{"id":24149330,"url":"https://github.com/vinukavinnath/ipcv","last_synced_at":"2026-04-15T09:31:36.451Z","repository":{"id":250301140,"uuid":"824670359","full_name":"vinukavinnath/ipcv","owner":"vinukavinnath","description":"A collection of image processing and computer vision tutorials covering topics from shape detection to deep learning-based image classification. 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The tutorials cover various topics, from basic shape detection to complex image classification tasks using deep learning models.\n\n\n## Table of Contents\n\n1. Shape Detection\n2. Face Recognition\n3. Vehicle Number Plate Detection\n4. Object Detection with MobileNet\n5. Pneumonia Detection with VGG16\n6. Gun Detection with VGG16\n7. Multiclass Image Classification\n\n### Shape Detection\nThis tutorial covers the basics of shape detection using OpenCV. It includes code for set of preprocessing tasks and identify the shape using contours in an image.\n\n### Face Recognition\nDemonstrates face recognition using OpenCV's pre-trained models and face_recognition library. It explains how to detect faces in an image and recognize known individuals.\n\n### Vehicle Number Plate Detection\nThis tutorial focuses on detecting vehicle number plates using OpenCV and easy_ocr. It covers image preprocessing, plate detection, and character recognition.\n\n### Object Detection with MobileNet\nExplains how to use the MobileNet SSD model for object detection. It includes steps for loading the model, processing images, and identifying objects in real-time.\n\n### Pneumonia Detection with VGG16\nThis tutorial uses the VGG16 deep learning model to detect pneumonia from chest X-ray images. It covers data preprocessing, model training, and evaluation using transfer learning techniques.\n\n### Gun Detection with VGG16\nSimilar to the pneumonia detection tutorial, this one uses the VGG16 model to detect guns in images. It includes steps for training the model and making predictions.\n\n### Multiclass Image Classification\nThis tutorial demonstrates how to perform multiclass image classification using CIFAR10 dataset and campares ANN and CNN characteristics.\n\n## Reflective Journal\n[Click here](https://kduac-my.sharepoint.com/:b:/g/personal/39-bcs-0005_kdu_ac_lk/EZZ_htCTLeBAmt1F-C2nG08B4biDHS6Ba-SkmXZUvKTL_A?e=lDKYJ7) to refer the Reflective Journal based on above activities.\n\n## Setting up the repository\n1. Clone this repository\n```bash\ngit clone https://github.com/vinukavinnath/ipcv.git \"IPCV\"\n```\n2. Download and install Anaconda Distribution\n\nClick [here](https://www.anaconda.com/download) to download.\n\n3. Create a virtual environment using `conda` commands and activate it.\n4. Open Anaconda Navigator and launch Jupyter Notebook.\n5. Open notebooks through jupyter notebook and run python commands.\n\n## Contributing\nContributions are welcome! If you have any improvements or new tutorials to add, please fork the repository and submit a pull request. Make sure to follow the existing code style and include detailed explanations for any new content.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvinukavinnath%2Fipcv","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvinukavinnath%2Fipcv","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvinukavinnath%2Fipcv/lists"}