def __init__(self, net_model):
labels_file = LABELS_DICT[net_model['Dataset'].lower()]
label_map = label_map_util.load_labelmap(
labels_file) # loads the labels map.
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=999999)
category_index = label_map_util.create_category_index(categories)
self.classes = {}
# We build is as a dict because of gaps on the labels definitions
for cat in category_index:
self.classes[cat] = str(category_index[cat]['name'])
# Frozen inference graph, written on the file
CKPT = os.path.join('Net', 'Models', net_model['Model'])
detection_graph = tf.compat.v1.Graph() # new graph instance.
with detection_graph.as_default():
od_graph_def = tf.compat.v1.GraphDef()
with tf.io.gfile.GFile(CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
# Set additional parameters for the TF session
# Set params depending on the availability of a GPU
if tf.test.is_gpu_available():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
config = tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False)
self.sess = tf.compat.v1.Session(graph=detection_graph,
config=config)
else:
self.sess = tf.compat.v1.Session(graph=detection_graph)
# Extract the placeholders
self.image_tensor = detection_graph.get_tensor_by_name(
'image_tensor:0')
# NCHW conversion. not possible
#self.image_tensor = tf.transpose(self.image_tensor, [0, 3, 1, 2])
self.detection_boxes = detection_graph.get_tensor_by_name(
'detection_boxes:0')
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.boxes = []
self.scores = []
self.predictions = []
# Dummy initialization (otherwise it takes longer then)
dummy_tensor = np.zeros((1, 1, 1, 3), dtype=np.int32)
self.sess.run([
self.detection_boxes, self.detection_scores,
self.detection_classes, self.num_detections
],
feed_dict={self.image_tensor: dummy_tensor})
self.confidence_threshold = 0.5
def __init__(self, net_model):
self.framework = "TensorFlow"
labels_file = LABELS_DICT[net_model['Dataset'].lower()]
label_map = label_map_util.load_labelmap(
labels_file) # loads the labels map.
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=999999)
category_index = label_map_util.create_category_index(categories)
self.classes = {}
# We build is as a dict because of gaps on the labels definitions
for cat in category_index:
self.classes[cat] = str(category_index[cat]['name'])
# Frozen inference graph, written on the file
CKPT = 'Net/TensorFlow/' + net_model['Model']
detection_graph = tf.Graph() # new graph instance.
with 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='')
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7)
self.sess = tf.Session(
graph=detection_graph,
config=tf.ConfigProto(log_device_placement=False,
#gpu_options=gpu_options))
))
self.image_tensor = detection_graph.get_tensor_by_name(
'image_tensor:0')
# NCHW conversion. not possible
#self.image_tensor = tf.transpose(self.image_tensor, [0, 3, 1, 2])
self.detection_boxes = detection_graph.get_tensor_by_name(
'detection_boxes:0')
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.boxes = []
self.scores = []
self.predictions = []
# Dummy initialization (otherwise it takes longer then)
dummy_tensor = np.zeros((1, 1, 1, 3), dtype=np.int32)
self.sess.run([
self.detection_boxes, self.detection_scores,
self.detection_classes, self.num_detections
],
feed_dict={self.image_tensor: dummy_tensor})
self.confidence_threshold = 0.5
print("Network ready!")
def __init__(self, net_model):
SystemExit('Keras not supported yet (for now...)')
self.framework = "Keras"
# Parse the dataset to get which labels to yield
labels_file = LABELS_DICT[net_model['Dataset'].lower()]
label_map = label_map_util.load_labelmap(
labels_file) # loads the labels map.
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=100000)
category_index = label_map_util.create_category_index(categories)
self.classes = {}
# We build is as a dict because of gaps on the labels definitions
for cat in category_index:
self.classes[cat] = str(category_index[cat]['name'])
MODEL_FILE = 'Net/Keras/' + net_model['Model']
file = h5py.File(MODEL_FILE, 'r')
ssd_loss = SSDLoss(neg_pos_ratio=3, n_neg_min=0, alpha=1.0)
K.clear_session()
if str(file.items()[0][0]) == 'model_weights':
print("Full model detected. Loading it...")
try:
self.model = load_model(MODEL_FILE,
custom_objects={
'AnchorBoxes': AnchorBoxes,
'L2Normalization': L2Normalization,
'DecodeDetections':
DecodeDetections,
'compute_loss':
ssd_loss.compute_loss
})
except Exception as e:
SystemExit(e)
else:
print(
"Weights file detected. Creating a model and loading the weights into it..."
)
print "Model file: ", MODEL_FILE
self.model = create_model_from_weights.create_model(
MODEL_FILE, ssd_loss, len(self.classes))
# the Keras network works on 300x300 images. Reference sizes:
input_size = self.model.input.shape.as_list()
self.img_height = input_size[1]
self.img_width = input_size[2]
# Output preallocation
self.predictions = np.asarray([])
self.boxes = np.asarray([])
self.scores = np.asarray([])
dummy = np.zeros([1, self.img_height, self.img_width, 3])
self.model.predict(dummy)
print("Network ready!")
def __init__(self, net_model):
# attributes from dl-objecttracker architecture
self.input_image = None
self.output_image = None
self.activated = True
self.detection = None
self.label = None
self.colors = None
self.frame = None
# attributes set by yml config
self.confidence_threshold = None
# new necessary attributes from dl-objectdetector network architecture
self.original_height = None
self.original_width = None
self.font = cv2.FONT_HERSHEY_SIMPLEX
self.scale = 0.5
COLORS = label_map_util.COLORS
self.framework = "Keras"
self.net_has_masks = False
self.log_network_results = []
self.fps_network_results = []
self.log_done = False
self.logger_status = True
self.image_scale = (None, None)
# Parse the dataset to get which labels to yield
labels_file = LABELS_DICT[net_model['Dataset'].lower()]
label_map = label_map_util.load_labelmap(
labels_file) # loads the labels map.
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=100000)
category_index = label_map_util.create_category_index(categories)
self.classes = {}
# We build is as a dict because of gaps on the labels definitions
for cat in category_index:
self.classes[cat] = str(category_index[cat]['name'])
# We create the color dictionary for the bounding boxes.
self.colors = {}
idx = 0
for _class in self.classes.values():
self.colors[_class] = COLORS[idx]
idx = +1
MODEL_FILE = 'Net/Keras/' + net_model['Model']
file = h5py.File(MODEL_FILE, 'r')
ssd_loss = SSDLoss(neg_pos_ratio=3, n_neg_min=0, alpha=1.0)
K.clear_session()
if str(file.items()[0][0]) == 'model_weights':
print("Full model detected. Loading it...")
try:
self.model = load_model(MODEL_FILE,
custom_objects={
'AnchorBoxes': AnchorBoxes,
'L2Normalization': L2Normalization,
'DecodeDetections':
DecodeDetections,
'compute_loss':
ssd_loss.compute_loss
})
except Exception as e:
SystemExit(e)
else:
print(
"Weights file detected. Creating a model and loading the weights into it..."
)
print("Model file: ", MODEL_FILE)
self.model = create_model_from_weights.create_model(
MODEL_FILE, ssd_loss, len(self.classes))
# the Keras network works on 300x300 images. Reference sizes:
input_size = self.model.input.shape.as_list()
self.img_height = input_size[1]
self.img_width = input_size[2]
# Factors to rescale the output bounding boxes
self.height_factor = np.true_divide(self.img_height, self.img_height)
self.width_factor = np.true_divide(self.img_width, self.img_width)
# Output preallocation
self.predictions = np.asarray([])
self.boxes = np.asarray([])
self.scores = np.asarray([])
dummy = np.zeros([1, self.img_height, self.img_width, 3])
self.model.predict(dummy)
print("Network ready!")
def __init__(self, net_model):
# attributes from dl-objecttracker network architecture
self.input_image = None
self.output_image = None
self.activated = True
self.detection = None
self.label = None
self.colors = None
self.frame = None
# attributes set by yml config
self.confidence_threshold = None
# new necessary attributes from dl-objectdetector network architecture
self.original_height = None
self.original_width = None
self.font = cv2.FONT_HERSHEY_SIMPLEX
self.scale = 0.5
COLORS = label_map_util.COLORS
self.framework = "TensorFlow"
self.net_has_masks = False
self.log_network_results = []
self.fps_network_results = []
self.log_done = False
self.logger_status = True
self.image_scale = (None, None)
labels_file = LABELS_DICT[net_model['Dataset'].lower()]
label_map = label_map_util.load_labelmap(
labels_file) # loads the labels map.
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=999999)
category_index = label_map_util.create_category_index(categories)
self.classes = {}
# We build is as a dict because of gaps on the labels definitions
for cat in category_index:
self.classes[cat] = str(category_index[cat]['name'])
# We create the color dictionary for the bounding boxes.
self.colors = {}
idx = 0
for _class in self.classes.values():
self.colors[_class] = COLORS[idx]
idx = +1
# Frozen inference graph, written on the file
CKPT = 'Net/TensorFlow/' + net_model['Model']
detection_graph = tf.Graph() # new graph instance.
with 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='')
# Set additional parameters for the TF session
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
config = tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False)
self.sess = tf.Session(graph=detection_graph, config=config)
self.image_tensor = detection_graph.get_tensor_by_name(
'image_tensor:0')
self.detection_boxes = detection_graph.get_tensor_by_name(
'detection_boxes:0')
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')
for op in detection_graph.get_operations():
if op.name == 'detection_masks':
self.net_has_masks = True
self.detection_masks = detection_graph.get_tensor_by_name(
'detection_masks:0')
self.scores = []
self.predictions = []
# Dummy initialization (otherwise it takes longer then)
dummy_tensor = np.zeros((1, 1, 1, 3), dtype=np.int32)
if self.net_has_masks:
self.sess.run([
self.detection_boxes, self.detection_scores,
self.detection_classes, self.num_detections,
self.detection_masks
],
feed_dict={self.image_tensor: dummy_tensor})
else:
self.sess.run([
self.detection_boxes, self.detection_scores,
self.detection_classes, self.num_detections
],
feed_dict={self.image_tensor: dummy_tensor})
print("Network ready!")