PETGreen / RotateObjectDetection

This repository is based on Ultralytics/yolov5, with adjustments to enable rotate prediction boxes.

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Rotate-Yolov5

This repository is based on Ultralytics/yolov5, with adjustments to enable rotate prediction boxes.

Section I. Description

The codes are based on Ultralytics/yolov5, and several functions are added and modified to enable rotate prediction boxes.

The modifications compared with Ultralytics/yolov5 and their brief descriptions are summarized below:

  1. data/rotate_ucas.yaml : Exemplar UCAS-AOD dataset to test the effects of rotate boxes

  2. data/images/UCAS-AOD : For the inference of rotate-yolov5s-ucas.pt

  3. models/common.py :
    3.1. class Rotate_NMS : Non-Maximum Suppression (NMS) module for Rotate Boxes
    3.2. class Rotate_AutoShape : Rotate Version of Original AutoShape, input-robust polygon model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and Rotate_NMS
    3.3. class Rotate_Detections : Rotate detections class for Rotate-YOLOv5 inference results

  4. models/rotate_yolov5s_ucas.yaml : Configuration file of rotate yolov5s for exemplar UCAS-AOD dataset

  5. models/yolo.py :
    5.1. class Rotate_Detect : Detect head for rotate-yolov5 models with rotate box prediction
    5.2. class Rotate_Model : Rotate yolov5 models with rotate box prediction

  6. utils/iou_cuda : CUDA extension for iou computation of polygon boxes
    6.1. extensions.cpp : CUDA extension file
    6.2. inter_union_cuda.cu : CUDA code for computing iou of polygon boxes
    6.3. setup.py : for building CUDA extensions module polygon_inter_union_cuda, with two functions polygon_inter_union_cuda and polygon_b_inter_union_cuda

  7. utils/autoanchor.py :
    7.1. def rotate_check_anchors : Rotate version of original check_anchors
    7.2. def rotate_kmean_anchors : Create kmeans-evolved anchors from rotate-enabled training dataset

  8. utils/datasets.py :
    8.1. def polygon_random_perspective : Data augmentation for datasets with polygon boxes (augmentation effects: HSV-Hue, HSV-Saturation, HSV-Value, rotation, translation, scale, shear, perspective, flip up-down, flip left-right, mosaic, mixup)
    8.2. def polygon_box_candidates : Polygon version of original box_candidates
    8.3. def rotate_random_perspective : Data augmentation for datasets with rotate boxes (augmentation effects: HSV-Hue, HSV-Saturation, HSV-Value, rotation, translation, scale, shear, perspective, flip up-down, flip left-right, mosaic, mixup)
    8.4. class Rotate_LoadImagesAndLabels : Rotate version of original LoadImagesAndLabels
    8.5. def rotate_load_mosaic : Loads images in a 4-mosaic, with rotate boxes
    8.6. def rotate_load_mosaic9 : Loads images in a 9-mosaic, with rotate boxes
    8.7. def rotate_verify_image_label : Verify one image-label pair for rotate datasets
    8.8. def create_dataloader : Has been modified to include rotate datasets
    8.9. class Albumentations : For albumentation augmentation

  9. utils/general.py :
    9.1. def xyxyxyxyn2xyxyxyxy : Convert normalized xyxyxyxy or segments into pixel xyxyxyxy or segments
    9.2. def polygon_segment2box : Convert 1 segment label to 1 polygon box label
    9.3. def polygon_inter_union_cpu : iou computation (polygon) with cpu
    9.4. def polygon_box_iou : Compute iou of polygon boxes via cpu or cuda
    9.5. def polygon_b_inter_union_cpu : iou computation (polygon) with cpu
    9.6. def polygon_bbox_iou : Compute iou of polygon boxes via cpu or cuda
    9.7. def polygon_nms_kernel : Non maximum suppression kernel for polygon-enabled boxes
    9.8. def order_corners : Return sorted corners
    9.9. def xywhrm2xyxyxyxy : Convert rotate xywhrm into xyxyxyxy, suitable for both pixel-level or normalized
    9.10. def xyxyxyxy2xywhrm : Convert xyxyxyxy into rotate xywhrm, suitable for both pixel-level and normalized
    9.11. def xywhn2xywh : Convert normalized xywh into pixel xywh
    9.12. def rotate_segments2boxes : Convert segment labels to rotate box labels, i.e. (xy1, xy2, ...) to rotated boxes (x, y, w, h, re, im)
    9.13. def rotate_scale_coords : Rescale coords (x, y, w, h, re, im) from img1_shape to img0_shape
    9.14. def rotate_box_iou : Compute iou of rotate boxes via cpu or cuda
    9.15. def rotate_bbox_iou : Compute iou of rotated boxes for class Rotate_ComputeLoss in loss.py via cpu or cuda
    9.16. def rotate_non_max_suppression : Runs Non-Maximum Suppression (NMS) on inference results for rotated boxes

  10. utils/loss.py :
    10.1. class Rotate_ComputeLoss : Compute loss for rotate boxes

  11. utils/metrics.py :
    11.1. class Rotate_ConfusionMatrix : Rotate version of original ConfusionMatrix

  12. utils/plots.py :
    12.1. def polygon_plot_one_box : Plot one polygon box on image
    12.2. def polygon_plot_one_box_PIL : Plot one polygon box on image via PIL
    12.3. def polygon_plot_images : Polygon version of original plot_images
    12.4. def rotate_plot_one_box : Plot one rotate box on image
    12.5. def rotate_plot_one_box_PIL : Plot one rotate box on image via PIL
    12.6. def rotate_output_to_target : Convert model output format [x, y, w, h, re, im, conf, class_id] to target format [batch_id, class_id, x, y, w, h, re, im, conf]
    12.7. def rotate_plot_images : Rotate version of original plot_images
    12.8. def rotate_plot_test_txt : Rotate version of original plot_test_txt
    12.9. def rotate_plot_targets_txt : Rotate version of original plot_targets_txt
    12.10. def rotate_plot_labels : Rotate version of original plot_labels

  13. rotate_train.py : For training rotate-yolov5 models

  14. rotate_test.py : For testing rotate-yolov5 models

  15. rotate_detect.py : For detecting rotate-yolov5 models

  16. requirements.py : Added python model shapely

Section II. How Does Rotate Boxes Work? How Does Rotate Boxes Different from Polygon Boxes?

  1. Comparisons between Rotate-Yolov5 and Polygon-Yolov5

2. Model Head of Rotate-Yolov5

3. Illustration of Box Loss of Rotated Boxes

Section III. Installation

For the CUDA extension to be successfully built without error, please use CUDA version >= 11.2. The codes have been verified in Ubuntu 16.04 with Tesla K80 GPU.

# The following codes install CUDA 11.2 from scratch on Ubuntu 16.04, if you have installed it, please ignore
# If you are using other versions of systems, please check https://tutorialforlinux.com/2019/12/01/how-to-add-cuda-repository-for-ubuntu-based-oses-2/
# Install Ubuntu kernel head
sudo apt install linux-headers-$(uname -r)

# Pinning CUDA repo wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-ubuntu1604.pin sudo mv cuda-ubuntu1604.pin /etc/apt/preferences.d/cuda-repository-pin-600
# Add CUDA GPG key sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
# Setting up CUDA repo sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/ /"
# Refresh apt repositories sudo apt update
# Installing CUDA 11.2 sudo apt install cuda-11-2 -y sudo apt install cuda-toolkit-11-2 -y
# Setting up path echo 'export PATH=/usr/local/cuda-11.2/bin${PATH:+:${PATH}}' >> $HOME/.bashrc # You are done installing CUDA 11.2
# Check NVIDIA nvidia-smi # Update all apts sudo apt-get update sudo apt-get -y upgrade
# Begin installing python 3.7 curl -o ~/miniconda.sh -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh chmod +x ~/miniconda.sh ./miniconda.sh -b echo "PATH=~/miniconda3/bin:$PATH" >> ~/.bashrc source ~/.bashrc conda install -y python=3.7 # You are done installing python

The following codes set you up with the Rotate Yolov5.

# clone git repo
git clone https://github.com/XinzeLee/RotateObjectDetection
cd RotateObjectDetection/rotate-yolov5
# install python package requirements
pip install -r requirements.txt
# install CUDA extensions
cd utils/iou_cuda
python setup.py install
# cd back to rotate-yolov5 folder
cd .. && cd ..

Section IV. Rotate-Tutorial 1: Deploy the Rotate Yolov5s

Try Rotate Yolov5s Model by Following Rotate-Tutorial 1

  1. Inference
     $ python rotate_detect.py --weights rotate-yolov5s-ucas.pt --img 1024 --conf 0.75 \
         --source data/images/UCAS-AOD --iou-thres 0.4 --hide-labels

  2. Test
     $ python rotate_test.py --weights rotate-yolov5s-ucas.pt --data rotate_ucas.yaml \
         --img 1024 --iou 0.65 --task val

  3. Train
     $ python rotate_train.py --weights rotate-yolov5s-ucas.pt --cfg rotate_yolov5s_ucas.yaml \
         --data rotate_ucas.yaml --hyp hyp.ucas.yaml --img-size 1024 \
         --epochs 3 --batch-size 12 --noautoanchor --rotate --cache
  4. Performance
    4.1. Confusion Matrix

    4.2. Precision Curve

    4.3. Recall Curve

    4.4. Precision-Recall Curve

    4.5. F1 Curve

Section V. Rotate-Tutorial 2: Transform COCO Dataset to Rotate Labels Using Segmentation

Transform COCO Dataset to Rotate Labels by Following Rotate-Tutorial 2

Transformed Exemplar Figure

Section VI. References

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This repository is based on Ultralytics/yolov5, with adjustments to enable rotate prediction boxes.


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