戴口罩辨識

參考 face_mask_detection

參考 Nvidia TLT(Transfer Learning Toolkit) 轉移學習工具 安裝所需工具

參考資料集

  戴口罩

    Kaggle Medical Mask Dataset

    MAFA - MAsked FAces

  無口罩

    FDDB Dataset

    WiderFace Dataset

下載後依據目錄格式擺放

$ docker run --name face-mask --runtime=nvidia --gpus all -it -v /home/user/Data/DeepStreamer/tlt-experiments/:/workspace/tlt-experiments -p 8888:8888 nvcr.io/nvidia/tlt-streamanalytics:v2.0_py3 /bin/bash

root@0aa10155a3bd:/workspace/tlt-experiments# exit

exit

$ docker ps

CONTAINER ID        IMAGE                                         COMMAND                  CREATED             STATUS              PORTS                    NAMES

0aa10155a3bd        nvcr.io/nvidia/tlt-streamanalytics:v2.0_py3   "install_ngc_cli.sh …"   8 minutes ago       Up 6 minutes        0.0.0.0:8888->8888/tcp   face-mask

$ docker exec -it face-mask /bin/bash

# cd tlt-experiments/face-mask-detection

# python -m pip install -r requirements.txt

修改 face-mask-detection/tlt_specs/detectnet_v2_tfrecords_kitti_trainval.txt

root_directory_path: "/workspace/tlt-experiments/face-mask-detection-data/kitti/train"

修改 face-mask-detection/tlt_specs/detectnet_v2_train_resnet18_kitti.txt

tfrecords_path: "/workspace/tlt-experiments/face-mask-detection-data/kitti/tfrecords/kitti_trainval/*"

image_directory_path: "/workspace/tlt-experiments/face-mask-detection-data/kitti/train"

pretrained_model_file: "/workspace/tlt-experiments/detectnet_v2/pretrained_resnet18/tlt_pretrained_detectnet_v2_vresnet18/resnet18.hdf5"

修改 face-mask-detection/tlt_specs/detectnet_v2_retrain_resnet18_kitti.txt

tfrecords_path: "/workspace/tlt-experiments/face-mask-detection-data/kitti/tfrecords/kitti_trainval/*"

mage_directory_path: "/workspace/tlt-experiments/face-mask-detection-data/kitti/train"

pretrained_model_file: "/workspace/tlt-experiments/face-mask-detection-data/detectnet_v2/experiment_dir_pruned/resnet18_nopool_bn_detectnet_v2_pruned.tlt"

修改 face-mask-detection/tlt_specs/detectnet_v2_inference_kitti_tlt.txt

model: "/workspace/tlt-experiments/detectnet_v2/experiment_dir_retrain/weights/resnet18_detector_pruned.tlt"

修改 face-mask-detection/tlt_specs/detectnet_v2_inference_kitti_etlt.txt

trt_engine: "/workspace/tlt-experiments/detectnet_v2/experiment_dir_final_unpruned/resnet18_detector.trt"

產生 kitti 格式的標註

# vi data2kitti.sh

/usr/bin/python data2kitti.py --kaggle-dataset-path '/workspace/tlt-experiments/face-mask-detection-data/Kaggle Medical Mask Dataset/medical-masks-dataset' --mafa-dataset-path '/workspace/tlt-experiments/face-mask-detection-data/MAFA Dataset' --fddb-dataset-path '/workspace/tlt-experiments/face-mask-detection-data/FDDB Dataset' --widerface-dataset-path '/workspace/tlt-experiments/face-mask-detection-data/Wider Face Dataset' --kitti-base-path '/workspace/tlt-experiments/face-mask-detection-data/kitti' --train

/usr/bin/python data2kitti.py --kaggle-dataset-path '/workspace/tlt-experiments/face-mask-detection-data/Kaggle Medical Mask Dataset/medical-masks-dataset' --mafa-dataset-path '/workspace/tlt-experiments/face-mask-detection-data/MAFA Dataset' --fddb-dataset-path '/workspace/tlt-experiments/face-mask-detection-data/FDDB Dataset' --widerface-dataset-path '/workspace/tlt-experiments/face-mask-detection-data/Wider Face Dataset' --kitti-base-path '/workspace/tlt-experiments/face-mask-detection-data/kitti' --val

# ./data2kitti.sh

# jupyter notebook --ip 0.0.0.0 --allow-root

    To access the notebook, open this file in a browser:

        file:///root/.local/share/jupyter/runtime/nbserver-241-open.html

    Or copy and paste one of these URLs:

        http://0aa10155a3bd:8888/?token=29cbb63890972635e00320fe6c66bda30b2c81daa7400f3d

     or http://127.0.0.1:8888/?token=29cbb63890972635e00320fe6c66bda30b2c81daa7400f3d

用瀏覽器開啟 http://127.0.0.1:8888/?token=29cbb63890972635e00320fe6c66bda30b2c81daa7400f3d

進入 http://192.168.0.101:8888/notebooks/face-mask-detection.ipynb

修改下列環境變數到正確目錄

# Setting up env variables for cleaner command line commands.

%env KEY=tlt_encode

# User directory

%env USER_EXPERIMENT_DIR=/workspace/tlt-experiments/detectnet_v2

# Download directory

%env DATA_DOWNLOAD_DIR=/workspace/tlt-experiments/face-mask-detection-data/kitti

%env SPECS_DIR=/workspace/tlt-experiments/face-mask-detection/tlt_specs

產生 tfrecords

!tlt-dataset-convert -d $SPECS_DIR/detectnet_v2_tfrecords_kitti_trainval.txt \

                     -o $DATA_DOWNLOAD_DIR/tfrecords/kitti_trainval/kitti_trainval

查詢 tlt_pretrained_detectnet_v2

!ngc registry model list nvidia/tlt_pretrained_detectnet_v2:*

!mkdir -p $USER_EXPERIMENT_DIR/pretrained_resnet18/

下載 tlt_pretrained_detectnet_v2

!ngc registry model download-version nvidia/tlt_pretrained_detectnet_v2:resnet18 \

    --dest $USER_EXPERIMENT_DIR/pretrained_resnet18

訓練開始

!tlt-train detectnet_v2 -e $SPECS_DIR/detectnet_v2_train_resnet18_kitti.txt \

                        -r $USER_EXPERIMENT_DIR/experiment_dir_unpruned \

                        -k $KEY \

                        -n resnet18_detector

測試訓練結果

!tlt-evaluate detectnet_v2 -e $SPECS_DIR/detectnet_v2_train_resnet18_kitti.txt\

                           -m $USER_EXPERIMENT_DIR/experiment_dir_unpruned/weights/resnet18_detector.tlt \

                           -k $KEY

修剪模型， -ph (threshold), 越大產生的模型越小，推論速度越快，但會不準

!mkdir -p $USER_EXPERIMENT_DIR/experiment_dir_pruned

!tlt-prune -m $USER_EXPERIMENT_DIR/experiment_dir_unpruned/weights/resnet18_detector.tlt \

           -o $USER_EXPERIMENT_DIR/experiment_dir_pruned/resnet18_nopool_bn_detectnet_v2_pruned.tlt \

           -eq union \

           -pth 0.7 \

           -k $KEY

再次訓練修剪過的模型

!tlt-train detectnet_v2 -e $SPECS_DIR/detectnet_v2_retrain_resnet18_kitti.txt \

                        -r $USER_EXPERIMENT_DIR/experiment_dir_retrain \

                        -k $KEY \

                        -n resnet18_detector_pruned

測試再次訓練的模型

!tlt-evaluate detectnet_v2 -e $SPECS_DIR/detectnet_v2_retrain_resnet18_kitti.txt \

                           -m $USER_EXPERIMENT_DIR/experiment_dir_retrain/weights/resnet18_detector_pruned.tlt \

                           -k $KEY

利用模型推論測試的圖形

!tlt-infer detectnet_v2 -e $SPECS_DIR/detectnet_v2_inference_kitti_tlt.txt \

                        -o $USER_EXPERIMENT_DIR/tlt_infer_testing \

                        -i $DATA_DOWNLOAD_DIR/test/images \

                        -k $KEY

輸出可被 DeepStream 使用的模型

!mkdir -p $USER_EXPERIMENT_DIR/experiment_dir_final

# Removing a pre-existing copy of the etlt if there has been any.

import os

output_file=os.path.join(os.environ['USER_EXPERIMENT_DIR'],

                         "experiment_dir_final/resnet18_detector_thermal.etlt")

if os.path.exists(output_file):

    os.system("rm {}".format(output_file))

!tlt-export detectnet_v2 \

            -m $USER_EXPERIMENT_DIR/experiment_dir_retrain/weights/resnet18_detector_pruned.tlt \

            -o $USER_EXPERIMENT_DIR/experiment_dir_final/resnet18_detector_thermal.etlt \

            -k $KEY

修改 ds_configs/config_infer_primary_masknet_gpu.txt

tlt-encoded-model=/home/nano/Data/DeepStream/face-mask-detection/experiment_dir_final/resnet18_detector_thermal.etlt

# 不要用 model-engine-file

input-dims=3;480;640;0

修改 ds_configs/deepstream_app_source1_camera_masknet_gpu.txt

width=640

height=480

完成

$ deepstream-app -c deepstream_app_source1_camera_masknet_gpu.txt