Add application file

Dockerfile

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+FROM python:3.10.16
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+
+ENV NO_ALBUMENTATIONS_UPDATE=1
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import os
+import onnxruntime
+from insightface.model_zoo import SCRFD
+from insightface.model_zoo import ArcFaceONNX
+from insightface.app.common import Face
+import numpy as np
+import cv2
+from fastapi import FastAPI, UploadFile
+
+detection_model_file = os.path.abspath(os.path.join(os.getcwd(), "det.onnx"))
+recognition_model_file = os.path.abspath(os.path.join(os.getcwd(), "rec.onnx"))
+
+detection = SCRFD(model_file=detection_model_file)
+detection.prepare(ctx_id=-1, input_size=(640, 640))
+
+recognition = ArcFaceONNX(model_file=recognition_model_file)
+recognition.prepare(ctx_id=-1)
+
+def read_image_from_spooled_temporary_file(temp_file):
+    temp_file.seek(0)
+    return cv2.imdecode(np.frombuffer(temp_file.read(), np.uint8), cv2.IMREAD_COLOR)
+
+app = FastAPI()
+
+@app.post("/v1/detection")
+async def v1_detection(photo: UploadFile):
+  if not detection:
+    return {"faces": []}
+
+  image = read_image_from_spooled_temporary_file(photo.file)
+  bboxes, kpss = detection.detect(image)
+  if bboxes.shape[0] == 0:
+    return {"faces": []}
+
+  faces = []
+  for i in range(bboxes.shape[0]):
+    bbox = bboxes[i, :4]
+    det_score = bboxes[i, 4]
+    kps = None
+    if kpss is not None:
+      kps = kpss[i]
+    faces.append({
+      "detectionScore": det_score.item(),
+      "boundingBox": {
+        "topLeft": {
+          "x": bbox[0].item(),
+          "y": bbox[1].item()
+        },
+        "bottomRight": {
+          "x": bbox[2].item(),
+          "y": bbox[3].item()
+        }
+      },
+      "keyPoints": [{"x": pt[0].item(), "y": pt[1].item()} for pt in kps]
+    })
+  
+  return {"faces": faces}
+
+@app.post("/v1/recognition")
+async def v1_recognition(photo: UploadFile):
+  if not detection or not recognition:
+    return {"faces": []}
+
+  image = read_image_from_spooled_temporary_file(photo.file)
+  bboxes, kpss = detection.detect(image)
+  if bboxes.shape[0] == 0:
+    return {"faces": []}
+
+  faces = []
+  for i in range(bboxes.shape[0]):
+    bbox = bboxes[i, :4]
+    det_score = bboxes[i, 4]
+    kps = None
+    if kpss is not None:
+      kps = kpss[i]
+    face = Face(bbox=bbox, kps=kps, det_score=det_score)
+    recognition.get(image, face)
+    faces.append({
+      "boundingBox": {
+        "topLeft": {
+          "x": face.bbox[0].item(),
+          "y": face.bbox[1].item()
+        },
+        "bottomRight": {
+          "x": face.bbox[2].item(),
+          "y": face.bbox[3].item()
+        }
+      },
+      "detectionScore": face.det_score.item(),
+      "keyPoints": [{"x": pt[0].item(), "y": pt[1].item()} for pt in face.kps],
+      "embedding": [v.item() for v in face.embedding.ravel()]
+    })
+
+  return {"faces": faces}

det.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5838f7fe053675b1c7a08b633df49e7af5495cee0493c7dcf6697200b85b5b91
+size 16923827

rec.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4c06341c33c2ca1f86781dab0e829f88ad5b64be9fba56e56bc9ebdefc619e43
+size 174383860

requirements.txt

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+numpy
+onnxruntime >= 1.17.0
+insightface >=0.7.3
+opencv-python-headless >=4.7.0.72
+fastapi[standard]