def __init__(self, FLAGS, darknet=None):
self.ntrain = 0
if isinstance(FLAGS, dict):
defaultSettings = {
"binary": "./bin/",
"config": "./cfg/",
"batch": 16,
"threshold": 0.1,
"train": False,
"verbalise": False,
"gpu": 0.0
}
defaultSettings.update(FLAGS)
FLAGS = dotdict(defaultSettings)
if darknet is None:
darknet = Darknet(FLAGS)
self.ntrain = len(darknet.layers)
#self.ntrain = 1
self.darknet = darknet
args = [darknet.meta, FLAGS]
self.num_layer = len(darknet.layers)
self.framework = create_framework(*args)
self.meta = darknet.meta
self.FLAGS = FLAGS
self.say('\nBuilding net ...')
start = time.time()
self.graph = tf.Graph()
with self.graph.as_default() as g:
self.build_forward()
self.setup_meta_ops()
self.say('Finished in {}s\n'.format(time.time() - start))
def __init__(self, FLAGS, darknet=None):
self.ntrain = 0
if isinstance(FLAGS, dict):
defaultSettings = {
"binary": "./bin/",
"config": "./cfg/",
"batch": 16,
"threshold": 0.1,
"train": False,
"verbalise": False,
"gpuName": "/gpu:0",
"gpu": 0.0
}
defaultSettings.update(FLAGS)
FLAGS = dotdict(defaultSettings)
self.FLAGS = FLAGS
if self.FLAGS.pbLoad and self.FLAGS.metaLoad:
self.say('\nLoading from .pb and .meta')
self.graph = tf.Graph()
with self.graph.as_default() as g:
device = FLAGS.gpuName \
if FLAGS.gpu > 0.0 else None
with g.device(device):
self.build_from_pb()
return
if darknet is None:
darknet = Darknet(FLAGS)
self.ntrain = len(darknet.layers)
self.darknet = darknet
args = [darknet.meta, FLAGS]
self.num_layer = len(darknet.layers)
self.framework = create_framework(*args)
self.meta = darknet.meta
self.say('\nBuilding net ...')
start = time.time()
self.graph = tf.Graph()
with self.graph.as_default() as g:
device = FLAGS.gpuName \
if FLAGS.gpu > 0.0 else None
with g.device(device):
self.build_forward()
self.setup_meta_ops()
self.say('Finished in {}s\n'.format(time.time() - start))
def __init__(self, FLAGS, darknet=None):
self.ntrain = 0
if isinstance(FLAGS, dict):
from darkflow.defaults import argHandler
newFLAGS = argHandler()
newFLAGS.setDefaults()
newFLAGS.update(FLAGS)
FLAGS = newFLAGS
self.FLAGS = FLAGS
if self.FLAGS.pbLoad and self.FLAGS.metaLoad:
self.say('\nLoading from .pb and .meta')
self.graph = tf.Graph()
device_name = FLAGS.gpuName \
if FLAGS.gpu > 0.0 else None
with tf.device(device_name):
with self.graph.as_default() as g:
self.build_from_pb()
return
if darknet is None:
darknet = Darknet(FLAGS)
self.ntrain = len(darknet.layers)
self.darknet = darknet
args = [darknet.meta, FLAGS]
self.num_layer = len(darknet.layers)
#self.framework = create_framework(*args)
self.framework = YOLOv2ForBusDetection(*args)
self.meta = darknet.meta
self.say('\nBuilding net ...')
start = time.time()
self.graph = tf.Graph()
device_name = FLAGS.gpuName \
if FLAGS.gpu > 0.0 else None
with tf.device(device_name):
with self.graph.as_default() as g:
self.build_forward()
self.setup_meta_ops()
self.say('Finished in {}s\n'.format(time.time() - start))
def __init__(self, FLAGS, darknet=None):
print('>>__init__')
self.ntrain = 0
self.male = 0
self.female = 0
self.FLAGS = FLAGS
self.frame_count = 0
self.record = np.array([['0000000000000000000'], [0], [0]])
if isinstance(FLAGS, dict):
newFLAGS = argHandler()
newFLAGS.update(FLAGS)
FLAGS = newFLAGS
self.FLAGS = FLAGS
if self.FLAGS.pbLoad and self.FLAGS.metaLoad:
self.say('\nLoading from .pb and .meta')
self.graph = tf.Graph()
device_name = FLAGS.gpuName if FLAGS.gpu > 0.0 else None
with tf.device(device_name):
with self.graph.as_default() as g:
self.build_from_pb()
return
# init socket
if self.FLAGS.socket == 'yes':
self.sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
self.sock.connect((FLAGS.hostname, FLAGS.port))
print('Client socket name is {}'.format(self.sock.getsockname()))
text = 'Client connected'
print('Socket connected')
data = text.encode('ascii')
self.sock.send(data)
if darknet is None:
print('>>darknet = Darknet(FLAGS)')
darknet = Darknet(FLAGS)
self.ntrain = len(darknet.layers)
self.darknet = darknet
args = [darknet.meta, FLAGS]
self.num_layer = len(darknet.layers)
print('>>layers = ', self.num_layer)
self.framework = create_framework(*args)
# meta dict
#self.meta={'net': {'type': '[net]', 'batch': 1, 'subdivisions': 1, 'width': 608, 'height': 608, 'channels': 3, 'momentum': 0.9, 'decay': 0.0005, 'angle': 0, 'saturation': 1.5, 'exposure': 1.5, 'hue': 0.1, 'learning_rate': 0.001, 'burn_in': 1000, 'max_batches': 500200, 'policy': 'steps', 'steps': '400000,450000', 'scales': '.1,.1'}, 'type': '[region]', 'anchors': [0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828], 'bias_match': 1, 'classes': 80, 'coords': 4, 'num': 5, 'softmax': 1, 'jitter': 0.3, 'rescore': 1, 'object_scale': 5, 'noobject_scale': 1, 'class_scale': 1, 'coord_scale': 1, 'absolute': 1, 'thresh': 0.3, 'random': 1, 'model': 'cfg/yolo.cfg', 'inp_size': [608, 608, 3], 'out_size': [19, 19, 425], 'name': 'yolo', 'labels': ['person', 'bicycle', 'car', 'motorbike', 'aeroplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'sofa', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tvmonitor', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'], 'colors': [(254.0, 254.0, 254), (248.92, 228.6, 127), (243.84, 203.20000000000002, 0), (238.76, 177.79999999999998, -127), (233.68, 152.4, -254), (228.6, 127.0, 254), (223.52, 101.60000000000001, 127), (218.44, 76.20000000000002, 0), (213.35999999999999, 50.79999999999999, -127), (208.28000000000003, 25.399999999999995, -254), (203.20000000000002, 0.0, 254), (198.12, -25.400000000000023, 127), (193.04, -50.79999999999999, 0), (187.96, -76.20000000000002, -127), (182.88, -101.59999999999998, -254), (177.79999999999998, -127.0, 254), (172.71999999999997, -152.40000000000003, 127), (167.64, -177.79999999999998, 0), (162.56, -203.20000000000002, -127), (157.48, -228.59999999999997, -254), (152.4, -254.0, 254), (147.32000000000002, -279.40000000000003, 127), (142.24, -304.80000000000007, 0), (137.16, -330.19999999999993, -127), (132.08, -355.59999999999997, -254), (127.0, 254.0, 254), (121.92, 228.6, 127), (116.83999999999999, 203.20000000000002, 0), (111.75999999999999, 177.79999999999998, -127), (106.68, 152.4, -254), (101.60000000000001, 127.0, 254), (96.52, 101.60000000000001, 127), (91.44, 76.20000000000002, 0), (86.35999999999999, 50.79999999999999, -127), (81.27999999999999, 25.399999999999995, -254), (76.20000000000002, 0.0, 254), (71.12, -25.400000000000023, 127), (66.04, -50.79999999999999, 0), (60.96, -76.20000000000002, -127), (55.879999999999995, -101.59999999999998, -254), (50.79999999999999, -127.0, 254), (45.72000000000001, -152.40000000000003, 127), (40.64000000000001, -177.79999999999998, 0), (35.56, -203.20000000000002, -127), (30.48, -228.59999999999997, -254), (25.399999999999995, -254.0, 254), (20.31999999999999, -279.40000000000003, 127), (15.240000000000013, -304.80000000000007, 0), (10.160000000000009, -330.19999999999993, -127), (5.0800000000000045, -355.59999999999997, -254), (0.0, 254.0, 254), (-5.0800000000000045, 228.6, 127), (-10.160000000000009, 203.20000000000002, 0), (-15.240000000000013, 177.79999999999998, -127), (-20.320000000000018, 152.4, -254), (-25.400000000000023, 127.0, 254), (-30.480000000000025, 101.60000000000001, 127), (-35.559999999999974, 76.20000000000002, 0), (-40.63999999999998, 50.79999999999999, -127), (-45.719999999999985, 25.399999999999995, -254), (-50.79999999999999, 0.0, 254), (-55.879999999999995, -25.400000000000023, 127), (-60.96, -50.79999999999999, 0), (-66.04, -76.20000000000002, -127), (-71.12, -101.59999999999998, -254), (-76.20000000000002, -127.0, 254), (-81.28000000000002, -152.40000000000003, 127), (-86.36000000000001, -177.79999999999998, 0), (-91.44000000000003, -203.20000000000002, -127), (-96.51999999999997, -228.59999999999997, -254), (-101.59999999999998, -254.0, 254), (-106.67999999999998, -279.40000000000003, 127), (-111.75999999999999, -304.80000000000007, 0), (-116.83999999999999, -330.19999999999993, -127), (-121.92, -355.59999999999997, -254), (-127.0, 254.0, 254), (-132.08, 228.6, 127), (-137.16, 203.20000000000002, 0), (-142.24, 177.79999999999998, -127), (-147.32000000000002, 152.4, -254)]}
self.meta = darknet.meta #auto define
print(self.meta)
print('>>meta class = ', len(self.meta['labels']))
self.say('\nBuilding net ...')
start = time.time()
self.graph = tf.Graph()
device_name = FLAGS.gpuName if FLAGS.gpu > 0.0 else None
with tf.device(device_name):
with self.graph.as_default() as g:
self.build_forward()
self.setup_meta_ops()
self.say('Finished in {}s\n'.format(time.time() - start))