class TLClassifier:
def __init__(self, is_site):
#TODO load classifier
assert not is_site
weights_file = r'light_classification/models/squeezenet_weights.h5' #Replace with real world classifier
image_shape = (224, 224, 3)
self.states = (TrafficLight.RED, TrafficLight.YELLOW,
TrafficLight.GREEN, TrafficLight.UNKNOWN)
print('Loading model..')
self.model = SqueezeNet(len(self.states), *image_shape)
self.model.load_weights(weights_file, by_name=True)
self.model._make_predict_function()
print('Loaded weights: %s' % weights_file)
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)
"""
mini_batch = cv2.resize(
image, (224, 224),
cv2.INTER_AREA).astype('float')[np.newaxis, ..., ::-1] / 255.
light = self.states[np.argmax(self.model.predict(mini_batch))]
return light
import tensorflow as tf
from tensorflow.python.keras.preprocessing import image
from tensorflow.python.keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np
from squeezenet import SqueezeNet
responses = ["record_response", "replay_response"]
session_conf = tf.ConfigProto(intra_op_parallelism_threads=1,
inter_op_parallelism_threads=1)
sess = tf.Session(config=session_conf)
img = image.load_img('/image.jpg', target_size=(227, 227))
model = SqueezeNet(weights='imagenet')
model._make_predict_function()
print('Model is ready')
img2 = image.load_img('/image2.jpg', target_size=(227, 227))
model2 = SqueezeNet(weights='imagenet')
model2._make_predict_function()
print('Model2 is ready')
class Greeter(helloworld_pb2_grpc.GreeterServicer):
def SayHello(self, request, context):
#res = decode_predictions(preds) # requires access to the Internet
if request.name == "record":
msg = 'Hello, %s!' % responses[0]
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
