Add formula detection service

src/client_demo.py

@@ -1,10 +1,12 @@
 import requests
 
-url = "http://127.0.0.1:8000/predict"
+rec_server_url = "http://127.0.0.1:8000/frec"
+det_server_url = "http://127.0.0.1:8000/fdet"
 
 img_path = "/your/image/path/"
 with open(img_path, 'rb') as img:
     files = {'img': img}
-    response = requests.post(url, files=files)
+    response = requests.post(det_server_url, files=files)
+    # response = requests.post(rec_server_url, files=files)
 
 print(response.text)

src/server.py

@@ -1,14 +1,19 @@
 import argparse
+import tempfile
 import time
 import numpy as np
 import cv2
 
+from pathlib import Path
 from starlette.requests import Request
 from ray import serve
 from ray.serve.handle import DeploymentHandle
+from onnxruntime import InferenceSession
 
-from models.ocr_model.utils.inference import inference
+from models.ocr_model.utils.inference import inference as rec_inference
+from models.det_model.inference import predict as det_inference
 from models.ocr_model.model.TexTeller import TexTeller
+from models.det_model.inference import PredictConfig
 from models.ocr_model.utils.to_katex import to_katex
 
 
@@ -39,7 +44,7 @@ if args.ngpu_per_replica > 0 and not args.inference_mode == 'cuda':
         "num_gpus": args.ngpu_per_replica
     }
 )
-class TexTellerServer:
+class TexTellerRecServer:
     def __init__(
         self, 
         checkpoint_path: str, 
@@ -55,41 +60,75 @@ class TexTellerServer:
         self.model = self.model.to(inf_mode) if inf_mode != 'cpu' else self.model
     
     def predict(self, image_nparray) -> str:
-        return to_katex(inference(
+        return to_katex(rec_inference(
             self.model, self.tokenizer, [image_nparray],
             accelerator=self.inf_mode, num_beams=self.num_beams
         )[0])
 
 
+@serve.deployment(num_replicas=args.num_replicas)
+class TexTellerDetServer:
+    def __init__(
+        self
+    ):
+        self.infer_config = PredictConfig("./models/det_model/model/infer_cfg.yml")
+        self.latex_det_model = InferenceSession("./models/det_model/model/rtdetr_r50vd_6x_coco.onnx")
+
+    async def predict(self, image_nparray) -> str:
+        with tempfile.TemporaryDirectory() as temp_dir:
+            img_path = f"{temp_dir}/temp_image.jpg"
+            cv2.imwrite(img_path, image_nparray)
+            
+            latex_bboxes = det_inference(img_path, self.latex_det_model, self.infer_config)
+            return latex_bboxes
+
+
 @serve.deployment()
 class Ingress:
-    def __init__(self, texteller_server: DeploymentHandle) -> None:
-        self.texteller_server = texteller_server
+    def __init__(self, det_server: DeploymentHandle, rec_server: DeploymentHandle) -> None:
+        self.det_server = det_server
+        self.texteller_server = rec_server
     
     async def __call__(self, request: Request) -> str:
+        request_path = request.url.path
         form   = await request.form()
         img_rb = await form['img'].read()
 
         img_nparray = np.frombuffer(img_rb, np.uint8)
         img_nparray = cv2.imdecode(img_nparray, cv2.IMREAD_COLOR)
         img_nparray = cv2.cvtColor(img_nparray, cv2.COLOR_BGR2RGB)
+
+        if request_path.startswith("/fdet"):
+            if self.det_server == None:
+                return "[ERROR] rtdetr_r50vd_6x_coco.onnx not found."
+            pred = await self.det_server.predict.remote(img_nparray)
+            return pred
+
+        elif request_path.startswith("/frec"):
             pred = await self.texteller_server.predict.remote(img_nparray)
             return pred
 
+        else:
+            return "[ERROR] Invalid request path"
+
 
 if __name__ == '__main__':
     ckpt_dir = args.checkpoint_dir
     tknz_dir = args.tokenizer_dir
 
     serve.start(http_options={"host": "0.0.0.0", "port": args.server_port})
-    texteller_server = TexTellerServer.bind(
+    rec_server = TexTellerRecServer.bind(
         ckpt_dir, tknz_dir, 
         inf_mode=args.inference_mode,
         num_beams=args.num_beams
     )
-    ingress = Ingress.bind(texteller_server)
+    det_server = None
+    if Path('./models/det_model/model/rtdetr_r50vd_6x_coco.onnx').exists():
+        det_server = TexTellerDetServer.bind()
+    ingress = Ingress.bind(det_server, rec_server)
 
-    ingress_handle = serve.run(ingress, route_prefix="/predict")  
+    # ingress_handle = serve.run(ingress, route_prefix="/predict")  
+    ingress_handle = serve.run(ingress, route_prefix="/") 
 
     while True:
         time.sleep(1)