OpenVINO---Human Pose Estimation模型C++和cpu環境下推理測試

OpenVINO的環境配置不在敘述，這裏主要採用2019R3版本。

測試環境：

win10

vs2015

硬件I74700

英特爾OpenVINO介紹如下模型：

• Object Detection Models / 目標檢測模型
• Object Recognition Models / 目標識別模型
• Reidentification Models / 迴歸模型
• Semantic Segmentation Models / 語義分割模型
• Instance Segmentation Models / 實例分割模型
• Human Pose Estimation Models / 人類姿勢識別模型
• Image Processing/ 圖像處理
• Text Detection / 文本檢測
• Text Recognition / 文本識別
• Text Spotting / 文本識別
• Action Recognition Models / 動作識別模型
• Image Retrieval / 圖像檢索
• Compressed models / 壓縮模型

void renderHumanPose(const std::vector<HumanPose>& poses, cv::Mat& image) {
		CV_Assert(image.type() == CV_8UC3);

		const std::vector<cv::Scalar> colors = {
			cv::Scalar(255, 0, 0), cv::Scalar(255, 85, 0), cv::Scalar(255, 170, 0),
			cv::Scalar(255, 255, 0), cv::Scalar(170, 255, 0), cv::Scalar(85, 255, 0),
			cv::Scalar(0, 255, 0), cv::Scalar(0, 255, 85), cv::Scalar(0, 255, 170),
			cv::Scalar(0, 255, 255), cv::Scalar(0, 170, 255), cv::Scalar(0, 85, 255),
			cv::Scalar(0, 0, 255), cv::Scalar(85, 0, 255), cv::Scalar(170, 0, 255),
			cv::Scalar(255, 0, 255), cv::Scalar(255, 0, 170), cv::Scalar(255, 0, 85)
		};
		const std::vector<std::pair<int, int> > limbKeypointsIds = {
			{ 1, 2 },{ 1, 5 },{ 2, 3 },
			{ 3, 4 },{ 5, 6 },{ 6, 7 },
			{ 1, 8 },{ 8, 9 },{ 9, 10 },
			{ 1, 11 },{ 11, 12 },{ 12, 13 },
			{ 1, 0 },{ 0, 14 },{ 14, 16 },
			{ 0, 15 },{ 15, 17 }
		};

		const int stickWidth = 4;
		const cv::Point2f absentKeypoint(-1.0f, -1.0f);
		for (const auto& pose : poses) {
			CV_Assert(pose.keypoints.size() == HumanPoseEstimator::keypointsNumber);

			for (size_t keypointIdx = 0; keypointIdx < pose.keypoints.size(); keypointIdx++) {
				if (pose.keypoints[keypointIdx] != absentKeypoint) {
					cv::circle(image, pose.keypoints[keypointIdx], 4, colors[keypointIdx], -1);
				}
			}
		}
		cv::Mat pane = image.clone();
		for (const auto& pose : poses) {
			for (const auto& limbKeypointsId : limbKeypointsIds) {
				std::pair<cv::Point2f, cv::Point2f> limbKeypoints(pose.keypoints[limbKeypointsId.first],
					pose.keypoints[limbKeypointsId.second]);
				if (limbKeypoints.first == absentKeypoint
					|| limbKeypoints.second == absentKeypoint) {
					continue;
				}

				float meanX = (limbKeypoints.first.x + limbKeypoints.second.x) / 2;
				float meanY = (limbKeypoints.first.y + limbKeypoints.second.y) / 2;
				cv::Point difference = limbKeypoints.first - limbKeypoints.second;
				double length = std::sqrt(difference.x * difference.x + difference.y * difference.y);
				int angle = static_cast<int>(std::atan2(difference.y, difference.x) * 180 / CV_PI);
				std::vector<cv::Point> polygon;
				cv::ellipse2Poly(cv::Point2d(meanX, meanY), cv::Size2d(length / 2, stickWidth),
					angle, 0, 360, 1, polygon);
				cv::fillConvexPoly(pane, polygon, colors[limbKeypointsId.second]);
			}
		}
		cv::addWeighted(image, 0.4, pane, 0.6, 0, image);
	}

 

相比於其他版本的Human Pose Estimation，openvino給出的Human Pose Estimation版本實時性發現很高，如圖在我的老cpu上fp32的精度居然可以達到17fps，而且效果精度很高，很驚喜有木有！