fix: 降低 NMS 阈值到 0.01，保留更多低分数检测结果

internal/nsfw/server.py

@@ -197,7 +197,8 @@ def _raw_detect(det, data):
     boxes_arr = np.array(boxes, dtype=np.float32)
     scores_arr = np.array(scores, dtype=np.float32)
 
-    indices = cv2.dnn.NMSBoxes(boxes_arr.tolist(), scores_arr.tolist(), 0.1, 0.45)
+    # NMS 参数：score_threshold=0.01 保留更多低分数结果，iou_threshold=0.45
+    indices = cv2.dnn.NMSBoxes(boxes_arr.tolist(), scores_arr.tolist(), 0.01, 0.45)
 
     detections = []
     if indices is not None and len(indices) > 0:

test_nsfw.py

@@ -0,0 +1,70 @@
+# -*- coding: utf-8 -*-
+import cv2
+import numpy as np
+from nudenet import NudeDetector
+
+# 测试图片路径
+img_path = r"E:\已测试分拣men\数据源\xxxx\图片_横屏_2K\dark_25\e0d12fd3a8c5436f801f766fad4a22fb.jpg"
+
+print(f"测试图片: {img_path}")
+
+# 加载模型
+det = NudeDetector()
+
+# 读取图片
+with open(img_path, 'rb') as f:
+    img_bytes = f.read()
+
+print(f"文件大小: {len(img_bytes)} bytes")
+
+# 解码图片
+mat = cv2.imdecode(np.frombuffer(img_bytes, np.uint8), -1)
+if mat is None:
+    print("图片解码失败")
+    exit(1)
+
+print(f"图片尺寸: {mat.shape}")
+
+# 预处理
+mat_c3 = cv2.cvtColor(mat, cv2.COLOR_RGBA2BGR)
+ow, oh = mat_c3.shape[1], mat_c3.shape[0]
+max_size = max(ow, oh)
+x_pad, y_pad = max_size - ow, max_size - oh
+mat_pad = cv2.copyMakeBorder(mat_c3, 0, y_pad, 0, x_pad, cv2.BORDER_CONSTANT)
+blob = cv2.dnn.blobFromImage(mat_pad, 1/255.0, (320, 320), (0, 0, 0), swapRB=True, crop=False)
+
+# 推理
+outputs = det.onnx_session.run(None, {det.input_name: blob})
+outs = np.transpose(np.squeeze(outputs[0]))
+
+# 标签列表
+labels = [
+    'FEMALE_GENITALIA_COVERED', 'FACE_FEMALE', 'BUTTOCKS_EXPOSED',
+    'FEMALE_BREAST_EXPOSED', 'FEMALE_GENITALIA_EXPOSED', 'MALE_BREAST_EXPOSED',
+    'ANUS_EXPOSED', 'FEET_EXPOSED', 'BELLY_COVERED', 'FEET_COVERED',
+    'ARMPITS_COVERED', 'ARMPITS_EXPOSED', 'FACE_MALE', 'BELLY_EXPOSED',
+    'MALE_GENITALIA_EXPOSED', 'ANUS_COVERED', 'FEMALE_BREAST_COVERED',
+    'BUTTOCKS_COVERED'
+]
+
+# 收集所有得分 > 0.01 的检测结果
+all_scores = []
+for i in range(outs.shape[0]):
+    cls_scores = outs[i][4:]
+    max_idx = int(np.argmax(cls_scores))
+    max_score = float(cls_scores[max_idx])
+    if max_score > 0.01:
+        all_scores.append((labels[max_idx], max_score))
+
+# 按得分排序
+all_scores.sort(key=lambda x: -x[1])
+
+print(f"\n检测到 {len(all_scores)} 个得分 > 0.01 的区域")
+print("\nTop 20 最高分:")
+for cls, sc in all_scores[:20]:
+    print(f"  {cls}: {sc:.4f}")
+
+# 使用标准 detect 方法对比
+print("\n--- 使用标准 detect 方法 ---")
+result = det.detect(img_bytes)
+print(f"标准检测结果: {result}")