commit 2412ebec3ac66741db1da9866c560fb71b5677a4 Author: haotian <2421912570@qq.com> Date: Tue Sep 2 16:11:43 2025 +0800 1.初始化仓库\n2.添加同时推流mp4文件脚本\n3.添加批量添加摄像头 diff --git a/001测试模型.py b/001测试模型.py new file mode 100644 index 0000000..da9c657 --- /dev/null +++ b/001测试模型.py @@ -0,0 +1,51 @@ +from ultralytics import YOLO +import cv2 + +def predict_and_visualize(model_path, image_path, output_path): + # 加载训练好的模型 + model = YOLO(model_path) + + # 进行预测 + results = model.predict(source=image_path, conf=0.25) # conf设置置信度阈值 + + # 读取原始图片 + img = cv2.imread(image_path) + + # 获取预测结果 + boxes = results[0].boxes + class_names = model.names # 获取类别名称字典 + + # 遍历每个检测结果 + for box in boxes: + # 获取坐标和类别信息 + x1, y1, x2, y2 = map(int, box.xyxy[0].tolist()) + cls_id = int(box.cls[0].item()) + conf = box.conf[0].item() + + # 绘制边界框 + color = (0, 255, 0) # 绿色边框 + cv2.rectangle(img, (x1, y1), (x2, y2), color, 2) + + # 准备显示文本 + label = f"{class_names[cls_id]}: {conf:.2f}" + + # 计算文本位置 + (w, h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 1) + + # 绘制文本背景 + cv2.rectangle(img, (x1, y1 - h - 5), (x1 + w, y1), color, -1) + # 绘制文本 + cv2.putText(img, label, (x1, y1 - 5), + cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 0), 1) + + # 保存结果 + cv2.imwrite(output_path, img) + print(f"结果已保存至: {output_path}") + +if __name__ == "__main__": + # 使用示例 + model_path = "models/安全帽检测模型/yolo11n_safehat.pt" # 替换为你的模型路径 + image_path = "images/mp4_509.jpg" # 替换为你的图片路径 + output_path = "output/mp4_509.jpg" # 输出文件名 + + predict_and_visualize(model_path, image_path, output_path) \ No newline at end of file diff --git a/002批量测试模型图片.py b/002批量测试模型图片.py new file mode 100644 index 0000000..84a2005 --- /dev/null +++ b/002批量测试模型图片.py @@ -0,0 +1,63 @@ +from ultralytics import YOLO +import cv2 +import os + +def predict_and_visualize(model_path, image_path, output_path): + # 加载训练好的模型 + model = YOLO(model_path) + + all_image_file = os.listdir(image_path) + all_image_path = [os.path.join(image_path, t) for t in all_image_file] + + for i in range(len(all_image_path)): + + # 进行预测 + results = model.predict(source=all_image_path[i], conf=0.25) # conf设置置信度阈值 + + # 读取原始图片 + img = cv2.imread(all_image_path[i]) + + # 获取预测结果 + boxes = results[0].boxes + class_names = model.names # 获取类别名称字典 + + + # {0: 'head', 1: 'safehat'} + print(class_names) + + # break + + # 遍历每个检测结果 + for box in boxes: + # 获取坐标和类别信息 + x1, y1, x2, y2 = map(int, box.xyxy[0].tolist()) + cls_id = int(box.cls[0].item()) + conf = box.conf[0].item() + + # 绘制边界框 + color = (0, 255, 0) # 绿色边框 + cv2.rectangle(img, (x1, y1), (x2, y2), color, 2) + + # 准备显示文本 + label = f"{class_names[cls_id]}: {conf:.2f}" + + # 计算文本位置 + (w, h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 1) + + # 绘制文本背景 + cv2.rectangle(img, (x1, y1 - h - 5), (x1 + w, y1), color, -1) + # 绘制文本 + cv2.putText(img, label, (x1, y1 - 5), + cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 0), 1) + + # 保存结果 + cv2.imwrite(output_path+f"{i}.jpg", img) + print(f"结果已保存至: {output_path}") + +if __name__ == "__main__": + # 使用示例 + model_path = "/home/admin-root/haotian/xcms/models/安全帽检测模型OpenVINO/best_s.xml" # 替换为你的模型路径 + image_path = "/home/admin-root/haotian/锻8/tensorrtx/yolov8/images" # 替换为你的图片路径 + output_path = "/home/admin-root/haotian/xcms/output/" # 输出文件名 + + predict_and_visualize(model_path, image_path, output_path) \ No newline at end of file diff --git a/003推流指定文件夹下所有mp4文件.sh b/003推流指定文件夹下所有mp4文件.sh new file mode 100644 index 0000000..a419440 --- /dev/null +++ b/003推流指定文件夹下所有mp4文件.sh @@ -0,0 +1,35 @@ +#!/usr/bin/env bash + +VIDEO_DIR="/home/admin-root/haotian/康达瑞贝斯机器狗/data_video" # 你的 .mp4 文件目录 +RTSP_SERVER="rtsp://10.0.0.17:8554/camera_test" + + +# 捕获 SIGINT（Ctrl+C）和 SIGTERM，触发 exit（进而触发 EXIT trap） +trap "exit" INT TERM + +# EXIT 触发时，终止当前进程组（包括所有子进程） +trap "kill 0" EXIT + +for filepath in "$VIDEO_DIR"/*.mp4; do + [ -e "$filepath" ] || continue + filename=$(basename "$filepath" .mp4) + RTSP_URL="${RTSP_SERVER}/${filename}" + + ( + while true; do + echo "$(date): 推流 -> $RTSP_URL" + ffmpeg -re -stream_loop -1 -i "$filepath" \ + -vf "scale=1920:1080" \ + -c:v libx264 -preset veryfast -tune zerolatency \ + -c:a aac -b:a 128k \ + -muxdelay 0 -muxpreload 0 \ + -f rtsp -rtsp_transport tcp \ + "$RTSP_URL" + echo "$(date): 推流中断，5 秒后重启 -> $RTSP_URL" + sleep 5 + done + ) & +done + +# 等待所有后台任务 +wait \ No newline at end of file diff --git a/004批量添加摄像头.py b/004批量添加摄像头.py new file mode 100644 index 0000000..a6384dc --- /dev/null +++ b/004批量添加摄像头.py @@ -0,0 +1,61 @@ +import requests +import uuid +import os +import time + +""" + 批量向xcms中添加添加摄像头 +""" + +RSTP_URL = "rtsp://10.0.0.17:8554/camera_test/" +ADD_CAMERA_URL = "http://10.0.0.81:9001/open/addStream" +def add_camera(rtsp_path, nick_name, code ,method="POST"): + + # code = uuid.uuid4().hex + + # print(code) + + data = { + "code": f"{code}",# 必填，摄像头编号（不能与已有摄像头编号重复） + "nickname": f"{nick_name}",#必填，名称 + "pull_stream_type": 1, # 必填，数值类型，1:RTSP,2:RTMP,3:FVL,4:HLS,21:GB28181 + "pull_stream_url": f"{rtsp_path}", # 必填，直播流地址 + "pull_stream_ip":"10.0.0.17", # 必填，摄像头IP + "pull_stream_port":8554, # 必填，数值类型，摄像头拉流服务对用的端口 + # "camera_name":"", # 非必填，摄像头名称 + # "camera_manufacturer":"", # 非必填，摄像头厂商 + # "camera_device_id":"group1",# （v4.638新增）非必填，摄像头所属分组编号，默认不填写时等于code + # "remark":"", # 非必填，备注 + # "onvif_username":"", # 非必填，探测ONVIF username + # "onvif_password":"", # 非必填，探测ONVIF password + "is_audio":0 # 必填，数值类型，0:静音 1:原始音频 + } + + headers = { + "Content-Type": "application/json", + "Safe":"aqxY9ps21fyhyKNRyYpGvJCTp1JBeGOM" + } + + response = requests.post(ADD_CAMERA_URL, json=data, headers=headers) + + response.raise_for_status() + + response_data = response.json() + + print(response_data["code"]) + + +def main(): + + video_path = "/home/admin-root/haotian/康达瑞贝斯机器狗/data_video" + video_name_list = os.listdir(video_path) + + for i, video_name in enumerate(video_name_list): + video_name = video_name.split(".")[0] + add_camera(RSTP_URL+video_name, video_name, f"t{i}") + time.sleep(0.2) + + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/docs/模型测试结果.md b/docs/模型测试结果.md new file mode 100644 index 0000000..faf7d5e --- /dev/null +++ b/docs/模型测试结果.md @@ -0,0 +1,15 @@ +## 模型可用性 + +- [X] 人脸检测模型 +- [X] 厨师帽检测模型 +- [ ] 反光衣检测模型 + AttributeError: Can't get attribute 'C3k2' on +- [X] 安全帽检测模型 + 报错同上. +- [X] 打架检测模型 +- [X] 抽烟检测模型 +- [X] 持械检测模型 +- [X] 火焰烟火检测模型 +- [X] 烟尘检测模型 +- [X] 睡岗检测模型 + diff --git a/models/安全帽检测模型OpenVINO/best_s.bin b/models/安全帽检测模型OpenVINO/best_s.bin new file mode 100644 index 0000000..c4eb440 Binary files /dev/null and b/models/安全帽检测模型OpenVINO/best_s.bin differ diff --git a/models/安全帽检测模型OpenVINO/best_s.xml b/models/安全帽检测模型OpenVINO/best_s.xml new file mode 100644 index 0000000..663f1a1 Binary files /dev/null and b/models/安全帽检测模型OpenVINO/best_s.xml differ diff --git a/models/安全帽检测模型OpenVINO/test.py b/models/安全帽检测模型OpenVINO/test.py new file mode 100644 index 0000000..570b611 --- /dev/null +++ b/models/安全帽检测模型OpenVINO/test.py @@ -0,0 +1,16 @@ +from ultralytics import YOLO +import openvino as ov +''' + 加载文件失败 +''' +# 加载 OpenVINO 模型 +core = ov.Core() +compiled_model = core.compile_model("best_s.xml", "CPU") + +# 通过 YOLO 包装推理 +model = YOLO("best_s.xml") # 使用 YOLO 接口 +model.model = compiled_model # 替换为 OpenVINO 模型 + + +# 执行推理 +results = model.predict("image.jpg") \ No newline at end of file