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Merge pull request '优化热成像' (#12) from wangjiaqi23 into master

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p82feo7wg 1 week ago
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src/热成像识别/README.md
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@ -37,22 +37,54 @@ SAFE → CAUTION → WARNING → DANGER → CRITICAL → SUSPECTED_FIRE
```
热成像识别/
├── cpp/ C++ 实现
│ ├── CMakeLists.txt 构建配置
├── cpp/ C++ 检测器实现
│ ├── CMakeLists.txt 构建配置
│ ├── include/
│ │ ├── thermal_types.h 类型定义（威胁类型、等级、配置参数）
│ │ ├── thermal_preprocessor.h 预处理模块接口
│ │ └── thermal_detector.h 检测模块接口
│ │ ├── thermal_types.h 类型定义（威胁类型、等级、配置参数）
│ │ ├── thermal_preprocessor.h 预处理模块接口
│ │ └── thermal_detector.h 检测模块接口
│ ├── src/
│ │ ├── thermal_preprocessor.cpp 预处理实现
│ │ ├── thermal_detector.cpp 检测实现
│ │ └── main.cpp 命令行入口
│ └── thermal_detector.exe 编译产物（Windows x64）
│ │ ├── thermal_preprocessor.cpp 预处理实现（自适应温度映射）
│ │ ├── thermal_detector.cpp 检测实现（三阶段检测）
│ │ └── main.cpp 命令行入口（classical / yolo 两种模式）
│ └── models/
│ └── yolov8n_thermal.onnx ONNX 推理模型
│
├── README.md 本文档
├── HIT-UAV-Infrared-Thermal-Dataset-v1.2/ HIT-UAV 红外热成像数据集 (2898张)
│ ├── normal_json/ JSON 标注（train/val/test 划分）
│ ├── normal_xml/ VOC XML 标注 + JPEGImages
│ ├── rotate_json/ 旋转增强 JSON 标注
│ ├── rotate_xml/ 旋转增强 VOC 标注
│ └── tools/ 数据集工具
│
└── test/
└── test_images/ 12 张测试热成像图片
├── hituav_dataset/ HIT-UAV 转换后的 YOLO 格式数据集
│ ├── data.yaml YOLO 数据集配置（1 类: Person）
│ ├── images/{train,val}/ 训练/验证图像
│ └── labels/{train,val}/ YOLO 格式标注
│
├── test/ 测试数据（手动标注）
│ ├── labels.json 24 条测试标注
│ ├── test_images/ 18 张测试热成像图片（含无人机实拍 + 仿真截图）
│ └── yolo_dataset/ 手动标注转换的 YOLO 数据集（2 类: Person, Hotspot）
│
├── runs/ YOLO 训练输出
│ └── train/thermal_yolov8n/ YOLOv8n 训练结果
│
├── README.md 本文档
│
├── convert_hituav.py HIT-UAV → YOLO 格式转换
├── convert_labels.py 手动标注 → YOLO 格式转换
├── train_yolo.py YOLOv8 模型训练
├── export_onnx.py PyTorch → ONNX 导出
├── thermal_yolo_detect.py YOLO 检测脚本（支持 classical + yolo 对比）
├── yolo_detector.py YOLO 推理封装
├── auto_tune.py 传统算法超参数自动调优
├── label_tool.py 手动标注工具
├── thermal_colorize.py 热成像伪彩色增强
├── best_params.json 自动调优最优参数
├── yolov8n_thermal.pt 微调后的 YOLOv8 权重
├── yolov8n_thermal.onnx 导出的 ONNX 模型
└── yolov8n.pt / yolov8n.onnx 预训练基线模型
```
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src/热成像识别/auto_tune.py
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"""
热成像检测参数自动优化 — 基于标注数据调优 C++ 检测器参数
通过调用 thermal_detector.exe (--json 模式) 运行检测，
将结果与手动标注的 ground truth 比对，搜索最优参数。
由于 C++ 参数在头文件中是编译时常量，本工具的优化策略是：
1. 运行默认参数检测，计算 baseline F1
2. 生成候选参数值，逐个写入临时配置文件
3. 输出最优参数供用户手动更新到 thermal_types.h
用法:
python auto_tune.py # 使用 labels.json + test/test_images/
python auto_tune.py --label-file my_labels.json # 指定标注文件
python auto_tune.py --image-dir path/to/images # 指定图片目录
python auto_tune.py --metric precision # 优化目标改为精确率
python auto_tune.py --export best_params.json # 导出最优参数
"""
import cv2
import numpy as np
import json
import os
import sys
import glob
import subprocess
import re
import copy
# 标注类型 → 检测器输出类型映射
LABEL_TO_DETECT = {
'Person': 'Person',
'Barrel_Overheat': 'Barrel Overheat',
'Cold_Weapon': 'Cold Weapon',
'Flash_Residue': 'Flash Residue',
'Hotspot': 'Hotspot',
}
def load_labels(label_path):
with open(label_path, 'r', encoding='utf-8') as f:
return json.load(f)
def iou(box_a, box_b):
"""计算两个 bbox 的 IoU。box = [x, y, w, h]"""
ax1, ay1 = box_a[0], box_a[1]
ax2, ay2 = ax1 + box_a[2], ay1 + box_a[3]
bx1, by1 = box_b[0], box_b[1]
bx2, by2 = bx1 + box_b[2], by1 + box_b[3]
ix1 = max(ax1, bx1)
iy1 = max(ay1, by1)
ix2 = min(ax2, bx2)
iy2 = min(ay2, by2)
inter = max(0, ix2 - ix1) * max(0, iy2 - iy1)
area_a = box_a[2] * box_a[3]
area_b = box_b[2] * box_b[3]
union = area_a + area_b - inter
return inter / max(union, 1)
def match_detections(gt_labels, detections, iou_threshold=0.1):
"""匹配 ground truth 和检测结果，返回 TP, FP, FN
使用混合匹配策略：先尝试 IoU，若 IoU < threshold 则用中心点距离匹配
"""
gt_by_type = {}
for lb in gt_labels:
t = LABEL_TO_DETECT.get(lb['type'], lb['type'])
gt_by_type.setdefault(t, []).append(lb)
det_by_type = {}
for d in detections:
t = d.get('type', '')
det_by_type.setdefault(t, []).append(d)
tp, fp, fn = 0, 0, 0
all_types = set(list(gt_by_type.keys()) + list(det_by_type.keys()))
for t in all_types:
gts = gt_by_type.get(t, [])
dets = det_by_type.get(t, [])
matched_gt = set()
matched_det = set()
pairs = []
for gi, g in enumerate(gts):
gx = g['bbox'][0] + g['bbox'][2] / 2
gy = g['bbox'][1] + g['bbox'][3] / 2
for di, d in enumerate(dets):
db = d.get('bbox', {})
dbox = [db.get('x', 0), db.get('y', 0), db.get('w', 0), db.get('h', 0)]
dx = dbox[0] + dbox[2] / 2
dy = dbox[1] + dbox[3] / 2
# IoU 匹配
iou_score = iou(g['bbox'], dbox)
# 中心点距离匹配（当 IoU 低时）
gt_size = max(g['bbox'][2], g['bbox'][3])
det_size = max(dbox[2], dbox[3])
max_size = max(gt_size, det_size, 1)
dist = ((gx - dx) ** 2 + (gy - dy) ** 2) ** 0.5
dist_ratio = dist / max_size
# 综合分数：IoU 优先，中心点距离兜底
if iou_score >= iou_threshold:
score = iou_score
elif dist_ratio < 1.5:
score = iou_threshold * 0.5 # 给一个较低的匹配分数
else:
continue
pairs.append((score, gi, di))
pairs.sort(reverse=True)
for score, gi, di in pairs:
if gi not in matched_gt and di not in matched_det:
matched_gt.add(gi)
matched_det.add(di)
tp += len(matched_gt)
fp += len(dets) - len(matched_det)
fn += len(gts) - len(matched_gt)
return tp, fp, fn
def _get_env_with_opencv():
"""构建包含 OpenCV DLL 路径的环境变量"""
import copy
env = copy.deepcopy(os.environ)
# 常见 OpenCV 安装位置
opencv_paths = [
os.path.join(os.path.expanduser('~'), '.conda', 'envs', 'opencv-build', 'Library', 'bin'),
os.path.join(os.path.expanduser('~'), 'miniconda3', 'envs', 'opencv-build', 'Library', 'bin'),
os.path.join(os.path.expanduser('~'), 'anaconda3', 'envs', 'opencv-build', 'Library', 'bin'),
os.path.join(os.path.expanduser('~'), 'opencv', 'build', 'x64', 'vc16', 'bin'),
os.path.join(os.path.expanduser('~'), 'opencv', 'build', 'x64', 'vc17', 'bin'),
]
extra = ';'.join(p for p in opencv_paths if os.path.isdir(p))
if extra:
env['PATH'] = extra + ';' + env.get('PATH', '')
return env
def run_detector(exe_path, image_path):
"""调用 C++ 检测器，返回 JSON 结果"""
try:
env = _get_env_with_opencv()
result = subprocess.run(
[exe_path, image_path, '--json', '--no-show'],
capture_output=True, text=True, timeout=30,
encoding='utf-8', errors='replace',
env=env,
)
output = result.stdout.strip()
# 找到 JSON 块
start = output.find('{')
end = output.rfind('}') + 1
if start >= 0 and end > start:
json_str = output[start:end]
# 移除 file 字段（中文路径含反斜杠会导致 JSON 解析失败）
import re
json_str = re.sub(r'"file"\s*:\s*"[^"]*"\s*,?', '', json_str)
# 修复尾部逗号
json_str = re.sub(r',\s*}', '}', json_str)
json_str = re.sub(r',\s*]', ']', json_str)
return json.loads(json_str)
except Exception as e:
print(f" 检测器运行失败: {e}")
return None
def evaluate_default(exe_path, image_paths, all_labels):
"""用默认参数评估所有图片"""
total_tp, total_fp, total_fn = 0, 0, 0
for path in image_paths:
fname = os.path.basename(path)
if fname not in all_labels:
continue
result = run_detector(exe_path, path)
if result is None:
continue
detections = result.get('detections', [])
tp, fp, fn = match_detections(all_labels[fname], detections)
total_tp += tp
total_fp += fp
total_fn += fn
precision = total_tp / max(total_tp + total_fp, 1)
recall = total_tp / max(total_tp + total_fn, 1)
f1 = 2 * precision * recall / max(precision + recall, 1e-8)
return {
'f1': f1, 'precision': precision, 'recall': recall,
'tp': total_tp, 'fp': total_fp, 'fn': total_fn,
}
def evaluate_with_filter(exe_path, image_paths, all_labels, filters):
"""用默认参数运行检测器，但对结果做后处理过滤来模拟参数变化
可以模拟的参数：
- confidence_threshold: 过滤低置信度检测
- hotspot_max_count: 限制热点数量
- 按类型过滤面积范围
"""
total_tp, total_fp, total_fn = 0, 0, 0
conf_thresh = filters.get('confidence_threshold', 0.0)
hotspot_max = filters.get('hotspot_max_count', 999)
person_min_area = filters.get('person_min_area', 0)
for path in image_paths:
fname = os.path.basename(path)
if fname not in all_labels:
continue
result = run_detector(exe_path, path)
if result is None:
continue
detections = result.get('detections', [])
# 后处理过滤
filtered = []
hotspot_count = 0
for d in detections:
# 置信度过滤
if d.get('confidence', 1.0) < conf_thresh:
continue
# 面积过滤 (Person)
bbox = d.get('bbox', {})
area = bbox.get('w', 0) * bbox.get('h', 0)
if d.get('type') == 'Person' and area < person_min_area:
continue
# 热点数量限制
if d.get('type') == 'Hotspot':
hotspot_count += 1
if hotspot_count > hotspot_max:
continue
filtered.append(d)
tp, fp, fn = match_detections(all_labels[fname], filtered)
total_tp += tp
total_fp += fp
total_fn += fn
precision = total_tp / max(total_tp + total_fp, 1)
recall = total_tp / max(total_tp + total_fn, 1)
f1 = 2 * precision * recall / max(precision + recall, 1e-8)
return {
'f1': f1, 'precision': precision, 'recall': recall,
'tp': total_tp, 'fp': total_fp, 'fn': total_fn,
}
def search_best_params(exe_path, image_paths, all_labels, metric='f1'):
"""搜索最优参数组合"""
print(f"\n{'='*60}")
print(f"参数优化 — 调用 C++ 检测器")
print(f"目标: {metric}")
print(f"标注图片: {len(all_labels)} 张, 标注数: {sum(len(v) for v in all_labels.values())}")
print(f"检测器: {exe_path}")
print(f"{'='*60}\n")
# 基准评估
print("基准参数评估...")
baseline = evaluate_default(exe_path, image_paths, all_labels)
print(f" 基准: F1={baseline['f1']:.3f} P={baseline['precision']:.3f} R={baseline['recall']:.3f}")
print(f" TP={baseline['tp']} FP={baseline['fp']} FN={baseline['fn']}\n")
# 可搜索参数 (通过后处理模拟)
search_grid = {
'confidence_threshold': [0.10, 0.20, 0.25, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80],
'hotspot_max_count': [3, 5, 8, 10, 15, 20],
'person_min_area': [100, 200, 300, 400, 500, 600, 800],
}
best_params = {}
best_score = baseline[metric]
improved = True
iteration = 0
while improved:
improved = False
iteration += 1
print(f"--- 第 {iteration} 轮搜索 ---")
for param_name, values in search_grid.items():
current_val = best_params.get(param_name, values[len(values)//2])
for val in values:
test_params = dict(best_params)
test_params[param_name] = val
result = evaluate_with_filter(
exe_path, image_paths, all_labels, test_params
)
score = result[metric]
if score > best_score + 0.001:
old_val = best_params.get(param_name, '?')
best_params[param_name] = val
best_score = score
improved = True
print(f" {param_name}: {old_val} -> {val} "
f"{metric}={score:.3f} "
f"(P={result['precision']:.3f} R={result['recall']:.3f} "
f"TP={result['tp']} FP={result['fp']} FN={result['fn']})")
if not improved:
print(" 未找到更优参数，搜索结束")
# 最终结果
print(f"\n{'='*60}")
print(f"优化结果 — {metric}={best_score:.3f}")
print(f"{'='*60}")
if best_params:
print("\n需要修改的参数 (在 cpp/include/thermal_types.h 中):")
param_map = {
'confidence_threshold': ('confidence_threshold', '通用置信度下限'),
'hotspot_max_count': ('hotspot_max_count', '最多保留热点数'),
'person_min_area': ('person_min_area', '人员最小面积'),
}
for k, v in best_params.items():
info = param_map.get(k, (k, ''))
cxx_name = info[0]
desc = info[1]
print(f" {cxx_name} = {v} // {desc}")
if cxx_name == 'confidence_threshold':
print(f" -> 在 thermal_detector.cpp 的 detect() 中修改阈值判断")
elif cxx_name == 'person_min_area':
print(f" -> 在 DetectorConfig::person_min_area 中修改")
elif cxx_name == 'hotspot_max_count':
print(f" -> 在 DetectorConfig::hotspot_max_count 中修改")
else:
print("\n默认参数已经是最优的，无需修改。")
final = evaluate_with_filter(exe_path, image_paths, all_labels, best_params) if best_params else baseline
print(f"\n最终指标: F1={final['f1']:.3f} P={final['precision']:.3f} R={final['recall']:.3f}")
print(f" TP={final['tp']} FP={final['fp']} FN={final['fn']}")
# 对比
if best_params:
print(f"\n对比:")
print(f" 优化前: F1={baseline['f1']:.3f} TP={baseline['tp']} FP={baseline['fp']} FN={baseline['fn']}")
print(f" 优化后: F1={final['f1']:.3f} TP={final['tp']} FP={final['fp']} FN={final['fn']}")
return best_params, final
def export_params(params, metrics, output_path):
data = {
'params': params,
'metrics': metrics,
'note': '由 auto_tune.py 自动优化生成，需手动更新到 cpp/include/thermal_types.h',
}
with open(output_path, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=2)
print(f"\n参数已导出: {output_path}")
def find_exe():
"""查找 thermal_detector.exe"""
script_dir = os.path.dirname(os.path.abspath(__file__))
# 同级 cpp 目录
exe = os.path.join(script_dir, 'cpp', 'thermal_detector.exe')
if os.path.isfile(exe):
return exe
# build 目录
exe = os.path.join(script_dir, 'cpp', 'build', 'Release', 'thermal_detector.exe')
if os.path.isfile(exe):
return exe
return None
def main():
script_dir = os.path.dirname(os.path.abspath(__file__))
args = sys.argv[1:]
label_file = None
image_dir = os.path.join(script_dir, 'test', 'test_images')
metric = 'f1'
export_path = None
exe_path = None
i = 0
while i < len(args):
if args[i] == '--label-file' and i + 1 < len(args):
label_file = args[i + 1]
i += 2
elif args[i] == '--image-dir' and i + 1 < len(args):
image_dir = args[i + 1]
i += 2
elif args[i] == '--metric' and i + 1 < len(args):
metric = args[i + 1]
i += 2
elif args[i] == '--export' and i + 1 < len(args):
export_path = args[i + 1]
i += 2
elif args[i] == '--exe' and i + 1 < len(args):
exe_path = args[i + 1]
i += 2
else:
i += 1
# 检测器
if exe_path is None:
exe_path = find_exe()
if exe_path is None:
print("找不到 thermal_detector.exe")
print("用法: python auto_tune.py --exe path/to/thermal_detector.exe")
return
print(f"检测器: {exe_path}")
# 标注
if label_file is None:
label_file = os.path.join(script_dir, 'test', 'labels.json')
if not os.path.exists(label_file):
label_file_alt = os.path.join(script_dir, 'labels.json')
if os.path.exists(label_file_alt):
label_file = label_file_alt
else:
print(f"标注文件不存在: {label_file}")
print("请先运行 label_tool.py 创建标注:")
print(" python label_tool.py")
return
all_labels = load_labels(label_file)
# 过滤掉 result_ 开头的图片（不对已标注的输出图做评估）
all_labels = {k: v for k, v in all_labels.items() if not os.path.basename(k).startswith('result_')}
print(f"标注文件: {label_file}")
print(f"标注图片数: {len(all_labels)} (已过滤 result_ 图像)")
if not all_labels:
print("标注为空，请先标注图片")
return
# 图片
image_paths = []
for ext in ['*.jpg', '*.jpeg', '*.png', '*.bmp', '*.tif']:
image_paths.extend(glob.glob(os.path.join(image_dir, ext)))
image_paths.sort()
labeled_images = [p for p in image_paths if os.path.basename(p) in all_labels]
print(f"图片目录: {image_dir}")
print(f"有标注的图片: {len(labeled_images)} / {len(image_paths)}")
if not labeled_images:
print("没有匹配到有标注的图片")
return
# 搜索
best_params, metrics = search_best_params(exe_path, labeled_images, all_labels, metric)
# 导出
if export_path:
export_params(best_params, metrics, export_path)
else:
default_export = os.path.join(script_dir, 'best_params.json')
export_params(best_params, metrics, default_export)
if __name__ == '__main__':
main()

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src/热成像识别/best_params.json
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{
"params": {
"confidence_threshold": 0.5
},
"metrics": {
"f1": 0.13793103448275862,
"precision": 0.09900990099009901,
"recall": 0.22727272727272727,
"tp": 10,
"fp": 91,
"fn": 34
},
"note": "由 auto_tune.py 自动优化生成，需手动更新到 cpp/include/thermal_types.h"
}

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src/热成像识别/cpp/CMakeLists.txt
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@ -6,12 +6,54 @@ set(CMAKE_CXX_STANDARD_REQUIRED ON)
find_package(OpenCV REQUIRED COMPONENTS core imgproc imgcodecs highgui videoio)
add_executable(thermal_detector
src/main.cpp
# ── ONNX Runtime (bundled) ──
set(ONNXRuntime_DIR "${CMAKE_CURRENT_SOURCE_DIR}/third_party/onnxruntime"
CACHE PATH "ONNX Runtime root")
set(ONNXRuntime_INCLUDE_DIRS "${ONNXRuntime_DIR}/include")
set(ONNXRuntime_FOUND FALSE)
if(WIN32)
set(ONNXRuntime_DLL "${ONNXRuntime_DIR}/lib/onnxruntime.dll")
set(ONNXRuntime_DEF "${ONNXRuntime_DIR}/lib/onnxruntime.def")
if(EXISTS "${ONNXRuntime_DLL}" AND EXISTS "${ONNXRuntime_DEF}")
set(ONNXRuntime_LIB "${CMAKE_CURRENT_BINARY_DIR}/onnxruntime.lib")
add_custom_command(
OUTPUT ${ONNXRuntime_LIB}
COMMAND lib.exe /def:${ONNXRuntime_DEF} /out:${ONNXRuntime_LIB} /machine:x64
DEPENDS ${ONNXRuntime_DEF}
COMMENT "Generating onnxruntime import library"
)
add_custom_target(onnxruntime_imp_lib DEPENDS ${ONNXRuntime_LIB})
set(ONNXRuntime_LIBS ${ONNXRuntime_LIB})
set(ONNXRuntime_FOUND TRUE)
endif()
else()
find_library(ONNXRuntime_LIBS onnxruntime
PATHS "${ONNXRuntime_DIR}/lib" NO_DEFAULT_PATH)
if(NOT ONNXRuntime_LIBS)
find_library(ONNXRuntime_LIBS onnxruntime)
endif()
if(ONNXRuntime_LIBS)
set(ONNXRuntime_FOUND TRUE)
endif()
endif()
# ── Source files ──
set(CORE_SOURCES
src/thermal_preprocessor.cpp
src/thermal_detector.cpp
)
set(YOLO_SOURCES
src/yolo_detector.cpp
)
add_executable(thermal_detector
src/main.cpp
${CORE_SOURCES}
)
target_include_directories(thermal_detector PRIVATE
${CMAKE_SOURCE_DIR}/include
${OpenCV_INCLUDE_DIRS}
@ -23,6 +65,36 @@ target_link_libraries(thermal_detector PRIVATE
target_compile_options(thermal_detector PRIVATE /utf-8 /EHsc)
# YOLO 模式 (需要 ONNX Runtime)
if(ONNXRuntime_FOUND)
target_sources(thermal_detector PRIVATE ${YOLO_SOURCES})
if(WIN32)
add_dependencies(thermal_detector onnxruntime_imp_lib)
endif()
target_include_directories(thermal_detector PRIVATE
${ONNXRuntime_INCLUDE_DIRS}
)
target_link_libraries(thermal_detector PRIVATE
${ONNXRuntime_LIBS}
)
target_compile_definitions(thermal_detector PRIVATE HAS_YOLO=1)
# 复制 DLL 到输出目录
if(WIN32 AND EXISTS "${ONNXRuntime_DLL}")
add_custom_command(TARGET thermal_detector POST_BUILD
COMMAND ${CMAKE_COMMAND} -E copy_if_different
"${ONNXRuntime_DLL}" "$<TARGET_FILE_DIR:thermal_detector>"
COMMENT "Copying onnxruntime.dll to output"
)
endif()
message(STATUS "YOLO mode: ENABLED (ONNX Runtime found)")
else()
message(STATUS "YOLO mode: DISABLED (ONNX Runtime not found at ${ONNXRuntime_DIR})")
endif()
# Windows: 输出到项目根目录方便测试
set_target_properties(thermal_detector PROPERTIES
RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}"

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CMAKE_PROJECT_COMPAT_VERSION:STATIC=
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CMAKE_VERBOSE_MAKEFILE:BOOL=FALSE
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exit /b %1
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^D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\THERMAL_DETECTOR.DIR\RELEASE\MAIN.OBJ|D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\THERMAL_DETECTOR.DIR\RELEASE\THERMAL_DETECTOR.OBJ|D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\THERMAL_DETECTOR.DIR\RELEASE\THERMAL_PREPROCESSOR.OBJ|D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\THERMAL_DETECTOR.DIR\RELEASE\YOLO_DETECTOR.OBJ

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@ -11,9 +11,15 @@ Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "ZERO_CHECK", "ZERO_CHECK.vc
ProjectSection(ProjectDependencies) = postProject
EndProjectSection
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ProjectSection(ProjectDependencies) = postProject
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EndProjectSection
EndProject
Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "thermal_detector", "thermal_detector.vcxproj", "{DF803312-3CFA-3263-AF8D-74A66844CD3A}"
ProjectSection(ProjectDependencies) = postProject
{D587A172-BD31-3460-AB25-5A3362814065} = {D587A172-BD31-3460-AB25-5A3362814065}
{84DC21BA-E13C-3E77-9EB5-4AD550232221} = {84DC21BA-E13C-3E77-9EB5-4AD550232221}
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@ -36,6 +42,14 @@ Global
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^D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\CMAKELISTS.TXT
setlocal
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if %errorlevel% neq 0 goto :cmEnd
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endlocal & call :cmErrorLevel %errorlevel% & goto :cmDone

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src/热成像识别/cpp/build/x64/Release/ZERO_CHECK/ZERO_CHECK.tlog/CustomBuild.command.1.tlog
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@ -1,6 +1,6 @@
^D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\CMAKEFILES\56E5ECA5E8F7B67EB6DC57ED2871D760\GENERATE.STAMP.RULE
setlocal
C:\Users\29578\.conda\envs\opencv-build\Library\bin\cmake.exe -SD:/29578/Documents/Study/computer/S_E/软件体系结构与设计/软件开发/热成像识别/cpp -BD:/29578/Documents/Study/computer/S_E/软件体系结构与设计/软件开发/热成像识别/cpp/build --check-stamp-list CMakeFiles/generate.stamp.list --vs-solution-file D:/29578/Documents/Study/computer/S_E/软件体系结构与设计/软件开发/热成像识别/cpp/build/thermal_detector.sln
D:\cmake\bin\cmake.exe -SD:/29578/Documents/Study/computer/S_E/软件体系结构与设计/软件开发/热成像识别/cpp -BD:/29578/Documents/Study/computer/S_E/软件体系结构与设计/软件开发/热成像识别/cpp/build --check-stamp-list CMakeFiles/generate.stamp.list --vs-solution-file D:/29578/Documents/Study/computer/S_E/软件体系结构与设计/软件开发/热成像识别/cpp/build/thermal_detector.sln
if %errorlevel% neq 0 goto :cmEnd
:cmEnd
endlocal & call :cmErrorLevel %errorlevel% & goto :cmDone

113
src/热成像识别/cpp/build/x64/Release/ZERO_CHECK/ZERO_CHECK.tlog/CustomBuild.read.1.tlog
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D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\CMAKEFILES\4.3.2\CMAKERCCOMPILER.CMAKE
D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\CMAKEFILES\4.3.2\CMAKESYSTEM.CMAKE
D:\CMAKE\SHARE\CMAKE-4.3\MODULES\CMAKECXXINFORMATION.CMAKE
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src/热成像识别/cpp/build/x64/Release/onnxruntime_imp_lib/onnxrunt.84DC21BA.tlog/CustomBuild.command.1.tlog
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^D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\CMAKEFILES\E9BD87DD49C8A00A589935B380B13B97\ONNXRUNTIME.LIB.RULE
setlocal
lib.exe /def:D:/29578/Documents/Study/computer/S_E/软件体系结构与设计/软件开发/热成像识别/cpp/third_party/onnxruntime/lib/onnxruntime.def /out:D:/29578/Documents/Study/computer/S_E/软件体系结构与设计/软件开发/热成像识别/cpp/build/onnxruntime.lib /machine:x64
if %errorlevel% neq 0 goto :cmEnd
:cmEnd
endlocal & call :cmErrorLevel %errorlevel% & goto :cmDone
:cmErrorLevel
exit /b %1
:cmDone
if %errorlevel% neq 0 goto :VCEnd
^D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\CMAKEFILES\56E5ECA5E8F7B67EB6DC57ED2871D760\ONNXRUNTIME_IMP_LIB.RULE
setlocal
:cmEnd
endlocal & call :cmErrorLevel %errorlevel% & goto :cmDone
:cmErrorLevel
exit /b %1
:cmDone
if %errorlevel% neq 0 goto :VCEnd
^D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\CMAKELISTS.TXT
setlocal
D:\cmake\bin\cmake.exe -SD:/29578/Documents/Study/computer/S_E/软件体系结构与设计/软件开发/热成像识别/cpp -BD:/29578/Documents/Study/computer/S_E/软件体系结构与设计/软件开发/热成像识别/cpp/build --check-stamp-file D:/29578/Documents/Study/computer/S_E/软件体系结构与设计/软件开发/热成像识别/cpp/build/CMakeFiles/generate.stamp
if %errorlevel% neq 0 goto :cmEnd
:cmEnd
endlocal & call :cmErrorLevel %errorlevel% & goto :cmDone
:cmErrorLevel
exit /b %1
:cmDone
if %errorlevel% neq 0 goto :VCEnd

37
src/热成像识别/cpp/build/x64/Release/onnxruntime_imp_lib/onnxrunt.84DC21BA.tlog/CustomBuild.read.1.tlog
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^D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\CMAKEFILES\E9BD87DD49C8A00A589935B380B13B97\ONNXRUNTIME.LIB.RULE
D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\THIRD_PARTY\ONNXRUNTIME\LIB\ONNXRUNTIME.DEF
^D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\CMAKEFILES\56E5ECA5E8F7B67EB6DC57ED2871D760\ONNXRUNTIME_IMP_LIB.RULE
D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\ONNXRUNTIME.LIB
^D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\CMAKELISTS.TXT
C:\USERS\29578\.CONDA\ENVS\OPENCV-BUILD\LIBRARY\CMAKE\OPENCVCONFIG-VERSION.CMAKE
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D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\CMAKEFILES\4.3.2\CMAKERCCOMPILER.CMAKE
D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\CMAKEFILES\4.3.2\CMAKESYSTEM.CMAKE
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D:\CMAKE\SHARE\CMAKE-4.3\MODULES\LINKER\MSVC-CXX.CMAKE
D:\CMAKE\SHARE\CMAKE-4.3\MODULES\LINKER\MSVC.CMAKE
D:\CMAKE\SHARE\CMAKE-4.3\MODULES\PLATFORM\LINKER\WINDOWS-MSVC-CXX.CMAKE
D:\CMAKE\SHARE\CMAKE-4.3\MODULES\PLATFORM\LINKER\WINDOWS-MSVC.CMAKE
D:\CMAKE\SHARE\CMAKE-4.3\MODULES\PLATFORM\WINDOWS-INITIALIZE.CMAKE
D:\CMAKE\SHARE\CMAKE-4.3\MODULES\PLATFORM\WINDOWS-MSVC-CXX.CMAKE
D:\CMAKE\SHARE\CMAKE-4.3\MODULES\PLATFORM\WINDOWS-MSVC.CMAKE
D:\CMAKE\SHARE\CMAKE-4.3\MODULES\PLATFORM\WINDOWS.CMAKE
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6
src/热成像识别/cpp/build/x64/Release/onnxruntime_imp_lib/onnxrunt.84DC21BA.tlog/CustomBuild.write.1.tlog
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@ -0,0 +1,6 @@
^D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\CMAKEFILES\E9BD87DD49C8A00A589935B380B13B97\ONNXRUNTIME.LIB.RULE
D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\ONNXRUNTIME.LIB
^D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\CMAKEFILES\56E5ECA5E8F7B67EB6DC57ED2871D760\ONNXRUNTIME_IMP_LIB.RULE
D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\CMAKEFILES\ONNXRUNTIME_IMP_LIB
^D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\CMAKELISTS.TXT
D:\29578\DOCUMENTS\STUDY\COMPUTER\S_E\软件体系结构与设计\软件开发\热成像识别\CPP\BUILD\CMAKEFILES\GENERATE.STAMP

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src/热成像识别/cpp/build/x64/Release/onnxruntime_imp_lib/onnxrunt.84DC21BA.tlog/onnxruntime_imp_lib.lastbuildstate
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PlatformToolSet=v143:VCToolArchitecture=Native64Bit:VCToolsVersion=14.43.34808:TargetPlatformVersion=10.0.22621.0:
Release|x64|D:\29578\Documents\Study\computer\S_E\软件体系结构与设计\软件开发\热成像识别\cpp\build\|

14
src/热成像识别/cpp/build/x64/Release/onnxruntime_imp_lib/onnxruntime_imp_lib.recipe
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<?xml version="1.0" encoding="utf-8"?>
<Project>
<ProjectOutputs>
<ProjectOutput>
<FullPath>D:\29578\Documents\Study\computer\S_E\软件体系结构与设计\软件开发\热成像识别\cpp\build\x64\Release\ZERO_CHECK</FullPath>
</ProjectOutput>
<ProjectOutput>
<FullPath>D:\29578\Documents\Study\computer\S_E\软件体系结构与设计\软件开发\热成像识别\cpp\build\x64\Release\onnxruntime_imp_lib</FullPath>
</ProjectOutput>
</ProjectOutputs>
<ContentFiles />
<SatelliteDlls />
<NonRecipeFileRefs />
</Project>

6
src/热成像识别/cpp/include/thermal_detector.h
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@ -13,12 +13,12 @@ public:
// 可视化
static cv::Mat draw_detections(const cv::Mat& image, const Assessment& assessment);
private:
DetectorConfig cfg_;
// 环境温度估计
static double estimate_ambient(const cv::Mat& temp_map);
private:
DetectorConfig cfg_;
// Pass 1: 人员检测 (体温段)
std::vector<DetectionResult> detect_persons(const cv::Mat& bands,
const cv::Mat& temp_map,

34
src/热成像识别/cpp/include/yolo_detector.h
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#pragma once
#include "thermal_types.h"
#include <opencv2/core.hpp>
#include <memory>
#include <string>
#include <vector>
struct YoloConfig {
std::string model_path;
int input_size = 640;
float conf_threshold = 0.25f;
float nms_threshold = 0.45f;
int max_detections = 100;
int intra_threads = 1;
};
class YoloDetector {
public:
explicit YoloDetector(const YoloConfig& config);
~YoloDetector();
YoloDetector(const YoloDetector&) = delete;
YoloDetector& operator=(const YoloDetector&) = delete;
YoloDetector(YoloDetector&&) noexcept;
YoloDetector& operator=(YoloDetector&&) noexcept;
std::vector<DetectionResult> detect(const cv::Mat& image);
bool is_loaded() const noexcept;
private:
struct Impl;
std::unique_ptr<Impl> impl_;
};

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src/热成像识别/cpp/src/main.cpp
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@ -1,5 +1,10 @@
#include "thermal_preprocessor.h"
#include "thermal_detector.h"
#ifdef HAS_YOLO
#include "yolo_detector.h"
#endif
#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <iostream>
@ -10,12 +15,14 @@ static void print_usage(const char* prog) {
std::cout << "Usage: " << prog << " <image_path> [options]\n"
<< " " << prog << " <directory> [options]\n\n"
<< "Options:\n"
<< " --no-show Don't display results (save only)\n"
<< " --json Output JSON format result\n"
<< " --help Show this help\n\n"
<< " --mode <mode> Detection mode: classical (default), yolo\n"
<< " --model <path> YOLO ONNX model path\n"
<< " --no-show Don't display results (save only)\n"
<< " --json Output JSON format result\n"
<< " --help Show this help\n\n"
<< "Examples:\n"
<< " thermal_detector test_images/photo.jpg\n"
<< " thermal_detector test_images/ --no-show\n";
<< " thermal_detector test_images/photo.jpg --mode yolo --model models/yolov8n_thermal.onnx\n";
}
static std::string threat_type_str(ThreatType t) {
@ -83,28 +90,95 @@ static std::string get_output_path(const std::string& input_path) {
return dir + "result_" + fname;
}
static bool has_image_ext(const std::string& path) {
std::string ext;
auto pos = path.rfind('.');
if (pos != std::string::npos) ext = path.substr(pos);
return ext == ".jpg" || ext == ".jpeg" || ext == ".png" || ext == ".bmp" || ext == ".tif";
#ifdef HAS_YOLO
static Assessment detect_with_yolo(
const cv::Mat& img,
YoloDetector& yolo,
ThermalPreprocessor& preprocessor)
{
auto yolo_dets = yolo.detect(img);
// 运行预处理获取温度信息
auto preprocessed = preprocessor.preprocess(img);
double ambient = ThermalDetector::estimate_ambient(preprocessed.temperature_map);
// 用温度图补充 YOLO 检测结果
for (auto& det : yolo_dets) {
if (!preprocessed.temperature_map.empty()) {
cv::Rect safe = det.bbox & cv::Rect(0, 0,
preprocessed.temperature_map.cols,
preprocessed.temperature_map.rows);
if (!safe.empty()) {
cv::Mat roi = preprocessed.temperature_map(safe);
double min_t, max_t;
cv::minMaxLoc(roi, &min_t, &max_t);
det.max_temp = max_t;
det.mean_temp = cv::mean(roi)[0];
}
}
det.area = det.bbox.width * det.bbox.height;
det.aspect_ratio = (double)std::max(det.bbox.width, det.bbox.height)
/ std::max(std::min(det.bbox.width, det.bbox.height), 1);
}
Assessment assessment;
assessment.detections = yolo_dets;
assessment.ambient_temp = ambient;
for (const auto& d : yolo_dets) {
switch (d.type) {
case ThreatType::PERSON: assessment.persons.push_back(d); break;
case ThreatType::ANOMALY_HOTSPOT: assessment.hotspots.push_back(d); break;
default: break;
}
}
return assessment;
}
#endif
int main(int argc, char* argv[]) {
if (argc < 2) { print_usage(argv[0]); return 1; }
std::string source = argv[1];
bool show = true, json_out = false;
std::string mode = "classical";
std::string model_path = "models/yolov8n_thermal.onnx";
for (int i = 2; i < argc; i++) {
if (std::strcmp(argv[i], "--no-show") == 0) show = false;
else if (std::strcmp(argv[i], "--json") == 0) json_out = true;
else if (std::strcmp(argv[i], "--help") == 0) { print_usage(argv[0]); return 0; }
else if (std::strcmp(argv[i], "--mode") == 0 && i + 1 < argc) {
mode = argv[++i];
}
else if (std::strcmp(argv[i], "--model") == 0 && i + 1 < argc) {
model_path = argv[++i];
}
}
ThermalPreprocessor preprocessor;
ThermalDetector detector;
#ifdef HAS_YOLO
std::unique_ptr<YoloDetector> yolo_detector;
if (mode == "yolo") {
YoloConfig yolo_cfg;
yolo_cfg.model_path = model_path;
yolo_detector = std::make_unique<YoloDetector>(yolo_cfg);
if (!yolo_detector->is_loaded()) {
std::cerr << "[WARN] Failed to load YOLO model, falling back to classical\n";
mode = "classical";
} else {
std::cerr << "[INFO] YOLO mode: " << model_path << "\n";
}
}
#else
if (mode == "yolo") {
std::cerr << "[WARN] YOLO mode not available (built without ONNX Runtime)\n";
mode = "classical";
}
#endif
auto process_one = [&](const std::string& path) {
cv::Mat img = cv::imread(path, cv::IMREAD_COLOR);
if (img.empty())
@ -115,18 +189,27 @@ int main(int argc, char* argv[]) {
}
auto t0 = std::chrono::high_resolution_clock::now();
auto preprocessed = preprocessor.preprocess(img);
auto assessment = detector.detect(preprocessed);
Assessment assessment;
if (mode == "yolo") {
#ifdef HAS_YOLO
assessment = detect_with_yolo(img, *yolo_detector, preprocessor);
#endif
} else {
auto preprocessed = preprocessor.preprocess(img);
assessment = detector.detect(preprocessed);
}
auto t1 = std::chrono::high_resolution_clock::now();
double ms = std::chrono::duration<double, std::milli>(t1 - t0).count();
if (json_out) {
print_json(assessment, path);
} else {
std::cout << "[" << mode << " mode]\n";
print_assessment(assessment, path, ms);
}
// 保存标注图
cv::Mat vis = ThermalDetector::draw_detections(img, assessment);
std::string out_path = get_output_path(path);
cv::imwrite(out_path, vis);

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#include "yolo_detector.h"
#include <onnxruntime_cxx_api.h>
#include <opencv2/imgproc.hpp>
#include <fstream>
#include <algorithm>
#include <iostream>
#include <numeric>
#ifdef _WIN32
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#endif
struct YoloDetector::Impl {
YoloConfig config;
Ort::Env env;
std::unique_ptr<Ort::Session> session;
std::unique_ptr<Ort::SessionOptions> session_options;
bool loaded = false;
int num_classes = 0;
int input_size = 640;
explicit Impl(const YoloConfig& cfg)
: config(cfg), env(ORT_LOGGING_LEVEL_WARNING, "thermal-yolo"),
input_size(cfg.input_size) {
session_options = std::make_unique<Ort::SessionOptions>();
session_options->SetIntraOpNumThreads(config.intra_threads);
session_options->SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_EXTENDED);
}
bool load_model(const std::string& path) {
std::ifstream check(path, std::ios::binary);
if (!check) {
std::cerr << "[YOLO] Model file not found: " << path << std::endl;
return false;
}
check.close();
try {
#ifdef _WIN32
int wlen = MultiByteToWideChar(CP_UTF8, 0, path.c_str(), -1, nullptr, 0);
std::wstring wpath;
if (wlen > 0) {
wpath.resize(static_cast<std::size_t>(wlen));
MultiByteToWideChar(CP_UTF8, 0, path.c_str(), -1, &wpath[0], wlen);
}
session = std::make_unique<Ort::Session>(env, wpath.c_str(), *session_options);
#else
session = std::make_unique<Ort::Session>(env, path.c_str(), *session_options);
#endif
} catch (const Ort::Exception& e) {
std::cerr << "[YOLO] ONNX Runtime load failed: " << e.what() << std::endl;
return false;
}
// 从输出形状推断类别数
auto output_count = session->GetOutputCount();
if (output_count > 0) {
auto type_info = session->GetOutputTypeInfo(0);
auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
auto shape = tensor_info.GetShape();
// YOLOv8 输出: [1, 4+nc, 8400]
if (shape.size() == 3 && shape[1] > 4) {
num_classes = static_cast<int>(shape[1]) - 4;
}
}
loaded = session != nullptr;
return loaded;
}
std::vector<float> preprocess(const cv::Mat& image, int& orig_w, int& orig_h) {
cv::Mat bgr;
if (image.channels() == 1)
cv::cvtColor(image, bgr, cv::COLOR_GRAY2BGR);
else
bgr = image;
orig_w = bgr.cols;
orig_h = bgr.rows;
cv::Mat resized;
cv::resize(bgr, resized, cv::Size(input_size, input_size));
cv::Mat rgb;
cv::cvtColor(resized, rgb, cv::COLOR_BGR2RGB);
cv::Mat f32;
rgb.convertTo(f32, CV_32F, 1.0 / 255.0);
std::vector<cv::Mat> channels(3);
cv::split(f32, channels);
int channel_size = input_size * input_size;
std::vector<float> input_data(3 * channel_size);
for (int c = 0; c < 3; c++) {
std::memcpy(input_data.data() + c * channel_size,
channels[c].data, channel_size * sizeof(float));
}
return input_data;
}
struct RawDet {
float cx, cy, w, h;
int class_id;
float score;
};
static double compute_iou(const cv::Rect& a, const cv::Rect& b) {
int ix1 = std::max(a.x, b.x);
int iy1 = std::max(a.y, b.y);
int ix2 = std::min(a.x + a.width, b.x + b.width);
int iy2 = std::min(a.y + a.height, b.y + b.height);
int inter = std::max(0, ix2 - ix1) * std::max(0, iy2 - iy1);
int area_a = a.width * a.height;
int area_b = b.width * b.height;
int union_area = area_a + area_b - inter;
return static_cast<double>(inter) / std::max(union_area, 1);
}
ThreatType map_class(int class_id) {
if (num_classes == 80) {
return (class_id == 0) ? ThreatType::PERSON : ThreatType::PERSON;
}
if (class_id == 0) return ThreatType::PERSON;
if (class_id == 1) return ThreatType::ANOMALY_HOTSPOT;
return ThreatType::PERSON;
}
std::vector<DetectionResult> postprocess(
std::vector<Ort::Value>& outputs,
int orig_w, int orig_h)
{
if (outputs.empty()) return {};
float* data = outputs[0].GetTensorMutableData<float>();
auto shape = outputs[0].GetTensorTypeAndShapeInfo().GetShape();
int nc = static_cast<int>(shape[1]) - 4;
int num_preds = static_cast<int>(shape[2]);
std::vector<RawDet> raw;
for (int i = 0; i < num_preds; i++) {
float best_score = 0;
int best_class = 0;
for (int c = 0; c < nc; c++) {
float s = data[(4 + c) * num_preds + i];
if (s > best_score) { best_score = s; best_class = c; }
}
if (best_score < config.conf_threshold) continue;
raw.push_back({
data[0 * num_preds + i],
data[1 * num_preds + i],
data[2 * num_preds + i],
data[3 * num_preds + i],
best_class, best_score
});
}
// NMS
std::sort(raw.begin(), raw.end(),
[](const RawDet& a, const RawDet& b) { return a.score > b.score; });
float scale_x = static_cast<float>(orig_w) / input_size;
float scale_y = static_cast<float>(orig_h) / input_size;
// 转换为 cv::Rect 并做 NMS
std::vector<std::pair<RawDet, cv::Rect>> boxed;
for (const auto& d : raw) {
int x = static_cast<int>((d.cx - d.w / 2) * scale_x);
int y = static_cast<int>((d.cy - d.h / 2) * scale_y);
int w = static_cast<int>(d.w * scale_x);
int h = static_cast<int>(d.h * scale_y);
boxed.push_back({d, cv::Rect(x, y, w, h)});
}
std::vector<int> keep;
for (int i = 0; i < static_cast<int>(boxed.size()); i++) {
bool suppressed = false;
for (int j : keep) {
if (compute_iou(boxed[i].second, boxed[j].second) > config.nms_threshold) {
suppressed = true;
break;
}
}
if (!suppressed) keep.push_back(i);
if (static_cast<int>(keep.size()) >= config.max_detections) break;
}
std::vector<DetectionResult> results;
for (int idx : keep) {
const auto& det = boxed[idx].first;
const auto& box = boxed[idx].second;
results.push_back({
map_class(det.class_id),
static_cast<double>(det.score),
box,
cv::Point2d(box.x + box.width / 2.0, box.y + box.height / 2.0),
0, 0,
box.width * box.height,
static_cast<double>(std::max(box.width, box.height)) /
std::max(std::min(box.width, box.height), 1)
});
}
return results;
}
std::vector<DetectionResult> detect(const cv::Mat& image) {
if (!loaded) return {};
int orig_w, orig_h;
auto input_data = preprocess(image, orig_w, orig_h);
auto memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
std::vector<int64_t> input_shape = {1, 3, input_size, input_size};
Ort::Value input_tensor = Ort::Value::CreateTensor<float>(
memory_info, input_data.data(), input_data.size(),
input_shape.data(), input_shape.size());
// 动态获取 I/O 名称
auto allocator = Ort::AllocatorWithDefaultOptions();
auto input_name = session->GetInputNameAllocated(0, allocator);
auto output_name = session->GetOutputNameAllocated(0, allocator);
const char* input_names[] = {input_name.get()};
const char* output_names[] = {output_name.get()};
auto outputs = session->Run(
Ort::RunOptions{nullptr},
input_names, &input_tensor, 1,
output_names, 1);
return postprocess(outputs, orig_w, orig_h);
}
};
YoloDetector::YoloDetector(const YoloConfig& config)
: impl_(std::make_unique<Impl>(config)) {
if (!config.model_path.empty()) {
impl_->load_model(config.model_path);
}
}
YoloDetector::~YoloDetector() = default;
YoloDetector::YoloDetector(YoloDetector&&) noexcept = default;
YoloDetector& YoloDetector::operator=(YoloDetector&&) noexcept = default;
std::vector<DetectionResult> YoloDetector::detect(const cv::Mat& image) {
return impl_->detect(image);
}
bool YoloDetector::is_loaded() const noexcept {
return impl_->loaded;
}

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// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
#pragma once
#include <stdint.h>
#include <cmath>
#include <cstring>
#include <limits>
namespace onnxruntime_float16 {
namespace detail {
enum class endian {
#if defined(_WIN32)
little = 0,
big = 1,
native = little,
#elif defined(__GNUC__) || defined(__clang__)
little = __ORDER_LITTLE_ENDIAN__,
big = __ORDER_BIG_ENDIAN__,
native = __BYTE_ORDER__,
#else
#error onnxruntime_float16::detail::endian is not implemented in this environment.
#endif
};
static_assert(
endian::native == endian::little || endian::native == endian::big,
"Only little-endian or big-endian native byte orders are supported.");
} // namespace detail
/// <summary>
/// Shared implementation between public and internal classes. CRTP pattern.
/// </summary>
template <class Derived>
struct Float16Impl {
protected:
/// <summary>
/// Converts from float to uint16_t float16 representation
/// </summary>
/// <param name="v"></param>
/// <returns></returns>
constexpr static uint16_t ToUint16Impl(float v) noexcept;
/// <summary>
/// Converts float16 to float
/// </summary>
/// <returns>float representation of float16 value</returns>
float ToFloatImpl() const noexcept;
/// <summary>
/// Creates an instance that represents absolute value.
/// </summary>
/// <returns>Absolute value</returns>
uint16_t AbsImpl() const noexcept {
return static_cast<uint16_t>(val & ~kSignMask);
}
/// <summary>
/// Creates a new instance with the sign flipped.
/// </summary>
/// <returns>Flipped sign instance</returns>
uint16_t NegateImpl() const noexcept {
return IsNaN() ? val : static_cast<uint16_t>(val ^ kSignMask);
}
public:
// uint16_t special values
static constexpr uint16_t kSignMask = 0x8000U;
static constexpr uint16_t kBiasedExponentMask = 0x7C00U;
static constexpr uint16_t kPositiveInfinityBits = 0x7C00U;
static constexpr uint16_t kNegativeInfinityBits = 0xFC00U;
static constexpr uint16_t kPositiveQNaNBits = 0x7E00U;
static constexpr uint16_t kNegativeQNaNBits = 0xFE00U;
static constexpr uint16_t kMaxValueBits = 0x7BFFU; // Largest normal number
static constexpr uint16_t kOneBits = 0x3C00U;
static constexpr uint16_t kMinusOneBits = 0xBC00U;
uint16_t val{0};
Float16Impl() = default;
/// <summary>
/// Checks if the value is negative
/// </summary>
/// <returns>true if negative</returns>
bool IsNegative() const noexcept {
return static_cast<int16_t>(val) < 0;
}
/// <summary>
/// Tests if the value is NaN
/// </summary>
/// <returns>true if NaN</returns>
bool IsNaN() const noexcept {
return AbsImpl() > kPositiveInfinityBits;
}
/// <summary>
/// Tests if the value is finite
/// </summary>
/// <returns>true if finite</returns>
bool IsFinite() const noexcept {
return AbsImpl() < kPositiveInfinityBits;
}
/// <summary>
/// Tests if the value represents positive infinity.
/// </summary>
/// <returns>true if positive infinity</returns>
bool IsPositiveInfinity() const noexcept {
return val == kPositiveInfinityBits;
}
/// <summary>
/// Tests if the value represents negative infinity
/// </summary>
/// <returns>true if negative infinity</returns>
bool IsNegativeInfinity() const noexcept {
return val == kNegativeInfinityBits;
}
/// <summary>
/// Tests if the value is either positive or negative infinity.
/// </summary>
/// <returns>True if absolute value is infinity</returns>
bool IsInfinity() const noexcept {
return AbsImpl() == kPositiveInfinityBits;
}
/// <summary>
/// Tests if the value is NaN or zero. Useful for comparisons.
/// </summary>
/// <returns>True if NaN or zero.</returns>
bool IsNaNOrZero() const noexcept {
auto abs = AbsImpl();
return (abs == 0 || abs > kPositiveInfinityBits);
}
/// <summary>
/// Tests if the value is normal (not zero, subnormal, infinite, or NaN).
/// </summary>
/// <returns>True if so</returns>
bool IsNormal() const noexcept {
auto abs = AbsImpl();
return (abs < kPositiveInfinityBits) // is finite
&& (abs != 0) // is not zero
&& ((abs & kBiasedExponentMask) != 0); // is not subnormal (has a non-zero exponent)
}
/// <summary>
/// Tests if the value is subnormal (denormal).
/// </summary>
/// <returns>True if so</returns>
bool IsSubnormal() const noexcept {
auto abs = AbsImpl();
return (abs < kPositiveInfinityBits) // is finite
&& (abs != 0) // is not zero
&& ((abs & kBiasedExponentMask) == 0); // is subnormal (has a zero exponent)
}
/// <summary>
/// Creates an instance that represents absolute value.
/// </summary>
/// <returns>Absolute value</returns>
Derived Abs() const noexcept { return Derived::FromBits(AbsImpl()); }
/// <summary>
/// Creates a new instance with the sign flipped.
/// </summary>
/// <returns>Flipped sign instance</returns>
Derived Negate() const noexcept { return Derived::FromBits(NegateImpl()); }
/// <summary>
/// IEEE defines that positive and negative zero are equal, this gives us a quick equality check
/// for two values by or'ing the private bits together and stripping the sign. They are both zero,
/// and therefore equivalent, if the resulting value is still zero.
/// </summary>
/// <param name="lhs">first value</param>
/// <param name="rhs">second value</param>
/// <returns>True if both arguments represent zero</returns>
static bool AreZero(const Float16Impl& lhs, const Float16Impl& rhs) noexcept {
return static_cast<uint16_t>((lhs.val | rhs.val) & ~kSignMask) == 0;
}
bool operator==(const Float16Impl& rhs) const noexcept {
if (IsNaN() || rhs.IsNaN()) {
// IEEE defines that NaN is not equal to anything, including itself.
return false;
}
return val == rhs.val;
}
bool operator!=(const Float16Impl& rhs) const noexcept { return !(*this == rhs); }
bool operator<(const Float16Impl& rhs) const noexcept {
if (IsNaN() || rhs.IsNaN()) {
// IEEE defines that NaN is unordered with respect to everything, including itself.
return false;
}
const bool left_is_negative = IsNegative();
if (left_is_negative != rhs.IsNegative()) {
// When the signs of left and right differ, we know that left is less than right if it is
// the negative value. The exception to this is if both values are zero, in which case IEEE
// says they should be equal, even if the signs differ.
return left_is_negative && !AreZero(*this, rhs);
}
return (val != rhs.val) && ((val < rhs.val) ^ left_is_negative);
}
};
// The following Float16_t conversions are based on the code from
// Eigen library.
// The conversion routines are Copyright (c) Fabian Giesen, 2016.
// The original license follows:
//
// Copyright (c) Fabian Giesen, 2016
// All rights reserved.
// Redistribution and use in source and binary forms, with or without
// modification, are permitted.
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
namespace detail {
union float32_bits {
unsigned int u;
float f;
};
} // namespace detail
template <class Derived>
inline constexpr uint16_t Float16Impl<Derived>::ToUint16Impl(float v) noexcept {
detail::float32_bits f{};
f.f = v;
constexpr detail::float32_bits f32infty = {255 << 23};
constexpr detail::float32_bits f16max = {(127 + 16) << 23};
constexpr detail::float32_bits denorm_magic = {((127 - 15) + (23 - 10) + 1) << 23};
constexpr unsigned int sign_mask = 0x80000000u;
uint16_t val = static_cast<uint16_t>(0x0u);
unsigned int sign = f.u & sign_mask;
f.u ^= sign;
// NOTE all the integer compares in this function can be safely
// compiled into signed compares since all operands are below
// 0x80000000. Important if you want fast straight SSE2 code
// (since there's no unsigned PCMPGTD).
if (f.u >= f16max.u) { // result is Inf or NaN (all exponent bits set)
val = (f.u > f32infty.u) ? 0x7e00 : 0x7c00; // NaN->qNaN and Inf->Inf
} else { // (De)normalized number or zero
if (f.u < (113 << 23)) { // resulting FP16 is subnormal or zero
// use a magic value to align our 10 mantissa bits at the bottom of
// the float. as long as FP addition is round-to-nearest-even this
// just works.
f.f += denorm_magic.f;
// and one integer subtract of the bias later, we have our final float!
val = static_cast<uint16_t>(f.u - denorm_magic.u);
} else {
unsigned int mant_odd = (f.u >> 13) & 1; // resulting mantissa is odd
// update exponent, rounding bias part 1
// Equivalent to `f.u += ((unsigned int)(15 - 127) << 23) + 0xfff`, but
// without arithmetic overflow.
f.u += 0xc8000fffU;
// rounding bias part 2
f.u += mant_odd;
// take the bits!
val = static_cast<uint16_t>(f.u >> 13);
}
}
val |= static_cast<uint16_t>(sign >> 16);
return val;
}
template <class Derived>
inline float Float16Impl<Derived>::ToFloatImpl() const noexcept {
constexpr detail::float32_bits magic = {113 << 23};
constexpr unsigned int shifted_exp = 0x7c00 << 13; // exponent mask after shift
detail::float32_bits o{};
o.u = (val & 0x7fff) << 13; // exponent/mantissa bits
unsigned int exp = shifted_exp & o.u; // just the exponent
o.u += (127 - 15) << 23; // exponent adjust
// handle exponent special cases
if (exp == shifted_exp) { // Inf/NaN?
o.u += (128 - 16) << 23; // extra exp adjust
} else if (exp == 0) { // Zero/Denormal?
o.u += 1 << 23; // extra exp adjust
o.f -= magic.f; // re-normalize
}
// Attempt to workaround the Internal Compiler Error on ARM64
// for bitwise | operator, including std::bitset
#if (defined _MSC_VER) && (defined _M_ARM || defined _M_ARM64 || defined _M_ARM64EC)
if (IsNegative()) {
return -o.f;
}
#else
// original code:
o.u |= (val & 0x8000U) << 16U; // sign bit
#endif
return o.f;
}
/// Shared implementation between public and internal classes. CRTP pattern.
template <class Derived>
struct BFloat16Impl {
protected:
/// <summary>
/// Converts from float to uint16_t float16 representation
/// </summary>
/// <param name="v"></param>
/// <returns></returns>
static uint16_t ToUint16Impl(float v) noexcept;
/// <summary>
/// Converts bfloat16 to float
/// </summary>
/// <returns>float representation of bfloat16 value</returns>
float ToFloatImpl() const noexcept;
/// <summary>
/// Creates an instance that represents absolute value.
/// </summary>
/// <returns>Absolute value</returns>
uint16_t AbsImpl() const noexcept {
return static_cast<uint16_t>(val & ~kSignMask);
}
/// <summary>
/// Creates a new instance with the sign flipped.
/// </summary>
/// <returns>Flipped sign instance</returns>
uint16_t NegateImpl() const noexcept {
return IsNaN() ? val : static_cast<uint16_t>(val ^ kSignMask);
}
public:
// uint16_t special values
static constexpr uint16_t kSignMask = 0x8000U;
static constexpr uint16_t kBiasedExponentMask = 0x7F80U;
static constexpr uint16_t kPositiveInfinityBits = 0x7F80U;
static constexpr uint16_t kNegativeInfinityBits = 0xFF80U;
static constexpr uint16_t kPositiveQNaNBits = 0x7FC1U;
static constexpr uint16_t kNegativeQNaNBits = 0xFFC1U;
static constexpr uint16_t kMaxValueBits = 0x7F7FU;
static constexpr uint16_t kRoundToNearest = 0x7FFFU;
static constexpr uint16_t kOneBits = 0x3F80U;
static constexpr uint16_t kMinusOneBits = 0xBF80U;
uint16_t val{0};
BFloat16Impl() = default;
/// <summary>
/// Checks if the value is negative
/// </summary>
/// <returns>true if negative</returns>
bool IsNegative() const noexcept {
return static_cast<int16_t>(val) < 0;
}
/// <summary>
/// Tests if the value is NaN
/// </summary>
/// <returns>true if NaN</returns>
bool IsNaN() const noexcept {
return AbsImpl() > kPositiveInfinityBits;
}
/// <summary>
/// Tests if the value is finite
/// </summary>
/// <returns>true if finite</returns>
bool IsFinite() const noexcept {
return AbsImpl() < kPositiveInfinityBits;
}
/// <summary>
/// Tests if the value represents positive infinity.
/// </summary>
/// <returns>true if positive infinity</returns>
bool IsPositiveInfinity() const noexcept {
return val == kPositiveInfinityBits;
}
/// <summary>
/// Tests if the value represents negative infinity
/// </summary>
/// <returns>true if negative infinity</returns>
bool IsNegativeInfinity() const noexcept {
return val == kNegativeInfinityBits;
}
/// <summary>
/// Tests if the value is either positive or negative infinity.
/// </summary>
/// <returns>True if absolute value is infinity</returns>
bool IsInfinity() const noexcept {
return AbsImpl() == kPositiveInfinityBits;
}
/// <summary>
/// Tests if the value is NaN or zero. Useful for comparisons.
/// </summary>
/// <returns>True if NaN or zero.</returns>
bool IsNaNOrZero() const noexcept {
auto abs = AbsImpl();
return (abs == 0 || abs > kPositiveInfinityBits);
}
/// <summary>
/// Tests if the value is normal (not zero, subnormal, infinite, or NaN).
/// </summary>
/// <returns>True if so</returns>
bool IsNormal() const noexcept {
auto abs = AbsImpl();
return (abs < kPositiveInfinityBits) // is finite
&& (abs != 0) // is not zero
&& ((abs & kBiasedExponentMask) != 0); // is not subnormal (has a non-zero exponent)
}
/// <summary>
/// Tests if the value is subnormal (denormal).
/// </summary>
/// <returns>True if so</returns>
bool IsSubnormal() const noexcept {
auto abs = AbsImpl();
return (abs < kPositiveInfinityBits) // is finite
&& (abs != 0) // is not zero
&& ((abs & kBiasedExponentMask) == 0); // is subnormal (has a zero exponent)
}
/// <summary>
/// Creates an instance that represents absolute value.
/// </summary>
/// <returns>Absolute value</returns>
Derived Abs() const noexcept { return Derived::FromBits(AbsImpl()); }
/// <summary>
/// Creates a new instance with the sign flipped.
/// </summary>
/// <returns>Flipped sign instance</returns>
Derived Negate() const noexcept { return Derived::FromBits(NegateImpl()); }
/// <summary>
/// IEEE defines that positive and negative zero are equal, this gives us a quick equality check
/// for two values by or'ing the private bits together and stripping the sign. They are both zero,
/// and therefore equivalent, if the resulting value is still zero.
/// </summary>
/// <param name="lhs">first value</param>
/// <param name="rhs">second value</param>
/// <returns>True if both arguments represent zero</returns>
static bool AreZero(const BFloat16Impl& lhs, const BFloat16Impl& rhs) noexcept {
// IEEE defines that positive and negative zero are equal, this gives us a quick equality check
// for two values by or'ing the private bits together and stripping the sign. They are both zero,
// and therefore equivalent, if the resulting value is still zero.
return static_cast<uint16_t>((lhs.val | rhs.val) & ~kSignMask) == 0;
}
};
template <class Derived>
inline uint16_t BFloat16Impl<Derived>::ToUint16Impl(float v) noexcept {
uint16_t result;
if (std::isnan(v)) {
result = kPositiveQNaNBits;
} else {
auto get_msb_half = [](float fl) {
uint16_t result;
#ifdef __cpp_if_constexpr
if constexpr (detail::endian::native == detail::endian::little) {
#else
if (detail::endian::native == detail::endian::little) {
#endif
std::memcpy(&result, reinterpret_cast<char*>(&fl) + sizeof(uint16_t), sizeof(uint16_t));
} else {
std::memcpy(&result, &fl, sizeof(uint16_t));
}
return result;
};
uint16_t upper_bits = get_msb_half(v);
union {
uint32_t U32;
float F32;
};
F32 = v;
U32 += (upper_bits & 1) + kRoundToNearest;
result = get_msb_half(F32);
}
return result;
}
template <class Derived>
inline float BFloat16Impl<Derived>::ToFloatImpl() const noexcept {
if (IsNaN()) {
return std::numeric_limits<float>::quiet_NaN();
}
float result;
char* const first = reinterpret_cast<char*>(&result);
char* const second = first + sizeof(uint16_t);
#ifdef __cpp_if_constexpr
if constexpr (detail::endian::native == detail::endian::little) {
#else
if (detail::endian::native == detail::endian::little) {
#endif
std::memset(first, 0, sizeof(uint16_t));
std::memcpy(second, &val, sizeof(uint16_t));
} else {
std::memcpy(first, &val, sizeof(uint16_t));
std::memset(second, 0, sizeof(uint16_t));
}
return result;
}
} // namespace onnxruntime_float16

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src/热成像识别/cpp/third_party/onnxruntime/lib/onnxruntime.def vendored
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;
; Definition file of onnxruntime.dll
; Automatic generated by gendef 1.1
; written by Kai Tietz 2008
; The def file has to be processed by --kill-at (-k) option of dlltool or ld
;
LIBRARY "onnxruntime.dll"
EXPORTS
OrtGetApiBase
OrtSessionOptionsAppendExecutionProvider_CPU

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src/热成像识别/label_tool.py
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"""
热成像威胁标注工具 — 手动标记训练数据
操作方式:
鼠标左键拖拽 → 绘制矩形框
P → 标记为 Person (人员)
B → 标记为 Barrel Overheat (枪管过热)
W → 标记为 Cold Weapon (冷武器)
F → 标记为 Flash Residue (闪光残留)
H → 标记为 Hotspot (热点)
D → 删除最后一个标注
C → 清除所有标注
S → 保存并下一张
N → 不保存，跳到下一张
Q → 退出
左右箭头 → 切换图片
用法:
python label_tool.py # 标注 test/test_images/ 下所有图片
python label_tool.py path/to/images/ # 标注指定目录
python label_tool.py photo1.jpg photo2.jpg # 标注指定图片
python label_tool.py --review # 只查看已有标注
"""
import cv2
import numpy as np
import json
import os
import sys
import glob
THREAT_TYPES = {
'P': ('Person', (0, 255, 0)),
'B': ('Barrel_Overheat', (0, 165, 255)),
'W': ('Cold_Weapon', (255, 255, 0)),
'F': ('Flash_Residue', (0, 255, 255)),
'H': ('Hotspot', (0, 0, 255)),
}
LABEL_FILE = 'labels.json'
def load_labels(label_path):
if os.path.exists(label_path):
with open(label_path, 'r', encoding='utf-8') as f:
return json.load(f)
return {}
def save_labels(label_path, data):
with open(label_path, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=2)
def draw_labels(image, labels):
vis = image.copy()
for lb in labels:
x, y, w, h = lb['bbox']
color = (0, 0, 0)
for key, (name, c) in THREAT_TYPES.items():
if name == lb['type']:
color = c
break
cv2.rectangle(vis, (x, y), (x + w, y + h), color, 2)
label_text = lb['type']
cv2.putText(vis, label_text, (x, y - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1)
return vis
class LabelTool:
def __init__(self, image_paths, label_path, review_only=False):
self.image_paths = image_paths
self.label_path = label_path
self.review_only = review_only
self.all_labels = load_labels(label_path)
self.idx = 0
self.current_labels = []
self.drawing = False
self.rect_start = None
self.rect_end = None
self.display = None
self.original = None
self._pending_rect = None
self.win = 'Label Tool'
cv2.namedWindow(self.win, cv2.WINDOW_NORMAL)
cv2.setMouseCallback(self.win, self._mouse_cb)
def _load_current(self):
path = self.image_paths[self.idx]
buf = np.fromfile(path, dtype=np.uint8)
img = cv2.imdecode(buf, cv2.IMREAD_COLOR)
if img is None:
img = cv2.imdecode(buf, cv2.IMREAD_GRAYSCALE)
if img is not None:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
if img is None:
print(f"无法读取: {path}")
return False
self.original = img.copy()
fname = os.path.basename(path)
self.current_labels = self.all_labels.get(fname, [])
self._redraw()
return True
def _redraw(self):
self.display = draw_labels(self.original, self.current_labels)
path = self.image_paths[self.idx]
fname = os.path.basename(path)
info = f"[{self.idx+1}/{len(self.image_paths)}] {fname} | {len(self.current_labels)} labels"
cv2.putText(self.display, info, (10, 25),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1)
help_y = self.display.shape[0] - 10
help_text = "P:Person B:Barrel W:Weapon F:Flash H:Hotspot D:Del C:Clear S:Save N:Skip Q:Quit"
cv2.putText(self.display, help_text, (10, help_y),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (200, 200, 200), 1)
cv2.imshow(self.win, self.display)
def _mouse_cb(self, event, x, y, flags, param):
if self.review_only:
return
if event == cv2.EVENT_LBUTTONDOWN:
self.drawing = True
self.rect_start = (x, y)
self.rect_end = (x, y)
elif event == cv2.EVENT_MOUSEMOVE and self.drawing:
self.rect_end = (x, y)
vis = self.display.copy()
cv2.rectangle(vis, self.rect_start, self.rect_end, (255, 255, 255), 1)
cv2.imshow(self.win, vis)
elif event == cv2.EVENT_LBUTTONUP:
self.drawing = False
self.rect_end = (x, y)
x1, y1 = self.rect_start
x2, y2 = self.rect_end
rx, ry = min(x1, x2), min(y1, y2)
rw, rh = abs(x2 - x1), abs(y2 - y1)
if rw < 5 or rh < 5:
return
self._pending_rect = (rx, ry, rw, rh)
print(f" 框选: ({rx},{ry}) {rw}x{rh} -> 按 P/B/W/F/H 选类型")
def _add_label(self, type_name):
if not self._pending_rect:
print(" 请先框选区域")
return
x, y, w, h = self._pending_rect
self.current_labels.append({
'type': type_name,
'bbox': [x, y, w, h],
})
print(f" + {type_name} at ({x},{y}) {w}x{h}")
self._pending_rect = None
self._redraw()
def _save_current(self):
path = self.image_paths[self.idx]
fname = os.path.basename(path)
self.all_labels[fname] = self.current_labels
save_labels(self.label_path, self.all_labels)
print(f" 已保存 {fname}: {len(self.current_labels)} labels")
def _next(self):
if self.idx + 1 < len(self.image_paths):
self.idx += 1
self._load_current()
else:
print("已是最后一张")
def _prev(self):
if self.idx > 0:
self.idx -= 1
self._load_current()
else:
print("已是第一张")
def run(self):
if not self.image_paths:
print("没有可标注的图片")
return
if not self._load_current():
return
print("\n=== 热成像标注工具 ===")
print("鼠标拖拽框选 -> 按键选类型 (P/B/W/F/H)")
print("D=删除最后 C=清空 S=保存 N=跳过 Q=退出")
print("左右箭头切换图片\n")
while True:
key = cv2.waitKey(50) & 0xFF
if key == ord('q') or key == 27:
break
elif key == ord('p'):
self._add_label('Person')
elif key == ord('b'):
self._add_label('Barrel_Overheat')
elif key == ord('w'):
self._add_label('Cold_Weapon')
elif key == ord('f'):
self._add_label('Flash_Residue')
elif key == ord('h'):
self._add_label('Hotspot')
elif key == ord('d') and not self.review_only:
if self.current_labels:
removed = self.current_labels.pop()
print(f" - 删除: {removed['type']}")
self._redraw()
elif key == ord('c') and not self.review_only:
self.current_labels = []
print(" 已清除所有标注")
self._redraw()
elif key == ord('s'):
self._save_current()
self._next()
elif key == ord('n'):
self._next()
elif key == 83 or key == ord('.'):
self._next()
elif key == 81 or key == ord(','):
self._prev()
path = self.image_paths[self.idx]
fname = os.path.basename(path)
if self.current_labels:
self.all_labels[fname] = self.current_labels
save_labels(self.label_path, self.all_labels)
print(f"\n自动保存 {fname}")
cv2.destroyAllWindows()
total = sum(len(v) for v in self.all_labels.values())
print(f"\n标注完成: {len(self.all_labels)} 张图片, {total} 个标注")
print(f"保存至: {self.label_path}")
def collect_images(source):
images = []
if isinstance(source, str) and os.path.isdir(source):
for ext in ['*.jpg', '*.jpeg', '*.png', '*.bmp', '*.tif']:
images.extend(glob.glob(os.path.join(source, ext)))
elif isinstance(source, list):
for s in source:
if os.path.isfile(s):
images.append(s)
elif os.path.isdir(s):
images.extend(collect_images(s))
images.sort()
return images
def main():
review = '--review' in sys.argv
args = [a for a in sys.argv[1:] if not a.startswith('--')]
if args:
if all(os.path.isfile(a) for a in args):
image_dir = None
image_paths = args
else:
image_dir = args[0]
image_paths = collect_images(image_dir)
else:
script_dir = os.path.dirname(os.path.abspath(__file__))
image_dir = os.path.join(script_dir, 'test', 'test_images')
image_paths = collect_images(image_dir)
if not image_paths:
print("没有找到可标注的图片")
print("用法: python label_tool.py [图片目录 | 图片1 图片2 ...]")
return
if image_dir:
label_path = os.path.join(image_dir, '..', LABEL_FILE)
else:
label_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), LABEL_FILE)
label_path = os.path.abspath(label_path)
print(f"图片: {len(image_paths)} 张")
print(f"标注文件: {label_path}")
tool = LabelTool(image_paths, label_path, review_only=review)
tool.run()
if __name__ == '__main__':
main()

380
src/热成像识别/test/labels.json
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@ -44,6 +44,33 @@
50,
38
]
},
{
"type": "Person",
"bbox": [
984,
519,
88,
68
]
},
{
"type": "Person",
"bbox": [
796,
609,
110,
93
]
},
{
"type": "Person",
"bbox": [
669,
602,
99,
86
]
}
],
"20260510170127_1.jpg": [
@ -485,5 +512,358 @@
396
]
}
],
"ArmA 3 Screenshot 2026.05.24 - 10.43.29.53.png": [
{
"type": "Person",
"bbox": [
785,
179,
182,
273
]
},
{
"type": "Person",
"bbox": [
558,
239,
155,
142
]
},
{
"type": "Person",
"bbox": [
1067,
273,
110,
116
]
},
{
"type": "Person",
"bbox": [
989,
262,
83,
119
]
},
{
"type": "Person",
"bbox": [
1249,
247,
94,
149
]
}
],
"ArmA 3 Screenshot 2026.05.24 - 10.47.49.85.png": [
{
"type": "Person",
"bbox": [
812,
587,
89,
97
]
},
{
"type": "Person",
"bbox": [
453,
654,
166,
119
]
},
{
"type": "Person",
"bbox": [
939,
497,
94,
101
]
}
],
"ArmA 3 Screenshot 2026.05.24 - 10.52.49.37.png": [
{
"type": "Person",
"bbox": [
1006,
475,
143,
153
]
},
{
"type": "Person",
"bbox": [
757,
430,
127,
82
]
},
{
"type": "Person",
"bbox": [
619,
404,
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]
},
{
"type": "Person",
"bbox": [
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362,
89,
98
]
},
{
"type": "Person",
"bbox": [
1183,
437,
105,
101
]
}
],
"ArmA 3 Screenshot 2026.05.24 - 10.53.10.76.png": [
{
"type": "Person",
"bbox": [
647,
396,
132,
112
]
},
{
"type": "Person",
"bbox": [
1033,
415,
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157
]
},
{
"type": "Person",
"bbox": [
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355,
77,
78
]
}
],
"ArmA 3 Screenshot 2026.05.24 - 10.53.24.08.png": [
{
"type": "Person",
"bbox": [
829,
467,
105,
235
]
},
{
"type": "Person",
"bbox": [
641,
370,
116,
142
]
}
],
"ArmA 3 Screenshot 2026.05.24 - 10.53.37.41.png": [
{
"type": "Person",
"bbox": [
1022,
340,
216,
213
]
},
{
"type": "Person",
"bbox": [
757,
247,
138,
186
]
},
{
"type": "Person",
"bbox": [
547,
239,
127,
146
]
}
],
"ArmA 3 Screenshot 2026.05.24 - 10.53.57.13.png": [
{
"type": "Person",
"bbox": [
801,
531,
89,
89
]
},
{
"type": "Person",
"bbox": [
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471,
77,
101
]
},
{
"type": "Person",
"bbox": [
658,
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121,
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]
},
{
"type": "Person",
"bbox": [
597,
407,
77,
68
]
},
{
"type": "Person",
"bbox": [
519,
366,
100,
82
]
}
],
"ArmA 3 Screenshot 2026.05.24 - 10.54.09.74.png": [
{
"type": "Person",
"bbox": [
906,
493,
105,
94
]
},
{
"type": "Person",
"bbox": [
630,
497,
99,
75
]
}
],
"ArmA 3 Screenshot 2026.05.24 - 10.54.59.57.png": [
{
"type": "Person",
"bbox": [
851,
557,
122,
78
]
},
{
"type": "Person",
"bbox": [
707,
658,
83,
104
]
},
{
"type": "Person",
"bbox": [
895,
277,
105,
123
]
},
{
"type": "Person",
"bbox": [
967,
179,
83,
101
]
},
{
"type": "Person",
"bbox": [
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172,
71,
82
]
}
],
"ArmA 3 Screenshot 2026.05.24 - 10.55.10.88.png": [
{
"type": "Person",
"bbox": [
807,
385,
72,
60
]
},
{
"type": "Person",
"bbox": [
901,
456,
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]
},
{
"type": "Person",
"bbox": [
729,
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133,
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]
},
{
"type": "Person",
"bbox": [
1210,
639,
149,
134
]
}
]
}

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