Update simulation_modules.py

simulation_modules.py

@@ -39,7 +39,7 @@ def find_peak_box_and_classify(data):
         for j in range(1, W + 1):
             confidence = det_data[i, j, 0]
             # تم رفع حد الثقة قليلاً لتقليل الاكتشافات الخاطئة
-            if confidence > 0.2:
+            if confidence > 0.15:
                 # البحث عن القمة المحلية
                 if (confidence >= det_data[i,j-1,0] and confidence >= det_data[i,j+1,0] and
                     confidence >= det_data[i-1,j,0] and confidence >= det_data[i+1,j,0]):
@@ -531,8 +531,8 @@ def render_bev(
     predicted_waypoints: np.ndarray,
     ego_pos_global: np.ndarray,
     ego_theta_global: float,
-    pixels_per_meter: int = 10,
-    grid_size_meters: tuple = (40, 40),
+    pixels_per_meter: int = 20,
+    grid_size_meters: tuple = (20, 20),
     future_time_steps: tuple = (1.0, 2.0) # أزمنة التنبؤ المستقبلية
 ) -> Dict[str, np.ndarray]:
     """
@@ -591,28 +591,27 @@ def render_bev(
             cv2.drawContours(bev_maps[key], [box_points_future.astype(np.int32)], 0, (255, 0, 128), 2) # بنفسجي
 
     # --- 2. رسم مركبة الأنا (يجب رسمها على كل الخرائط) ---
-    ego_center_pixel = (width_px / 2, height_px - 5)
-    ego_size_px = (1.8 * pixels_per_meter, 4.0 * pixels_per_meter)
+    ego_center_pixel = ((width_px / 2 ) + 20 , height_px - 10)
+    ego_size_px = (1.8 * pixels_per_meter, 2.0 * pixels_per_meter)
     ego_box = cv2.boxPoints((ego_center_pixel, ego_size_px, -90))
     for key in bev_maps:
         cv2.drawContours(bev_maps[key], [ego_box.astype(np.int32)], 0, (0, 255, 255), -1) # أصفر
 
     # --- 3. رسم نقاط المسار المتوقعة (فقط على خريطة الحاضر t0) ---
     if predicted_waypoints.size > 0:
-        waypoints_pixels = world_to_pixel(predicted_waypoints, (width_px, height_px), grid_size_meters)
+        waypoints_pixels = world_to_pixel(predicted_waypoints, (width_px+20, height_px), grid_size_meters)
         cv2.polylines(bev_maps['t0'], [waypoints_pixels.astype(np.int32)], isClosed=False, color=(0, 255, 0), thickness=3)
 
     return bev_maps
-
 # =========================================================
-# (دالة world_to_pixel تبقى كما هي من الرد السابق)
-def world_to_pixel(world_points, grid_size_pixels, grid_size_meters):
-    pixel_per_meter_x = grid_size_pixels[0] / grid_size_meters[0]
-    pixel_per_meter_y = grid_size_pixels[1] / grid_size_meters[1]
-
-    pixel_x = (world_points[:, 1] * pixel_per_meter_x) + (grid_size_pixels[0] / 2)
-    pixel_y = (grid_size_pixels[1]) - (world_points[:, 0] * pixel_per_meter_y)
-    return np.vstack((pixel_x, pixel_y)).T
+# # (دالة world_to_pixel تبقى كما هي من الرد السابق)
+# def world_to_pixel(world_points, grid_size_pixels, grid_size_meters):
+#     pixel_per_meter_x = grid_size_pixels[0] / grid_size_meters[0]
+#     pixel_per_meter_y = grid_size_pixels[1] / grid_size_meters[1]
+
+#     pixel_x = (world_points[:, 1] * pixel_per_meter_x) + (grid_size_pixels[0] / 2)
+#     pixel_y = (grid_size_pixels[1]) - (world_points[:, 0] * pixel_per_meter_y)
+#     return np.vstack((pixel_x, pixel_y)).T
 
 # ==========================================================
 #      الدالة الثانية: generate_bev_image (النسخة النهائية)