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convert_voc_to_line_dataset.py
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# -*-coding: utf-8 -*-
"""
@Project: PythonAPI
@File : vocDemo.py
@Author : panjq
@E-mail : [email protected]
@Date : 2019-05-09 19:10:16
"""
import os
import random
from utils import file_processing, image_processing
from modules.dataset_tool import face_body
from modules.dataset_tool.voc_tools import pascal_voc
# for SSD label，the first label is BACKGROUND：
# classes = ["BACKGROUND","aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
# for YOLO label,ignore the BACKGROUND
# classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog",
# "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
# for wall
# classes = ["PCwall"]
# classes = ["BACKGROUND","person", "dog"]
# print("class_name:{}".format(classes))
def convert_voc_to_linedataset(annotations_dir, image_list, class_names, out_filename, coordinatesType, show=True):
'''
label data format：
SSD = [label_id,x,y,w,h]
YOLO = [label_id,x_center/img_width ,y_center/img_height ,width/img_width ,height/img_height]
MMDET= [img_width,img_height,label_id,x,y,w,h]
line_image_label:[image_path,boxes_nums,x1, y1, w, h, label_id,x1, y1, w, h, label_id,...]
:param annotations_dir: 图片对应annotations所在目录
:param image_list: 图片列表
:param class_names:
:param out_filename
:param coordinatesType: 坐标类型：SSD,YOLO,MMDET格式
:param show: 显示
:return:
'''
with open(out_filename, mode='w', encoding='utf-8') as f:
for i, image_path in enumerate(image_list):
image_name = os.path.basename(image_path)
name_id = image_name[:-len(".jpg")]
ann_name = name_id + '.xml'
annotations_file = os.path.join(annotations_dir, ann_name)
if not os.path.exists(image_path):
print("no image_dict:{}".format(image_path))
continue
if not os.path.exists(annotations_file):
print("no annotations:{}".format(annotations_file))
continue
rects, class_name, class_id = pascal_voc.get_annotation(annotations_file,
class_names,
minAreaTH=0,
coordinatesType=coordinatesType)
if len(rects) == 0 or len(class_name) == 0 or len(class_id) == 0:
print("no class in annotations:{}".format(annotations_file))
continue
line_image_label = pascal_voc.convert_to_linedataset(image_name, rects, class_id)
# contents = face_body_tools.convert_pyramidbox_data(image_path, boxList, classes)
contents_line = " ".join('%s' % id for id in line_image_label)
f.write(contents_line + "\n")
if show:
image = image_processing.read_image(image_path)
image_processing.show_image_rects_text("image_dict", image, rects, class_name)
if i % 10 == 0 or i == len(image_list) - 1:
print("processing image_dict:{}/{}".format(i, len(image_list) - 1))
def linedataset_for_image(shuffle=True):
'''
label data format：
SSD = [label_id,x,y,w,h]
YOLO = [label_id,x_center/img_width ,y_center/img_height ,width/img_width ,height/img_height]
MMDET= [img_width,img_height,label_id,x,y,w,h]
:param annotations_dir:
:param image_dir:
:param label_out_dir:
:param out_train_val_path:
:param class_names:
:param coordinatesType: 坐标类型：SSD,YOLO,MMDET格式
:param show:
:return:
'''
show = False
coordinatesType = "SSD"
# PCwall
# train_filename = "/media/dm/dm2/project/dataset/VOC_wall/train.txt"
# val_filename = "/media/dm/dm2/project/dataset/VOC_wall/val.txt"
# annotations_dir = '/media/dm/dm2/project/dataset/VOC_wall/Annotations'
# image_dir = "/media/dm/dm2/project/dataset/VOC_wall/JPEGImages"
# VOC
# DATA_ROOT="/media/dm/dm2/project/dataset/VOCdevkit/VOC2007/"
# annotations_dir=DATA_ROOT+'Annotations'
# image_dir=DATA_ROOT+"JPEGImages"
# train_filename = DATA_ROOT+"train.txt"
# val_filename = DATA_ROOT+"val.txt"
# widerface
DATA_ROOT = "/media/dm/dm2/project/dataset/face/wider_face_voc/"
annotations_dir = DATA_ROOT + 'Annotations'
image_dir = DATA_ROOT + "JPEGImages"
train_filename = DATA_ROOT + "train.txt"
val_filename = DATA_ROOT + "val.txt"
image_list = file_processing.get_files_list(image_dir, postfix=["*.jpg"])
print("have {} images".format(len(image_list)))
if shuffle:
seeds = 100 # 固定种子,只要seed的值一样，后续生成的随机数都一样
random.seed(seeds)
random.shuffle(image_list)
# 分割成train和val数据集
factor = 0.95
train_num = int(factor * len(image_list))
train_image_list = image_list[:train_num]
val_image_list = image_list[train_num:]
# 转换label数据
print("doing train data .....")
convert_voc_to_linedataset(annotations_dir, train_image_list, classes, train_filename, coordinatesType,
show)
print("doing val data .....")
convert_voc_to_linedataset(annotations_dir, val_image_list, classes, val_filename, coordinatesType,
show)
print("done...ok!")
# test
linedataset_test(train_filename, classes, image_dir=image_dir, show=True)
def linedataset_test(filename, classes, image_dir=None, show=True):
'''
label_data:[image_path,boxes_nums,x1, y1, w, h, label_id,x1, y1, w, h, label_id]
:param filename:
:param image_dir:
:param show:
:return:
'''
with open(filename) as f:
lines = f.readlines()
for line in lines:
image_id, box, label = face_body.read_line_image_label(line)
if show:
if image_dir:
image_path = os.path.join(image_dir, image_id)
else:
image_path = image_id
image = image_processing.read_image(image_path)
name_list = file_processing.decode_label(label, classes)
image_processing.show_image_bboxes_text("image_dict", image, box, name_list)
if __name__ == "__main__":
# classes = ["BACKGROUND", 'face']
# classes = ["BACKGROUND", 'PCwall']
# classes = ["BACKGROUND","aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
# classes=pascal_voc.VOC_CLASSES_BG
# classes=["BACKGROUND","face"]
# print("class_name:{}".format(classes))
# linedataset_for_image()
classes = ["BACKGROUND", 'face', "body"]
DATASET_ROOT = "/media/dm/dm2/XMC/FaceDataset/NVR/NVR-Teacher2/"
# annotations_dir = DATASET_ROOT + 'Annotations'
# label_out_dir = DATASET_ROOT + 'label'
image_dir = DATASET_ROOT + "image_dict"
filename = DATASET_ROOT + "teacher_data_anno.txt"
linedataset_test(filename, classes, image_dir)