DIC适用于损伤力学和裂纹_Python_下载.zip

# DIC_LIPEC Python package to perform stereo calibration for Digital Image Correlation (DIC) # The Package in Brief - [x] A calibration patern that can be implemented in real life scenario - [x] A dual webcam setup ensuring the same view from both webcams and focused at a particular point in the calibration pattern - [x] Capture frames of the calibration pattern using the stereo system - [x] Frames must be on .jpg or .png format - [ ] The package works with .tiff format images - [x] Detect points from the captured frames using template matching algorithm and recognise a grid pattern - [ ] Template matching works on all captured frames - [x] Use the detected grid and points for stereo calibration - [x] Determine intrinsic and extrinsic parameters for both webcams # Main features * The package is built using Python 3.7.6 consisting of three different functions. * Function CaptureFrames() is used to setup the dual webcam framework required for DIC. Following this the webcams are used to capture frames of the calibration pattern for different rotations of the pattern. * Function SingleSurfaceCalibration() is used for stereo calibration involving only one calibration pattern, i.e., a single grid. * Function DoubleSurfaceCalibration() is used for stereo calibration involving two calibration patterns, i.e., two grids. # Quickstart If you know what you're doing, then this section is for you. Otherwise, you should go to the `Getting Started` section. #### Windows: ``` sudo apt install python3-pip python3-venv git clone https://github.com/stochasticmaterialism/Digital-Image-Correlation-DIC-/tree/DIC_LIPEC/DIC_LIPEC pip install -r requirements.txt cd DIC python setup.py install ``` Basic usage: ``` from DIC_LIPEC import * ``` # Getting started ## Installation The following pieces of software are required to implement the package: * Python 3.6 or higher * A Python editing platform like Jupyter Notebook which comes as a part of the Anaconda Distribution The list of necessary python packages is provided in the `requirements.txt` file and can be installed using pip: ``` pip install -r requirements.txt ``` # Function description ```yaml from DIC_LIPEC import CaptureFrames CaptureFrames(stamp) Input stamp: This parameter defines the delay between two webcams Output Displays the total time duration for which the webcams were on along with their time stamp along with the total number of frames captured by both webcams. Besides, with the user permission, the functions saves all the captured frames in the C drive of the devicebeing used. Images are saved in by the names xxxxx_y where xxxxx represents the frame number while y is 1 if the frame is captured by the left webcam and 0 for right webcam. ``` ```yaml from DIC_LIPEC import SingleSurfaceCalibration arr1,arr2,arr3,arr4=SingleSurfaceCalibration(location1,type1,location2,type2,location3,threshold1,threshold2) Input location1: Location of the folder where the captured frames are saved type1: Image format of the captured frames location2: Location of the folder where the templates to detect the special points are saved type1: Image format of the templates used to detect the special points location3: Location of the image template used to detect the normal points threshold1: parameter to detect normal points threshold2: parameter to detect special points Output arr1: Array with all pixel coordinates for the normal points detected for all frames arr2: Array with all pixel coordinates for the special points detected for all frames arr3: Array with the intrinsic and extrinsic parameters for left webcam arr4: Array with the intrinsic and extrinsic parameters for right webcam ``` # Functions expected to be available in the next version ```yaml from DIC_LIPEC import DoubleSurfaceCalibration arr1,arr2,arr3,arr4,arr5,arr6,arr7,arr8=DoubleSurfaceCalibration(location1,type1,location2,type2,location3,threshold1,threshold2) Input location1: Location of the folder where the captured frames are saved type1: Image format of the captured frames location2: Location of the folder where the templates to detect the special points are saved type1: Image format of the templates used to detect the special points location3: Location of the image template used to detect the normal points threshold1: parameter to detect normal points threshold2: parameter to detect special points Output arr1: Array with all pixel coordinates for the normal points detected for grid 1 for all frames arr2: Array with all pixel coordinates for the special points detected for grid 2 for all frames arr3: Array with all pixel coordinates for the normal points detected for grid 1 for all frames arr4: Array with all pixel coordinates for the special points detected for grid 2 for all frames arr5: Array with the intrinsic and extrinsic parameters for left webcam for grid 1 arr6: Array with the intrinsic and extrinsic parameters for left webcam for grid 2 arr7: Array with the intrinsic and extrinsic parameters for right webcam for grid 1 arr8: Array with the intrinsic and extrinsic parameters for right webcam for grid 2 ``` # Guide * Images folder consists of the templates used to detect normal and special points * Numerical aspects of Calibration explains the mathematical background to carry out calibration * test.py is used to test the package once installation is complete ## Contribute ? Maybe you need something for your own project, maybe you found a mistake or something you could improve and feeling like helping, in any case, we'll be happy to get in touch. Please leave us an Issue, or a Pull Request (we welcome those!). # Disclaimer This software is for educational and research purposes only. Use it at your own risks.