from styx_msgs.msg import TrafficLight

import shutil
import tensorflow as tf
from keras.models import load_model
import h5py
import keras
import numpy as np
import cv2

class TLClassifier(object):
    def __init__(self, is_site):        
        # check that model Keras version is same as local Keras version
        if not is_site:
            filename = './light_classification/model.h5'
        else:
            filename = './light_classification/model_site.h5'

        f = h5py.File(filename, mode='r')
        model_version = f.attrs.get('keras_version')
        keras_version = str(keras.__version__).encode('utf8')

        if model_version != keras_version:
            print('You are using Keras version ', keras_version,
                ', but the model was built using ', model_version)

        self.model = load_model(filename, custom_objects={"tf": tf})
        self.graph = tf.get_default_graph()

    def get_classification(self, image):
        """Determines the color of the traffic light in the image

        Args:
            image (cv::Mat): image containing the traffic light

        Returns:
            int: ID of traffic light color (specified in styx_msgs/TrafficLight)

        """
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        image = cv2.resize(image, (360,270), interpolation = cv2.INTER_CUBIC)

        with self.graph.as_default():
            result = self.model.predict(image[None, :, :, :], batch_size=1).squeeze()
            id = np.argmax(result)
            return id, result[id]
e7aaee983cc20a7c1bb59488f3861d890759dd35
<mask token>


class TLClassifier(object):
    <mask token>
    <mask token>

<mask token>


class TLClassifier(object):

    def __init__(self, is_site):
        if not is_site:
            filename = './light_classification/model.h5'
        else:
            filename = './light_classification/model_site.h5'
        f = h5py.File(filename, mode='r')
        model_version = f.attrs.get('keras_version')
        keras_version = str(keras.__version__).encode('utf8')
        if model_version != keras_version:
            print('You are using Keras version ', keras_version,
                ', but the model was built using ', model_version)
        self.model = load_model(filename, custom_objects={'tf': tf})
        self.graph = tf.get_default_graph()
    <mask token>

<mask token>


class TLClassifier(object):

    def __init__(self, is_site):
        if not is_site:
            filename = './light_classification/model.h5'
        else:
            filename = './light_classification/model_site.h5'
        f = h5py.File(filename, mode='r')
        model_version = f.attrs.get('keras_version')
        keras_version = str(keras.__version__).encode('utf8')
        if model_version != keras_version:
            print('You are using Keras version ', keras_version,
                ', but the model was built using ', model_version)
        self.model = load_model(filename, custom_objects={'tf': tf})
        self.graph = tf.get_default_graph()

    def get_classification(self, image):
        """Determines the color of the traffic light in the image

        Args:
            image (cv::Mat): image containing the traffic light

        Returns:
            int: ID of traffic light color (specified in styx_msgs/TrafficLight)

        """
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        image = cv2.resize(image, (360, 270), interpolation=cv2.INTER_CUBIC)
        with self.graph.as_default():
            result = self.model.predict(image[None, :, :, :], batch_size=1
                ).squeeze()
            id = np.argmax(result)
            return id, result[id]

from styx_msgs.msg import TrafficLight
import shutil
import tensorflow as tf
from keras.models import load_model
import h5py
import keras
import numpy as np
import cv2


class TLClassifier(object):

    def __init__(self, is_site):
        if not is_site:
            filename = './light_classification/model.h5'
        else:
            filename = './light_classification/model_site.h5'
        f = h5py.File(filename, mode='r')
        model_version = f.attrs.get('keras_version')
        keras_version = str(keras.__version__).encode('utf8')
        if model_version != keras_version:
            print('You are using Keras version ', keras_version,
                ', but the model was built using ', model_version)
        self.model = load_model(filename, custom_objects={'tf': tf})
        self.graph = tf.get_default_graph()

    def get_classification(self, image):
        """Determines the color of the traffic light in the image

        Args:
            image (cv::Mat): image containing the traffic light

        Returns:
            int: ID of traffic light color (specified in styx_msgs/TrafficLight)

        """
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        image = cv2.resize(image, (360, 270), interpolation=cv2.INTER_CUBIC)
        with self.graph.as_default():
            result = self.model.predict(image[None, :, :, :], batch_size=1
                ).squeeze()
            id = np.argmax(result)
            return id, result[id]

from styx_msgs.msg import TrafficLight

import shutil
import tensorflow as tf
from keras.models import load_model
import h5py
import keras
import numpy as np
import cv2

class TLClassifier(object):
    def __init__(self, is_site):        
        # check that model Keras version is same as local Keras version
        if not is_site:
            filename = './light_classification/model.h5'
        else:
            filename = './light_classification/model_site.h5'

        f = h5py.File(filename, mode='r')
        model_version = f.attrs.get('keras_version')
        keras_version = str(keras.__version__).encode('utf8')

        if model_version != keras_version:
            print('You are using Keras version ', keras_version,
                ', but the model was built using ', model_version)

        self.model = load_model(filename, custom_objects={"tf": tf})
        self.graph = tf.get_default_graph()

    def get_classification(self, image):
        """Determines the color of the traffic light in the image

        Args:
            image (cv::Mat): image containing the traffic light

        Returns:
            int: ID of traffic light color (specified in styx_msgs/TrafficLight)

        """
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        image = cv2.resize(image, (360,270), interpolation = cv2.INTER_CUBIC)

        with self.graph.as_default():
            result = self.model.predict(image[None, :, :, :], batch_size=1).squeeze()
            id = np.argmax(result)
            return id, result[id]