def __init__(self, voc_root_dir, voc_dir_ls, voc_names, class_num, batch_size, anchors, width=608, height=608, data_debug=False):
self.data_dirs = [path.join(path.join(voc_root_dir, voc_dir), "JPEGImages") for voc_dir in voc_dir_ls]
self.class_num = class_num # classify number
self.batch_size = batch_size
self.anchors = np.asarray(anchors).astype(np.float32).reshape([-1, 2]) / [width, height] #[9,2]
print("anchors:\n", self.anchors)
self.imgs_path = []
self.labels_path = []
self.num_batch = 0 # total batch number
self.num_imgs = 0 # total number of images
self.steps_per_epoch = 1
self.data_debug = data_debug
self.width = width
self.height = height
self.names_dict = tools.word2id(voc_names) # dictionary of name to id
# 初始化数据增强策略的参数
self.flip_img = 0.5 # probility of flip image
self.is_flip = False #
self.gray_img = 0.02 # probility to gray picture
self.smooth_delta = 0.001 # label smooth delta
self.erase_img = 0 # probility of random erase some area
self.gasuss = 0.0 # probility of gasuss noise
self.__init_args()
def __init__(self, voc_root_dir, voc_dir_ls, voc_names, class_num, batch_size, anchors, multi_scale_img=True, width=608, height=608):
self.data_dirs = [path.join(path.join(voc_root_dir, voc_dir), "JPEGImages") for voc_dir in voc_dir_ls] # 数据文件路径
self.class_num = class_num # 分类数
self.batch_size = batch_size
self.anchors = np.asarray(anchors).astype(np.float32).reshape([-1, 2]) / [width, height] #[9,2]
print("anchors:\n", self.anchors)
self.multi_scale_img = multi_scale_img # 多尺度缩放图片
self.imgs_path = []
self.labels_path = []
self.num_batch = 0 # 多少个 batch 了
self.num_imgs = 0 # 一共多少张图片
self.width = width
self.height = height
self.names_dict = tools.word2id(voc_names) # 名字到 id 的字典
# 初始化各项参数
self.__init_args()
def __init__(self,
voc_root_dir,
voc_dir_ls,
voc_names,
class_num,
batch_size,
anchors,
agument,
width=608,
height=608,
data_debug=False):
self.data_dirs = [
path.join(path.join(voc_root_dir, voc_dir), "JPEGImages")
for voc_dir in voc_dir_ls
] # 数据文件路径
self.class_num = class_num # 分类数
self.batch_size = batch_size
self.anchors = np.asarray(anchors).astype(np.float32).reshape(
[-1, 2]) / [width, height] #[9,2]
print("anchors:\n", self.anchors)
self.imgs_path = []
self.labels_path = []
self.num_batch = 0 # 多少个 batch 了
self.num_imgs = 0 # 一共多少张图片
self.data_debug = data_debug
self.width = width
self.height = height
self.agument = agument # data agument strategy
self.smooth_delta = 0.01 # label smooth delta
self.names_dict = tools.word2id(voc_names) # 名字到 id 的字典
# 初始化各项参数
self.__init_args()
def __init__(self, voc_root_dir, voc_dir_ls, voc_names, class_num, batch_size, anchors, agument, width=608, height=608, data_debug=False):
self.data_dirs = [path.join(path.join(voc_root_dir, voc_dir), "JPEGImages") for voc_dir in voc_dir_ls]
self.class_num = class_num # classify number
self.batch_size = batch_size
self.anchors = np.asarray(anchors).astype(np.float32).reshape([-1, 2]) / [width, height] #[9,2]
print("anchors:\n", self.anchors)
self.imgs_path = []
self.labels_path = []
self.num_batch = 0 # total batch number
self.num_imgs = 0 # total number of images
self.data_debug = data_debug
self.width = width
self.height = height
self.agument = agument # data agument strategy
self.smooth_delta = 0.01 # label smooth delta
self.names_dict = tools.word2id(voc_names) # dictionary of name to id
self.__init_args()
def __init__(self,
voc_root_dir,
voc_names_file,
class_num,
batch_size,
anchors,
is_tiny=False,
size=416):
'''
is_tiny:the flag that loading the yolo_tiny's data
'''
Log.add_log("message:voc_root_dir '" + str(voc_root_dir) +
"', batch_size '" + str(batch_size) + "', size '" +
str(size) + "'")
self.data_dirs = [
path.join(voc_dir, "JPEGImages") for voc_dir in voc_root_dir
]
self.label_dirs = [
path.join(voc_dir, "Annotations") for voc_dir in voc_root_dir
]
self.class_num = class_num # classify number
self.batch_size = batch_size
self.anchors = anchors
self.debug_img = False # show the image
self.is_tiny = is_tiny # load data of yolo_tiny
self.imgs_path = []
self.labels_path = []
self.num_batch = 0 # total batch number
self.num_imgs = 0 # total number of images
self.size = size
self.names_dict = tools.word2id(
voc_names_file) # dictionary of name to id
self.steps_per_epoch = 1000
# ################## data augment ##################
self.flip = Flip()
self.flip_img = 0.5 # probility of flip image
self.gray_img = 0.1 # probility to gray picture
self.label_smooth = 0.001 # label smooth delta
# random erase
self.erase = Erase(max_erase=5, max_w=30, max_h=30)
self.erase_img = 0.5 # probility of random erase some area
# color enhance
self.color_enhance = Color_enhancement()
# rotate : not use
self.rotate_img = 0.0 # probility to rorate the image
# translate
self.translate = Translate(self.size, self.size, pad_thresh=30)
self.trans_img = 0.5 # probility of translate the image
self.gasuss = 0.0 # gasuss norse
# initial all parameters
self.__init_args()