dyhBUPT
commented
1 year ago It seems that there is some error in your annotation file.
You can refer to: https://github.com/open-mmlab/mmtracking/blob/master/docs/en/dataset.md#2-convert-annotations
Open eatbreakfast111 opened 1 year ago
dyhBUPT
commented
1 year ago It seems that there is some error in your annotation file.
You can refer to: https://github.com/open-mmlab/mmtracking/blob/master/docs/en/dataset.md#2-convert-annotations
eatbreakfast111
commented
1 year ago Thank you so much. It work.
I had write a script labelme2cocovid, but when i try to train the dataset, it occur this problem:
Here is my labelme2cocovid, could you please give me some advice?
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import os
import cv2
import numpy as np
from tqdm import tqdm
import json
############################################################
# 存在图像和json的目录
srcpath = '/home/music/Downloads/mmtracking-master/data/youtube_vis_2019/valid/JPEGImages/'
# 要保存的结果json
dstjson = 'valid.json'
############################################################
# 读取图像，解决imread不能读取中文路径的问题
def cv_imread(filePath):
cv_img = cv2.imdecode(np.fromfile(filePath,dtype=np.uint8),-1)
return cv_img
if __name__ == '__main__':
# 最终的json
dst_ann = {
"categories": [],
"videos": [],
"images": [],
"annotations": []}
categories_list = []
# 遍历目录
videos_list = os.listdir(srcpath)
for videoname in videos_list:
print('================='+videoname+'=================')
videopath = srcpath + videoname + '/'
fileslist = os.listdir(videopath)
#############################################################
# 写video相关信息
curr_video_id = len(dst_ann['videos'])+1
dst_ann['videos'].append({"id": curr_video_id,"name": videoname})
#############################################################
# 遍历一个文件夹下的图像及标注
for filename in tqdm(fileslist):
if '.json' in filename:
jsonpath = videopath + filename
imgpath = jsonpath[:-4] + 'jpg'
img = cv_imread(imgpath)
width,height,_ = img.shape
#############################################################
# 写images相关信息
curr_img_id = len(dst_ann['images'])+1
img_info = {
"file_name": filename[:-4] + 'jpg',
"height": height,"width": width,
"id": curr_img_id,
"video_id": curr_video_id,
"frame_id": int(filename[:-5]) }
dst_ann['images'].append(img_info)
#############################################################
# 读json
with open(jsonpath,encoding="utf-8") as f:
data = json.load(f)
shapes = data['shapes']
for s in shapes:
label = s['label']
points = s['points']
curr_category_id = -1
#############################################################
# 写类别相关信息
if not label in categories_list:
curr_category_id = len(categories_list)+1
categories_list.append(label)
dst_ann['categories'].append({"id": curr_category_id,"name": label})
else:
for c in dst_ann['categories']:
if c['name'] == label:
curr_category_id = c['id']
break
#############################################################
# 求点的外接矩形
xmin,xmax,ymin,ymax = width,0,height,0
for p in points:
x,y = p[0], p[1]
xmin = min(xmin, x)
xmax = max(xmax,x)
ymin = min(ymin, y)
ymax = max(ymax, y)
# check bbox
if 0:
cv2.rectangle(img, (int(xmin),int(ymin)), (int(xmax),int(ymax)), (255, 0, 255), 3)
cv2.imshow('',img)
cv2.waitKey(-1)
# 计算面积
area = (xmax-xmin) * (ymax-ymin)
#############################################################
# 写annotations相关信息
curr_ann_id = len(dst_ann['annotations'])+1
ann = {"id": curr_ann_id,
"image_id": curr_img_id,
"video_id": curr_video_id,
"category_id": curr_category_id,
"instance_id": -1,
"bbox": [xmin,ymin,xmax-xmin,ymax-ymin],
"area": area,
"mask":points,
"occluded": False,
"truncated": False,
"iscrowd": False,
"ignore": False,
"is_vid_train_frame": True,
"visibility": 1.0}
dst_ann['annotations'].append(ann)
#############################################################
#############################################################
# 写结果文件
with open(dstjson,"w") as f:
json.dump(dst_ann, f,
indent=2, # 空格缩进符，写入多行
sort_keys=False, # 键的排序
ensure_ascii=True) # 显示中文
Thank you!
Hello! I want to train the masktrackrcnn by the official youtube_vis_dataset but it occur :KeyError: "YouTubeVISDataset: 'image_id'". Here is my datatree
Thank you!