
-ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
-FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
-You should have received a copy of the GNU General Public License along with
-this program. If not, see <http://www.gnu.org/licenses/>.
-
-Original idea from: https://github.com/JdeRobot/DetectionSuite/blob/master/DeepLearningSuite/DeepLearningSuiteLib/python_modules/keras_detect.py
-Code adapted by: fqez
-Original code by: vmartinezf and chanfr
-"""
-
-import os
-
-import cv2
-import numpy as np
-import tensorflow as tf
-from keras.backend import set_session
-from keras.models import load_model
-from utils.logger import logger
-
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-
-class KerasPredictor:
-    """This class takes care of the prediction of the models that controls the behaviors of the robots.
-
-    It's used for keras-based models and it's meant to predict a value (tipically a velocity) based on image inputs.
-
-    Attributes:
-        sess {tf.Session} -- Tensorflow session
-        graph {tf.Graph} -- Tensorflow graph
-        model -- Keras model instance
-        img_width {int} -- Input images width
-        img_height {int} -- Input images height
-    """
-
-    def __init__(self, path_to_hdf5):
-        """Constructor of the class.
-
-        Arguments:
-            path_to_hdf5 {str} -- Path to the model file.
-        """
-
-        # Obtain the graph
-        logger.info("Loading keras model {}".format(path_to_hdf5))
-        self.sess = tf.compat.v1.Session()
-        self.graph = tf.compat.v1.get_default_graph()
-        tf.compat.v1.keras.backend.set_session(self.sess)
-        self.model = tf.keras.models.load_model(path_to_hdf5)
-
-        input_size = self.model.input.shape.as_list()
-        self.img_height = input_size[1]
-        self.img_width = input_size[2]
-
-    def predict(self, img, type='classification'):
-        """Make a prediction of a velocity based on an input images.
-
-        The model takes the image one of the robot's cameras and makes a predictino based on that information.
-
-        Arguments:
-            img {robot.interfaces.camera.Image} -- Image obtanied from one of the robot's cameras.
-            type {str} -- Specify if the network is a classification or regression network (default: 'classification')
-
-        Returns:
-            int, float -- Predicted class or value based on the image information depending on the type of network.
-        """
-
-        img_resized = cv2.resize(img, (self.img_width, self.img_height))
-        input_img = np.stack([img_resized], axis=0)
-
-        with self.graph.as_default():
-            set_session(self.sess)
-            y_pred = self.model.predict(input_img)
-
-        if type == 'classification':
-            return [np.argmax(prediction) for prediction in y_pred][0]
-        else:
-            return [float(prediction[0]) for prediction in y_pred][0]
+#!/usr/bin/env python
+
+""" This module contains the logic to make predictions of keras-based neural networks.
+
+This is module contains a class called KerasPredictor which is responsible of making predictions based
+on a specific model. This is a generic class used by all the keras-based models.
+
+This program is free software: you can redistribute it and/or modify it under
+the terms of the GNU General Public License as published by the Free Software
+Foundation, either version 3 of the License, or (at your option) any later
+version.
+This program is distributed in the hope that it will be useful, but WITHOUT
+ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
+You should have received a copy of the GNU General Public License along with
+this program. If not, see <http://www.gnu.org/licenses/>.
+
+Original idea from: https://github.com/JdeRobot/DetectionSuite/blob/master/DeepLearningSuite/DeepLearningSuiteLib/python_modules/keras_detect.py
+Code adapted by: fqez
+Original code by: vmartinezf and chanfr
+"""
+
+import os
+
+import cv2
+import numpy as np
+import tensorflow as tf
+from keras.backend import set_session
+from keras.models import load_model
+from utils.logger import logger
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+
+class KerasPredictor:
+    """This class takes care of the prediction of the models that controls the behaviors of the robots.
+
+    It's used for keras-based models and it's meant to predict a value (tipically a velocity) based on image inputs.
+
+    Attributes:
+        sess {tf.Session} -- Tensorflow session
+        graph {tf.Graph} -- Tensorflow graph
+        model -- Keras model instance
+        img_width {int} -- Input images width
+        img_height {int} -- Input images height
+    """
+
+    def __init__(self, path_to_hdf5):
+        """Constructor of the class.
+
+        Arguments:
+            path_to_hdf5 {str} -- Path to the model file.
+        """
+
+        # Obtain the graph
+        logger.info("Loading keras model {}".format(path_to_hdf5))
+        self.sess = tf.compat.v1.Session()
+        self.graph = tf.compat.v1.get_default_graph()
+        tf.compat.v1.keras.backend.set_session(self.sess)
+        self.model = tf.keras.models.load_model(path_to_hdf5)
+
+        input_size = self.model.input.shape.as_list()
+        self.img_height = input_size[1]
+        self.img_width = input_size[2]
+
+    def predict(self, img, type='classification'):
+        """Make a prediction of a velocity based on an input images.
+
+        The model takes the image one of the robot's cameras and makes a predictino based on that information.
+
+        Arguments:
+            img {robot.interfaces.camera.Image} -- Image obtanied from one of the robot's cameras.
+            type {str} -- Specify if the network is a classification or regression network (default: 'classification')
+
+        Returns:
+            int, float -- Predicted class or value based on the image information depending on the type of network.
+        """
+
+        img_resized = cv2.resize(img, (self.img_width, self.img_height))
+        input_img = np.stack([img_resized], axis=0)
+
+        with self.graph.as_default():
+            set_session(self.sess)
+            y_pred = self.model.predict(input_img)
+
+        if type == 'classification':
+            return [np.argmax(prediction) for prediction in y_pred][0]
+        else:
+            return [float(prediction[0]) for prediction in y_pred][0]

behavior_metrics/brains/__init__.py

@@ -1 +1 @@
-from .brains_handler import Brains  # FQDN
+from .brains_handler import Brains  # FQDN