Update services/detection.py

services/detection.py

@@ -0,0 +1,47 @@
+import cv2
+import numpy as np
+import tensorflow as tf
+
+class FaceDetector:
+    def __init__(self, model_path="models/face_detection.tflite"):
+        self.interpreter = tf.lite.Interpreter(model_path=model_path)
+        self.interpreter.allocate_tensors()
+        
+        self.input_details = self.interpreter.get_input_details()
+        self.output_details = self.interpreter.get_output_details()
+        
+    def detect_faces(self, image):
+        """كشف الوجوه في الصورة"""
+        # تحضير الصورة
+        input_shape = self.input_details[0]['shape'][1:3]
+        img_resized = cv2.resize(image, (input_shape[1], input_shape[0]))
+        img_normalized = img_resized.astype(np.float32) / 255.0
+        img_input = np.expand_dims(img_normalized, axis=0)
+        
+        # تنفيذ النموذج
+        self.interpreter.set_tensor(self.input_details[0]['index'], img_input)
+        self.interpreter.invoke()
+        
+        # استخراج النتائج
+        boxes = self.interpreter.get_tensor(self.output_details[0]['index'])
+        scores = self.interpreter.get_tensor(self.output_details[1]['index'])
+        
+        return self._process_detections(boxes[0], scores[0], image.shape)
+    
+    def _process_detections(self, boxes, scores, image_shape):
+        """معالجة صناديق الكشف"""
+        h, w = image_shape[:2]
+        faces = []
+        
+        for i, score in enumerate(scores):
+            if score > 0.5:  # عتبة الثقة
+                y1, x1, y2, x2 = boxes[i]
+                x1, y1 = int(x1 * w), int(y1 * h)
+                x2, y2 = int(x2 * w), int(y2 * h)
+                
+                faces.append({
+                    'bbox': [x1, y1, x2, y2],
+                    'confidence': float(score)
+                })
+        
+        return faces