def __init__(self, yolo_dir, gpu_num, mode=0):
darknet.set_gpu(gpu_num)
self.metaMain = None
self.netMain = None
altNames = None
# Use tiny yolov3
configPath = None
weightPath = None
if (mode == 0):
configPath = os.path.join(yolo_dir, "cfg/tiny-yolo.cfg")
weightPath = os.path.join(yolo_dir, "yolov3-tiny.weights")
# Use yolov3
elif (mode == 1):
configPath = os.path.join(yolo_dir, "cfg/yolov3.cfg")
weightPath = os.path.join(yolo_dir, "yolov3.weights")
metaPath = os.path.join(yolo_dir, "cfg/coco.data")
if not os.path.exists(configPath):
raise ValueError("Invalid config path `" +
os.path.abspath(configPath) + "`")
if not os.path.exists(weightPath):
raise ValueError("Invalid weight path `" +
os.path.abspath(weightPath) + "`")
if not os.path.exists(metaPath):
raise ValueError("Invalid data file path `" +
os.path.abspath(metaPath) + "`")
if self.netMain is None:
self.netMain = darknet.load_net_custom(configPath.encode("ascii"),
weightPath.encode("ascii"),
0, 1) # batch size = 1
if self.metaMain is None:
self.metaMain = darknet.load_meta(metaPath.encode("ascii"))
if altNames is None:
try:
with open(metaPath) as metaFH:
metaContents = metaFH.read()
import re
match = re.search("names *= *(.*)$", metaContents,
re.IGNORECASE | re.MULTILINE)
if match:
result = match.group(1)
else:
result = None
try:
if os.path.exists(result):
with open(result) as namesFH:
namesList = namesFH.read().strip().split("\n")
altNames = [x.strip() for x in namesList]
except TypeError as e:
print(e)
pass
except Exception as e:
print(e)
pass
self.darknet_image = darknet.make_image(
darknet.network_width(self.netMain),
darknet.network_height(self.netMain), 3)
if i.state == 2 and (time.time() - i.time) > shutdownTime:
i.state = 0
i.time = 0
i.index = -1
elif i.state == 1:
if findList(indices, i.index) == -1:
i.state = 2
i.time = time.time()
if __name__ == '__main__':
tracker = Sort()
colors = [(random.randrange(0, 255), random.randrange(0, 255),
random.randrange(0, 255)) for i in range(10)]
dn.set_gpu(0)
net = dn.load_net(
"/home/bardoe/sources/sceneLight/model/yolov3.cfg".encode("utf-8"),
"/home/bardoe/sources/sceneLight/model/yolov3.weights".encode("utf-8"),
0)
meta = dn.load_meta(
"/home/bardoe/sources/sceneLight/model/coco.data".encode("utf-8"))
cap = cv2.VideoCapture(0)
frames = 0
start = time.time()
sender.start()
sender.activate_output(1)
sender[1].multicast = True
while cap.isOpened():
def __init__(self, gpu, cfg, weights, data):
darknet.set_gpu(gpu)
self.net = darknet.load_net(str.encode(cfg), str.encode(weights), 0)
self.meta = darknet.load_meta(str.encode(data))
def to_box(r):
boxes = []
scores = []
for rc in r:
if rc[0] == b'text':
cx, cy, w, h = rc[-1]
scores.append(rc[1])
prob = rc[1]
xmin, ymin, xmax, ymax = cx - w / 2, cy - h / 2, cx + w / 2, cy + h / 2
boxes.append([int(xmin), int(ymin), int(xmax), int(ymax)])
return boxes, scores
import pdb
if GPU:
try:
dn.set_gpu(GPUID)
except:
pass
net = dn.load_net(yoloCfg.encode('utf-8'), yoloWeights.encode('utf-8'), 0)
meta = dn.load_meta(yoloData.encode('utf-8'))
os.chdir(pwd)
def text_detect(img):
r = detect_np(net, meta, img, thresh=0, hier_thresh=0.5,
nms=None) ##输出所有box,与opencv dnn统一
bboxes = to_box(r)
return bboxes