def Eval(path, net_path=None):
img, expand, scale = resize(cv2.imread(path))
data = torch.tensor(img.transpose([2, 0, 1]) / 255 - 0.5,
dtype=torch.float32).unsqueeze(dim=0)
setting = init_settings()
if net_path:
setting['net_path'] = net_path
print('[{}]指定的网络文件路径已起效'.format(datetime.datetime.now()))
yolo = Detector(setting['net_path'])
startTime = time.time()
boxes = yolo.detect(
data, 0.5, setting['anchors']) # Box <- [batch,confi,cx,cy,w,h,cls]
boxes = boxes.cpu()
stopTime = time.time()
print('* ------------------------------------------ *')
print('* PROCESSING TIME COST : {}'.format(stopTime - startTime))
if boxes.size()[0] == 0:
print('* NO THINGS CAUGHT')
return boxes.numpy()
frame = nms(
boxes, 0.5,
True).cpu().detach().numpy() # box_idx, [N, IOU, CX, CY, W, H, CLS]
frame[:, 2:6] = return2RealPosition(frame[:, 2:6], expand, scale)
print('* NUM OF BOXES : {} / {}'.format(frame.shape[0], boxes.size()[0]))
return frame
def __init__(self):
app = wx.App(False)
settings = utils.init_settings()
if settings['storepages']:
win_info, page_dict = utils.init_page_dict()
else:
win_info = (wx.DefaultPosition, wx.DefaultSize)
page_dict = {}
frame = MainFrame(None, sys.argv[1:], settings,
win_info[0], win_info[1],
page_dict)
frame.Show()
app.MainLoop()
def Eval(path, net_path=None, img=None):
"""
网络的使用函数
:param img: 可选参数，可传入图片数据
:param path: 图片文件路径
:param net_path: 网络文件路径
:return: 侦测到的物体的位置、IOU、和分类信息
"""
if img:
img, expand, scale = resize(img)
else:
img, expand, scale = resize(cv2.imread(path))
# 数据转换， 转换图像数组数据为tensor且数据缩放到-0.5~0.5
data = torch.tensor(img.transpose([2, 0, 1]) / 255 - 0.5,
dtype=torch.float32).unsqueeze(dim=0)
# 初始化设置
setting = init_settings()
# 检查是否有试着网络路径
if net_path:
setting['net_path'] = net_path
print('[{}]指定的网络文件路径已起效'.format(datetime.datetime.now()))
# 获取侦测类对象
yolo = Detector(setting['net_path'])
startTime = time.time()
# 调用侦测类中的侦测处理函数
boxes = yolo.detect(
data, 0.5, setting['anchors']) # Box <- [batch,confi,cx,cy,w,h,cls]
# 数据取到cpu（如果本来就在CPU也不会有问题）
boxes = boxes.cpu()
stopTime = time.time()
print('* ------------------------------------------ *')
print('* PROCESSING TIME COST : {}'.format(stopTime - startTime))
if boxes.size()[0] == 0:
print('* NO THINGS CAUGHT')
return boxes.numpy()
# 合并重合框
frame = nms(
boxes, 0.5,
True).cpu().detach().numpy() # box_idx, [N, IOU, CX, CY, W, H, CLS]
# 反算416*416下的位置到图片的实际位置
frame[:, 2:6] = return2RealPosition(frame[:, 2:6], expand, scale)
print('* NUM OF BOXES : {} / {}'.format(frame.shape[0], boxes.size()[0]))
return frame
settings['user_exceptions'].append(current_user.id)
for group_entity in settings['target_groups']:
try:
number_of_users = await process_group(group_entity)
total_users_scraped += number_of_users
total_groups_scraped += 1
except (ChannelPrivateError, UsernameInvalidError,
UsernameNotOccupiedError, ValueError) as e:
logger.error(f"{stringify_group_entity(group_entity)}: {e}")
continue
time_delta = datetime.now() - start_time
logger.info('Statistics')
logger.info(f'Total users scraped: {total_users_scraped}')
logger.info(f'Total groups scraped: {total_groups_scraped}')
logger.info(f'Total time: {time_delta}')
if __name__ == '__main__':
makedirs(data_path, exist_ok=True)
settings = init_settings(settings_file_path)
logger.debug(f'Settings loaded: {settings}')
with TelegramClient('iron_dome',
settings['api_id'],
settings['api_hash'],
flood_sleep_threshold=86400,
base_logger='telethon') as client:
client.loop.run_until_complete(main(settings, client))
def __init__(self, settings=None):
# 初始化设置
self.settings = init_settings()
# 有效化控制台输入的新设置
self.check_new_settings(settings)
def __init__(self, settings=None):
self.settings = init_settings()
self.check_new_settings(settings)