first trail

Dockerfile

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+FROM python:3.12.3
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN apt-get update && apt-get install -y ffmpeg libsm6 libxext6
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import cv2
+from deepface import DeepFace
+import tempfile
+import requests
+import shutil
+import logging
+from typing import Optional
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+app = FastAPI()
+
+# Define the request model
+class FaceVerificationRequest(BaseModel):
+    id_url: str
+    ref_url: str
+
+def download_image(url: str) -> Optional[str]:
+    """Downloads an image from a URL and saves it to a temporary file."""
+    try:
+        response = requests.get(url, stream=True)
+        response.raise_for_status()  
+        temp_path = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg").name
+        with open(temp_path, "wb") as f:
+            shutil.copyfileobj(response.raw, f)
+        logger.info(f"Image downloaded successfully: {url}")
+        return temp_path
+    except requests.exceptions.RequestException as e:
+        logger.error(f"Failed to download image from {url}: {e}")
+        return None
+
+def detect_and_crop_face(image_path, detector_backend="mtcnn") -> Optional[str]:
+    """Detects and crops the face from an image."""
+    try:
+        faces = DeepFace.extract_faces(img_path=image_path, detector_backend=detector_backend, enforce_detection=False)
+        if not faces:
+            logger.warning("No faces detected.")
+            return None
+        
+        face_info = faces[0]
+        facial_area = face_info.get("facial_area", {})
+        if not facial_area:
+            logger.warning("No valid facial area found.")
+            return None
+        
+        x, y, w, h = facial_area["x"], facial_area["y"], facial_area["w"], facial_area["h"]
+        image = cv2.imread(image_path)
+        if image is None or w <= 0 or h <= 0:
+            logger.error("Invalid face cropping dimensions.")
+            return None
+        
+        cropped_face = image[y:y+h, x:x+w]
+        temp_path = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg").name
+        cv2.imwrite(temp_path, cropped_face)
+        logger.info(f"Face successfully cropped and saved at {temp_path}")
+        return temp_path  
+    except Exception as e:
+        logger.error(f"Error during face detection: {e}")
+        return None
+
+@app.post("/verify")
+async def verify_face(request: FaceVerificationRequest):
+    """Verifies whether two faces belong to the same person."""
+    try:
+        id_path = download_image(request.id_url)
+        ref_path = download_image(request.ref_url)
+        
+        if not id_path or not ref_path:
+            return {"error": "Failed to download images."}
+        
+        cropped_face_path = detect_and_crop_face(id_path)
+        if cropped_face_path:
+            result = DeepFace.verify(
+                img1_path=cropped_face_path, 
+                img2_path=ref_path, 
+                model_name="Facenet", 
+                detector_backend="mtcnn"
+            )
+            threshold = 0.6  
+            distance = result.get("distance", 1.0)
+            is_match = distance < threshold
+            
+            logger.info(f"Face verification result: {result}")
+            return {"match": is_match}
+        else:
+            return {"error": "Face detection failed for ID card image."}
+    except Exception as e:
+        logger.error(f"Verification failed: {e}")
+        return {"error": str(e)}

requirements.txt

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+fastapi
+pydantic
+opencv-python
+numpy==2.0.2
+deepface==0.0.93
+requests
+uvicorn