Introduction

Developed by four talented young men as their graduation project.
Name	Github	Twitter
Ziad Mansour	ZiadMansourM	@ziad_m_404
Mohamed Wael	-	-
Maged Elosail	-	-
Yousif adel	-	-

ScanMate: Implementation of Close Range Photogrammetry using Classical Image Processing Techniques

📝 Pipeline - docs - videos

*** We have the following 7 steps in our pipeline:
$ prepare_images
- Load Dataset Images
- Compute Mask
$ compute_sift_keypoints_descriptors
$ image_matching
$ data_feature_matching
- Apply crossCheck BF Matcher
- Apply Ransac on BF Matcher Output
- Loop without repetition using Itertools
$ compute_k_matrix
$ generate_point_cloud
- Recover Pose of reference camera
- Recover rest camera poses using solvePNPRansac
- Apply Triangulation
$ 3D reconstruction
- Use PointsCloud to generate a 3D Object (.stl) file

🏛️ Datasets

Status	Dataset Link	Description
❌	Gingerbread Man	3D model of a gingerbread cookie. Created in RealityCapture from 158 images.
✅	Hammer	Hammer dataset with size of 750 MB.
✅	Small Cottage	Objects Scanned from all sides using Masks.
✅	Fountain	3D reconstruction images from the popular Strecha dataset.

🧐 Production Folder Structure

(venv) ziadh@Ziads-MacBook-Air production % tree 
.
├── conf
│   ├── certs
│   ├── html
│   ├── kong-config
│   │   └── kong.yaml
│   ├── logs
│   └── nginx.conf
├── data
├── docker-compose.yml
└── src
    ├── Dockerfile
    ├── main.py
    ├── scanmate.py
    └── under_the_hood
        ├── __init__.py
        ├── compute_sift_features.py
        ├── data_feature_match.py
        ├── data_structures
        │   ├── __init__.py
        │   ├── feature_matches.py
        │   ├── image.py
        │   └── images.py
        ├── generate_points_cloud.py
        ├── image_match.py
        ├── prepare_images.py
        └── utils
            ├── __init__.py
            └── utils.py

10 directories, 18 files

⚖️ License

This project is licensed under the terms of the GNU General Public License version 3.0 (GPLv3). See the LICENSE file for details.