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.dockerignore

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+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+*.db
+.env
+.vscode/
+.idea/

Dockerfile

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+# Base image with Python
+FROM python:3.10-slim
+
+# Set environment variables
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+ENV KMP_DUPLICATE_LIB_OK=TRUE
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    libsm6 \
+    libxrender1 \
+    libxext6 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements and install Python packages
+COPY requirements.txt .
+RUN pip install --upgrade pip
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the app code
+COPY . .
+
+# Expose FastAPI default port
+EXPOSE 8000
+
+# Run the FastAPI app with uvicorn
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

alpr_module.py

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+import cv2
+import numpy as np
+import easyocr
+from inference_sdk import InferenceHTTPClient
+
+# تعريف العميل
+CLIENT = InferenceHTTPClient(
+    api_url="https://serverless.roboflow.com",
+    api_key="4m9tZRxBfEK8C4no7zsZ"
+)
+reader = easyocr.Reader(['en'])
+def detect_license_plate(image_path, model_id="license-plate-recognition-rxg4e/11"):
+    """إجراء التنبؤ باستخدام نموذج Roboflow"""
+    result = CLIENT.infer(image_path, model_id=model_id)
+    return result
+
+def extract_plate_from_image(image_path, predictions):
+    """استخلاص صورة اللوحة من الصورة الكاملة بناءً على التنبؤات"""
+    image = cv2.imread(image_path)
+    max_confidence = 0
+    best_coords = None
+
+    for prediction in predictions['predictions']:
+        confidence = prediction['confidence']
+        if confidence > max_confidence:
+            max_confidence = confidence
+            x = int(prediction['x'])
+            y = int(prediction['y'])
+            width = int(prediction['width'])
+            height = int(prediction['height'])
+            x1 = int(x - width / 2)
+            y1 = int(y - height / 2)
+            x2 = int(x + width / 2)
+            y2 = int(y + height / 2)
+            best_coords = (x1, y1, x2, y2)
+
+    if not best_coords:
+        return None  # لم يتم الكشف عن أي لوحة
+
+    x1, y1, x2, y2 = best_coords
+    polygon_points = np.array([
+        [x1, y1],
+        [x2, y1],
+        [x2, y2],
+        [x1, y2]
+    ], dtype=np.int32)
+
+    mask = np.zeros(image.shape[:2], dtype=np.uint8)
+    cv2.fillPoly(mask, [polygon_points], 255)
+
+    masked_image = cv2.bitwise_and(image, image, mask=mask)
+    x, y, w, h = cv2.boundingRect(polygon_points)
+    cropped_plate = masked_image[y:y+h, x:x+w]
+
+    return cropped_plate
+
+def enhance_image_for_ocr(image):
+    """تحسين الصورة لاستخلاص النصوص باستخدام EasyOCR"""
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    return gray
+
+def extract_license_number(image):
+    """استخراج الأرقام من صورة اللوحة"""
+    result = reader.readtext(image)
+    plate_numbers = []
+
+    for detection in result:
+        text = detection[1]
+        numbers_only = ''.join(c for c in text if c.isdigit())
+        if numbers_only:
+            plate_numbers.append(numbers_only)
+    return plate_numbers

main.py

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+import os
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+from fastapi import FastAPI,UploadFile,File
+from alpr_module import detect_license_plate, extract_plate_from_image, enhance_image_for_ocr, extract_license_number
+import tempfile
+
+
+
+
+app = FastAPI()
+
+
+
+
+
+@app.post("/")
+async def recognize_plate(file: UploadFile = File(...)):
+    try:
+        # حفظ الصورة مؤقتاً
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
+            tmp.write(await file.read())
+            tmp_path = tmp.name
+        # الخطوات
+        predictions = detect_license_plate(tmp_path)
+        plate_img = extract_plate_from_image(tmp_path, predictions)
+        if plate_img is None:
+            os.remove(tmp_path)
+            return JSONResponse(status_code=404, content={"error": "لم يتم اكتشاف أي لوحة"})
+        enhanced_img = enhance_image_for_ocr(plate_img)
+        numbers = extract_license_number(enhanced_img)
+        os.remove(tmp_path)
+        if numbers:
+            longest = max(numbers, key=len)
+            return {"plate_number": longest}
+        else:
+            return {"plate_number": None}
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": str(e)})

requirements.txt

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+opencv-python
+numpy
+easyocr
+inference-sdk
+fastapi
+uvicorn