def main():
parser = argparse.ArgumentParser()
parser.add_argument('--weights', default='yolo_v2.ckpt',
type=str) # darknet-19.ckpt
parser.add_argument('--weight_dir', default='output', type=str)
parser.add_argument('--data_dir', default='data', type=str)
parser.add_argument('--gpu', default='',
type=str) # which gpu to be selected
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu # configure gpu
weights_file = os.path.join(args.data_dir, args.weight_dir, args.weights)
yolo = yolo_v2(False) # 'False' mean 'test'
# yolo = Darknet19(False)
detector = Detector(yolo, weights_file)
#detect the video
#cap = cv2.VideoCapture('asd.mp4')
#cap = cv2.VideoCapture(0)
#detector.video_detect(cap)
#detect the image
imagename = './test/01.jpg'
detector.image_detect(imagename)
def main():
start0 = time.clock()
parser = argparse.ArgumentParser()
parser.add_argument('--weights', default='yolo_v2.ckpt-4000',
type=str) # darknet-19.ckpt
parser.add_argument('--weight_dir', default='output', type=str)
parser.add_argument('--data_dir', default='data', type=str)
parser.add_argument('--gpu', default='0',
type=str) # which gpu to be selected
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu # configure gpu
weights_file = os.path.join(args.data_dir, args.weight_dir, args.weights)
# yolo_obj = yolo_v2(False) # 'False' mean 'test'
yolo_obj = yolo_v2(True) # 单张图片预测，设置True效果好
detector = Detector(yolo_obj, weights_file)
elapsed = (time.clock() - start0)
print("Time used:", elapsed)
#detect the video
#cap = cv2.VideoCapture('asd.mp4')
#cap = cv2.VideoCapture(0)
#detector.video_detect(cap)
#detect the image
start1 = time.clock()
file_path = os.getcwd() + r'/data/data_set/defect_data/train_image'
for i in os.listdir(file_path):
imagename = os.path.join(file_path, i)
detector.image_detect(imagename)
elapsed1 = (time.clock() - start1)
print("Time used:", elapsed1)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--weights',
default='D:\\reference\\5-dataset\\yolo2_coco_weights\\yolo2_coco.ckpt',
type=str) # darknet-19.ckpt
parser.add_argument('--gpu', default='0',
type=str) # which gpu to be selected
args = parser.parse_args()
if args.gpu is not None:
cfg.GPU = args.gpu
if args.weights is not None:
cfg.WEIGHTS_FILE = args.weights
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.GPU
yolo = yolo_v2()
# yolo = Darknet19()
pre_data = Pascal_voc()
train = Train(yolo, pre_data)
print('start training ...')
train.train()
print('successful training.')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--weights', default='yolo_v2.ckpt-4000',
type=str) # darknet-19.ckpt
# parser.add_argument('--weights', default = '', type = str) # darknet-19.ckpt
#voc的预训练权重
# parser.add_argument('--weights', default = 'yolo_weights.ckpt', type = str)
parser.add_argument('--gpu', default="0",
type=str) # which gpu to be selected
args = parser.parse_args()
if args.gpu is not None:
cfg.GPU = args.gpu
if args.weights is not None:
cfg.WEIGHTS_FILE = args.weights
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
# os.environ['CUDA_VISIBLE_DEVICES'] = '1,0'
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.GPU
yolo_obj = yolo_v2()
pre_data = Data_preprocess()
train = Train(yolo_obj, pre_data)
print('start training ...')
train.train()
print('successful training.')
def main():
parser = argparse.ArgumentParser() # 创建一个解析器对象，并告诉它将会有些什么参数
parser.add_argument('--weights', default = 'yolo_v2.ckpt', type = str) # darknet-19.ckpt
parser.add_argument('--gpu', default = '', type = str) # 可以使用的GPU
args = parser.parse_args()
if args.gpu is not None:
cfg.GPU = args.gpu
if args.weights is not None:
cfg.WEIGHTS_FILE = args.weights
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.GPU
yolo = yolo_v2()
# yolo = Darknet19()
pre_data = Pascal_voc() #数据集类
train = Train(yolo, pre_data)
print('start training ...')
train.train()
print('successful training.')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-w', '--weights', default=None,
type=str) # darknet-19.ckpt
parser.add_argument('--weight_dir', default='output', type=str)
parser.add_argument('--data_dir', default='data', type=str)
parser.add_argument('-o', '--optimizer', default=1, type=int)
parser.add_argument('-v',
'--var_set',
default='all',
type=str,
choices=['all', 'back'])
parser.add_argument('-g', '--gpu', default='',
type=str) # which gpu to be selected
args = parser.parse_args()
random.seed(cfg.RANDOM_SEED)
np.random.seed(cfg.RANDOM_SEED)
tf.set_random_seed(cfg.RANDOM_SEED)
if args.gpu is not None:
cfg.GPU = args.gpu
if args.weights is not None:
cfg.WEIGHTS_FILE = args.weights
else:
w_dir = (os.path.join(cfg.DATA_DIR, args.data_dir))
latest = tf.train.latest_checkpoint(w_dir)
if latest is not None and len(latest) > 0: cfg.WEIGHTS_FILE = latest
print("** resore weights file:", cfg.WEIGHTS_FILE)
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.GPU
yolo = yolo_v2()
# yolo = Darknet19()
pre_data2012 = Pascal_voc_VOC2012()
pre_data2007 = Pascal_voc_VOC2007()
pre_data = pre_data2012.takeIn(pre_data2007)
train = Train(yolo,
pre_data,
optimizer_no=args.optimizer,
var_set=args.var_set)
print('** start training ...')
train.train()
print('** successful training.')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--weights', default='yolo_v2.ckpt', type=str)
parser.add_argument('--gpu', default='', type=str)
args = parser.parse_args()
if args.gpu is not None:
cfg.GPU = args.gpu
if args.weights is not None:
cfg.WEIGHTS_FILE = args.weights
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.GPU
yolo = yolo_v2()
pre_data = Pascal_voc()
train = Train(yolo, pre_data)
print('start training ...')
train.train()
print('successful training.')
def main():
parser = argparse.ArgumentParser() #创建一个解析器
parser.add_argument('--weights', default = 'yolo_v2.ckpt', type = str) # darknet-19.ckpt
parser.add_argument('--weight_dir', default = 'output', type = str)
parser.add_argument('--data_dir', default = 'data', type = str)
parser.add_argument('--gpu', default = '', type = str) # which gpu to be selected
args = parser.parse_args()# 解析输入的命令行，参数默认是从sys.argv[1:]中获取，parse_args()返回一个命名空间
# 包含传递给命令行的参数，该对象将参数保存为属性
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu # 可见GPU
weights_file = os.path.join(args.data_dir, args.weight_dir, args.weights)
yolo = yolo_v2(False) # 'False' mean 'test'
# yolo = Darknet19(False)
detector = Detector(yolo, weights_file)
#detect the video
#cap = cv2.VideoCapture('asd.mp4')
#cap = cv2.VideoCapture(0)
#detector.video_detect(cap)
#detect the image
imagename = './test/02.jpg'
detector.image_detect(imagename)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--weights', default = 'yolo_v2.ckpt-30000', type = str) # darknet-19.ckpt
parser.add_argument('--gpu', default = '', type = str) # which gpu to be selected
args = parser.parse_args()
if args.gpu is not None:
cfg.GPU = args.gpu
if args.weights is not None:
cfg.WEIGHTS_FILE = args.weights
os.environ['CUDA_VISIBLE_DEVICES'] = '2'#cfg.GPU
yolo = yolo_v2()
# yolo = Darknet19()
# pre_data = Pascal_voc()
AIZOO = AIZOO_dataset()
train = Train(yolo, AIZOO)
print('start training ...')
train.train()
print('successful training.')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--weights', default='yolo_v2.ckpt',
type=str) # darknet-19.ckpt
parser.add_argument('--weight_dir', default='output', type=str)
parser.add_argument('--data_dir', default='data', type=str)
parser.add_argument('--gpu', default='',
type=str) # which gpu to be selected
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu # configure gpu
weights_file = os.path.join(args.data_dir, args.weight_dir, args.weights)
yolo = yolo_v2(False) # 'False' mean 'test'
# yolo = Darknet19(False)
detector = Detector(yolo, weights_file)
#detect the image
image_files_path = './linemod/cfg/test_shuf_labels.txt'
#imagename = './test/02.jpg'
#detector.image_detect(imagename)
detector.test(image_files_path)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--images', nargs='+', type=str, required=True)
parser.add_argument('--weights', default=None, type=str) # darknet-19.ckpt
parser.add_argument('--weight_dir', default='output', type=str)
parser.add_argument('--data_dir', default='data', type=str)
parser.add_argument('--gpu', default='',
type=str) # which gpu to be selected
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu # configure gpu
weights_dir = os.path.join(args.data_dir, args.weight_dir)
if args.weights is not None:
cfg.WEIGHTS_FILE = args.weights
else:
latest = tf.train.latest_checkpoint(weights_dir)
if latest is not None and len(latest) > 0:
cfg.WEIGHTS_FILE = os.path.basename(latest)
weights_file = os.path.join(weights_dir, cfg.WEIGHTS_FILE)
print("using weigts file:", weights_file)
yolo = yolo_v2(False) # 'False' mean 'test'
# yolo = Darknet19(False)
detector = Detector(yolo, weights_file)
#detect the video
#cap = cv2.VideoCapture('asd.mp4')
#cap = cv2.VideoCapture(0)
#detector.video_detect(cap)
#detect the image
for imagename in args.images:
key = detector.image_detect(imagename)
print('hitted key=', key)
if key == 27 or key == 1048603: break # means ESC via SSH
break
cap.release()
cv2.destroyAllWindows()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--weights', default = 'yolo_v2.ckpt', type = str) # darknet-19.ckpt
parser.add_argument('--weight_dir', default = 'output', type = str)
parser.add_argument('--data_dir', default = 'data', type = str)
parser.add_argument('--gpu', default = '', type = str) # which gpu to be selected
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu # configure gpu
weights_file = os.path.join(args.data_dir, args.weight_dir, args.weights)
yolo = yolo_v2(False) # 'False' mean 'test'
# yolo = Darknet19(False)
detector = Detector(yolo, weights_file)
#detect the video
#cap = cv2.VideoCapture('asd.mp4')
#cap = cv2.VideoCapture(0)
#detector.video_detect(cap)
#detect the image
imagename = './test/01.jpg'
detector.image_detect(imagename)
if __name__ == '__main__':
main()