\u60f3\u8c61\u4e00\u4e0b，\u4f60\u6234\u4e0a\u4e00\u526f\u770b\u4f3c\u666e\u901a\u7684\u773c\u955c，\u773c\u524d\u7684\u4e16\u754c\u7acb\u523b\u53d8\u5f97\u4e0d\u4e00\u6837\u4e86\u3002\u811a\u4e0b\u7684\u76f2\u9053\u88ab\u9ad8\u4eae\u6807\u6ce8，\u524d\u65b9\u7684\u7ea2\u7eff\u706f\u72b6\u6001\u6e05\u6670\u53ef\u89c1，\u751a\u81f3\u4f60\u968f\u53e3\u8bf4\u4e00\u53e5\u201c\u5e2e\u6211\u627e\u4e00\u4e0b\u94a5\u5319\u201d，\u773c\u955c\u5c31\u80fd\u5f15\u5bfc\u4f60\u7684\u89c6\u7ebf\u770b\u5411\u76ee\u6807\u3002\u8fd9\u4e0d\u662f\u79d1\u5e7b\u7535\u5f71，\u800c\u662fAIGlasses_for_navigation\u6b63\u5728\u5b9e\u73b0\u7684\u6280\u672f\u573a\u666f\u3002<\/p>\n
AIGlasses_for_navigation\u662f\u4e00\u6b3e\u96c6\u6210\u4e86AI\u6280\u672f\u3001\u4f20\u611f\u6280\u672f\u4e0e\u5bfc\u822a\u529f\u80fd\u7684\u53ef\u7a7f\u6234\u667a\u80fd\u8bbe\u5907\u3002\u5b83\u7684\u6838\u5fc3\u76ee\u6807\u5f88\u7b80\u5355：\u901a\u8fc7\u865a\u5b9e\u878d\u5408\u3001\u591a\u6a21\u6001\u4ea4\u4e92，\u4e3a\u7528\u6237\u63d0\u4f9b\u76f4\u89c2\u4e14\u5b89\u5168\u7684\u5bfc\u822a\u6307\u5f15\u3002\u8fd9\u5957\u7cfb\u7edf\u76ee\u524d\u5df2\u7ecf\u80fd\u591f\u9002\u914d\u666e\u901a\u5927\u4f17\u7684\u65e5\u5e38\u51fa\u884c\u9700\u6c42，\u66f4\u91cd\u8981\u7684\u662f，\u5b83\u4e3a\u89c6\u969c\u4eba\u7fa4\u7b49\u7279\u6b8a\u7fa4\u4f53\u63d0\u4f9b\u4e86\u5b9a\u5236\u5316\u7684\u8f85\u52a9\u65b9\u6848，\u8ba9\u79d1\u6280\u771f\u6b63\u670d\u52a1\u4e8e\u4eba\u3002<\/p>\n
\u4eca\u5929，\u6211\u4eec\u4e0d\u53ea\u804a\u5b83\u7684\u529f\u80fd\u6709\u591a\u9177，\u66f4\u8981\u6df1\u5165\u63a2\u8ba8\u4e00\u4e2a\u66f4\u5b9e\u9645\u7684\u95ee\u9898：\u5982\u4f55\u628a\u8fd9\u6837\u4e00\u5957\u590d\u6742\u7684AI\u7cfb\u7edf，\u90e8\u7f72\u5230\u5404\u79cd\u4e0d\u540c\u7684\u786c\u4ef6\u5e73\u53f0\u4e0a？\u4ece\u6027\u80fd\u5f3a\u5927\u7684\u8fb9\u7f18\u670d\u52a1\u5668，\u5230\u5c0f\u5de7\u4fbf\u643a\u7684\u6811\u8393\u6d3e，\u518d\u5230\u56fd\u4ea7\u7684\u6607\u817e\u82af\u7247，\u6211\u4eec\u5c06\u4e00\u6b65\u6b65\u62c6\u89e3AIGlasses_for_navigation\u7684\u90e8\u7f72\u65b9\u6848，\u8ba9\u4f60\u65e0\u8bba\u624b\u5934\u6709\u4ec0\u4e48\u8bbe\u5907，\u90fd\u80fd\u5feb\u901f\u642d\u5efa\u8d77\u5c5e\u4e8e\u81ea\u5df1\u7684\u667a\u80fd\u5bfc\u822a\u773c\u955c\u7cfb\u7edf\u3002<\/p>\n
\u5728\u5f00\u59cb\u90e8\u7f72\u4e4b\u524d，\u6211\u4eec\u5148\u8981\u641e\u6e05\u695aAIGlasses_for_navigation\u5230\u5e95\u7531\u54ea\u4e9b\u90e8\u5206\u7ec4\u6210，\u4ee5\u53ca\u5b83\u662f\u5982\u4f55\u5de5\u4f5c\u7684\u3002\u7406\u89e3\u4e86\u6574\u4f53\u67b6\u6784，\u540e\u7eed\u7684\u90e8\u7f72\u548c\u9002\u914d\u5c31\u4f1a\u4e8b\u534a\u529f\u500d\u3002<\/p>\n
AIGlasses_for_navigation\u91c7\u7528\u4e86\u5178\u578b\u7684\u5ba2\u6237\u7aef-\u670d\u52a1\u5668\u67b6\u6784，\u4f46\u505a\u4e86\u5f88\u591a\u9002\u5e94\u8fb9\u7f18\u8ba1\u7b97\u7684\u4f18\u5316\u3002\u6574\u4e2a\u7cfb\u7edf\u53ef\u4ee5\u5206\u6210\u4e09\u4e2a\u4e3b\u8981\u90e8\u5206：<\/p>\n
\u524d\u7aef\u611f\u77e5\u5c42：<\/p>\n
\u8fb9\u7f18\u8ba1\u7b97\u5c42：<\/p>\n
\u7528\u6237\u4ea4\u4e92\u5c42：<\/p>\n
\u8fd9\u662f\u7cfb\u7edf\u7684\u6838\u5fc3\u529f\u80fd\u4e4b\u4e00\u3002\u901a\u8fc7YOLO\u5206\u5272\u6a21\u578b，\u7cfb\u7edf\u80fd\u591f\u5b9e\u65f6\u8bc6\u522b\u753b\u9762\u4e2d\u7684\u76f2\u9053\u533a\u57df，\u5e76\u5224\u65ad\u7528\u6237\u7684\u884c\u8d70\u65b9\u5411\u662f\u5426\u504f\u79bb\u3002\u5f53\u68c0\u6d4b\u5230\u76f2\u9053\u65f6，\u7cfb\u7edf\u4f1a\u901a\u8fc7\u8bed\u97f3\u63d0\u793a\u201c\u6cbf\u76f2\u9053\u76f4\u884c\u201d；\u5f53\u76f2\u9053\u51fa\u73b0\u8f6c\u5f2f\u65f6，\u4f1a\u63d0\u793a\u201c\u5411\u5de6\u8f6c\u201d\u6216\u201c\u5411\u53f3\u8f6c\u201d；\u5982\u679c\u524d\u65b9\u6709\u969c\u788d\u7269\u906e\u6321\u76f2\u9053，\u4f1a\u53ca\u65f6\u9884\u8b66\u3002<\/p>\n
\u8fc7\u9a6c\u8def\u5bf9\u5f88\u591a\u4eba\u6765\u8bf4\u662f\u65e5\u5e38\u5c0f\u4e8b，\u4f46\u5bf9\u89c6\u969c\u4eba\u58eb\u5374\u5145\u6ee1\u6311\u6218\u3002\u7cfb\u7edf\u96c6\u6210\u4e86\u6591\u9a6c\u7ebf\u68c0\u6d4b\u548c\u7ea2\u7eff\u706f\u8bc6\u522b\u53cc\u91cd\u529f\u80fd\u3002\u5f53\u7528\u6237\u9700\u8981\u8fc7\u9a6c\u8def\u65f6，\u7cfb\u7edf\u4f1a\u5148\u5f15\u5bfc\u7528\u6237\u5bf9\u51c6\u6591\u9a6c\u7ebf\u4e2d\u5fc3，\u7136\u540e\u6301\u7eed\u76d1\u63a7\u7ea2\u7eff\u706f\u72b6\u6001\u3002\u53ea\u6709\u5728\u7eff\u706f\u4eae\u8d77\u4e14\u6ca1\u6709\u8f66\u8f86\u901a\u8fc7\u65f6，\u624d\u4f1a\u63d0\u793a\u201c\u53ef\u4ee5\u5b89\u5168\u901a\u8fc7\u201d\u3002<\/p>\n
\u201c\u6211\u7684\u624b\u673a\u653e\u54ea\u513f\u4e86？\u201d\u8fd9\u79cd\u65e5\u5e38\u56f0\u6270，\u7cfb\u7edf\u4e5f\u80fd\u5e2e\u5fd9\u89e3\u51b3\u3002\u7528\u6237\u53ea\u9700\u8bf4\u51fa\u201c\u5e2e\u6211\u627e\u4e00\u4e0b\u624b\u673a\u201d，\u7cfb\u7edf\u5c31\u4f1a\u542f\u52a8\u7269\u54c1\u8bc6\u522b\u6a21\u5f0f\u3002\u5f53\u6444\u50cf\u5934\u6355\u6349\u5230\u76ee\u6807\u7269\u54c1\u65f6，\u4f1a\u7528\u8bed\u97f3\u5f15\u5bfc\u7528\u6237：\u201c\u624b\u673a\u5728\u4f60\u5de6\u524d\u65b9\u7ea62\u7c73\u5904，\u8bf7\u5411\u5de6\u8f6c30\u5ea6\u201d\u3002<\/p>\n
\u57fa\u4e8e\u963f\u91cc\u4e91DashScope\u7684\u591a\u6a21\u6001\u7406\u89e3\u80fd\u529b，\u7cfb\u7edf\u652f\u6301\u81ea\u7136\u7684\u8bed\u97f3\u5bf9\u8bdd\u3002\u4f60\u53ef\u4ee5\u76f4\u63a5\u95ee\u201c\u524d\u9762\u662f\u4ec0\u4e48\u5e97？\u201d，\u7cfb\u7edf\u4f1a\u8bc6\u522b\u753b\u9762\u4e2d\u7684\u5e97\u94fa\u62db\u724c\u5e76\u56de\u7b54\u3002\u8fd9\u79cd\u4ea4\u4e92\u65b9\u5f0f\u8ba9\u8bbe\u5907\u4f7f\u7528\u8d77\u6765\u66f4\u52a0\u81ea\u7136，\u4e0d\u9700\u8981\u8bb0\u5fc6\u590d\u6742\u7684\u547d\u4ee4\u8bcd\u3002<\/p>\n
\u7cfb\u7edf\u5728\u8bbe\u8ba1\u4e4b\u521d\u5c31\u8003\u8651\u4e86\u591a\u5e73\u53f0\u90e8\u7f72\u7684\u9700\u6c42\u3002\u901a\u8fc7\u62bd\u8c61\u786c\u4ef6\u63a5\u53e3\u3001\u6a21\u5757\u5316\u8bbe\u8ba1\u548c\u670d\u52a1\u5316\u67b6\u6784，\u540c\u4e00\u5957\u4ee3\u7801\u53ef\u4ee5\u5728\u4e0d\u540c\u786c\u4ef6\u5e73\u53f0\u4e0a\u8fd0\u884c，\u53ea\u9700\u9488\u5bf9\u7279\u5b9a\u5e73\u53f0\u8fdb\u884c\u4f18\u5316\u9002\u914d\u3002<\/p>\n
\u5982\u679c\u4f60\u6709\u6027\u80fd\u8f83\u5f3a\u7684\u8fb9\u7f18\u670d\u52a1\u5668（\u6bd4\u5982NVIDIA Jetson\u7cfb\u5217\u3001Intel NUC\u7b49），\u8fd9\u662f\u83b7\u5f97\u6700\u4f73\u4f53\u9a8c\u7684\u90e8\u7f72\u65b9\u5f0f\u3002\u8fb9\u7f18\u670d\u52a1\u5668\u901a\u5e38\u6709\u66f4\u5f3a\u7684\u7b97\u529b\u548c\u66f4\u4e30\u5bcc\u7684\u63a5\u53e3，\u80fd\u591f\u652f\u6301\u66f4\u590d\u6742\u7684AI\u6a21\u578b\u548c\u66f4\u9ad8\u7684\u5904\u7406\u5e27\u7387\u3002<\/p>\n
\u9996\u5148，\u6211\u4eec\u9700\u8981\u51c6\u5907\u4e00\u4e2a\u5e72\u51c0\u7684Linux\u73af\u5883\u3002\u8fd9\u91cc\u4ee5Ubuntu 20.04\u4e3a\u4f8b，\u5176\u4ed6\u53d1\u884c\u7248\u7684\u547d\u4ee4\u53ef\u80fd\u7565\u6709\u4e0d\u540c\u3002<\/p>\n
# \u66f4\u65b0\u7cfb\u7edf\u5305
\nsudo apt update && sudo apt upgrade -y<\/p>\n
# \u5b89\u88c5Python\u73af\u5883（\u5efa\u8bae\u4f7f\u7528Python 3.8+）
\nsudo apt install python3.8 python3.8-venv python3.8-dev -y<\/p>\n
# \u5b89\u88c5\u7cfb\u7edf\u4f9d\u8d56
\nsudo apt install git wget curl supervisor ffmpeg libsm6 libxext6 -y<\/p>\n
# \u5b89\u88c5CUDA\u548ccuDNN（\u5982\u679c\u4f7f\u7528NVIDIA GPU）
\n# \u8fd9\u91cc\u5047\u8bbe\u5df2\u7ecf\u5b89\u88c5\u4e86\u5408\u9002\u7684\u9a71\u52a8\u548cCUDA\u73af\u5883<\/p>\n
# \u514b\u9686\u9879\u76ee\u4ee3\u7801
\ncd \/root
\ngit clone https:\/\/github.com\/AI-FanGe\/OpenAIglasses_for_Navigation.git
\ncd AIGlasses_for_navigation<\/p>\n
# \u521b\u5efa\u865a\u62df\u73af\u5883
\npython3.8 -m venv venv
\nsource venv\/bin\/activate<\/p>\n
# \u5b89\u88c5Python\u4f9d\u8d56
\npip install –upgrade pip
\npip install -r requirements.txt<\/p>\n
# \u4e0b\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b
\n# \u7cfb\u7edf\u4f1a\u81ea\u52a8\u4e0b\u8f7d\u6240\u9700\u6a21\u578b，\u5982\u679c\u9700\u8981\u624b\u52a8\u4e0b\u8f7d：
\nwget -P model\/ https:\/\/example.com\/models\/yolo-seg.pt
\nwget -P model\/ https:\/\/example.com\/models\/yoloe-11l-seg.pt
\nwget -P model\/ https:\/\/example.com\/models\/shoppingbest5.pt<\/p>\n
\u4e3a\u4e86\u8ba9\u7cfb\u7edf\u80fd\u591f\u7a33\u5b9a\u8fd0\u884c\u5e76\u5728\u5d29\u6e83\u65f6\u81ea\u52a8\u91cd\u542f，\u6211\u4eec\u4f7f\u7528Supervisor\u6765\u7ba1\u7406\u670d\u52a1\u3002<\/p>\n
# \u521b\u5efaSupervisor\u914d\u7f6e\u6587\u4ef6
\nsudo tee \/etc\/supervisor\/conf.d\/aiglasses.conf << 'EOF'
\n[program:aiglasses]
\ncommand=\/root\/AIGlasses_for_navigation\/venv\/bin\/python \/root\/AIGlasses_for_navigation\/app_main.py
\ndirectory=\/root\/AIGlasses_for_navigation
\nautostart=true
\nautorestart=true
\nstartretries=3
\nuser=root
\nredirect_stderr=true
\nstdout_logfile=\/root\/AIGlasses_for_navigation\/logs\/supervisor.log
\nstdout_logfile_maxbytes=50MB
\nstdout_logfile_backups=10
\nenvironment=PYTHONUNBUFFERED="1"
\nEOF<\/p>\n
# \u91cd\u65b0\u52a0\u8f7dSupervisor\u914d\u7f6e
\nsudo supervisorctl reread
\nsudo supervisorctl update<\/p>\n
# \u542f\u52a8\u670d\u52a1
\nsudo supervisorctl start aiglasses<\/p>\n
# \u67e5\u770b\u670d\u52a1\u72b6\u6001
\nsudo supervisorctl status aiglasses<\/p>\n
\u5728\u8fb9\u7f18\u670d\u52a1\u5668\u4e0a，\u6211\u4eec\u53ef\u4ee5\u542f\u7528\u4e00\u4e9b\u9ad8\u7ea7\u7279\u6027\u6765\u63d0\u5347\u6027\u80fd：<\/p>\n
# \u5728app_main.py\u4e2d，\u53ef\u4ee5\u8c03\u6574\u4ee5\u4e0b\u53c2\u6570\u8fdb\u884c\u4f18\u5316
\nconfig = {
\n 'gpu_id': 0, # \u4f7f\u7528GPU\u52a0\u901f
\n 'batch_size': 4, # \u6279\u5904\u7406\u5927\u5c0f，\u6839\u636e\u663e\u5b58\u8c03\u6574
\n 'fp16': True, # \u4f7f\u7528\u534a\u7cbe\u5ea6\u6d6e\u70b9\u6570，\u63d0\u5347\u63a8\u7406\u901f\u5ea6
\n 'trt': False, # \u662f\u5426\u4f7f\u7528TensorRT\u52a0\u901f（\u9700\u8981\u989d\u5916\u914d\u7f6e）
\n 'num_workers': 4, # \u6570\u636e\u5904\u7406\u7ebf\u7a0b\u6570
\n}<\/p>\n
# \u5bf9\u4e8e\u591a\u6444\u50cf\u5934\u652f\u6301，\u53ef\u4ee5\u8fd9\u6837\u914d\u7f6e
\ncamera_configs = [
\n {'id': 0, 'type': 'main', 'fps': 30, 'resolution': (1280, 720)},
\n {'id': 1, 'type': 'auxiliary', 'fps': 15, 'resolution': (640, 480)},
\n]<\/p>\n
\u8fb9\u7f18\u670d\u52a1\u5668\u7684\u4f18\u52bf：<\/p>\n
\u9002\u7528\u573a\u666f：<\/p>\n
\u6811\u8393\u6d3e\u4ee5\u5176\u5c0f\u5de7\u7684\u4f53\u79ef\u548c\u4f4e\u5ec9\u7684\u6210\u672c，\u6210\u4e3a\u5f88\u591a\u5d4c\u5165\u5f0f\u9879\u76ee\u7684\u9996\u9009\u3002\u867d\u7136\u6027\u80fd\u6709\u9650，\u4f46\u901a\u8fc7\u5408\u7406\u7684\u4f18\u5316，AIGlasses_for_navigation\u4e5f\u80fd\u5728\u6811\u8393\u6d3e\u4e0a\u6d41\u7545\u8fd0\u884c\u3002<\/p>\n
\u63a8\u8350\u4f7f\u7528\u6811\u8393\u6d3e4B\u6216\u66f4\u65b0\u578b\u53f7，\u81f3\u5c114GB\u5185\u5b58\u3002<\/p>\n
# \u5b89\u88c5Raspberry Pi OS（64\u4f4d\u7248\u672c）
\n# \u4ece\u5b98\u7f51\u4e0b\u8f7d\u955c\u50cf\u5e76\u5237\u5165SD\u5361<\/p>\n
# \u9996\u6b21\u542f\u52a8\u540e，\u8fdb\u884c\u57fa\u672c\u914d\u7f6e
\nsudo raspi-config
\n# \u542f\u7528SSH\u3001VNC\u3001\u6444\u50cf\u5934\u63a5\u53e3
\n# \u6269\u5c55\u6587\u4ef6\u7cfb\u7edf
\n# \u8bbe\u7f6e\u5408\u9002\u7684GPU\u5185\u5b58（\u5efa\u8bae256MB）<\/p>\n
# \u66f4\u65b0\u7cfb\u7edf
\nsudo apt update && sudo apt upgrade -y<\/p>\n
\u7531\u4e8e\u6811\u8393\u6d3e\u7b97\u529b\u6709\u9650，\u6211\u4eec\u9700\u8981\u5bf9\u7cfb\u7edf\u8fdb\u884c\u4e00\u4e9b\u8f7b\u91cf\u5316\u8c03\u6574\u3002<\/p>\n
# \u5b89\u88c5\u6700\u5c0f\u5316\u4f9d\u8d56
\nsudo apt install python3-pip python3-venv git supervisor -y<\/p>\n
# \u514b\u9686\u9879\u76ee（\u4f7f\u7528\u8f7b\u91cf\u5316\u5206\u652f）
\ncd \/home\/pi
\ngit clone -b lightweight https:\/\/github.com\/AI-FanGe\/OpenAIglasses_for_Navigation.git
\ncd AIGlasses_for_navigation<\/p>\n
# \u521b\u5efa\u865a\u62df\u73af\u5883
\npython3 -m venv venv
\nsource venv\/bin\/activate<\/p>\n
# \u5b89\u88c5\u7cbe\u7b80\u7248\u4f9d\u8d56
\npip install –upgrade pip
\n# \u4f7f\u7528\u4e13\u95e8\u4e3a\u6811\u8393\u6d3e\u4f18\u5316\u7684requirements\u6587\u4ef6
\npip install -r requirements_rpi.txt<\/p>\n
\u6811\u8393\u6d3e\u65e0\u6cd5\u8fd0\u884c\u5b8c\u6574\u7684YOLO\u6a21\u578b，\u6211\u4eec\u9700\u8981\u4f7f\u7528\u8f7b\u91cf\u5316\u7248\u672c\u3002<\/p>\n
# \u6a21\u578b\u914d\u7f6e\u4f18\u5316（\u9488\u5bf9\u6811\u8393\u6d3e）
\nmodel_config = {
\n 'blind_road_model': 'model\/yolo-nano-seg.pt', # \u4f7f\u7528Nano\u7248\u672c
\n 'traffic_light_model': 'model\/trafficlight-tiny.pt', # \u4f7f\u7528Tiny\u7248\u672c
\n 'shopping_model': 'model\/shoppingbest3.pt', # \u7cbe\u7b80\u7c7b\u522b\u6570
\n 'inference_size': 320, # \u51cf\u5c0f\u8f93\u5165\u5c3a\u5bf8
\n 'confidence_threshold': 0.5, # \u63d0\u9ad8\u7f6e\u4fe1\u5ea6\u9608\u503c，\u51cf\u5c11\u8bef\u68c0
\n}<\/p>\n
# \u5e27\u7387\u63a7\u5236，\u907f\u514d\u8fc7\u70ed
\nfps_limit = 10 # \u9650\u5236\u523010FPS
\nuse_hardware_acceleration = True # \u542f\u7528\u786c\u4ef6\u52a0\u901f<\/p>\n
\u6811\u8393\u6d3e\u6709\u4e13\u7528\u7684\u6444\u50cf\u5934\u63a5\u53e3，\u914d\u7f6e\u8d77\u6765\u66f4\u52a0\u65b9\u4fbf\u3002<\/p>\n
# \u542f\u7528\u6444\u50cf\u5934\u6a21\u5757
\nsudo raspi-config
\n# \u9009\u62e9 Interface Options -> Camera -> Enable<\/p>\n
# \u6d4b\u8bd5\u6444\u50cf\u5934
\nlibcamera-hello -t 0<\/p>\n
# \u5b89\u88c5\u97f3\u9891\u76f8\u5173\u9a71\u52a8
\nsudo apt install pulseaudio alsa-utils -y<\/p>\n
# \u914d\u7f6eUSB\u9ea6\u514b\u98ce（\u5982\u679c\u4f7f\u7528）
\narecord -l # \u67e5\u770b\u97f3\u9891\u8bbe\u5907\u5217\u8868<\/p>\n
\u6811\u8393\u6d3e\u5728\u957f\u65f6\u95f4\u8fd0\u884c\u65f6\u9700\u8981\u6ce8\u610f\u6563\u70ed\u548c\u529f\u8017\u3002<\/p>\n
# \u5b89\u88c5\u6e29\u5ea6\u76d1\u63a7
\nsudo apt install lm-sensors -y
\nsensors<\/p>\n
# \u914d\u7f6e\u52a8\u6001\u9891\u7387\u8c03\u6574（\u51cf\u5c11\u53d1\u70ed）
\nsudo nano \/boot\/config.txt
\n# \u6dfb\u52a0\u4ee5\u4e0b\u914d\u7f6e
\narm_freq=1200
\nover_voltage=2
\ntemp_limit=70 # \u6e29\u5ea6\u9650\u5236<\/p>\n
# \u4f7f\u7528\u6563\u70ed\u7247\u6216\u98ce\u6247
\n# \u5efa\u8bae\u5b89\u88c5\u4e3b\u52a8\u6563\u70ed\u98ce\u6247，\u7279\u522b\u662f\u8fd0\u884cAI\u63a8\u7406\u65f6<\/p>\n
\u6811\u8393\u6d3e\u7684\u4f18\u52bf：<\/p>\n
\u9700\u8981\u6ce8\u610f\u7684\u6311\u6218：<\/p>\n
\u9002\u7528\u573a\u666f：<\/p>\n
\u6607\u817e（Ascend）\u662f\u534e\u4e3a\u63a8\u51fa\u7684AI\u5904\u7406\u5668，\u5728\u56fd\u4ea7\u5316\u66ff\u4ee3\u548c\u7279\u5b9a\u573a\u666f\u4e0b\u6709\u7740\u91cd\u8981\u5e94\u7528\u3002\u5c06AIGlasses_for_navigation\u9002\u914d\u5230\u6607\u817e\u5e73\u53f0，\u65e2\u80fd\u6ee1\u8db3\u56fd\u4ea7\u5316\u9700\u6c42，\u4e5f\u80fd\u53d1\u6325\u5176\u7279\u5b9a\u4f18\u52bf\u3002<\/p>\n
\u9996\u5148\u9700\u8981\u5728\u6607\u817e\u8bbe\u5907\u4e0a\u642d\u5efa\u57fa\u7840\u73af\u5883\u3002<\/p>\n
# \u5b89\u88c5CANN（Compute Architecture for Neural Networks）\u5de5\u5177\u5305
\n# \u4ece\u534e\u4e3a\u5b98\u7f51\u4e0b\u8f7d\u5bf9\u5e94\u7248\u672c\u7684CANN\u5305
\nwget https:\/\/ascend-repo.xxx.com\/CANN-6.0.0.alpha001.zip
\nunzip CANN-6.0.0.alpha001.zip
\ncd CANN-6.0.0.alpha001
\nsudo .\/install.sh –install-path=\/usr\/local\/Ascend<\/p>\n
# \u8bbe\u7f6e\u73af\u5883\u53d8\u91cf
\necho 'source \/usr\/local\/Ascend\/ascend-toolkit\/set_env.sh' >> ~\/.bashrc
\nsource ~\/.bashrc<\/p>\n
# \u5b89\u88c5Python\u63a5\u53e3
\npip install te-0.4.0-cp38-cp38-linux_aarch64.whl
\npip install topi-0.4.0-cp38-cp38-linux_aarch64.whl<\/p>\n
\u6607\u817e\u4f7f\u7528OM（Offline Model）\u6a21\u578b\u683c\u5f0f，\u9700\u8981\u5c06PyTorch\u6a21\u578b\u8fdb\u884c\u8f6c\u6362\u3002<\/p>\n
# \u6a21\u578b\u8f6c\u6362\u811a\u672c\u793a\u4f8b
\nfrom ais_bench.infer.interface import InferSession
\nimport torch
\nimport onnx
\nimport numpy as np<\/p>\n
def convert_to_om(model_path, output_path):
\n """
\n \u5c06PyTorch\u6a21\u578b\u8f6c\u6362\u4e3a\u6607\u817eOM\u683c\u5f0f
\n """
\n # 1. \u5bfc\u51fa\u4e3aONNX\u683c\u5f0f
\n model = torch.load(model_path)
\n dummy_input = torch.randn(1, 3, 640, 640)
\n torch.onnx.export(model, dummy_input, "temp.onnx")<\/p>\n
# 2. \u4f7f\u7528ATC\u5de5\u5177\u8f6c\u6362\u4e3aOM
\n atc_cmd = f"""
\n atc –model=temp.onnx \\\\
\n –framework=5 \\\\
\n –output={output_path} \\\\
\n –input_format=NCHW \\\\
\n –input_shape="input:1,3,640,640" \\\\
\n –log=error \\\\
\n –soc_version=Ascend310 \\\\
\n –insert_op_conf=aipp.config
\n """
\n os.system(atc_cmd)<\/p>\n
return output_path<\/p>\n
# \u8f6c\u6362\u6240\u6709\u9700\u8981\u7684\u6a21\u578b
\nmodels_to_convert = [
\n ('yolo-seg.pt', 'yolo-seg.om'),
\n ('trafficlight.pt', 'trafficlight.om'),
\n ('shoppingbest5.pt', 'shoppingbest5.om'),
\n]<\/p>\n
\u9700\u8981\u4fee\u6539\u63a8\u7406\u4ee3\u7801\u4ee5\u4f7f\u7528\u6607\u817eNPU\u3002<\/p>\n
class AscendInferenceEngine:
\n """\u6607\u817eNPU\u63a8\u7406\u5f15\u64ce"""<\/p>\n
def __init__(self, model_path):
\n self.session = InferSession(device_id=0, model_path=model_path)<\/p>\n
def infer(self, image):
\n # \u56fe\u50cf\u9884\u5904\u7406
\n processed = self.preprocess(image)<\/p>\n
# NPU\u63a8\u7406
\n outputs = self.session.infer([processed])<\/p>\n
# \u540e\u5904\u7406
\n results = self.postprocess(outputs)<\/p>\n
return results<\/p>\n
def preprocess(self, image):
\n """\u56fe\u50cf\u9884\u5904\u7406，\u7b26\u5408\u6607\u817e\u8f93\u5165\u8981\u6c42"""
\n # \u8c03\u6574\u5c3a\u5bf8
\n resized = cv2.resize(image, (640, 640))
\n # \u5f52\u4e00\u5316
\n normalized = resized \/ 255.0
\n # NCHW\u8f6c\u6362
\n nchw = normalized.transpose(2, 0, 1)
\n # \u6dfb\u52a0batch\u7ef4\u5ea6
\n batched = np.expand_dims(nchw, axis=0)<\/p>\n
return batched.astype(np.float32)<\/p>\n
\u6607\u817e\u5e73\u53f0\u6709\u5176\u72ec\u7279\u7684\u6027\u80fd\u7279\u6027，\u9700\u8981\u9488\u5bf9\u6027\u4f18\u5316\u3002<\/p>\n
# \u6607\u817e\u4e13\u7528\u914d\u7f6e
\nascend_config = {
\n 'device_id': 0, # \u8bbe\u5907ID
\n 'batch_size': 8, # \u6607\u817e\u9002\u5408\u8f83\u5927batch
\n 'precision_mode': 'allow_fp32_to_fp16', # \u6df7\u5408\u7cbe\u5ea6
\n 'op_select_impl_mode': 'high_precision', # \u7b97\u5b50\u9009\u62e9\u6a21\u5f0f
\n 'dynamic_batch_size': '1,2,4,8', # \u52a8\u6001batch\u652f\u6301
\n 'buffer_optimize': 'l2_optimize', # \u5185\u5b58\u4f18\u5316
\n}<\/p>\n
# \u6d41\u6c34\u7ebf\u4f18\u5316
\nclass PipelineOptimizer:
\n """\u6d41\u6c34\u7ebf\u4f18\u5316，\u63d0\u5347\u541e\u5410\u91cf"""<\/p>\n
def __init__(self):
\n self.preprocess_queue = Queue(maxsize=4)
\n self.inference_queue = Queue(maxsize=4)
\n self.postprocess_queue = Queue(maxsize=4)<\/p>\n
def start_pipeline(self):
\n # \u521b\u5efa\u591a\u4e2a\u7ebf\u7a0b\u5904\u7406\u4e0d\u540c\u9636\u6bb5
\n preprocess_thread = Thread(target=self.preprocess_worker)
\n inference_thread = Thread(target=self.inference_worker)
\n postprocess_thread = Thread(target=self.postprocess_worker)<\/p>\n
preprocess_thread.start()
\n inference_thread.start()
\n postprocess_thread.start()<\/p>\n
\u6607\u817e\u82af\u7247\u5728\u5185\u5b58\u548c\u529f\u8017\u65b9\u9762\u9700\u8981\u7279\u522b\u5173\u6ce8\u3002<\/p>\n
# \u67e5\u770bNPU\u5185\u5b58\u4f7f\u7528
\nnpu-smi info<\/p>\n
# \u76d1\u63a7\u529f\u8017
\ncat \/sys\/class\/hisi_hwt\/hisi_sec0\/power<\/p>\n
# \u52a8\u6001\u9891\u7387\u8c03\u6574
\necho "performance" > \/sys\/devices\/system\/cpu\/cpufreq\/policy0\/scaling_governor
\necho "powersave" > \/sys\/devices\/system\/cpu\/cpufreq\/policy0\/scaling_governor<\/p>\n
\u6607\u817e\u5e73\u53f0\u7684\u4f18\u52bf：<\/p>\n
\u9700\u8981\u6ce8\u610f\u7684\u6311\u6218：<\/p>\n
\u9002\u7528\u573a\u666f：<\/p>\n
\u4e86\u89e3\u4e86\u5404\u4e2a\u5e73\u53f0\u7684\u90e8\u7f72\u65b9\u6848\u540e，\u6211\u4eec\u6765\u5b9e\u9645\u64cd\u4f5c\u4e00\u4e0b，\u4ece\u96f6\u5f00\u59cb\u642d\u5efa\u4e00\u4e2a\u5b8c\u6574\u7684AIGlasses_for_navigation\u7cfb\u7edf\u3002\u6211\u4f1a\u4ee5\u6811\u8393\u6d3e\u4e3a\u4f8b，\u56e0\u4e3a\u8fd9\u662f\u6700\u5e38\u7528\u4e14\u6210\u672c\u6700\u4f4e\u7684\u65b9\u6848\u3002<\/p>\n
\u5728\u5f00\u59cb\u4e4b\u524d，\u786e\u4fdd\u4f60\u51c6\u5907\u597d\u4e86\u4ee5\u4e0b\u786c\u4ef6：<\/p>\n
\u5fc5\u9700\u786c\u4ef6：<\/p>\n
\u53ef\u9009\u786c\u4ef6：<\/p>\n
# \u4f7f\u7528Raspberry Pi Imager\u70e7\u5f55\u7cfb\u7edf
\n# \u9009\u62e9Raspberry Pi OS（64\u4f4d）Lite\u7248\u672c<\/p>\n
# \u9996\u6b21\u542f\u52a8\u914d\u7f6e
\nsudo raspi-config
\n# 1. \u66f4\u6539\u5bc6\u7801
\n# 2. \u8bbe\u7f6eWi-Fi\u56fd\u5bb6
\n# 3. \u542f\u7528SSH
\n# 4. \u542f\u7528\u6444\u50cf\u5934
\n# 5. \u6269\u5c55\u6587\u4ef6\u7cfb\u7edf
\n# 6. \u8bbe\u7f6eGPU\u5185\u5b58\u4e3a256MB<\/p>\n
# \u66f4\u65b0\u7cfb\u7edf
\nsudo apt update && sudo apt upgrade -y<\/p>\n
# \u5b89\u88c5Python\u548c\u57fa\u7840\u5de5\u5177
\nsudo apt install python3.9 python3.9-venv python3.9-dev git -y<\/p>\n
# \u5b89\u88c5\u97f3\u9891\u76f8\u5173
\nsudo apt install pulseaudio alsa-utils libportaudio2 -y<\/p>\n
# \u5b89\u88c5\u89c6\u9891\u76f8\u5173
\nsudo apt install ffmpeg libsm6 libxext6 libgl1-mesa-glx -y<\/p>\n
# \u5b89\u88c5\u8fdb\u7a0b\u7ba1\u7406
\nsudo apt install supervisor -y<\/p>\n
# \u514b\u9686\u9879\u76ee\u4ee3\u7801
\ncd \/home\/pi
\ngit clone https:\/\/github.com\/AI-FanGe\/OpenAIglasses_for_Navigation.git
\ncd AIGlasses_for_navigation<\/p>\n
# \u5207\u6362\u5230\u8f7b\u91cf\u5316\u5206\u652f
\ngit checkout lightweight<\/p>\n
# \u521b\u5efa\u865a\u62df\u73af\u5883
\npython3.9 -m venv venv
\nsource venv\/bin\/activate<\/p>\n
# \u5b89\u88c5\u4f9d\u8d56（\u4f7f\u7528\u6811\u8393\u6d3e\u4f18\u5316\u7248）
\npip install –upgrade pip setuptools wheel
\npip install -r requirements_rpi.txt<\/p>\n
# \u4e0b\u8f7d\u8f7b\u91cf\u5316\u6a21\u578b
\ncd model
\nwget https:\/\/example.com\/models\/yolo-nano-seg.pt
\nwget https:\/\/example.com\/models\/trafficlight-tiny.pt
\nwget https:\/\/example.com\/models\/shoppingbest3.pt
\ncd ..<\/p>\n
# \u6d4b\u8bd5\u6444\u50cf\u5934
\nimport cv2
\ncap = cv2.VideoCapture(0)
\nif cap.isOpened():
\n ret, frame = cap.read()
\n if ret:
\n print("\u6444\u50cf\u5934\u6d4b\u8bd5\u6210\u529f！")
\n cv2.imwrite('test_camera.jpg', frame)
\n cap.release()<\/p>\n
# \u6d4b\u8bd5\u9ea6\u514b\u98ce
\nimport pyaudio
\np = pyaudio.PyAudio()
\ninfo = p.get_host_api_info_by_index(0)
\nnumdevices = info.get('deviceCount')
\nfor i in range(numdevices):
\n device_info = p.get_device_info_by_host_api_device_index(0, i)
\n if device_info.get('maxInputChannels') > 0:
\n print(f"\u8f93\u5165\u8bbe\u5907 {i}: {device_info.get('name')}")<\/p>\n
# \u6d4b\u8bd5\u626c\u58f0\u5668
\nimport pygame
\npygame.mixer.init()
\npygame.mixer.music.load('test.mp3')
\npygame.mixer.music.play()<\/p>\n
# \u521b\u5efa\u65e5\u5fd7\u76ee\u5f55
\nmkdir -p \/home\/pi\/AIGlasses_for_navigation\/logs<\/p>\n
# \u521b\u5efaSupervisor\u914d\u7f6e
\nsudo tee \/etc\/supervisor\/conf.d\/aiglasses.conf << 'EOF'
\n[program:aiglasses]
\ncommand=\/home\/pi\/AIGlasses_for_navigation\/venv\/bin\/python \/home\/pi\/AIGlasses_for_navigation\/app_main.py
\ndirectory=\/home\/pi\/AIGlasses_for_navigation
\nautostart=true
\nautorestart=true
\nstartretries=3
\nuser=pi
\nenvironment=HOME="\/home\/pi",USER="pi"
\nstdout_logfile=\/home\/pi\/AIGlasses_for_navigation\/logs\/supervisor.log
\nstdout_logfile_maxbytes=50MB
\nstdout_logfile_backups=10
\nstderr_logfile=\/home\/pi\/AIGlasses_for_navigation\/logs\/supervisor_err.log
\nstderr_logfile_maxbytes=50MB
\nstderr_logfile_backups=10
\nEOF<\/p>\n
# \u5e94\u7528\u914d\u7f6e
\nsudo supervisorctl reread
\nsudo supervisorctl update
\nsudo supervisorctl start aiglasses<\/p>\n
# \u83b7\u53d6\u6811\u8393\u6d3eIP\u5730\u5740
\nhostname -I<\/p>\n
# \u5728\u6d4f\u89c8\u5668\u4e2d\u8bbf\u95ee（\u5047\u8bbeIP\u662f192.168.1.100）
\n# http:\/\/192.168.1.100:8081<\/p>\n
# \u914d\u7f6e\u963f\u91cc\u4e91API Key
\n# 1. \u70b9\u51fb\u53f3\u4e0a\u89d2\u9f7f\u8f6e\u56fe\u6807
\n# 2. \u8f93\u5165\u4f60\u7684DashScope API Key
\n# 3. \u70b9\u51fb\u4fdd\u5b58<\/p>\n
\u5728\u90e8\u7f72\u8fc7\u7a0b\u4e2d，\u4f60\u53ef\u80fd\u4f1a\u9047\u5230\u4e00\u4e9b\u95ee\u9898\u3002\u8fd9\u91cc\u5217\u51fa\u4e86\u4e00\u4e9b\u5e38\u89c1\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6cd5：<\/p>\n
\u95ee\u98981：\u6444\u50cf\u5934\u65e0\u6cd5\u8bc6\u522b<\/p>\n
# \u68c0\u67e5\u6444\u50cf\u5934\u8fde\u63a5
\nvcgencmd get_camera
\n# \u5e94\u8be5\u8fd4\u56de supported=1 detected=1<\/p>\n
# \u68c0\u67e5\u6743\u9650
\ngroups pi
\n# \u786e\u4fddpi\u7528\u6237\u5728video\u7ec4\u4e2d
\nsudo usermod -a -G video pi<\/p>\n
# \u91cd\u542f\u670d\u52a1
\nsudo supervisorctl restart aiglasses<\/p>\n
\u95ee\u98982：\u97f3\u9891\u8bbe\u5907\u95ee\u9898<\/p>\n
# \u5217\u51fa\u97f3\u9891\u8bbe\u5907
\naplay -l
\narecord -l<\/p>\n
# \u8bbe\u7f6e\u9ed8\u8ba4\u8bbe\u5907
\n# \u7f16\u8f91 \/etc\/asound.conf
\npcm.!default {
\n type plug
\n slave.pcm "hw:1,0" # \u4fee\u6539\u4e3a\u4f60\u7684\u8bbe\u5907\u53f7
\n}<\/p>\n
\u95ee\u98983：\u5185\u5b58\u4e0d\u8db3<\/p>\n
# \u542f\u7528\u4ea4\u6362\u7a7a\u95f4
\nsudo dphys-swapfile swapoff
\nsudo nano \/etc\/dphys-swapfile
\n# \u4fee\u6539 CONF_SWAPSIZE=1024
\nsudo dphys-swapfile setup
\nsudo dphys-swapfile swapon<\/p>\n
# \u6e05\u7406\u5185\u5b58
\nsudo sync && echo 3 | sudo tee \/proc\/sys\/vm\/drop_caches<\/p>\n
\u95ee\u98984：\u670d\u52a1\u542f\u52a8\u5931\u8d25<\/p>\n
# \u67e5\u770b\u8be6\u7ec6\u65e5\u5fd7
\ntail -100 \/home\/pi\/AIGlasses_for_navigation\/logs\/supervisor.log<\/p>\n
# \u68c0\u67e5\u7aef\u53e3\u5360\u7528
\nsudo netstat -tlnp | grep 8081<\/p>\n
# \u624b\u52a8\u6d4b\u8bd5\u542f\u52a8
\ncd \/home\/pi\/AIGlasses_for_navigation
\nsource venv\/bin\/activate
\npython app_main.py<\/p>\n
\u4e3a\u4e86\u8ba9\u7cfb\u7edf\u5728\u6811\u8393\u6d3e\u4e0a\u8fd0\u884c\u5f97\u66f4\u6d41\u7545，\u53ef\u4ee5\u5c1d\u8bd5\u4ee5\u4e0b\u4f18\u5316：<\/p>\n
# \u5728config.py\u4e2d\u6dfb\u52a0\u6811\u8393\u6d3e\u4e13\u7528\u914d\u7f6e
\nRASPBERRY_PI_CONFIG = {
\n 'camera_fps': 15, # \u964d\u4f4e\u5e27\u7387
\n 'inference_size': 320, # \u51cf\u5c0f\u63a8\u7406\u5c3a\u5bf8
\n 'enable_hardware_acceleration': True, # \u542f\u7528\u786c\u4ef6\u52a0\u901f
\n 'use_lightweight_models': True, # \u4f7f\u7528\u8f7b\u91cf\u5316\u6a21\u578b
\n 'audio_sample_rate': 16000, # \u964d\u4f4e\u97f3\u9891\u91c7\u6837\u7387
\n 'max_concurrent_requests': 2, # \u9650\u5236\u5e76\u53d1\u8bf7\u6c42
\n}<\/p>\n
# \u5185\u5b58\u4f18\u5316
\nimport gc
\ndef optimize_memory():
\n """\u5b9a\u671f\u6e05\u7406\u5185\u5b58"""
\n gc.collect()<\/p>\n
# \u5728\u957f\u65f6\u95f4\u8fd0\u884c\u7684\u5faa\u73af\u4e2d\u6dfb\u52a0
\nwhile True:
\n process_frame()
\n if frame_count % 100 == 0:
\n optimize_memory()<\/p>\n
\u901a\u8fc7\u524d\u9762\u7684\u8be6\u7ec6\u4ecb\u7ecd，\u6211\u4eec\u53ef\u4ee5\u770b\u5230AIGlasses_for_navigation\u5728\u4e0d\u540c\u5e73\u53f0\u4e0a\u7684\u90e8\u7f72\u5404\u6709\u7279\u70b9\u3002\u8fd9\u91cc\u505a\u4e00\u4e2a\u7b80\u5355\u7684\u5bf9\u6bd4\u603b\u7ed3：<\/p>\n
| \u8fb9\u7f18\u670d\u52a1\u5668<\/td>\n | \u2b50\u2b50\u2b50\u2b50\u2b50<\/td>\n | \u2b50\u2b50\u2b50<\/td>\n | \u2b50\u2b50\u2b50\u2b50<\/td>\n | \u56fa\u5b9a\u573a\u6240\u3001\u9ad8\u7cbe\u5ea6\u9700\u6c42<\/td>\n | \u2b50\u2b50\u2b50\u2b50\u2b50<\/td>\n<\/tr>\n |
| \u6811\u8393\u6d3e<\/td>\n | \u2b50\u2b50\u2b50<\/td>\n | \u2b50\u2b50<\/td>\n | \u2b50<\/td>\n | \u4e2a\u4eba\u4f7f\u7528\u3001\u539f\u578b\u5f00\u53d1<\/td>\n | \u2b50\u2b50\u2b50\u2b50<\/td>\n<\/tr>\n |
| \u6607\u817e\u82af\u7247<\/td>\n | \u2b50\u2b50\u2b50\u2b50<\/td>\n | \u2b50\u2b50\u2b50\u2b50<\/td>\n | \u2b50\u2b50\u2b50<\/td>\n | \u56fd\u4ea7\u5316\u9879\u76ee\u3001\u6279\u91cf\u90e8\u7f72<\/td>\n | \u2b50\u2b50\u2b50<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u8fb9\u7f18\u670d\u52a1\u5668\u9002\u5408\u5bf9\u6027\u80fd\u8981\u6c42\u9ad8\u7684\u573a\u666f，\u6bd4\u5982\u5546\u573a\u3001\u533b\u9662\u7684\u56fa\u5b9a\u5bfc\u822a\u70b9\u3002\u6811\u8393\u6d3e\u6700\u9002\u5408\u4e2a\u4eba\u5f00\u53d1\u8005\u548c\u5c0f\u89c4\u6a21\u6d4b\u8bd5，\u6210\u672c\u4f4e\u4e14\u7075\u6d3b\u6027\u9ad8\u3002\u6607\u817e\u82af\u7247\u5219\u5728\u7279\u5b9a\u9886\u57df（\u5982\u653f\u52a1\u3001\u56fd\u4f01）\u6709\u4e0d\u53ef\u66ff\u4ee3\u7684\u4f18\u52bf\u3002<\/p>\n 7.2 \u5b9e\u9645\u5e94\u7528\u5efa\u8bae<\/h4>\n\u6839\u636e\u6211\u7684\u5b9e\u8df5\u7ecf\u9a8c，\u7ed9\u4e0d\u540c\u9700\u6c42\u7684\u7528\u6237\u4e00\u4e9b\u5177\u4f53\u5efa\u8bae：<\/p>\n \u5982\u679c\u4f60\u662f\u4e2a\u4eba\u5f00\u53d1\u8005\u6216\u7814\u7a76\u8005：<\/p>\n
|