- Simple image-based image search engine using Keras + Flask. You can launch the search engine just by running two python scripts.
offline.py: This script extracts a deep-feature from each database image. Each feature is a 4096D fc6 activation from a VGG16 model with ImageNet pre-trained weights.server.py: This script runs a web-server. You can send your query image to the server via a Flask web-interface. The server finds similar images to the query by a simple linear scan.- GPUs are not required.
- Tested on windows 10
git clone https://github.com/BRFabdelilah/indexation.git
cd indexation
pip install -r requirements.txt
# Put your image files (*.jpg) on static/img
# Then fc6 features are extracted and saved on static/feature
# Note that it takes time for the first time because Keras downloads the VGG weights.
python offline.py
# Now you can do the search via localhost:5000
python server.py
cd indexation
pip install -r requirements.txt
# Put your image files (*.jpg) on static/img
# Then fc6 features are extracted and saved on static/feature
# Note that it takes time for the first time because Keras downloads the VGG weights.
python offline.py
# Now you can do the search via localhost:5000
python server.py.git
cd indexation
pip install -r requirements.txt
# Put your image files (*.jpg) on static/img
# Then fc6 features are extracted and saved on static/feature
# Note that it takes time for the first time because Keras downloads the VGG weights.
python offline.py
# Now you can do the search via localhost:5000
python server.py