fastapi deploy

.vscode/launch.json

@@ -0,0 +1,30 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Uvicorn",
+            "type": "debugpy",
+            "request": "launch",
+            "module": "uvicorn",
+            "args": [
+                "api:app",
+                "--reload"
+            ],
+            "jinja": true,
+            "justMyCode": false,
+            "env": {
+                "PYTHONPATH": "${workspaceFolder}:${PYTHONPATH}",
+            }
+        },
+        {
+            "name": "Python Debugger: Current File",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal"
+        }
+    ]
+}

api.py

@@ -0,0 +1,57 @@
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+import io
+import base64
+from ultralytics import YOLO
+import torch
+from PIL import Image, ImageOps
+
+import utils
+from drawing import draw_keypoints
+
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+print('Using device:', device)
+
+model_pose = YOLO('yolov8l-pose.pt')
+model_pose.to(device)
+
+app = FastAPI()
+
+@app.post("/predict-image")
+async def predict_image(file: UploadFile = File(...)):
+    contents = await file.read()
+    input_image = Image.open(io.BytesIO(contents)).convert("RGB")
+    input_image = ImageOps.exif_transpose(input_image)
+
+    # predict
+    result = model_pose(input_image)[0]
+    keypoints = utils.get_keypoints(result)
+
+    # draw keypoints
+    output_image = draw_keypoints(input_image, keypoints).convert("RGB")
+
+    # calculate angles
+    lea, rea = utils.get_eye_angles(keypoints)
+    lba, rba = utils.get_elbow_angles(keypoints)
+    angles  = {'left_eye_angle': lea, 'right_eye_angle': rea, 'left_elbow_angle': lba, 'right_elbow_angle': rba}
+
+    # encode to base64
+    img_buffer = io.BytesIO()
+    output_image.save(img_buffer, format="JPEG")
+    img_buffer.seek(0)
+    img_base64 = base64.b64encode(img_buffer.getvalue()).decode("utf-8")
+
+    # prepare json response
+    json_data = {
+        "keypoints": keypoints,
+        "angles": angles,
+        "output_image": img_base64
+    }
+
+    return JSONResponse(content=json_data)
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)