dk - edit

preprocess.py

@@ -0,0 +1,48 @@
+import io
+from typing import Any, Union
+
+from PIL import Image
+
+from clearml import StorageManager
+
+
+# Notice Preprocess class Must be named "Preprocess"
+class Preprocess(object):
+    def __init__(self):
+        # set internal state, this will be called only once. (i.e. not per request)
+        pass
+
+    def preprocess(self, body: Union[bytes, dict], state: dict, collect_custom_statistics_fn=None) -> Any:
+        # we expect to get two valid on the dict x0, and x1
+        if isinstance(body, bytes):
+            # we expect to get a stream of encoded image bytes
+            try:
+                Image.open(io.BytesIO(body)).convert("RGB")
+            except Exception:
+                # value error would return 404, we want to return 500 so any other exception
+                raise RuntimeError("Image could not be decoded")
+            raise ValueError("send JSON with 'url' (or 'image_url') and 'xywh'")
+
+        if isinstance(body, dict) and ("url" in body.keys() or "image_url" in body.keys()):
+            # image is given as url, and is fetched
+            url = body.get("image_url") or body.get("url")
+            local_file = StorageManager.get_local_copy(remote_url=url)
+            xywh = body.get("xywh")
+            if not xywh:
+                raise ValueError("body must include 'xywh' as 'x,y,w,h'")
+            # PersonAttrService.predict 에 전달 (로컬 캐시 경로 + bbox)
+            return {"image_url": local_file, "xywh": str(xywh).strip()}
+
+        raise ValueError("body must include 'url' (or 'image_url') and 'xywh'")
+
+    def postprocess(self, data: Any, state: dict, collect_custom_statistics_fn=None) -> dict:
+        # post process the data returned from the model inference engine
+        # data is the return value from model.predict we will put is inside a return value as Y
+        if not isinstance(data, dict):
+            # this should not happen
+            return dict(output={}, status="FAIL")
+
+        # data is returned as attribute dict from PersonAttrService.predict
+        if "output" in data and "status" in data:
+            return data
+        return dict(output=data, status="PASS")