Merge pull request #35 from robmarkcole/ISSUE33

solve issue

README.md

@@ -9,21 +9,21 @@ Simple app consisting of a form where you can upload an image, and see the infer
 then visit http://localhost:5000/ in your browser:
 
 <p align="center">
-<img src="https://github.com/robmarkcole/yolov5-flask/blob/master/docs/app_form.jpg" width="450">
+<img src="docs/app_form.jpg" width="450">
 </p>
 
 <p align="center">
-<img src="https://github.com/robmarkcole/yolov5-flask/blob/master/docs/app_result.jpg" width="450">
+<img src="docs/app_result.jpg" width="450">
 </p>
 
 ## Rest API
 Simple rest API exposing the model for consumption by another service. Run:
 
-`$ python3 restapi.py --port 5000`
+`$ python3 restapi.py --port 5000 --model yolov5s`
 
 Then use [curl](https://curl.se/) to perform a request:
 
-`$ curl -X POST -F image=@tests/zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s'`
+`$ curl -X POST -F image=@tests/zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5'`
 
 The model inference results are returned:
 

requirements.txt

@@ -1,5 +1,4 @@
 flask
-requests
 
 # YOLOv5 requirements
 matplotlib>=3.2.2

restapi.py

@@ -10,7 +10,7 @@
 
 app = Flask(__name__)
 
-DETECTION_URL = "/v1/object-detection/yolov5s"
+DETECTION_URL = "/v1/object-detection/yolov5"
 
 
 @app.route(DETECTION_URL, methods=["POST"])
@@ -22,16 +22,14 @@ def predict():
         image_file = request.files["image"]
         image_bytes = image_file.read()
         img = Image.open(io.BytesIO(image_bytes))
-        results = model(img, size=640)
-        data = results.pandas().xyxy[0].to_json(orient="records")
-        return data
-
+        results = model(img, 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', default='yolov5s', help='model to run, i.e. --model yolov5s')
     args = parser.parse_args()
 
-    model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)  # force_reload = recache latest code
-    model.eval()
+    model = torch.hub.load('ultralytics/yolov5', args.model)
     app.run(host="0.0.0.0", port=args.port)  # debug=True causes Restarting with stat