Installation & Quick start

Environment环境

The code is developed using python 3.6.12, pytorch-1.4.0, and CUDA 10.0 on Ubuntu 18.04.
For our experiments, we used 2 NVIDIA 2080Ti GPUs.
该代码是在ubuntu18.04上使用python3.6.12、pytorch-1.4.0和cuda10.0开发的。
在我们的实验中，我们使用了2个nvidia2080ti gpu。

Installation安装

1.Create a conda virtual environment and activate it
1.创建一个conda虚拟环境并激活它

conda create -n DCPose python=3.6.12

source activate DCPose

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2.Install dependencies through DCPose_requirements.txt
通过[DCPose_requirements.txt]安装依赖项

原始论文：pip install -r DCPose_requirement.txt
我的：pip install -r /home/cdj/CODE/DCPose-main/DCPose_requirement.txt
按照你的修改

3.Install DCN

cd thirdparty/deform_conv
python setup.py develop
-----------------------
注意需要先找到你文件所在的位置
我的：
cd /home/cdj/CODE/DCPose-main/thirdparty/deform_conv
python setup.py develop

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4.Download our pretrained models and supplementary .Put it in the directory ${DCPose_SUPP_DIR}. (Note that part of the pre-trained models are available, and more pre-train models will be released soon later.)
下载我们的预训练模型和补充
把它放在目录里DCPose_SUPP_DIR
请注意，部分预训练模型可用，更多预训练模型将很快发布。
放在主目录下
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路径不对会报错

上面网址打不开可以从我的网盘下载
（链接：https://pan.baidu.com/s/1kpdf95H67WH6EGy2uTvDbg
提取码：xgd1
复制这段内容后打开百度网盘手机App，操作更方便哦）

Data preparation数据准备

First, create a folder ${DATASET_DIR} to store the data of PoseTrack17 and PoseTrack18.
首先，创建一个文件夹 ${DATASET_DIR} 来存储PoseTrack17和PoseTrack18的数据。

The directory structure should look like this:
目录结构应如下所示：

${DATASET_DIR}
	|--${POSETRACK17_DIR}  
	|--${POSETRACK18_DIR}
	
# For example, our directory structure is as follows.例如，我们的目录结构如下。
# If you don't know much about configuration file(.yaml), please refer to our settings.如果您不太了解配置文件（.yaml），请参阅我们的设置。
DataSet
	|--PoseTrack17
	|--PoseTrack18

For PoseTrack17 data, we use a slightly modified version of the PoseTrack dataset where we rename the frames to follow %08d format, with first frame indexed as 1 (i.e. 00000001.jpg). First, download the data from PoseTrack download page. Then, rename the frames for each video as described above using this script.
我们使用了PoseTrack数据集的一个稍微修改过的版本，其中我们将帧重命名为%08d格式，第一帧索引为1（即00000001.jpg）。首先，从[PoseTrack下载页]下载数据(https://posetrack.net/users/download.php). 然后，使用[此脚本]如上所述重命名每个视频的帧(https://github.com/facebookresearch/DetectAndTrack/blob/master/tools/gen_posetrack_json.py).

Like PoseWarper, We provide all the required JSON files, which have already been converted to COCO format. Evaluation is performed using the official PoseTrack evaluation code, poseval, which uses py-motmetrics internally. We also provide required MAT/JSON files that are required for the evaluation.
使用py motmetrics内部。我们还提供了评估所需的MAT/JSON文件。

我们提供了所有需要的JSON文件，这些文件已经转换成COCO格式。使用官方的PoseTrack评估代码执行评估poseval

Your extracted PoseTrack17 directory should look like this:
解压缩的PoseTrack17目录应如下所示：

|--${POSETRACK17_DIR}
	|--images
        |-- bonn
        `-- bonn_5sec
        `-- bonn_mpii_test_5sec
        `-- bonn_mpii_test_v2_5sec
        `-- bonn_mpii_train_5sec
        `-- bonn_mpii_train_v2_5sec
        `-- mpii
        `-- mpii_5sec
    |--images_renamed   # first frame indexed as 1  (i.e. 00000001.jpg)
     	|-- bonn
        `-- bonn_5sec
        `-- bonn_mpii_test_5sec
        `-- bonn_mpii_test_v2_5sec
        `-- bonn_mpii_train_5sec
        `-- bonn_mpii_train_v2_5sec
        `-- mpii
        `-- mpii_5sec

For PoseTrack18 data, please download the data from PoseTrack download page. Since the video frames are named properly, you only need to extract them into a directory of your choice (no need to rename the video frames). As with PoseTrack17, we provide all required JSON files for PoseTrack18 dataset as well.
对于PoseTrack18数据，请从[PoseTrack下载页]下载数据(https://posetrack.net/users/download.php). 由于视频帧的命名正确，您只需将它们提取到您选择的目录中（无需重命名视频帧）。与PoseTrack17一样，我们也为PoseTrack18数据集提供了所有必需的JSON文件。

Your extracted PoseTrack18 images directory should look like this:
提取的PoseTrack18图像目录应如下所示：

${POSETRACK18_DIR}
    |--images
        |-- test
        `-- train
        `-- val

Create Symbolic link创建符号链接

ln -s  ${DCPose_SUPP_DIR}  ${DCPose_Project_Dir}  # For DCPose supplementary file
ln -s  ${DATASET_DIR}  ${DCPose_Project_Dir}      #  For Dataset


# For example
${DCPose_Project_Dir} = /your/project/path/Pose_Estimation_DCPose
${DCPose_SUPP_DIR}    = /your/supp/path/DcPose_supp_files
${DATASET_DIR}        = /your/dataset/path/DataSet

ln -s /your/supp/path/DcPose_supp_files  /your/project/path/Pose_Estimation_DCPose  # SUP File Symbolic link 
ln -s /your/dataset/path/DataSet         /your/project/path/Pose_Estimation_DCPose  # DATASET Symbolic link 2

Training from scratch从无到有的训练

For PoseTrack17

cd tools
# train & val 
python run.py --cfg ../configs/posetimation/DcPose/posetrack17/model_RSN.yaml --train --val

The results are saved in ${DCPose_Project_Dir}/output/PE/DcPose/DCPose_Network_Model_RSN/PoseTrack17/{Network_structure _hyperparameters} by default
默认情况下，结果保存在 ${DCPose_Project_Dir}/output/PE/DcPose/DCPose_Network_Model_RSN/PoseTrack17/{Network_structure _hyperparameters}中

For PoseTrack18

cd tools
# train & val 
python run.py --cfg ../configs/posetimation/DcPose/posetrack18/model_RSN.yaml --train --val

The results are saved in ${DCPose_Project_Dir}/output/PE/DcPose/DCPose_Network_Model_RSN/PoseTrack18/{Network_structure _hyperparameters} by default

Validating/Testing from our pretrained models从预训练模型验证/测试

# Evaluate on the PoseTrack17 validation set
python run.py --cfg ../configs/posetimation/DcPose/posetrack17/model_RSN_trained.yaml --val  
# Evaluate on the PoseTrack17 test set
python run.py --cfg ../configs/posetimation/DcPose/posetrack17/model_RSN_trained.yaml --test

Run on video

cd demo/                   
mkdir input/
# Put your video in the input directory
python video.py

##运行过程中的一些错误记录
###错误一缺少yolov3.weights
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https://pjreddie.com/media/files/yolov3.weights
自己下载了一个权重，放在了提示的路径下面（我上面网盘链接也有，之前下载好就不用再下载了。再粘贴一次链接：https://pan.baidu.com/s/1kpdf95H67WH6EGy2uTvDbg
提取码：xgd1
复制这段内容后打开百度网盘手机App，操作更方便哦）
在这里插入图片描述
##Stoplteration
原因input文件放错位置
方法一
刚开始就怀疑，因为有人说Stoplteration可能是因为读取不到文件，所以先把路径变成绝对路径，如下成功。
debug看一下