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Getting Started

This section provides a guide on how to install and use OTValidate.

Installation

Note

Installation instructions for Windows are following soon.

Install OTValidate by cloning the repository with git:

git clone https://github.com/OpenTrafficCam/OTValidate.git
cd OTValidate
pip install -r requirements.txt
pip install .

Or install by using the Makefile:

git clone https://github.com/OpenTrafficCam/OTValidate.git
cd OTValidate
make install

The installation for machines running on Apple's M1 chip is not as straightforward. There are two ways to install OTValidate on an M1 Mac. As a prerequisite the package manager Homebrew is required.

  1. By executing these commands in the following order:

    git clone https://github.com/OpenTrafficCam/OTValidate.git
    cd OTValidate
    brew install openblas
    OPENBLAS=$(brew --prefix openblas) CFLAGS="-falign-functions=8 ${CFLAGS}" pip install scipy==1.7.2
    pip install -r requirements.txt
    pip install .
    
  2. By using the Makefile

    git clone https://github.com/OpenTrafficCam/OTValidate.git
    cd OTValidate
    make install_m1
    

Prerequisites

Image Annotation Data

The folder containing the ground truth annotations of the images need to be in the YOLO format:

annotation_data
│   obj.data
│   obj.names
│   train.txt
│
└───obj_train_data
    │   frame_01.png
    │   frame_01.txt
    │   frame_02.png
    │   frame_02.txt
    │   ...
    │

Usage

Analyse Object Detection Performance

Quickstart Guide

from OTValidate  import evaluate_detection_performance

# path to the directory containing the annotated dataset in otdet format
gt_data = "path/to/data1"

# model weights
model1 = "path/to/model_weights1.pt"
model2 = "path/to/model_weights2.pt"
model3 = "path/to/model_weights3.pt"

Use the evaluate_detection_performance function to calculate a set of object detection metrics of the respective models:

evaluate_detection_performance(
    path_to_model_weights=[model1, model2, model3],
    yolo_path=yolo_path,
    otdet_gt_dir=gt_data,
    is_gt_xyxy_format=False, # whether the ground truth's bounding box is in xyxy or xywh format
    normalized=True,
)

Results

The evaluation results of the models will be saved in the directories containing the annotation data. An out directory containing all the results will be created there.


Last update: November 28, 2021
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