\u4e0b\u9762\uff0c\u672c\u6587\u5c06\u4f9d\u6b21\u8be6\u8ff0\u3002<\/p>\n
\u7b2c\u4e00\u6b65<\/strong>\uff0c\u914d\u7f6eOpenVINO C++\u5f00\u53d1\u73af\u5883\uff0c\u8bf7\u53c2\u8003\u300a\u5728Windows\u4e2d\u57fa\u4e8eVisual Studio\u914d\u7f6eOpenVINO C++\u5f00\u53d1\u73af\u5883\u300b<\/p>\n \u7b2c\u4e8c\u6b65<\/strong>\uff0c\u53c2\u8003\u300a\u57fa\u4e8eOpenVNO\u90e8\u7f72YOLOv5-seg\u5b9e\u65f6\u5b9e\u4f8b\u5206\u5272\u6a21\u578b\u300b \u514b\u9686YOLOv5 Github \u4ee3\u7801\u4ed3\u5230\u672c\u5730\uff0c\u7136\u540e\u8fd0\u884c\u547d\u4ee4\u83b7\u5f97 yolov5s-seg ONNX \u683c\u5f0f\u6a21\u578b\uff1ayolov5s-seg.onnx\uff1a<\/p>\n python export.py –weights yolov5s-seg.pt –include onnx<\/p>\n<\/blockquote>\n \u63a5\u7740\u8fd0\u884c\u547d\u4ee4\u83b7\u5f97yolov5s-seg IR\u683c\u5f0f\u6a21\u578b\uff1ayolov5s-seg.xml\u548cyolov5s-seg.bin\uff0c\u5982\u4e0b\u56fe\u6240\u793a<\/p>\n mo -m yolov5s-seg.onnx –compress_to_fp16<\/p>\n<\/blockquote>\n \u7b2c\u4e09\u6b65<\/strong>\uff1a\u4f7f\u7528OpenVINO Runtime C++ API\u7f16\u5199\u63a8\u7406\u7a0b\u5e8f\u3002\u4e00\u4e2a\u7aef\u5230\u7aef\u7684AI\u63a8\u7406\u7a0b\u5e8f\uff0c\u4e3b\u8981\u5305\u542b\u4e94\u4e2a\u5178\u578b\u7684\u5904\u7406\u6d41\u7a0b\uff1a<\/p>\n \u57fa\u4e8eOpenVINO Runtime C++API\u7684\u540c\u6b65\u63a8\u7406\u4ee3\u7801\u7684\u5173\u952e\u7247\u6bb5\u5982\u4e0b\u6240\u793a\uff1a<\/p>\n \u5b8c\u6574\u8303\u4f8b\u4ee3\u7801\uff1ahttps:\/\/gitee.com\/ppov-nuc\/yolov5_infer\/blob\/main\/yolov5seg_openvino_dGPU.cpp<\/p>\n<\/p>\n \u8fd0\u884c\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n Please credit: OBA Blog<\/a> » \u57fa\u4e8eOpenVINO C++ API\u90e8\u7f72YOLOv5-Seg\u5b9e\u4f8b\u5206\u5272\u6a21\u578b<\/a><\/p>","protected":false},"excerpt":{"rendered":" \u4e0a\u4e00\u7bc7\u6587\u7ae0\u300a\u57fa\u4e8eOpenVNO\u90e8\u7f72YOLOv5-seg\u5b9e\u65f6\u5b9e\u4f8b\u5206\u5272\u6a21\u578b\u300b \u4ecb\u7ecd\u4e86\u57fa\u4e8eOpenVINO Python API\u90e8\u7f72YOLOv5-Seg\u5b9e\u4f8b\u5206\u5272\u6a21\u578b\uff0c\u672c\u6587\u4ecb\u7ecd\u57fa\u4e8eOpenVINO C++ API\u90e8\u7f72YOLOv5-Seg\u5b9e\u4f8b\u5206\u5272\u6a21\u578b\uff0c\u4e3b\u8981\u6b65\u9aa4\u6709\uff1a \u914d\u7f6eOpenVINO C++\u5f00\u53d1\u73af\u5883 \u4e0b\u8f7d\u5e76\u8f6c\u6362YOLOv5-Seg\u9884\u8bad\u7ec3\u6a21\u578b \u4f7f\u7528OpenVINO Runtime C++ API\u7f16\u5199\u63a8\u7406\u7a0b\u5e8f \u4e0b\u9762\uff0c\u672c\u6587\u5c06\u4f9d\u6b21\u8be6\u8ff0\u3002 \u7b2c\u4e00\u6b65\uff0c\u914d\u7f6eOpenVINO C++\u5f00\u53d1\u73af\u5883\uff0c\u8bf7\u53c2\u8003\u300a\u5728Windows\u4e2d\u57fa\u4e8eVisual Studio\u914d\u7f6eOpenVINO C++\u5f00\u53d1\u73af\u5883\u300b \u7b2c\u4e8c\u6b65\uff0c\u53c2\u8003\u300a\u57fa\u4e8eOpenVNO\u90e8\u7f72YOLOv5-seg\u5b9e\u65f6\u5b9e\u4f8b\u5206\u5272\u6a21\u578b\u300b \u514b\u9686YOLOv5 Github \u4ee3\u7801\u4ed3\u5230\u672c\u5730\uff0c\u7136\u540e\u8fd0\u884c\u547d\u4ee4\u83b7\u5f97 yolov5s-seg ONNX \u683c\u5f0f\u6a21\u578b\uff1ayolov5s-seg.onnx\uff1a python export.py –weights yolov5s-seg.pt –include onnx \u63a5\u7740\u8fd0\u884c\u547d\u4ee4\u83b7\u5f97yolov5s-seg IR\u683c\u5f0f\u6a21\u578b\uff1ayolov5s-seg.xml\u548cyolov5s-seg.bin\uff0c\u5982\u4e0b\u56fe\u6240\u793a mo -m yolov5s-seg.onnx –compress_to_fp16 yolov5-seg ONNX\u683c\u5f0f\u548cIR\u683c\u5f0f\u6a21\u578b \u7b2c\u4e09\u6b65\uff1a\u4f7f\u7528OpenVINO Runtime C++ API\u7f16\u5199\u63a8\u7406\u7a0b\u5e8f\u3002\u4e00\u4e2a\u7aef\u5230\u7aef\u7684AI\u63a8\u7406\u7a0b\u5e8f\uff0c\u4e3b\u8981\u5305\u542b\u4e94\u4e2a\u5178\u578b\u7684\u5904\u7406\u6d41\u7a0b\uff1a \u91c7\u96c6\u56fe\u50cf&\u56fe\u50cf\u89e3\u7801 \u56fe\u50cf\u6570\u636e\u9884\u5904\u7406 AI\u63a8\u7406\u8ba1\u7b97 \u5bf9\u63a8\u7406\u7ed3\u679c\u8fdb\u884c\u540e\u5904\u7406 \u5c06\u5904\u7406\u540e\u7684\u7ed3\u679c\u96c6\u6210\u5230\u4e1a\u52a1\u6d41\u7a0b image.png \u57fa\u4e8eOpenVINO Runtime C++API\u7684\u540c\u6b65\u63a8\u7406\u4ee3\u7801\u7684\u5173\u952e\u7247\u6bb5\u5982\u4e0b\u6240\u793a\uff1a int main(int argc, char* argv[]) { \/\/ ——– Get OpenVINO runtime version ——– std::cout paddings(3); \/\/scale, half_h, half_w cv::Mat resized_img = letterbox(img, paddings); \/\/resize to (640,640) by letterbox \/\/ BGR->RGB, u8(0-255)->f32(0.0-1.0), HWC->NCHW cv::Mat blob = cv::dnn::blobFromImage(resized_img, 1 \/ 255.0, cv::Size(640, 640), cv::Scalar(0, 0, 0), true); \/\/ ——– Step 5. Feed the blob into the input node of YOLOv5 ——- \/\/ Get input port for model with one input auto input_port = compiled_model.input(); \/\/ […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-256","post","type-post","status-publish","format-standard","hentry","category-share"],"_links":{"self":[{"href":"https:\/\/obagg.com\/index.php\/wp-json\/wp\/v2\/posts\/256","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/obagg.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/obagg.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/obagg.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/obagg.com\/index.php\/wp-json\/wp\/v2\/comments?post=256"}],"version-history":[{"count":0,"href":"https:\/\/obagg.com\/index.php\/wp-json\/wp\/v2\/posts\/256\/revisions"}],"wp:attachment":[{"href":"https:\/\/obagg.com\/index.php\/wp-json\/wp\/v2\/media?parent=256"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/obagg.com\/index.php\/wp-json\/wp\/v2\/categories?post=256"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/obagg.com\/index.php\/wp-json\/wp\/v2\/tags?post=256"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
\n
![](\"https:\/\/obagg.com\/wp-content\/uploads\/2024\/11\/frc-45f55ea3b7cf37a334b490f6b00ffe5f.png\")
<\/div>\n<\/div>\n\n
![](\"https:\/\/obagg.com\/wp-content\/uploads\/2024\/11\/frc-38eff8649b88b4a36c6849eb99d74ca1.png\")
<\/div>\n<\/div>\nint main(int argc, char* argv[]) {\n \/\/ -------- Get OpenVINO runtime version --------\n std::cout paddings(3); \/\/scale, half_h, half_w\n cv::Mat resized_img = letterbox(img, paddings); \/\/resize to (640,640) by letterbox\n \/\/ BGR->RGB, u8(0-255)->f32(0.0-1.0), HWC->NCHW\n cv::Mat blob = cv::dnn::blobFromImage(resized_img, 1 \/ 255.0, cv::Size(640, 640), cv::Scalar(0, 0, 0), true);\n\n \/\/ -------- Step 5. Feed the blob into the input node of YOLOv5 -------\n \/\/ Get input port for model with one input\n auto input_port = compiled_model.input();\n \/\/ Create tensor from external memory\n ov::Tensor input_tensor(input_port.get_element_type(), input_port.get_shape(), blob.ptr(0));\n \/\/ Set input tensor for model with one input\n infer_request.set_input_tensor(input_tensor);\n\n \/\/ -------- Step 6. Start inference --------\n infer_request.infer();\n\n \/\/ -------- Step 7. Get the inference result --------\n auto detect = infer_request.get_output_tensor(0);\n auto detect_shape = detect.get_shape();\n std::cout 32 x 25600\n cv::Mat proto_buffer(proto_shape[1], proto_shape[2] * proto_shape[3], CV_32F, proto.data());\n\n \/\/ -------- Step 8. Post-process the inference result -----------\n ...\n}\n<\/code><\/pre>\n![](\"https:\/\/obagg.com\/wp-content\/uploads\/2024\/11\/frc-4265cfafc132ab9b12795053c49db092.png\")
<\/div>\n<\/div>\n