def __init__(self,
gpu_id=1,
name=None,
is_conv_4=True,
mn_checkpoint=None,
dataset_name=MyDataset.dataset_name_miniimagenet,
is_check=False):
self.gpu_id = gpu_id
os.environ["CUDA_VISIBLE_DEVICES"] = str(self.gpu_id)
self.name = name
self.is_conv_4 = is_conv_4
self.dataset_name = dataset_name
self.num_way = 5
self.num_shot = 1
self.num_workers = 8
self.episode_size = 15
self.test_episode = 600
self.mn_checkpoint = mn_checkpoint
###############################################################################################
if self.is_conv_4:
self.matching_net, self.batch_size = C4Net(hid_dim=64,
z_dim=64), 64
else:
self.matching_net, self.batch_size = ResNet12Small(
avg_pool=True, drop_rate=0.1), 32
###############################################################################################
self.is_check = is_check
if self.is_check:
self.log_file = None
return
self.log_file = Tools.new_dir(
os.path.join(
"../models_abl/{}/mn/result".format(self.dataset_name),
"{}_{}.txt".format(self.name, Tools.get_format_time())))
###############################################################################################
self.is_png = True
self.data_root = MyDataset.get_data_root(
dataset_name=self.dataset_name, is_png=self.is_png)
_, _, self.transform_test = MyTransforms.get_transform(
dataset_name=self.dataset_name,
has_ic=True,
is_fsl_simple=True,
is_css=False)
###############################################################################################
pass
def __init__(self,
gpu_id,
ic_out_dim=512,
modify_head=False,
net_name=net_name_conv4):
self.gpu_id = gpu_id
os.environ["CUDA_VISIBLE_DEVICES"] = str(self.gpu_id)
self.num_workers = 8
self.batch_size = 64
self.val_freq = 10
# ic
self.ic_out_dim = ic_out_dim
self.ic_ratio = 1
self.modify_head = modify_head
self.net_name = net_name
self.net = self.get_net(self.net_name)
self.ic_net = ICNet(net=self.net)
self.is_png = True
# self.is_png = False
self.learning_rate = 0.01
self.train_epoch = 1700
self.first_epoch, self.t_epoch = 500, 200
self.adjust_learning_rate = Runner.adjust_learning_rate1
self.model_name = "{}_{}_{}_{}_{}_{}_{}_{}_{}{}{}".format(
self.gpu_id, self.net_name, self.batch_size, self.ic_out_dim,
self.ic_ratio, self.train_epoch, self.first_epoch, self.t_epoch,
self.learning_rate, "_head" if self.modify_head else "",
"_png" if self.is_png else "")
self.time = Tools.get_format_time()
self.ic_dir = Tools.new_dir(
"../models_abl/ic_res_xx/{}_{}_ic.pkl".format(
self.time, self.model_name))
self.log_file = self.ic_dir.replace(".pkl", ".txt")
self.data_root = self.get_data_root()
Tools.print(self.model_name, txt_path=self.log_file)
Tools.print(self.data_root, txt_path=self.log_file)
pass
def __init__(self,
gpu_id=1,
dataset_name=MyDataset.dataset_name_miniimagenet,
is_conv_4=True,
is_res34=True,
is_modify_head=True):
self.gpu_id = gpu_id
os.environ["CUDA_VISIBLE_DEVICES"] = str(self.gpu_id)
self.dataset_name = dataset_name
self.is_conv_4 = is_conv_4
self.is_res34 = is_res34
self.modify_head = is_modify_head
self.num_workers = 8
self.num_way = 5
self.num_shot = 1
self.val_freq = 10
self.episode_size = 15
self.test_episode = 600
self.ic_out_dim = 512
self.ic_ratio = 1
self.learning_rate = 0.01
self.loss_fsl_ratio = 1.0
self.loss_ic_ratio = 1.0
###############################################################################################
self.train_epoch = 1500
self.first_epoch, self.t_epoch = 300, 200
self.adjust_learning_rate = RunnerTool.adjust_learning_rate1
###############################################################################################
###############################################################################################
self.is_png = True
self.data_root = MyDataset.get_data_root(
dataset_name=self.dataset_name, is_png=self.is_png)
self.transform_train_ic, self.transform_train_fsl, self.transform_test = MyTransforms.get_transform(
dataset_name=self.dataset_name,
has_ic=True,
is_fsl_simple=True,
is_css=False)
if self.is_res34:
self.resnet = resnet34
self.ic_net_name = "res34{}".format(
"_head" if self.modify_head else "")
else:
self.resnet = resnet18
self.ic_net_name = "res18{}".format(
"_head" if self.modify_head else "")
pass
if self.is_conv_4:
self.matching_net, self.batch_size, self.e_net_name = C4Net(
hid_dim=64, z_dim=64), 64, "C4"
else:
self.matching_net, self.batch_size, self.e_net_name = ResNet12Small(
avg_pool=True, drop_rate=0.1), 32, "R12S"
###############################################################################################
self.model_name = "{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}{}".format(
self.gpu_id, self.ic_net_name, self.e_net_name, self.train_epoch,
self.batch_size, self.num_way, self.num_shot, self.first_epoch,
self.t_epoch, self.ic_out_dim, self.ic_ratio, self.loss_fsl_ratio,
self.loss_ic_ratio, "_png" if self.is_png else "")
self.time = Tools.get_format_time()
_root_path = "../models_abl/{}/mn".format(self.dataset_name)
self.mn_dir = "{}/{}_{}_mn.pkl".format(_root_path, self.time,
self.model_name)
self.ic_dir = "{}/{}_{}_ic.pkl".format(_root_path, self.time,
self.model_name)
self.log_file = self.ic_dir.replace(".pkl", ".txt")
Tools.print(self.data_root, txt_path=self.log_file)
Tools.print(self.model_name, txt_path=self.log_file)
Tools.print(self.mn_dir, txt_path=self.log_file)
Tools.print(self.ic_dir, txt_path=self.log_file)
pass
result_name = "08-14-2"
cap = cv2.VideoCapture(1)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
background_sub_tractor_mog2 = cv2.createBackgroundSubtractorMOG2()
count = 0
while True:
ret, frame = cap.read()
if ret:
mask = background_sub_tractor_mog2.apply(frame)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
if np.sum(mask // 125) > 1000:
count += 1
Tools.print("{} {}".format(Tools.get_format_time(), count))
if count > 5:
file_name = "{}_{}.png".format(
Tools.get_format_time().replace(":", "_"), count)
result_file_name = os.path.join(result_dir, result_name,
file_name)
cv2.imwrite(Tools.new_dir(result_file_name), frame)
pass
cv2.imshow('frame', frame)
cv2.imshow('mask', mask)
if cv2.waitKey(30) & 0xff == "q":
break
else:
break
