def __init__(self, line, sline1, sline2):
#line is for counting
#slines for speed estimation
self.deepsort=deepsort_rbc()
self.yolov3 = YOLOV3("cfg/yolo_2k_reanchored.cfg","weights/yolo_2k_reanchored_70000.weights","cfg/2k_aug.data")
#New yolov3 model
#self.yolov3 = YOLOV3("cfg/yolov3_rbc.cfg","weights/yolov3_rbc_60000.weights","cfg/2k_aug.data")
self.below_line=set()
self.above_line=set()
self.considered=set()
self.speed1=set()
self.idtocoords=dict()
self.classes = ['car','bus','two-wheeler','three-wheeler','people','lcv','bicycle','truck']
self.v_count_up= defaultdict(int)
self.v_count_down= defaultdict(int)
#Initialize each value to zero
for c in self.classes:
self.v_count_up[c]
self.v_count_down[c]
self.sline1=sline1
self.sline2=sline2
self.line=line
self.tot_dist=0
self.tot_time=0
self.speed=0
self.speed_dict = defaultdict(int)
self.frame_count = defaultdict(int)
def freeze_graph(checkpoint_path, output_node_names, savename):
with tf.name_scope('input'):
input_data = tf.placeholder(dtype=tf.float32,
shape=(1, INPUTSIZE, INPUTSIZE, 3),
name='input_data')
training = tf.placeholder(dtype=tf.bool, name='training')
prefixdict = collectpth(checkpoint_path)
output = YOLOV3(training).build_network_dynamic(input_data,
prefixdict,
inputsize=INPUTSIZE)
with tf.Session() as sess:
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess=sess,
input_graph_def=sess.graph_def,
output_node_names=output_node_names.split(","))
with tf.gfile.GFile(savename, "wb") as f:
f.write(output_graph_def.SerializeToString())
print("%d ops in the final graph." % len(output_graph_def.node))
def __init__(self, line, sline1, sline2):
#line is for counting
#slines for speed estimation
self.deepsort = deepsort_rbc()
self.yolov3 = YOLOV3(
"/home/shantam/alexDarknet/darknet/weights_2903/yolov3_rbc.cfg",
"/home/shantam/alexDarknet/darknet/weights_2903/weights2/yolov3_rbc_180000.weights",
"/home/shantam/alexDarknet/darknet/weights_2903/ta.data")
#New yolov3 model
#self.yolov3 = YOLOV3("cfg/yolov3_rbc.cfg","weights/yolov3_rbc_60000.weights","cfg/2k_aug.data")
#self.below_line=set()
#self.above_line=set()
#self.considered=set()
#self.speed1=set()
#self.idtocoords=dict()
self.classes = [
'car', 'bus', 'two-wheeler', 'three-wheeler', 'people', 'lcv',
'bicycle', 'truck'
]
default='streams.txt',
help='source')
parser.add_argument(
"-fi",
"--face_identified",
help=
"Tipo do identificador de faces utilizado: yolov3, mtcnn,mtcnno,retina",
type=str,
default='yolov3')
args = parser.parse_args()
conf = get_config(False)
mtcnn = MTCNN()
print('mtcnn loaded')
yolov3 = YOLOV3()
print("Yolov3 loaded")
mtcnno = MTCNN_O()
print("mtcnno loaded")
retinanet = RETINANET("mobile0.25")
print("retinanet loaded")
learner = face_learner(conf, True)
learner.threshold = args.threshold
if conf.device.type == 'cpu':
learner.load_state(conf, 'cpu_final.pth', True, True)
else:
learner.load_state(conf, 'mobilefacenet.pth', True, True)
learner.model.eval()
print('learner loaded')
heatmap_output, offset_output, wh_output = net(
torch.autograd.Variable(torch.randn(3, 3, image_h, image_w)))
annotations = torch.FloatTensor([[[113, 120, 183, 255, 5],
[13, 45, 175, 210, 2]],
[[11, 18, 223, 225, 1],
[-1, -1, -1, -1, -1]],
[[-1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1]]])
decode = CenterNetDecoder(image_w, image_h)
batch_scores3, batch_classes3, batch_pred_bboxes3 = decode(
heatmap_output, offset_output, wh_output)
print("3333", batch_scores3.shape, batch_classes3.shape,
batch_pred_bboxes3.shape)
from yolov3 import YOLOV3
net = YOLOV3(backbone_type="darknet53")
image_h, image_w = 416, 416
obj_heads, reg_heads, cls_heads, batch_anchors = net(
torch.autograd.Variable(torch.randn(3, 3, image_h, image_w)))
annotations = torch.FloatTensor([[[113, 120, 183, 255, 5],
[13, 45, 175, 210, 2]],
[[11, 18, 223, 225, 1],
[-1, -1, -1, -1, -1]],
[[-1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1]]])
decode = YOLOV3Decoder(image_w, image_h)
batch_scores4, batch_classes4, batch_pred_bboxes4 = decode(
obj_heads, reg_heads, cls_heads, batch_anchors)
print("4444", batch_scores4.shape, batch_classes4.shape,
batch_pred_bboxes4.shape)
# def load_model(cfg_path,weights_path,data_path):
# net = load_net(cfg_path,weights_path, 0)
# meta = load_meta(data_path)
if __name__ == '__main__':
mp.set_start_method('spawn')
# establish queues
m = mp.Manager()
lock = m.Lock()
tasks = mp.JoinableQueue()
results = mp.Queue()
filepath = sys.argv[1]
yv3 = YOLOV3("cfg/yolo_2k_reanchored.cfg",
"weights/yolo_2k_reanchored_70000.weights", "cfg/2k_aug.data")
# fchkr = folder_checker()
#start consumers
# number_consumers = mp.cpu_count() * 2
number_consumers = 2
print("cpu count = {}".format(number_consumers))
consumers = [yolo_load(tasks, results) for i in range(number_consumers)]
# print(consumers)
for w in consumers:
# print(w)
w.start()
streams = glob.glob(os.path.join(filepath, '*'))
for strm in streams: