def __init__(self, graph, labels):
num_classes = 14
label_map = label_map_util.load_labelmap(labels)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=num_classes, use_display_name=True)
self.category_index = label_map_util.create_category_index(categories)
# Load Tensorflow model into memory
self.detection_graph = tf.Graph()
with self.detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(graph, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.sess = tf.Session(graph=self.detection_graph)
self.image_tensor = self.detection_graph.get_tensor_by_name(
'image_tensor:0')
self.detection_boxes = self.detection_graph.get_tensor_by_name(
'detection_boxes:0')
self.detection_scores = self.detection_graph.get_tensor_by_name(
'detection_scores:0')
self.detection_classes = self.detection_graph.get_tensor_by_name(
'detection_classes:0')
self.num_detections = self.detection_graph.get_tensor_by_name(
'num_detections:0')
def __init__(self, draw_box=False):
self.draw_box = draw_box
self.score_thresh = 0.5
self.skip_cnt = 0
self.max_cnt = 4
self.last_light = TrafficLight.UNKNOWN
curr_dir = os.path.dirname(os.path.realpath(__file__))
model = curr_dir + '/model/frozen_inference_graph.pb'
labels_file = curr_dir + '/model/label_map.pbtxt'
label_map = label_map_util.load_labelmap(labels_file)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=4, use_display_name=True)
self.category_index = label_map_util.create_category_index(categories)
self.graph = tf.Graph()
with self.graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(model, 'rb') as fid:
od_graph_def.ParseFromString(fid.read())
tf.import_graph_def(od_graph_def, name='')
self.sess = tf.Session(graph=self.graph)
self.image_tensor = self.graph.get_tensor_by_name('image_tensor:0')
self.boxes = self.graph.get_tensor_by_name('detection_boxes:0')
self.scores = self.graph.get_tensor_by_name('detection_scores:0')
self.classes = self.graph.get_tensor_by_name('detection_classes:0')
self.num_detections = self.graph.get_tensor_by_name('num_detections:0')
print("Loaded frozen model graph")
def __init__(self):
#TODO load classifier
self.current_light = TrafficLight.UNKNOWN
cwd = os.path.dirname(
os.path.realpath(__file__)) # Current working directory
self.simulation = True
if self.simulation is True:
CKPT = cwd + '/graphs/simulator_graph.pb'
else:
CKPT = cwd + '/graphs/test_site_graph.pb'
PATH_TO_LABELS = cwd + '/graphs/label_map.pbtxt'
NUM_CLASSES = 4
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
self.category_index = label_map_util.create_category_index(categories)
##### Build network
self.image_np_deep = None
self.detection_graph = tf.Graph()
# https://github.com/tensorflow/tensorflow/issues/6698
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# end
with self.detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.sess = tf.Session(graph=self.detection_graph, config=config)
# Definite input and output Tensors for detection_graph
self.image_tensor = self.detection_graph.get_tensor_by_name(
'image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
self.detection_boxes = self.detection_graph.get_tensor_by_name(
'detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
self.detection_scores = self.detection_graph.get_tensor_by_name(
'detection_scores:0')
self.detection_classes = self.detection_graph.get_tensor_by_name(
'detection_classes:0')
self.num_detections = self.detection_graph.get_tensor_by_name(
'num_detections:0')
print("Detection graph loaded")
def __init__(self):
# set default value for no detection
self.current_light = TrafficLight.UNKNOWN
curr_dir = os.path.dirname(os.path.realpath(__file__))
model = curr_dir + '/sim-udacity-model/frozen_inference_graph.pb'
## this is for selecting model trained on simulator or real images:
#if sim:
# model = curr_dir + '/sim-udacity_model/frozen_inference_graph.pb'
#else:
# model = curr_dir + '/real-udacity_model/frozen_inference_graph.pb'
labels_file = curr_dir + '/labelmap.pbtxt'
num_classes = 4 #14
label_map = label_map_util.load_labelmap(labels_file)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=num_classes, use_display_name=True)
self.category_index = label_map_util.create_category_index(categories)
self.image_np_deep = None
self.detection_graph = tf.Graph()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with self.detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(model, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.sess = tf.Session(graph=self.detection_graph, config=config)
# Definite input and output Tensors for detection_graph
self.image_tensor = self.detection_graph.get_tensor_by_name(
'image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
self.detection_boxes = self.detection_graph.get_tensor_by_name(
'detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
self.detection_scores = self.detection_graph.get_tensor_by_name(
'detection_scores:0')
self.detection_classes = self.detection_graph.get_tensor_by_name(
'detection_classes:0')
self.num_detections = self.detection_graph.get_tensor_by_name(
'num_detections:0')
print("Loaded frozen model graph")
def __init__(self):
#self.simulator = True #Set to false for real car.
self.simulator = rospy.get_param(
"/simulator") #Set to false for real car.
cwd = os.path.dirname(os.path.realpath(__file__))
if self.simulator == True:
print("In SIMULATOR")
CKPT = cwd + "/graphs/frozen_inference_graph.pb"
else:
print("In CAR")
CKPT = cwd + "/graphs/frozen_inference_graph_real.pb"
PATH_TO_LABELS = cwd + '/graphs/object-detection.pbtxt'
NUM_CLASSES = 3 # 3, as pretrained on udacity real car dataset with 3 classes
#Label maps map indices to category names
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
self.category_index = label_map_util.create_category_index(categories)
#Build network
self.detection_graph = tf.Graph()
with self.detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.sess = tf.Session(graph=self.detection_graph)
# Definite input and output Tensors for detection_graph
self.image_tensor = self.detection_graph.get_tensor_by_name(
'image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
self.detection_boxes = self.detection_graph.get_tensor_by_name(
'detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
self.detection_scores = self.detection_graph.get_tensor_by_name(
'detection_scores:0')
self.detection_classes = self.detection_graph.get_tensor_by_name(
'detection_classes:0')
self.num_detections = self.detection_graph.get_tensor_by_name(
'num_detections:0')
print("Loaded model")
def __init__(self, *args):
self.current_light = TrafficLight.RED
cwd = os.path.dirname(os.path.realpath(__file__))
# Path to frozen detection graph. This is the actual model that is used for the object detection.
base_path = os.path.dirname(os.path.abspath(__file__))
PATH_TO_CKPT = os.path.join(base_path, 'model',
'frozen_inference_graph.pb')
# Load label map
PATH_TO_LABELS = os.path.join(base_path, 'model',
'traffic_lights_label_map.pbtxt')
NUM_CLASSES = 14
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
self.category_index = label_map_util.create_category_index(categories)
# Build network
self.detection_graph = tf.Graph()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # https://github.com/tensorflow/tensorflow/issues/6698
with self.detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.sess = tf.Session(graph=self.detection_graph, config=config)
# Definite input and output Tensors for detection_graph
self.image_tensor = self.detection_graph.get_tensor_by_name(
'image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
self.detection_boxes = self.detection_graph.get_tensor_by_name(
'detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
self.detection_scores = self.detection_graph.get_tensor_by_name(
'detection_scores:0')
self.detection_classes = self.detection_graph.get_tensor_by_name(
'detection_classes:0')
self.num_detections = self.detection_graph.get_tensor_by_name(
'num_detections:0')
print("Classifier initialisation complete!")
def __init__(self, is_site):
# set default value for no detection
self.current_light = TrafficLight.UNKNOWN
curr_dir = os.path.dirname(os.path.realpath(__file__))
model_dir = curr_dir + '/../../../../image_classification_model/'
if is_site:
model = model_dir + '/protobuf/frozen_inference_graph_ssd_real.pb'
else:
model = model_dir + '/protobuf/frozen_inference_graph_ssd_sim.pb'
num_classes = 4
labels_file = model_dir + '/label_map.pbtxt'
label_map = label_map_util.load_labelmap(labels_file)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=num_classes, use_display_name=True)
self.category_index = label_map_util.create_category_index(categories)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.detection_graph = tf.Graph()
with self.detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(model, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.sess = tf.Session(graph=self.detection_graph, config=config)
# Input tensor
self.image_tensor = self.detection_graph.get_tensor_by_name(
'image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
self.detection_boxes = self.detection_graph.get_tensor_by_name(
'detection_boxes:0')
# Each score represents the confidence level for each object
self.detection_scores = self.detection_graph.get_tensor_by_name(
'detection_scores:0')
self.detection_classes = self.detection_graph.get_tensor_by_name(
'detection_classes:0')
self.num_detections = self.detection_graph.get_tensor_by_name(
'num_detections:0')
self.image_classified = None
print("Frozen graph loaded! model: {}".format(model))
def __init__(self, threshold=0.5):
dir_path = os.path.dirname(os.path.realpath(__file__))
self.threshold = threshold
self.model_name = 'ssd_mobilenet_v1_udacity_all_1.4'
self.model_path = dir_path + '/models/' + self.model_name + \
'/frozen_inference_graph.pb'
self.labels_name = 'udacity_all'
self.labels_path = dir_path + '/labels/' + self.labels_name + ".pbtxt"
# add classification boxes to image
label_map = label_map_util.load_labelmap(self.labels_path)
categories = label_map_util.convert_label_map_to_categories(label_map,
max_num_classes=3, use_display_name=True)
self.category_index = label_map_util.create_category_index(categories)
self.detection_graph = self.load_tf_model()
self.classified_image = None
def __init__(self,model_path):
self.current_light = TrafficLight.UNKNOWN # Default value if pass on network / or nothing detected.
cwd = os.path.dirname(os.path.realpath(__file__))
self.simulation = True # Set to false for real (sim one somehow works sorta for real too it's spooky)
self.faster = True # 10 for faster run time or anything else for larger network.
# Default to Simulation with 10 regions.
#print("cwd: {}".format(cwd))
if self.simulation is True:
if self.faster is True:
CKPT = cwd+'/../../../../classifier/faster-R-CNN/checkpoints/frozen_inference_graph.pb'
else:
CKPT = cwd+'/../../../../classifier/faster-R-CNN/checkpoints/frozen_inference_graph.pb'
else:
CKPT = cwd+'/../../../../classifier/faster-R-CNN/checkpoints/frozen_inference_graph.pb'
#print("cwd: {}".format(cwd))
PATH_TO_LABELS = cwd+'/../../../../classifier/label_map.pbtxt'
NUM_CLASSES = 14
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
self.category_index = label_map_util.create_category_index(categories)
##### Build network
self.image_np_deep = None
self.detection_graph = tf.Graph()
# https://github.com/tensorflow/tensorflow/issues/6698
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# end
with self.detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.sess = tf.Session(graph=self.detection_graph, config=config)
# Definite input and output Tensors for detection_graph
self.image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
self.detection_boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
self.detection_scores = self.detection_graph.get_tensor_by_name('detection_scores:0')
self.detection_classes = self.detection_graph.get_tensor_by_name('detection_classes:0')
self.num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')
print("Loaded graph")
def __init__(self, *args):
#print("Classifier launched in site mode : ", args[0])
#self.current_light = TrafficLight.RED
self.current_light = 0
#cwd = os.path.dirname(os.path.realpath(__file__))
# Path to frozen detection graph. This is the actual model that is used for the object detection.
base_path = '/home/ashis/ashis/Udacity/SDC/Term_3/TL_Model_Test/CarND-Capstone-Ashis-master/ros/src/tl_detector/light_classification/' #os.path.dirname(os.path.abspath(__file__))
#MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
MODEL_NAME = 'ssd_mobilenet_tl'
PATH_TO_CKPT = os.path.join(base_path, MODEL_NAME,
'frozen_inference_graph.pb')
#CHUNK_SIZE = 10485760 # 10MB
#PATH_TO_CHUNKS = os.path.join(base_path, MODEL_NAME, 'chunks')
#print('checkpoint', PATH_TO_CKPT)
#print('chunks', PATH_TO_CHUNKS)
# If the frozen model does not exist trying creating it from file chunks
#if not os.path.exists(PATH_TO_CKPT): #(MODEL_NAME + '/frozen_inference_graph.pb'):
# print("frozen inference graph not found - building from chunks")
# output = open(PATH_TO_CKPT, 'wb')
# chunks = os.listdir(PATH_TO_CHUNKS)
# chunks.sort()
# for fname in chunks:
# fpath = os.path.join(PATH_TO_CHUNKS, fname)
# with open(fpath, 'rb') as fileobj:
# for chunk in iter(lambda: fileobj.read(CHUNK_SIZE), b''):
# output.write(chunk)
# output.close()
# Load label map
PATH_TO_LABELS = os.path.join(base_path, 'data',
'traffic_lights_label_map.pbtxt')
NUM_CLASSES = 14
#label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
label_map = label_map_util.load_labelmap(
'/home/ashis/ashis/Udacity/SDC/Term_3/TL_Model_Test/CarND-Capstone-Ashis-master/ros/src/tl_detector/light_classification/ssd_mobilenet_tl/traffic_lights_label_map.pbtxt'
)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
self.category_index = label_map_util.create_category_index(categories)
# Build network
self.detection_graph = tf.Graph()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # https://github.com/tensorflow/tensorflow/issues/6698
with self.detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.sess = tf.Session(graph=self.detection_graph, config=config)
# Definite input and output Tensors for detection_graph
self.image_tensor = self.detection_graph.get_tensor_by_name(
'image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
self.detection_boxes = self.detection_graph.get_tensor_by_name(
'detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
self.detection_scores = self.detection_graph.get_tensor_by_name(
'detection_scores:0')
self.detection_classes = self.detection_graph.get_tensor_by_name(
'detection_classes:0')
self.num_detections = self.detection_graph.get_tensor_by_name(
'num_detections:0')
print("Classifier initialisation complete!")
def load_graph(self):
dir_path = os.path.dirname(os.path.realpath(__file__))
dir_path = os.path.join(dir_path, 'frozen_models')
# The trained tf model
# Accurate
MODEL_NAME = 'frozen_resnet101'
# Fast
# MODEL_NAME = 'frozen_ssd_mobilenet'
MODEL_NAME += ('_sim' if self.on_sim else '_real')
MODEL_NAME = os.path.join(dir_path, MODEL_NAME)
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = MODEL_NAME + '/label_map.pbtxt'
NUM_CLASSES = 14
###
# Load a (frozen) Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
###
# Loading label map
# label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
# categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
# category_index = label_map_util.create_category_index(categories)
# category_index = {
# category_index[1] = {'id': 1, 'name': TrafficLight.GREEN, 'description': 'Green'}
# category_index[2] = {'id': 2, 'name': TrafficLight.RED, 'description': 'Red'}
# category_index[3] = {'id': 3, 'name': TrafficLight.YELLOW, 'description': 'Yellow'}
# category_index[4] = {'id': 4, 'name': TrafficLight.UNKNOWN, 'description': 'off'}}
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES,
use_display_name=True)
self.category_index = label_map_util.create_category_index(categories)
rospy.loginfo('%s', self.category_index)
rospy.loginfo('Loaded model: %s', PATH_TO_CKPT)
with detection_graph.as_default():
# Definite input and output Tensors for detection_graph
self.image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
self.detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
self.detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
self.detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
self.num_detections = detection_graph.get_tensor_by_name('num_detections:0')
self.sess = tf.Session(graph=detection_graph)
self.detection_graph = detection_graph
