人脸抽取，支持摄像头分辨率选择

Author: lzy98276

CHANGELOG/v2.3.0-beta.3/CHANGELOG.md CHANGELOG/v2.3.0/CHANGELOG.mdCHANGELOG/v2.3.0-beta.3/CHANGELOG.md renamed to CHANGELOG/v2.3.0/CHANGELOG.md

@@ -1,20 +1,22 @@
-<img width="1800" height="766" alt="新版本" src="https://github.com/SECTL/SecRandom/blob/master/data/assets/icon/secrandom-beta.png" />
+<img width="1800" height="766" alt="新版本" src="https://github.com/SECTL/SecRandom/blob/master/data/assets/icon/secrandom-release.png" />
 
-v2.3 - Shiroko (砂狼白子) beta 3
+v2.3 - Shiroko (砂狼白子) release 1
 
 ## 🚀 主要更新
 
 - 新增 **设置搜索**，支持子项页定位，且能高亮显示搜索结果
+- 新增 **人脸抽取**，支持摄像头分辨率选择
 
 ## 💡 功能优化
 
-- 无
+- 优化 **人脸抽取**，默认模型更改为 version-RFB-640.onnx
 
 ## 🐛 修复问题
 
 - 修复 **人脸抽选**，打包后模型无法读取
 - 修复 **设置窗口**，最大化侧边栏无法展开
-- 修复 **摄像头预热**，退出时 QThread 报错
+- 修复 **摄像头预热**，避免 QThread 自等待
+- 修复 **人脸抽取**，启动多线程崩溃
 
 ## 🔧 其它变更
 

app/Language/modules/extraction_settings.py

@@ -1203,6 +1203,11 @@
             "name": "摄像头",
             "description": "选择人脸抽取使用的摄像头设备",
         },
+        "camera_display_resolution": {
+            "name": "摄像头显示分辨率",
+            "description": "选择摄像头预览采集分辨率（默认使用最高）",
+            "recommended_suffix": "推荐",
+        },
         "detector_type": {
             "name": "检测模型",
             "description": "选择相机预览的人脸检测模型文件（ONNX）",
@@ -1258,6 +1263,11 @@
             "name": "Camera",
             "description": "Select the camera device for face picking",
         },
+        "camera_display_resolution": {
+            "name": "Camera resolution",
+            "description": "Select capture resolution for camera preview (default: highest)",
+            "recommended_suffix": "Recommended",
+        },
         "detector_type": {
             "name": "Detection model",
             "description": "Select ONNX model file for camera preview",
@@ -1310,6 +1320,11 @@
             "name": "カメラ",
             "description": "顔抽選に使用するカメラデバイスを選択",
         },
+        "camera_display_resolution": {
+            "name": "カメラ解像度",
+            "description": "カメラプレビューの解像度を選択（デフォルトは最大）",
+            "recommended_suffix": "推奨",
+        },
         "detector_type": {
             "name": "検出モデル",
             "description": "カメラプレビューで使用する ONNX モデルファイルを選択",

app/common/camera_preview_backend/__init__.py

@@ -3,7 +3,9 @@
 from app.common.camera_preview_backend.devices import (
     CameraDeviceInfo,
     get_cached_camera_devices,
+    get_recommended_camera_resolution,
     list_camera_devices,
+    list_camera_resolutions,
     warmup_camera_devices,
     warmup_camera_devices_async,
 )
@@ -16,7 +18,9 @@
 __all__ = [
     "CameraDeviceInfo",
     "get_cached_camera_devices",
+    "get_recommended_camera_resolution",
     "list_camera_devices",
+    "list_camera_resolutions",
     "warmup_camera_devices",
     "warmup_camera_devices_async",
     "OpenCVCaptureWorker",

app/common/camera_preview_backend/detection.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import math
+import threading
 from pathlib import Path
 from typing import Optional, Tuple
 
@@ -10,6 +11,7 @@
 
 
 Rect = Tuple[int, int, int, int]
+_CV2_IMPORT_LOCK = threading.Lock()
 
 
 def merge_face_rects(frame_size: Tuple[int, int], rects: list[Rect]) -> list[Rect]:
@@ -348,17 +350,12 @@ def _ultralight_input_size_from_name(model_name: str) -> Tuple[int, int]:
 
 
 def create_ultralight_net(*, model_path: Path):
-    import cv2
+    with _CV2_IMPORT_LOCK:
+        import cv2
 
     if not hasattr(cv2, "dnn"):
         raise RuntimeError("OpenCV dnn is not available in this build")
-    try:
-        blob = model_path.read_bytes()
-        if not blob:
-            raise RuntimeError("ONNX file is empty")
-        return cv2.dnn.readNetFromONNX(blob)
-    except Exception:
-        return cv2.dnn.readNetFromONNX(str(model_path))
+    return cv2.dnn.readNetFromONNX(str(model_path))
 
 
 def _generate_ultralight_priors(input_size: Tuple[int, int]):
@@ -631,17 +628,12 @@ def ensure_scrfd_model_path(
 
 
 def create_scrfd_net(*, model_path: Path):
-    import cv2
+    with _CV2_IMPORT_LOCK:
+        import cv2
 
     if not hasattr(cv2, "dnn"):
         raise RuntimeError("OpenCV dnn is not available in this build")
-    try:
-        blob = model_path.read_bytes()
-        if not blob:
-            raise RuntimeError("ONNX file is empty")
-        return cv2.dnn.readNetFromONNX(blob)
-    except Exception:
-        return cv2.dnn.readNetFromONNX(str(model_path))
+    return cv2.dnn.readNetFromONNX(str(model_path))
 
 
 def detect_faces_scrfd(