基于keras-yolov3进行烟火数据集训练及识别（python开发源码+项目说明）.zip

# mAP (mean Average Precision) [](https://github.com/Cartucho/mAP) This code will evaluate the performance of your neural net for object recognition. <p align="center"> <img src="https://user-images.githubusercontent.com/15831541/37559643-6738bcc8-2a21-11e8-8a07-ed836f19c5d9.gif" width="450" height="300" /> </p> In practice, a **higher mAP** value indicates a **better performance** of your neural net, given your ground-truth and set of classes. ## Citation This project was developed for the following paper, please consider citing it: ```bibtex @INPROCEEDINGS{8594067, author={J. {Cartucho} and R. {Ventura} and M. {Veloso}}, booktitle={2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, title={Robust Object Recognition Through Symbiotic Deep Learning In Mobile Robots}, year={2018}, pages={2336-2341}, } ``` ## Table of contents - [Explanation](#explanation) - [Prerequisites](#prerequisites) - [Quick start](#quick-start) - [Running the code](#running-the-code) - [Authors](#authors) ## Explanation The performance of your neural net will be judged using the mAP criterium defined in the [PASCAL VOC 2012 competition](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/). We simply adapted the [official Matlab code](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/#devkit) into Python (in our tests they both give the same results). First (**1.**), we calculate the Average Precision (AP), for each of the classes present in the ground-truth. Finally (**2.**), we calculate the mAP (mean Average Precision) value. #### 1. Calculate AP For each class: First, your neural net **detection-results** are sorted by decreasing confidence and are assigned to **ground-truth objects**. We have "a match" when they share the **same label and an IoU >= 0.5** (Intersection over Union greater than 50%). This "match" is considered a true positive if that ground-truth object has not been already used (to avoid multiple detections of the same object). <img src="https://user-images.githubusercontent.com/15831541/37725175-45b9e1a6-2d2a-11e8-8c15-2fb4d716ca9a.png" width="35%" height="35%" /> Using this criterium, we calculate the precision/recall curve. E.g: <img src="https://user-images.githubusercontent.com/15831541/43008995-64dd53ce-8c34-11e8-8a2c-4567b1311910.png" width="45%" height="45%" /> Then we compute a version of the measured precision/recall curve with **precision monotonically decreasing** (shown in light red), by setting the precision for recall `r` to the maximum precision obtained for any recall `r' > r`. Finally, we compute the AP as the **area under this curve** (shown in light blue) by numerical integration. No approximation is involved since the curve is piecewise constant. #### 2. Calculate mAP We calculate the mean of all the AP's, resulting in an mAP value from 0 to 100%. E.g: <img src="https://user-images.githubusercontent.com/15831541/38933241-5f9556ae-4310-11e8-9d47-cb205f9b103b.png"/> <img src="https://user-images.githubusercontent.com/15831541/38933180-366b6fca-4310-11e8-99b9-17ad4b159b86.png" /> ## Prerequisites You need to install: - [Python](https://www.python.org/downloads/) Optional: - **plot** the results by [installing Matplotlib](https://matplotlib.org/users/installing.html) - Linux, macOS and Windows: 1. `python -mpip install -U pip` 2. `python -mpip install -U matplotlib` - show **animation** by installing [OpenCV](https://www.opencv.org/): 1. `python -mpip install -U pip` 2. `python -mpip install -U opencv-python` ## Quick-start To start using the mAP you need to clone the repo: ``` git clone https://github.com/Cartucho/mAP ``` ## Running the code Step by step: 1. [Create the ground-truth files](#create-the-ground-truth-files) 2. Copy the ground-truth files into the folder **input/ground-truth/** 3. [Create the detection-results files](#create-the-detection-results-files) 4. Copy the detection-results files into the folder **input/detection-results/** 5. Run the code: ``` python main.py ``` Optional (if you want to see the **animation**): 6. Insert the images into the folder **input/images-optional/** #### PASCAL VOC, Darkflow and YOLO users In the [scripts/extra](https://github.com/Cartucho/mAP/tree/master/scripts/extra) folder you can find additional scripts to convert **PASCAL VOC**, **darkflow** and **YOLO** files into the required format. #### Create the ground-truth files - Create a separate ground-truth text file for each image. - Use **matching names** for the files (e.g. image: "image_1.jpg", ground-truth: "image_1.txt"). - In these files, each line should be in the following format: ``` <class_name> <left> <top> <right> <bottom> [<difficult>] ``` - The `difficult` parameter is optional, use it if you want the calculation to ignore a specific detection. - E.g. "image_1.txt": ``` tvmonitor 2 10 173 238 book 439 157 556 241 book 437 246 518 351 difficult pottedplant 272 190 316 259 ``` #### Create the detection-results files - Create a separate detection-results text file for each image. - Use **matching names** for the files (e.g. image: "image_1.jpg", detection-results: "image_1.txt"). - In these files, each line should be in the following format: ``` <class_name> <confidence> <left> <top> <right> <bottom> ``` - E.g. "image_1.txt": ``` tvmonitor 0.471781 0 13 174 244 cup 0.414941 274 226 301 265 book 0.460851 429 219 528 247 chair 0.292345 0 199 88 436 book 0.269833 433 260 506 336 ``` ## Authors: * **João Cartucho** Feel free to contribute [](https://github.com/Cartucho/mAP/graphs/contributors)