基于python开发的光伏电池板红外缺陷检测系统软件.rar

 Branch|Linux|Windows| :----:|:----:|:----:| Master|[](https://travis-ci.org/cdeldon)|[](https://ci.appveyor.com/project/cdeldon/thermography/branch/master) Devel|[](https://travis-ci.org/cdeldon)|[](https://ci.appveyor.com/project/cdeldon/thermography/branch/devel) This repository contains the implementation of a feasibility study for automatic detection of defected solar panel modules. The developed framework has been coined _Thermography_ due to the fact that the input data to the system is a sequence of images in the infrared spectrum.  ### Structure The repository is structured as follows: 1. [Documentation](docs) of the _Thermography_ repository. 2. [GUI](gui) source code associated to the graphical user interface for interacting with the _Thermography_ framework. 3. [Log files](logs) generated at runtime. 4. [Resources](resources) used by the _Thermography_ framework. 5. [Thermography](thermography) core source code related to detection and classification of solar panel modules. The _python_ scripts located in the root directory can be used to launch different executables which exploit the _Thermography_ framework for solar panel module detection and classification. ### Installation *Thermography* has been tested using the 64-bit version of *python 3.5*. If you are using an other version of python, consider installing and using *Thermography* inside a virtual environment. #### System-wide installation Here follow the steps to install *Thermography* system-wide. ##### Get the source Download the git repository: ``` lang=bash $ git clone https://github.com/cdeldon/thermography.git $ cd thermography/ ``` Or download the following [zip](https://github.com/cdeldon/thermography/archive/master.zip). ##### Prerequisites Install the prerequisites: ``` lang=bash $ pip install -r requirements.txt ``` #### Anaconda Here follow the steps to install *Thermography* in a virtual environment created using [Anaconda](https://www.anaconda.com/download/). ##### Get the source Download the git repository: ``` lang=bash $ git clone https://github.com/cdeldon/thermography.git $ cd thermography/ ``` Or download the following [zip](https://github.com/cdeldon/thermography/archive/master.zip). ##### Virtual environment Create a new virtual environment ``` lang=bash $ conda create --name my_env python=3.5 $ activate my_env ``` ##### Prerequisites Install the prerequisites: ``` lang=bash (my_env) $ pip install -r requirements.txt ``` ### Example scripts Here follows a description of the example scripts in the [root](.) directory of the _Thermography_ repository. ##### Application Running the [main_app.py](main_app.py) script a default video is loaded and each frame is processed for module extraction. This script's purpose is to show the workflow of the _Thermography_ framework for a simple video. ##### GUIs A graphical user interface is provided for interacting with the _Thermography_ framework. In particular the following executables are available: 1. [Dataset creation](main_create_dataset.py) script used to facilitate the creation of a labeled dataset of images representing solar panel modules. 2. [ThermoGUI](main_thermogui.py) graphical interface which allows the used to interact with the _Thermography_ framework and to analyze a new sequence of frames on the fly. The executables with a graphical interface offer the following tools and visualizations:  The GUI presents different views of the processed input video, in particular the following views are available: Attention image|Edge image :---:|:---: | Segment image|Rectangle image :---:|:---: | The lateral toolbar offers runtime parameter tuning with immediate application: Video tab|Prepr. tab|Segment tab|Modules tab :---:|:---:|:---:|:---: ||| ##### Training and restoring Executables for training and restoring a learning system are offered with the _Thermography_ framework. These scripts can be used and adapted for training a new classifier which can the be integrated with the GUIs for real time classification of the detected solar panel modules. 1. [Training](main_training.py) trains a model to classify input images with the correct label. 2. [Restoring](main_training_restorer.py) restores a trained model with associated weights and outputs the classification for a set of input images. ### Tests The base functionalities of the _Thermography_ framework are tested using [unittests](https://docs.python.org/3/library/unittest.html). The tests can be executed as follows: ```lang=bash $ cd thermography/ $ python -m unittest discover thermography/test [-v] ``` The same tests can be run as a normal python script as follows: ```lang=bash $ cd thermography/ $ python main_test.py ``` ### Documentation The documentation of the code is available [here](https://cdeldon.github.io/thermography/).