# Flask REST API
[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/).
## Requirements
[Flask](https://palletsprojects.com/p/flask/) is required. Install with:
```shell
$ pip install Flask
```
## Run
After Flask installation run:
```shell
$ python3 restapi.py --port 5000
```
Then use [curl](https://curl.se/) to perform a request:
```shell
$ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s'`
```
The model inference results are returned as a JSON response:
```json
[
{
"class": 0,
"confidence": 0.8900438547,
"height": 0.9318675399,
"name": "person",
"width": 0.3264600933,
"xcenter": 0.7438579798,
"ycenter": 0.5207948685
},
{
"class": 0,
"confidence": 0.8440024257,
"height": 0.7155083418,
"name": "person",
"width": 0.6546785235,
"xcenter": 0.427829951,
"ycenter": 0.6334488392
},
{
"class": 27,
"confidence": 0.3771208823,
"height": 0.3902671337,
"name": "tie",
"width": 0.0696444362,
"xcenter": 0.3675483763,
"ycenter": 0.7991207838
},
{
"class": 27,
"confidence": 0.3527112305,
"height": 0.1540903747,
"name": "tie",
"width": 0.0336618312,
"xcenter": 0.7814827561,
"ycenter": 0.5065554976
}
]
```
An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given in `example_request.py`
基于YOLOv5+Deepsort实现车辆行人追踪和计数（完整源码+说明文档+数据）.zip
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基于YOLOv5+Deepsort实现车辆行人追踪和计数（完整源码+说明文档+数据）.zip （134个子文件） 
Dockerfile 821B
手册.docx 28KB .gitkeep 0B
train.jpg 59KB README.md 2KB README.md 65B
result.mp4 8.55MB
image.png 799KB yolov5s.pt 14.11MB datasets.py 44KB general.py 28KB plots.py 18KB wandb_utils.py 16KB common.py 16KB yolo.py 13KB torch_utils.py 12KB json_logger.py 11KB loss.py 9KB metrics.py 9KB kalman_filter.py 8KB autoanchor.py 7KB export.py 7KB linear_assignment.py 6KB train.py 6KB nn_matching.py 5KB experimental.py 5KB google_utils.py 5KB track.py 5KB io.py 4KB tracker.py 4KB activations.py 4KB evaluation.py 3KB deep_sort.py 3KB original_model.py 3KB model.py 3KB tracker.py 3KB iou_matching.py 3KB test.py 2KB AIDetector_pytorch.py 2KB preprocessing.py 2KB feature_extractor.py 2KB demo.py 1KB draw.py 1KB resume.py 1KB BaseDetector.py 1KB restapi.py 1KB parser.py 976B detection.py 825B log_dataset.py 800B tools.py 734B __init__.py 500B log.py 463B asserts.py 316B example_request.py 299B evaluate.py 293B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B datasets.cpython-37.pyc 34KB general.cpython-37.pyc 23KB common.cpython-36.pyc 19KB common.cpython-37.pyc 19KB plots.cpython-37.pyc 16KB torch_utils.cpython-37.pyc 11KB yolo.cpython-37.pyc 11KB metrics.cpython-37.pyc 8KB linear_assignment.cpython-36.pyc 7KB kalman_filter.cpython-36.pyc 7KB kalman_filter.cpython-37.pyc 7KB autoanchor.cpython-37.pyc 6KB experimental.cpython-36.pyc 6KB experimental.cpython-37.pyc 6KB nn_matching.cpython-36.pyc 6KB nn_matching.cpython-37.pyc 6KB track.cpython-36.pyc 5KB linear_assignment.cpython-37.pyc 5KB track.cpython-37.pyc 5KB tracker.cpython-36.pyc 5KB tracker.cpython-37.pyc 4KB deep_sort.cpython-36.pyc 4KB deep_sort.cpython-37.pyc 4KB google_utils.cpython-37.pyc 3KB iou_matching.cpython-37.pyc 3KB iou_matching.cpython-36.pyc 3KB model.cpython-37.pyc 3KB model.cpython-36.pyc 3KB feature_extractor.cpython-36.pyc 3KB feature_extractor.cpython-37.pyc 3KB preprocessing.cpython-37.pyc 2KB preprocessing.cpython-36.pyc 2KB detection.cpython-36.pyc 2KB BaseDetector.cpython-37.pyc 2KB parser.cpython-37.pyc 1KB parser.cpython-36.pyc 1KB detection.cpython-37.pyc 1KB __init__.cpython-37.pyc 676B共 134 条
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