feat: 批处理多ROI推理 - 添加 batch_process_detections 批量后处理 - 重构 _process_frame 收集多ROI统一推理

core/postprocessor.py

@@ -701,6 +701,98 @@ class PostProcessor:
             self._buffer_class_ids[:valid_count].copy()
         )
     
+    def batch_process_detections(
+        self,
+        batch_outputs: List[np.ndarray],
+        batch_size: int,
+        conf_threshold: Optional[float] = None,
+        nms_threshold: Optional[float] = None
+    ) -> List[Tuple[np.ndarray, np.ndarray, np.ndarray]]:
+        """
+        批量处理多 ROI 检测结果
+        
+        Args:
+            batch_outputs: 模型输出 (可能是 [output] 或单个 batch 数组)
+            batch_size: batch 大小
+            conf_threshold: 置信度阈值
+            nms_threshold: NMS阈值
+        
+        Returns:
+            List of (检测框, 置信度, 类别ID) per ROI
+        """
+        if conf_threshold is None:
+            conf_threshold = self.conf_threshold
+        if nms_threshold is None:
+            nms_threshold = self.nms_threshold
+        
+        if len(batch_outputs) == 1:
+            first_output = batch_outputs[0]
+            if isinstance(first_output, np.ndarray) and first_output.ndim == 3:
+                if first_output.shape[0] == batch_size:
+                    outputs_array = first_output
+                else:
+                    outputs_array = first_output
+            else:
+                outputs_array = np.array([first_output])
+        else:
+            outputs_array = np.array(batch_outputs)
+        
+        results = []
+        nms_processor = NMSProcessor(nms_threshold, use_gpu=True)
+        
+        for batch_idx in range(batch_size):
+            if outputs_array.ndim == 3:
+                if outputs_array.shape[0] == batch_size:
+                    output = outputs_array[batch_idx]
+                else:
+                    output = outputs_array[0]
+            elif outputs_array.ndim == 2:
+                output = outputs_array
+            else:
+                if batch_idx < len(outputs_array):
+                    output = outputs_array[batch_idx]
+                else:
+                    output = outputs_array[0]
+            
+            if output.ndim == 3:
+                output = output[0]
+            
+            if output.shape[0] != 84:
+                results.append((np.array([]), np.array([]), np.array([])))
+                continue
+            
+            boxes_xywh = output[0:4, :].T
+            obj_conf = output[4, :]
+            person_scores = output[5, :]
+            scores = obj_conf * person_scores
+            
+            coarse_mask = scores > 0.3
+            if not np.any(coarse_mask):
+                results.append((np.array([]), np.array([]), np.array([])))
+                continue
+            
+            boxes = boxes_xywh[coarse_mask]
+            scores_coarse = scores[coarse_mask]
+            class_ids = np.zeros(len(boxes), dtype=np.int32)
+            
+            valid_count = len(boxes)
+            
+            self._buffer_xyxy[:valid_count, 0] = boxes[:, 0] - boxes[:, 2] / 2
+            self._buffer_xyxy[:valid_count, 1] = boxes[:, 1] - boxes[:, 3] / 2
+            self._buffer_xyxy[:valid_count, 2] = boxes[:, 0] + boxes[:, 2] / 2
+            self._buffer_xyxy[:valid_count, 3] = boxes[:, 1] + boxes[:, 3] / 2
+            
+            keep_boxes, keep_scores, keep_classes = nms_processor.process_with_confidence_filter(
+                self._buffer_xyxy[:valid_count],
+                scores_coarse.astype(np.float32),
+                class_ids,
+                conf_threshold
+            )
+            
+            results.append((keep_boxes, keep_scores, keep_classes))
+        
+        return results
+    
     def filter_by_roi(
         self,
         boxes: np.ndarray,