khanhhoangadt / people-counting Goto Github PK

1. brew install socat
2. Download and install xquartz https://www.xquartz.org/
3. Setting open -a XQuartz then config “allow connections from network clients” is checked “on” on Security tab of Preferences (Source: https://cntnr.io/running-guis-with-docker-on-mac-os-x-a14df6a76efc)
4. docker-compose pull

sh run or docker-compose run --rm app python app/app.py after start socat & XQuartz or sh run python app/detect.py to run a custom command

sh run python app/detect.py

sh run darknet detect cfg/yolo.cfg yolo.weights data/dog.jpg

sh run darknet detector demo cfg/coco.data cfg/yolo.cfg yolo.weights app/storage/sample.mp4

sh run darknet detector test cfg/voc.data cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights data/dog.jpg

sh run darknet detector demo cfg/voc.data cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights app/storage/sample.mp4

Learn more: https://pjreddie.com/darknet/yolo/#train-voc

1. Get The Pascal VOC Data Download files and unzip to train folder

2. Generate Labels for VOC

docker-compose run --rm app bash -c "cd app/train && python voc_label.py"

3. Modify Cfg for Pascal Data I updated the config file in train/cfg/voc.data and volume it into docker

4. Download Pretrained Convolutional Weights Download file to train folder I've downloaded already darknet19_448.conv.23 file to /darknet

5. Train The Model

docker-compose run --rm app ./darknet detector train app/train/cfg/voc.data cfg/yolo-voc.cfg darknet19_448.conv.23

• Train from a checkpoint

docker-compose run --rm app ./darknet detector train app/train/cfg/voc.data cfg/yolo-voc.cfg app/train/backup/yolo-voc.backup

• Example some detect

sh run darknet detect app/train/cfg/yolo-voc.cfg app/train/backup/yolo-voc.backup app/train/test.jpg -thresh 0.9
sh run darknet detect cfg/yolo.cfg yolo.weights app/train/test.jpg
sh run darknet detect cfg/yolo.cfg app/train/backup/yolo-voc_400.weights app/train/test.jpg

Learn more: https://timebutt.github.io/static/how-to-train-yolov2-to-detect-custom-objects/

1. Prepare dataset for training Use crawler to search image from google

docker-compose run --rm se npm install
docker-compose run --rm -T se node crawler

2. Run BBox Tool to label image

sh run bblt

• Name folder with 001, 002, .. respectively with class
• Subfolder in Examples, Images, Labels with 001, 002, .. is required
• See more in tutorial
• BBox only see images with '.JPEG' ext
• rename 's/\.jpeg/\.jpg/' *.jpeg && rename 's/\.jpg/\.JPEG/' *.jpg Rename from jpeg to JPEG

3. Run convert format from BBox to Yolo

docker-compose run --rm app bash -c "cd app/train && python bblt2yolo.py"

The output is a folder named Labels/voc// what list bounding box of objects in txt files. Copy the files to your Images// folder. We will have images and txt file in same together

4. Spliting for training set and test set

docker-compose run --rm app bash -c "cd app/train && python split.py"

The output is 2 txt file: train.txt and test.txt. Name is itself explanation

5. Configuration yolo Configed them in cfg/object*

6. Run train

docker-compose run --rm app ./darknet detector train app/train/cfg/obj.data app/train/cfg/obj.cfg darknet19_448.conv.23

Or continue train from a checkpoint

docker-compose run --rm app ./darknet detector train app/train/cfg/obj.data app/train/cfg/obj.cfg app/train/backup/obj.backup

7. Test

sh run darknet detector test app/train/cfg/obj.data app/train/cfg/obj.cfg app/train/backup/obj.backup app/train/Images/001/1-0.jpeg -thresh 0.9

có 1 vài output quan trọng trong quá trình train:

• Region Avg IOU: 0.164715 càng tiến về 1 càng tốt, tức là tỉ lệ giao hợp càng gần 100% càng tốt
• 426.823456 avg: trung bình lỗi càng thấp càng tốt, 0.060730 avg, you can stop training
• kết quả sau khi train nằm trong folder backup định nghĩa trong config, ở trường hợp của mình là sau mỗi 100 iteration thì nó backup 1 lần để làm checkpoint