Humans of Paris 1900: Difference between revisions
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The fetching can easily be done using the wrapper provided by Raphaël Barman. | The fetching can easily be done using the wrapper provided by Raphaël Barman. | ||
Preprocessing involves processing of the metadata to extract tags as well as processing of the images to obtain a vector encoding of an individual's face, extracting [https://pypi.org/project/py-agender/ age and gender] and cropping it. | <ul> | ||
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Preprocessing involves processing of the metadata to extract tags as well as processing of the images to obtain a vector encoding of an individual's face using [http://cmusatyalab.github.io/openface/ OpenFace], extracting [https://pypi.org/project/py-agender/ age and gender using the pyAgender python library] and cropping it using OpenCV. | |||
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Storing the data involves [https://www.sqlite.org/index.html Sqlite] and Django ORM [https://docs.djangoproject.com/en/2.2/topics/db/ Django ORM] | Storing the data involves [https://www.sqlite.org/index.html Sqlite] and Django ORM [https://docs.djangoproject.com/en/2.2/topics/db/ Django ORM] | ||
Serving the data: | </li> | ||
Front end development | <li> | ||
Serving the data: [https://www.djangoproject.com/ Django] | |||
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Front end development uses Bootstrap to enable quick and beautiful UI design and [https://d3js.org/ D3.js] for displaying the FaceMap. | |||
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We organized the tasks according to the following schedule and milestones. | We organized the tasks according to the following schedule and milestones. | ||
Revision as of 14:44, 20 November 2019
Project plan
The final goal is to present the user with a way to navigate the collection and understand who the people is the photos are and what life they lived. We develop a website that provides the user with 4 core functionalities.
- An overview of the most famous individuals in the collection
- A search page to explore individuals based on tag matching
- A cluster view of individuals in the database, based on their looks
- A way to upload ones image and find ones’ doppelgänger in the collection
To make this vision reality, we went through 4 stages in the processing, namely fetching the data, processing it, storing and serving data and front end development.
The fetching can easily be done using the wrapper provided by Raphaël Barman.
- Preprocessing involves processing of the metadata to extract tags as well as processing of the images to obtain a vector encoding of an individual's face using OpenFace, extracting age and gender using the pyAgender python library and cropping it using OpenCV.
- Storing the data involves Sqlite and Django ORM Django ORM
- Serving the data: Django
- Front end development uses Bootstrap to enable quick and beautiful UI design and D3.js for displaying the FaceMap. We organized the tasks according to the following schedule and milestones.
Milestones
| Timeframe | Task | Completion |
|---|---|---|
| Week 4 | ||
| 07.11 | Understanding Gallica Query Gallica API | ✓ |
| Query Gallica API | ||
| Week 5 | ||
| 14.10 | Start preprocessing images | ✓ |
| Choose suitable Wikipedia API | ||
| Week 6 | ||
| 21.10 | Choose face recognition library | ✓ |
| Get facial vectors | ||
| Try database design with Docker & Flask | ||
| Week 7 | ||
| 28.10 | Remove irrelevant backgrounds of images | ✓ |
| Extract age and gender from images | ||
| Design data model | ||
| Extract tags, names, birth and death years out of metadata | ||
| Week 8 | ||
| 04.11 | Set up database environment | ✓ |
| Set up mockup user-interface | ||
| Prepare midterm presentation | ||
| Week 9 | ||
| 11.11 | Get tags, names, birth and death years in ready-to-use format | ✓ |
| Handle Wikipedia false positives | ||
| Integrate face recognition functionalities into database | ||
| Week 10 | ||
| 18.11 | Create draft of the website (frontend) | |
| Create FaceMap using D3 | ||
| Week 11 | ||
| 25.11 | Integrate all functionalities | |
| Finalize project website | ||
| Week 12 | ||
| 02.12 | Write Project report | |