A FastAPI object detection application based on Yolov5 model.
- Clone the project
cdinto the codebase- run
poetry shellandpoetry installto set the virtual environment and install the necessary dependencies
Since we used docker, we can start the app by running these commands:
sudo docker-compose build
sudo docker-compose up -d
The app will be served on port 8000
An example curl request:
curl --location '127.0.0.1:8000/object_detect' \
--form 'input_file=@"/Users/Downloads/download.jpeg"'
Valid response:
{
"data": [
[
{
"class": 0,
"class_name": "person",
"bbox": [
134,
330,
187,
579
],
"confidence": 0.7723177671432495
},
{
"class": 0,
"class_name": "person",
"bbox": [
90,
373,
140,
587
],
"confidence": 0.7619432210922241
},
{
"class": 2,
"class_name": "car",
"bbox": [
492,
314,
566,
430
],
"confidence": 0.7516090273857117
},
{
"class": 2,
"class_name": "car",
"bbox": [
379,
321,
455,
460
],
"confidence": 0.7398971915245056
},
{
"class": 0,
"class_name": "person",
"bbox": [
267,
332,
315,
541
],
"confidence": 0.4890599846839905
},
{
"class": 0,
"class_name": "person",
"bbox": [
592,
322,
611,
389
],
"confidence": 0.45573562383651733
},
{
"class": 2,
"class_name": "car",
"bbox": [
444,
313,
477,
400
],
"confidence": 0.38912448287010193
},
{
"class": 0,
"class_name": "person",
"bbox": [
209,
324,
260,
553
],
"confidence": 0.30747783184051514
}
]
],
"message": "object detected successfully",
"errors": null,
"status": 200
}
Error response:
{
"message": "object detection failed",
"errors": "error",
"status": 400
}