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# YOLOv5 π by Ultralytics, AGPL-3.0 license | |
""" | |
Run a Flask REST API exposing one or more YOLOv5s models | |
""" | |
import argparse | |
import io | |
import torch | |
from flask import Flask, request | |
from PIL import Image | |
app = Flask(__name__) | |
models = {} | |
DETECTION_URL = '/v1/object-detection/<model>' | |
def predict(model): | |
if request.method != 'POST': | |
return | |
if request.files.get('image'): | |
# Method 1 | |
# with request.files["image"] as f: | |
# im = Image.open(io.BytesIO(f.read())) | |
# Method 2 | |
im_file = request.files['image'] | |
im_bytes = im_file.read() | |
im = Image.open(io.BytesIO(im_bytes)) | |
if model in models: | |
results = models[model](im, size=640) # reduce size=320 for faster inference | |
return results.pandas().xyxy[0].to_json(orient='records') | |
if __name__ == '__main__': | |
parser = argparse.ArgumentParser(description='Flask API exposing YOLOv5 model') | |
parser.add_argument('--port', default=5000, type=int, help='port number') | |
parser.add_argument('--model', nargs='+', default=['yolov5s'], help='model(s) to run, i.e. --model yolov5n yolov5s') | |
opt = parser.parse_args() | |
for m in opt.model: | |
models[m] = torch.hub.load('ultralytics/yolov5', m, force_reload=True, skip_validation=True) | |
app.run(host='0.0.0.0', port=opt.port) # debug=True causes Restarting with stat | |