def __init__(self, cfg_path):
'''
Argument
--------
cfg_path: str
A path to config file.
Example: github.com/pjreddie/darknet/blob/master/cfg/yolov3.cfg
'''
super(Darknet, self).__init__()
self.layers_info = parse_cfg(cfg_path)
self.net_info, self.layers_list = self.create_layers(self.layers_info)
# take the number of classes from the last (yolo) layer of the network.
self.classes = self.layers_list[-1][0].classes
self.model_width = self.layers_list[-1][0].model_width
# print('INFO: shortcut is using output[i-1] instead of x check whether works with x')
print('INFO: changing predictions in the NMS loop make sure that it is not used later')
print('INFO: not adding +1 in nms')
print('INFO: loss: w and h aren`t put through sqroot' )
draw.text((5, 5), class_name, (0, 255, 0), font=font_text)
img = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
print('Class name:', class_name, 'Confidence:', str(confidence) + '%')
self.statusbar.showMessage('Class name: ' + class_name +
' Confidence: ' + str(confidence) + '%')
h, w = img.shape[:2]
img = QImage(img, w, h, QImage.Format_RGB888)
img = QPixmap.fromImage(img)
self.img_label.setPixmap(img) # show on img label
self.img_label.setScaledContents(True) # self adaption
self.isChecking = True
self.dish_label.setText("菜品名称：" + class_name)
self.price_label.setText("金额：" + self.prices[class_name] + "元")
def confirm(self):
self.isChecking = False
self.dish_label.setText("菜品名称：")
self.price_label.setText("金额：")
if __name__ == '__main__':
weight_path, cfg_path, cam_width, cam_height = args.weights, args.cfg, args.cam_width, args.cam_height
cfg = parse_cfg(cfg_path)
app = QApplication(sys.argv)
window = Window(weight_path, cfg, cam_width, cam_height)
sys.exit(app.exec_())
from test import TESTS
from utils import _init_fn, set_seed, parse_cfg, create_logger, resume_last, resume_best, resume_from, save_model, apply_dataparallel, model_to_device
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('cfg')
parser.add_argument('--resume_best', action='store_true', help='Resume the model from the best checkpoint')
parser.add_argument('--resume_last', action='store_true', help='Resume the model from the latest checkpoint')
parser.add_argument('--resume_from', help='Resume from a specified checkpoint')
parser.add_argument('--non_deterministic', help='Do not use a fixed seed')
parser.add_argument('--no_save', action='store_true', help='Do not save checkpoints')
parser.add_argument('--max_epoch', type=int, default=200, help='Max epoch during training')
parser.add_argument('--runs', type=int, default=1, help='Number of times to run')
args = parser.parse_args()
cfg, stem = parse_cfg(args.cfg)
if not args.non_deterministic:
set_seed(666)
# work directory
work_dir = join('work_dirs', stem)
if not isdir(work_dir):
makedirs(work_dir)
# create logger
logger = create_logger(work_dir)
best_test_acc_runs = []
# multiple runs
if cfg['dataset']['type'] == 'ShapeNet':
def __init__(self, cfgfile):
super(Darknet, self).__init__()
self.blocks = utils.parse_cfg(cfgfile)
self.net_info, self.module_list = utils.create_modules(self.blocks)
def create_model(self, cfg_file):
blocks = parse_cfg(cfg_file)
self.model = create_model(blocks)
self.initialized = False
self.loaded = False
print("Model created")
def __init__(self, cfgfile, reso):
super(YOLOv3, self).__init__()
self.blocks = parse_cfg(cfgfile)
self.reso = reso
self.module_list = self.build_model(self.blocks)
self.nms = NMSLayer()