diff --git a/README.md b/README.md
index 930634c..b2c40a0 100644
--- a/README.md
+++ b/README.md
@@ -143,6 +143,17 @@ As explained for the **/detect** endpoint, the query parameter must be the docke
This endpoint returns a list of all *JPG* or *PNG* files inside the docker directory received as query parameter. It is useful for checking which directory to apply inference to via the **/detect** endpoint.
+
+#### POST /detect_img_visual
+
+This endpoint **receives as payload the image on which inference should be applied**. The return of this api call is a PNG image showing the results of the inference as bounding boxes and labels.
+This endpoint should not be used for batch inference, as the image is sent through the network before performing inference, leading to high API response times, and thus to lower classification frequencies..
+
+
+#### GET /upload_form
+
+A simple HTML forms to serve as a GUI to upload an image from the client computer for testing/demo purpose.
+
# YOLOv8 Model
The YOLOv8 model running on the API backend is the YOLOv8m model, which provides a good balance between inference time and accuracy.
diff --git a/app/main.py b/app/main.py
index bc06ad2..6f03e06 100644
--- a/app/main.py
+++ b/app/main.py
@@ -1,10 +1,11 @@
"""Main script for exposing the FastAPI API for YOLO inference"""
import os
-from PIL import Image
+from PIL import Image, ImageDraw, ImageFont
from io import BytesIO
from typing import Dict, Any, Union
from fastapi import FastAPI, Query, File, UploadFile
+from fastapi.responses import HTMLResponse, Response
from app.model.model import inference_on_img, inference_on_path
from app.model.model import __version__ as model_version
@@ -102,3 +103,76 @@ async def detect_img(
'image_size': img.size,
'inference_results': inference_results_data
}}
+
+""" simple HTML forms to upload an image via GUI """
+@app.get("/upload_form", response_class=HTMLResponse, include_in_schema=False)
+async def root():
+ return '''
+
+
+
+
+
+
+
+'''
+
+""" drawing the result of the inference on the image then return it (for visual inspection) """
+@app.post('/detect_img_visual', responses = {
+ 200: {
+ "content": {"image/png": {}}
+ }
+ },response_class=Response)
+async def mirror_img(
+ img: UploadFile = File(...)
+) -> Dict[str, Union[int, Dict[str, Any]]]:
+ """Performs YOLO inference on the received image"""
+ if not img:
+ return {
+ 'status_code': 400,
+ 'data': {
+ 'message': 'No upload file sent'
+ }}
+
+ # Load the received image as PIL Image
+ img_content = await img.read()
+ image_stream = BytesIO(img_content)
+ image = Image.open(image_stream)
+
+ # Perform inference for the received image
+ try:
+ inference_results_data = inference_on_img(img=image)
+ except Exception as err:
+ print(f'An error occurred while trying to perform inference. {err}')
+ return {
+ 'status_code': 500,
+ 'data': {}
+ }
+
+ # ImageDraw allow to draw rectangle and text on the image
+ draw = ImageDraw.Draw(image)
+ font = ImageFont.load_default(20)
+ # For each result, draw the bounding box and write the class name and confidence
+ for result in inference_results_data:
+ draw.rectangle(list(result['box'].values()),outline="green")
+ draw.text(xy=[result['box']['x1'],result['box']['y1'] +30], text="{} ({:.1f})".format(result['name'], 100*result['confidence']), font=font, fill="green" )
+
+ # Generate the output as a bytes stream of an PNG image.
+ output_stream = BytesIO()
+ image.save(output_stream, 'PNG')
+
+ return Response (content=output_stream.getvalue(), media_type="image/png")