def __init__(self):
#TODO load classifier
# relative to file path, otherwise we have got problems with launch-type specific working directory
base_path = os.path.dirname(os.path.abspath(__file__))
graph_pth = os.path.join(base_path, 'fine_tuned_model_real_mobilenet', 'frozen_inference_graph.pb')
label_pth = os.path.join(base_path, 'fine_tuned_model_real_mobilenet', 'labels_map.pbtxt')
self.graph_pth = graph_pth
self.detection_graph = self.load_tf_graph(self.graph_pth)
self.label_pth = label_pth
self.label_map = label_map_util.load_labelmap(label_pth)
self.categories = label_map_util.convert_label_map_to_categories(self.label_map, max_num_classes=3, use_display_name=True)
self.category_index = label_map_util.create_category_index(self.categories)
self.class_map = {
1: TrafficLight.GREEN,
2: TrafficLight.YELLOW,
3: TrafficLight.RED
}
def class_text_to_int(row_label):
label = lmu.load_labelmap('/images/labelmap.pbtxt')
for lb in label.item:
if row_label == lb.name:
return lb.id
else:
None
def Open(self):
#Loading label map
self.label_map = label_map_util.load_labelmap(self.PATH_TO_LABELS)
self.categories = label_map_util.convert_label_map_to_categories(self.label_map, max_num_classes=self.NUM_CLASSES, use_display_name=True)
self.category_index = label_map_util.create_category_index(self.categories)
#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(self.PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
config = tf.ConfigProto(
device_count = {'GPU': 0}
)
detection_graph.as_default()
self.detector = tf.Session(graph=detection_graph, config=config)
# 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')
def set_model(model_name):
for file in glob.glob("*"):
if (file == model_name):
pass
# 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 = os.path.join('data', 'mscoco_label_map.pbtxt')
num_classes = 90
# 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 maps map indices to category names, so that when our convolution network predicts 5, we know that this corresponds to airplane. Here I use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine
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)
return detection_graph, category_index
def __init__(self):
# Get paths
TFWD_PATH = os.path.join(os.path.dirname(__file__), 'tf')
PATH_TO_CKPT = os.path.join(TFWD_PATH,'frozen_inference_graph.pb')
PATH_TO_LABELS = os.path.join(TFWD_PATH,'annotation.pbtxt')
# get our 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=3, use_display_name=True)
self.category_index = label_map_util.create_category_index(categories)
# Load the 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='')
# get our graph and save to class
self.sess = tf.Session(graph=detection_graph)
# Define input and output tensors for the object detection classifier
self.it = detection_graph.get_tensor_by_name('image_tensor:0')
self.db = detection_graph.get_tensor_by_name('detection_boxes:0')
self.ds = detection_graph.get_tensor_by_name('detection_scores:0')
self.dc = detection_graph.get_tensor_by_name('detection_classes:0')
self.nd = detection_graph.get_tensor_by_name('num_detections:0')
def _load_mapphx(self):
label_map = label_map_util.load_labelmap(self.PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=self.NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
return category_index
def load_model():
# 添加模型路径：
CWD_PATH = os.getcwd() # os.getcwd() 方法用于返回当前工作目录。
PATH_TO_CKPT = os.path.join(CWD_PATH,
'../ssd_mobilenet_v1_coco_2018_01_28',
'frozen_inference_graph.pb')
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join(CWD_PATH, 'data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 90
# 加载模型
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='')
# 加载lable 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)
print('models have already loaded.')
return detection_graph, category_index
def __displayDetectedImage(self, item):
dcmFileName = item.data(0, Qt.UserRole)
self.ui.statusBar.showMessage(dcmFileName)
#预处理
dcmFile = pydicom.read_file(dcmFileName)
origin = dcmFile.pixel_array # type:numpy.ndarray
intercept = dcmFile.RescaleIntercept
slope = dcmFile.RescaleSlope
origin = origin * slope + intercept
origin[origin < -100] = -100 # 去除低亮部分
origin[origin > 750] = 750 # 去除高亮部分
# 归一化到0-255
origin = cv2.normalize(origin,
None,
0,
255,
norm_type=cv2.NORM_MINMAX,
dtype=cv2.CV_8UC3)
image_np = np.expand_dims(origin, -1) # 将origin增加一个维度
image_np = np.repeat(image_np, 3, 2) # 将二维矩阵重复三次
#plt.imsave("output/undetected.jpg", image_np)
NUM_CLASSES = 6
PATH_TO_LABELS = 'label_map.pbtxt'
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)
result = item.data(1, Qt.UserRole)
boxes = result[1]
scores = result[2]
classes = result[3]
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
boxes,
classes,
scores,
category_index,
use_normalized_coordinates=True,
line_thickness=1)
#plt.imsave("output/detected.jpg", image_np)
image = QImage(image_np, image_np.shape[1], image_np.shape[0],
QImage.Format_RGB888)
self.curPixmap = QPixmap(image)
self.on_actZoomFitH_triggered()
self.ui.actZoomIn.setEnabled(True)
self.ui.actZoomOut.setEnabled(True)
self.ui.actZoomRealSize.setEnabled(True)
self.ui.actZoomFitW.setEnabled(True)
self.ui.actZoomFitH.setEnabled(True)
def __init__(self):
self.saved_image_limit = 500
self.saved_image_counter = 1
self.save_images = rospy.get_param("~save_image", False)
self.current_light = TrafficLight.UNKNOWN
self.model_path = rospy.get_param('~model')
self.readsize = 1024
# Build the model
self.detection_graph = tf.Graph()
# create config
config = tf.ConfigProto()
# reassemble the model from chunks. credit goes to team vulture for this idea
if not os.path.exists(self.model_path):
if not os.path.exists(self.model_path+'/chunks'):
output = open(self.model_path, 'wb')
frozen_model_path = os.path.dirname(self.model_path)+'/frozen_model_chunks'
chunks = os.listdir(frozen_model_path)
chunks.sort()
for filename in chunks:
filepath = os.path.join(frozen_model_path, filename)
with open(filepath, 'rb') as fileobj:
for chunk in iter(partial(fileobj.read, self.readsize), ''):
output.write(chunk)
output.close()
# Create the graph
with self.detection_graph.as_default():
graph_def = tf.GraphDef()
with tf.gfile.GFile(self.model_path, 'rb') as fid:
serialized_graph = fid.read()
graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(graph_def, name='')
rospy.loginfo('Loaded frozen tensorflow model: %s', self.model_path)
# Create a reusable sesion attribute
self.sess = tf.Session(graph=self.detection_graph, config=config)
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')
self.path = './light_classification/UTL_label_map.pbtxt'
print(self.path)
PATH_TO_LABELS = self.path
NUM_CLASSES = 3
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)
self.count = 1
def main(_):
# step 2: send a request
options = [('grpc.max_send_message_length', 1000 * 1024 * 1024),
('grpc.max_receive_message_length', 1000 * 1024 * 1024)]
channel = grpc.insecure_channel(FLAGS.server, options=options)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
request = predict_pb2.PredictRequest()
request.model_spec.name = 'model'
request.model_spec.signature_name = 'serving_default'
# step 1: prepare input
img = cv2.imread(FLAGS.image)
h, w = img.shape[:2]
if compress:
ratio = w / h
h1 = height
w1 = round(h1 * ratio)
scaled_img = cv2.resize(img, (w1, h1), interpolation=cv2.INTER_AREA)
tensor = tf.contrib.util.make_tensor_proto(scaled_img,
shape=[1] +
list(scaled_img.shape))
else:
tensor = tf.contrib.util.make_tensor_proto(img,
shape=[1] + list(img.shape))
request.inputs['inputs'].CopyFrom(tensor)
start = time.time()
# step 3: get the results
result_future = stub.Predict.future(request, 10.0) # 10 secs timeout
result = result_future.result()
stop = time.time()
print('time is ', stop - start)
NUM_CLASSES = 30
label_map = label_map_util.load_labelmap('annotations/label_map.pbtxt')
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)
boxes = result.outputs['detection_boxes'].float_val
classes = result.outputs['detection_classes'].float_val
scores = result.outputs['detection_scores'].float_val
result = vis_util.visualize_boxes_and_labels_on_image_array(
img,
np.reshape(boxes, [100, 4]),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8)
cv2.imwrite('result.jpg', result)
def get_labeled_image(image_path, path_to_labels, num_classes, boxes, classes,
scores):
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)
image = Image.open(image_path)
image_np = load_image_into_numpy_array(image)
image_process = vis_util.visualize_boxes_and_labels_on_image_array(
image_np, boxes, classes, scores, category_index)
return image_process
def get_category_index(self):
""" Transforms label map into category index for visualization.
Returns:
category_index: The category index corresponding to the given
label map.
"""
label_map = label_map_util.load_labelmap(self.label_path)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=1, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
return category_index
def _init_category_index(label_map_path):
"""Creates category index from class indexes to name of the classes.
Args:
label_map_path: path to the mapping.
Returns:
A map for mapping int keys to string categories.
"""
label_map = label_map_util.load_labelmap(label_map_path)
num_classes = np.max(x.id for x in label_map.item)
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)
return category_index
def main(unused_argv):
assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
assert FLAGS.eval_dir, '`eval_dir` is missing.'
tf.gfile.MakeDirs(FLAGS.eval_dir)
if FLAGS.pipeline_config_path:
configs = config_util.get_configs_from_pipeline_file(
FLAGS.pipeline_config_path)
tf.gfile.Copy(FLAGS.pipeline_config_path,
os.path.join(FLAGS.eval_dir, 'pipeline.config'),
overwrite=True)
else:
configs = config_util.get_configs_from_multiple_files(
model_config_path=FLAGS.model_config_path,
eval_config_path=FLAGS.eval_config_path,
eval_input_config_path=FLAGS.input_config_path)
for name, config in [('model.config', FLAGS.model_config_path),
('eval.config', FLAGS.eval_config_path),
('input.config', FLAGS.input_config_path)]:
tf.gfile.Copy(config,
os.path.join(FLAGS.eval_dir, name),
overwrite=True)
model_config = configs['model']
eval_config = configs['eval_config']
if FLAGS.eval_training_data:
input_config = configs['train_input_config']
else:
input_config = configs['eval_input_config']
model_fn = functools.partial(
model_builder.build,
model_config=model_config,
is_training=False)
create_input_dict_fn = functools.partial(
input_reader_builder.build,
input_config)
label_map = label_map_util.load_labelmap(input_config.label_map_path)
max_num_classes = max([item.id for item in label_map.item])
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes)
if FLAGS.run_once:
eval_config.max_evals = 1
evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
FLAGS.checkpoint_dir, FLAGS.eval_dir)
def load(self):
config = tf.ConfigProto()
config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
self.detection_graph = tf.Graph()
with tf.Session(graph=self.detection_graph, config=config) as sess:
self.session = sess
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='')
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)
def __init__(self, model_path, label_path):
#TODO load classifier
self.label_map = label_map_util.load_labelmap(label_path)
self.categories = label_map_util.convert_label_map_to_categories(
self.label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
self.category_index = label_map_util.create_category_index(
self.categories)
self.model_path = model_path
self.detection_graph = None
self.tf_session = None
self.image_tensor = None
self.detection_boxes = None
self.detection_scores = None
self.detection_classes = None
self.num_detections = None
def __init__(self):
self.modelpath = rospy.get_param('~model_path')
# PATH_TO_MODEL = 'models/trial19_ssd_inception_sim_frozen_inference_graph.pb' # model resnet-udacity-sim-large-10-regions
PATH_TO_MODEL = self.modelpath
self.saved_image_limit = 500
self.saved_image_counter = 1
self.save_images = False
self.readsize = 1024
# Build the model
self.detection_graph = tf.Graph()
# Create the graph
with self.detection_graph.as_default():
graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_MODEL, 'rb') as fid:
serialized_graph = fid.read()
graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(graph_def, name='')
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')
self.path = './light_classification/Conversion_label_map.pbtxt'
# Create a reusable sesion attribute
self.sess = tf.Session(graph=self.detection_graph)
PATH_TO_LABELS = self.path
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)
self.count = 1
def __init__(self, num_class):
threading.Thread.__init__(self)
self.map = {1: "bottle", 2: "speaker", 3: "scissors"}
self.current_Path = os.getcwd()
self.Load_from_location = os.path.join(self.current_Path, 'images')
self.Store_Search = store_Search(
os.path.join(self.current_Path, 'static'))
self.NUM_CLASSES = num_class
self.PATH_TO_LABELS = os.path.join(self.current_Path, 'labelmap.pbtxt')
self.label_map = label_map_util.load_labelmap(self.PATH_TO_LABELS)
self.PATH_TO_CKPT = os.path.join(self.current_Path,
'frozen_inference_graph.pb')
self.categories = label_map_util.convert_label_map_to_categories(
self.label_map,
max_num_classes=self.NUM_CLASSES,
use_display_name=True)
self.category_index = label_map_util.create_category_index(
self.categories)
def init_detector(self):
# load graph and label map from default folder
if self.graph_path is None:
self.graph_path = './frozen_model/frozen_inference_graph.pb'
if self.label_path is None:
self.label_path = './annotations/label_map.pbtxt'
# check existence of the two files
if not os.path.exists(self.graph_path):
raise IOError('Invalid detector_graph path! {}'.format(
self.graph_path))
if not os.path.exists(self.label_path):
raise IOError('Invalid label path! {}'.format(self.label_path))
# load frozen graph
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(self.graph_path, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.session = tf.Session(graph=detection_graph)
# prepare input and output request
self.input = detection_graph.get_tensor_by_name('image_tensor:0')
self.output.append(
detection_graph.get_tensor_by_name('detection_boxes:0'))
self.output.append(
detection_graph.get_tensor_by_name('detection_scores:0'))
self.output.append(
detection_graph.get_tensor_by_name('detection_classes:0'))
self.output.append(
detection_graph.get_tensor_by_name('num_detections:0'))
# Load label map
label_map = label_map_util.load_labelmap(self.label_path)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=self.class_num, use_display_name=True)
self.category_index = label_map_util.create_category_index(categories)
