template-matching
There are 13 repositories under template-matching topic.
rmislam / PythonSIFT
A clean and concise Python implementation of SIFT (Scale-Invariant Feature Transform)
Language:Python 883DennisLiu1993 / Fastest_Image_Pattern_Matching
C++ implementation of a ScienceDirect paper "An accelerating cpu-based correlation-based image alignment for real-time automatic optical inspection"
Language:C++ 680- Language:C++ 293
kelltom / OS-Bot-COLOR
A lightweight desktop client & toolkit for writing, controlling and monitoring color-based automation scripts.
Language:Python 229stb-tester / stb-tester
Automated Testing for Set-Top Boxes and Smart TVs
Language:Python 178slyautomation / osrs_basic_botting_functions
Functions for automating osrs botting using Python.
Language:Python 177multi-template-matching / MultiTemplateMatching-Python
Object-recognition using multiple templates in python
Language:Jupyter Notebook 173kamata1729 / QATM_pytorch
Pytorch Implementation of QATM:Quality-Aware Template Matching For Deep Learning
Language:Jupyter Notebook 166eqcorrscan / EQcorrscan
Earthquake detection and analysis in Python.
Language:Python 164mohammaduzair9 / Basic-Image-Processing
Implementation of Basic Digital Image Processing Tasks in Python / OpenCV
Language:Python 162- Language:Python 117
- play-game-with-computer-vision
paulonteri / play-game-with-computer-vision
A simple python bot (powered by computer vision) used to play a game (City Island 5). The bot is able to play the game and collect points without any human intervention.
Language:Python 110 cozheyuanzhangde / Invariant-TemplateMatching
Rotation & scale invariant template matching
Language:Python 87hakaboom / py_image_registration
图像配准算法。包括 SIFT、ORB、SURF、AKAZE、BRIEF、matchTemplate
Language:Python 84spyking-circus / spyking-circus
Fast and scalable spike sorting in python
Language:Python 82m-hamza-mughal / Aerial-Template-Matching
This project focuses on development of an algorithm for Template Matching on aerial images by implementing classical Computer Vision based techniques and deep-learning based techniques.
Language:Python 61- ComputerVision-Essentials
codeperfectplus / ComputerVision-Essentials
Computer Vision Essentials in Python Programming Language :tada:
Language:Python 59 felixchenfy / ros_yolo_as_template_matching
Run 3 scripts to (1) Synthesize images (by putting few template images onto backgrounds), (2) Train YOLOv3, and (3) Detect objects for: one image, images, video, webcam, or ROS topic.
Language:Python 55- Language:MATLAB 49
PennyHow / PyTrx
PyTrx is a Python object-oriented programme created for the purpose of calculating real-world measurements from oblique images and time-lapse image series. Its primary purpose is to obtain velocities, surface areas, and distances from oblique, optical imagery of glacial environments.
Language:Python 42abnan / DRMIME
DRMIME - Differentiable Mutual Information and Matrix Exponential for Multi-Resolution Image Registration
Language:Jupyter Notebook 41pageauc / cam-track
Windows, Unix, Raspberry Pi Computer python program to Track Camera X, Y Movements and Convert to Camera Pointing Position. Useful for Stabilization or Robotics Course Correction
Language:Python 38- Language:C++ 35
- MultiTemplateMatching-Fiji
Fiji plugin for object(s) detection using template(s) matching
Language:Python 34 YijianZhou / MESS
Matched filter earthquake detector
Language:Python 32MinhasKamal / SkinDetector
Detects Human Skin From Image (color-region-segmentation-photo-detection-extraction-detect)
Language:Java 28andreped / super-ml-pets
🐢 AI for Super Auto Pets
Language:Python 25- TomoBEAR
KudryashevLab / TomoBEAR
TomoBEAR is a configurable and customizable modular pipeline for streamlined processing of cryo-electron tomographic data for subtomogram averaging.
Language:MATLAB 24 strarsis / node-opencv-templatematching-test
Test for template matching using node-opencv
Language:JavaScript 23A more object-oriented python implementation of multi-template matching (mtm), relying on scikit-image and shapely.
Language:Jupyter Notebook 22Yeaseen / ML_Pattern
:trident: Some recognized algorithms[Decision Tree, Adaboost, Perceptron, Clustering, Neural network etc. ] of machine learning and pattern recognition are implemented from scratch using python. Data sets are also included to test the algorithms.
Language:Python 22magesh-technovator / feature-matching-opencv-python
Image key points Extraction, Description, Feature Matching
Language:Python 19robertarvind / Interactive-Semi-Automatic-Image-2D-Bounding-Box-Annotation-Tool-using-Multi-Template_Matching
Interactive Semi Automatic Image 2D Bounding Box Annotation and Labelling Tool using Multi Template Matching An Interactive Semi Automatic Image 2D Bounding Box Annotation/Labelling Tool to aid the Annotater/User to rapidly create 2D Bounding Box Single Object Detection masks for large number of training images in a semi automatic manner in order to train an object detection deep neural network such as Mask R-CNN or U-Net. As the Annotater/User starts annotating/labelling by drawing a bounding box for a few number of images in the selected folder then the algorithm suggests bounding box predictions for the rest of the yet to be annotated/labelled images in the folder. If the predictions are right then the user/annotater can simply press the keyboard key 'y' which indicates that the detected bounding box is correct. If the prediction is wrong then the user/annotater can manually draw a rectangular 2D bounding box over the correct ROI (Region of interest) in the image and then press the key 'y' to proceed further to the rest of the images in the folder. If the user/annotater made a mistake while drawing the 2D bounding box, then he/she can press the key 'n' in order to remove the incorrectly marked 2D bounding box and he/she can repeat the process for the same image until he/she draws the correct 2D bounding box and then after drawing the correct 2D bounding box, the user/annotater may press the key 'y' to continue to the rest of the images. The 2D bounding box prediction over the whole image data set improves as the user/annotater annotates/labels more number of images by drawing 2D bounding boxes. This tool allows the user/annotater to not only interactively and rapidly annotate large number of images but also to validate the predictions at the same time interactively. This tool helps the user/annotater to save a lot of time when annotating/labelling and validating the predictions for a large number of training images in a folder. Instructions to use:- 1. If the training images are in JPEG or any other format, then convert them to PNG format using some other tool or program before using these images for annotation. 2. All the training images must contain the object of interest which is to be annotated. 3. Currently the application only supports 2D bounding box annotation for single object detection per image, but in the future semantic segmentation based annotation features will be added which will allow precise boundary segmentation masks of an object in an image. 4. If some or all of the training images have varying dimensions(shapes/resolutions), then resize them to the same dimensions using this tool by providing the height and width to which all the training images need to be resized to. The height and width are inputed separately in two different dialog boxes which pop up once the program is executed. If the training images need not be resized then press the cancel button in the dialog boxes requesting the height and width. 5. Select the folder containing the training images by navigating to the folder containing the training images through a dialog box which pops up after the program is executed. If the images need to be resized then two dialog boxes pop up. The first dialog box is to navigate to the destination folder containing the unresized raw training images and after resizing another dialog box pops up to navigate to the folder containing the saved resized training images named as "resized_data". If the images need not be resized then only one dialog box pops up so that the user can navigate to the raw training images folder directly. 6. The images in the folder pop up one by one. After drawing the correct 2D bounding box over the ROI (region of Interest), press the 'y' key. Except the first image, the rest of the images will have a 2D bounding box drawn over them. If the predicted box is accurate, then continue by pressing the 'y' key. If the prediction is incorrect, then draw the accurate bounding box and press the 'y' key. If any mistake occured while drawing the 2D box, then reset the image by removing the incorrect drawing by pressing the 'n' key and then draw the correct box and press the 'y' key. 7. The output images are stored in four different folders in the same directory containing the training images folder. among the four folders, one contains the cropped templates of the bounding boxes, black and white mask images, training images and the images with 2D box detection markings.
Language:Python 19michaeltroger / template-matching-android
Augmented Reality Template Matching using OpenCV 4 for >= Android 5
Language:Kotlin 18neelgajjar / Neural-Network-Gender-Classification-Skin-Segmentation
Neural Network-male-female-recognition-human-skin-detection
Language:Java 18stgl / scarplet
Topographic edge detection of fault scarps and other landforms in digital elevation data
Language:Python 17
