Object Recognition Api with Client using opencv4node and SSD with coco models.
Objects he can recognize
- background
- person
- bicycle
- car
- motorcycle
- airplane
- bus
- train
- truck
- boat
- traffic light
- fire hydrant
- stop sign
- parking meter
- bench
- bird
- cat
- dog
- horse
- sheep
- cow
- elephant
- bear
- zebra
- giraffe
- backpack
- umbrella
- handbag
- tie
- suitcase
- frisbee
- skis
- snowboard
- sports ball
- kite
- baseball bat
- baseball glove
- skateboard
- surfboard
- tennis racket
- bottle
- wine glass
- cup
- fork
- knife
- spoon
- bowl
- banana
- apple
- sandwich
- orange
- broccoli
- carrot
- hot dog
- pizza
- donut
- cake
- chair
- couch
- potted plant
- bed
- dining table
- toilet
- tv
- laptop
- mouse
- remote
- keyboard
- cell phone
- microwave
- oven
- toaster
- sink
- refrigerator
- book
- clock
- vase
- scissors
- teddy bear
- hair drier
- toothbrush
-
Download SSD trained model
https://drive.google.com/file/d/1Er4yCQjQaeg1UoGrBGFNG5fh4ZmhtIVX
-
Install docker on your machine.
Extract SSD file previously downloaded
tar xvzf SSD_300x300.tar.gzMove SSD trained model file into api/src/ObjectRecognition folder it must be called SSD_300x300
api/src/objectRecognition
⊢ dnnCocoClassNames.js
⊢ dnnSSDCoco.js
⊢ index.js
⊢ utils.js
L SSD_300x300 # <-- Place model folder heredocker-compose build
docker-compose up
- Go to http://localhost:8080
- Upload image
you can choose an image from /images folder - Select your level of confidence
- Send request
- Sorted data response
- Add additional coco models
- Add more custom themes for material-ui
- React-boilerplate - Used to generate client
- Opencv4nodejs - Used to create API
- Lange Julien
This project is licensed under the WTFPL License - see the LICENSE.md file for details