diven
2 years ago
7 changed files with 397 additions and 0 deletions
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package com.visual.open.anpr.core.domain; |
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import org.opencv.core.Mat; |
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public class BorderMat { |
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/**图片数据*/ |
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public Mat mat; |
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/**图片的缩放比率**/ |
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public float scale; |
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/**往上补充的像素宽度**/ |
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public int top; |
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/**往下补充的像素宽度**/ |
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public int bottom; |
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/**往左补充的像素宽度**/ |
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public int left; |
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/**往右补充的像素宽度**/ |
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public int right; |
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public BorderMat(Mat mat, float scale, int top, int bottom, int left, int right) { |
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this.mat = mat; |
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this.scale = scale; |
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this.top = top; |
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this.bottom = bottom; |
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this.left = left; |
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this.right = right; |
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} |
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/** |
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* 释放资源 |
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*/ |
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public void release(){ |
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if(this.mat != null){ |
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try { |
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this.mat.release(); |
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this.mat = null; |
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}catch (Exception e){ |
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e.printStackTrace(); |
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} |
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} |
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} |
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} |
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package com.visual.open.anpr.core.models; |
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import ai.onnxruntime.OnnxTensor; |
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import ai.onnxruntime.OrtSession; |
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import com.visual.open.anpr.core.base.BaseOnnxInfer; |
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import com.visual.open.anpr.core.base.PlateDetection; |
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import com.visual.open.anpr.core.domain.ImageMat; |
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import com.visual.open.anpr.core.domain.BorderMat; |
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import com.visual.open.anpr.core.domain.PlateInfo; |
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import com.visual.open.anpr.core.utils.ReleaseUtil; |
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import org.opencv.core.*; |
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import org.opencv.imgproc.Imgproc; |
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import java.util.*; |
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import java.util.stream.Collectors; |
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public class TorchPlateDetection extends BaseOnnxInfer implements PlateDetection { |
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private static int imageWidth = 640; |
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private static int imageHeight= 640; |
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private static Scalar border = new Scalar(114, 114, 114); |
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public TorchPlateDetection(String modelPath, int threads) { |
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super(modelPath, threads); |
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} |
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@Override |
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public List<PlateInfo> inference(ImageMat image, float scoreTh, float iouTh, Map<String, Object> params) { |
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OnnxTensor tensor = null; |
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OrtSession.Result output = null; |
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BorderMat makeBorderMat = null; |
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ImageMat imageMat = image.clone(); |
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try { |
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//对图像进行标准宽高的处理
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makeBorderMat = resizeAndMakeBorderMat(imageMat.toCvMat(), imageWidth, imageHeight); |
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//转换数据为张量
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tensor = ImageMat.fromCVMat(makeBorderMat.mat) |
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.blobFromImageAndDoReleaseMat(1.0/255, new Scalar(0, 0, 0), true) |
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.to4dFloatOnnxTensorAndNoReleaseMat(new float[]{1,1,1},true); |
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//ONNX推理
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output = getSession().run(Collections.singletonMap(getInputName(), tensor)); |
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float[][][] result = (float[][][]) output.get(0).getValue(); |
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//候选框的处理
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List<float[]> boxes = filterCandidateBoxes(result[0], scoreTh, iouTh, params); |
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//根据入模一起对图片的处理参数对box进行还原
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List<float[]> restoreBoxes = restoreBoxes(boxes, makeBorderMat); |
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//模型后处理，转换为标准的结构化模型
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List<PlateInfo> plateInfos = new ArrayList<>(); |
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for (float[] item : restoreBoxes){ |
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//数据模型转换
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PlateInfo plateInfo = PlateInfo.build(item[4], PlateInfo.PlateBox.build( |
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PlateInfo.Point.build( |
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clip(item[5], 0, imageMat.getWidth()), |
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clip(item[6], 0, imageMat.getHeight())), |
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PlateInfo.Point.build( |
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clip(item[7], 0, imageMat.getWidth()), |
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clip(item[8], 0, imageMat.getHeight())), |
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PlateInfo.Point.build( |
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clip(item[9], 0, imageMat.getWidth()), |
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clip(item[10], 0, imageMat.getHeight())), |
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PlateInfo.Point.build( |
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clip(item[11], 0, imageMat.getWidth()), |
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clip(item[12], 0, imageMat.getHeight())) |
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)); |
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plateInfos.add(plateInfo); |
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} |
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//返回
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return plateInfos; |
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}catch (Exception e){ |
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//抛出异常
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throw new RuntimeException(e); |
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}finally { |
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//释放资源
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if(null != tensor){ |
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ReleaseUtil.release(tensor); |
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} |
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if(null != output){ |
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ReleaseUtil.release(output); |
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} |
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if(null != makeBorderMat){ |
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ReleaseUtil.release(makeBorderMat); |
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} |
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if(null != imageMat){ |
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ReleaseUtil.release(imageMat); |
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} |
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} |
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} |
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/** |
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* 候选框的处理 |
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* @param result 预测结果 |
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* @param scoreTh 候选框的分数阈值 |
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* @param iouTh 重叠比率 |
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* @param params 额外的参数 |
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* @return |
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*/ |
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private static List<float[]> filterCandidateBoxes(float[][] result, float scoreTh, float iouTh, Map<String, Object> params){ |
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//对预测的候选框进行预处理
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List<float[]> boxesForPretreatment = pretreatmentBoxes(result, scoreTh); |
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//根据iou进行车牌框过滤
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List<float[]> boxesForNms = filterByNmsForIou(boxesForPretreatment, iouTh); |
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//返回
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return boxesForNms; |
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} |
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/** |
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* 对图像进行标准宽高的处理 |
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* @param image 原始图片 |
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* @param targetWidth 目标图片的宽度 |
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* @param targetHeight 目标图片的高度 |
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* @return |
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*/ |
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private static BorderMat resizeAndMakeBorderMat(Mat image, int targetWidth, int targetHeight){ |
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Mat resizeDst = null; |
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try { |
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int imageWidth = image.width(); |
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int imageHeight = image.height(); |
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float scaling = Math.min(1.0f * targetHeight / imageHeight, 1.0f * targetWidth / imageWidth); |
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int newHeight = Double.valueOf(imageHeight * scaling).intValue(); |
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int newWidth = Double.valueOf(imageWidth * scaling).intValue(); |
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int topOffset = Double.valueOf((targetHeight - newHeight ) / 2.0).intValue(); |
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int leftOffset = Double.valueOf((targetWidth-newWidth) / 2.0).intValue(); |
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int bottomOffset = targetHeight - newHeight -topOffset ; |
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int rightOffset = targetWidth - newWidth-leftOffset ; |
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resizeDst = new Mat(); |
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Imgproc.resize(image, resizeDst, new Size(newWidth,newHeight ), 0, 0, Imgproc.INTER_AREA); |
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Mat res = new Mat(); |
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Core.copyMakeBorder(resizeDst, res, topOffset, bottomOffset, leftOffset, rightOffset, Core.BORDER_CONSTANT, border); |
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return new BorderMat(res, scaling, topOffset, bottomOffset, leftOffset, rightOffset); |
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}finally { |
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ReleaseUtil.release(resizeDst); |
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} |
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} |
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/** |
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* 对预测的候选框进行预处理 |
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* @param result 模型预测的候选框 |
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* @param scoreThresh 候选框的分数阈值 |
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* @return 处理后的待选框 |
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*/ |
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private static List<float[]> pretreatmentBoxes(float[][] result, float scoreThresh){ |
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return |
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Arrays.stream(result) |
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.filter(item -> item[4] > scoreThresh) |
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.map(item -> { |
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float[] temp = new float[14]; |
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//计算分数
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item[13] = item[13] * item[4]; |
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item[14] = item[14] * item[4]; |
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//计算坐标
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temp[0] = item[0] - item[2] / 2; |
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temp[1] = item[1] - item[3] / 2; |
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temp[2] = item[0] + item[2] / 2; |
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temp[3] = item[1] + item[3] / 2; |
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//计算车牌的预测分数
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temp[4] = Math.max(item[13], item[14]); |
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//标记点数据
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temp[5] = item[5]; |
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temp[6] = item[6]; |
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temp[7] = item[7]; |
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temp[8] = item[8]; |
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temp[9] = item[9]; |
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temp[10] = item[10]; |
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temp[11] = item[11]; |
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temp[12] = item[12]; |
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//计算是双层还是单层车牌
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temp[13] = item[13] >= item[14] ? 0 : 1; |
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return temp; |
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}) |
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.sorted((a, b) -> Float.compare(b[4], a[4])) |
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.collect(Collectors.toList()); |
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} |
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/** |
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* 根据iou进行车牌框过滤 |
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* @param boxes 待处理的boxes |
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* @param iouTh 重叠比率 |
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* @return 过滤后的车牌坐标 |
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*/ |
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private static List<float[]> filterByNmsForIou(List<float[]>boxes, float iouTh){ |
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List<float[]> result = new ArrayList<>(); |
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while(!boxes.isEmpty()){ |
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Iterator<float[]> iterator = boxes.iterator(); |
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//获取第一个元素，并删除元素
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float[] firstFace = iterator.next(); |
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iterator.remove(); |
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//对比后面元素与第一个元素之间的iou
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while (iterator.hasNext()) { |
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float[] nextFace = iterator.next(); |
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float x1=Math.max(firstFace[0], nextFace[0]); |
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float y1=Math.max(firstFace[1], nextFace[1]); |
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float x2=Math.min(firstFace[2], nextFace[2]); |
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float y2=Math.min(firstFace[3], nextFace[3]); |
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float w = Math.max(0, x2-x1); |
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float h = Math.max(0, y2-y1); |
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float inter_area = w * h; |
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float union_area = (firstFace[2] - firstFace[0]) * (firstFace[3] - firstFace[1]) + |
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(nextFace[2] - nextFace[0]) * (nextFace[3] - nextFace[1]); |
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float iou = inter_area/(union_area-inter_area); |
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if(iou >= iouTh){ |
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iterator.remove(); |
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} |
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} |
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result.add(firstFace); |
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} |
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return result; |
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} |
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/** |
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* 根据入模一起对图片的处理参数对box进行还原 |
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* @param boxes 候选框 |
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* @param border 边框及缩放信息 |
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* @return |
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*/ |
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private static List<float[]> restoreBoxes(List<float[]>boxes, BorderMat border){ |
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return boxes.stream().peek(item -> { |
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item[0] = (item[0] - border.left) / border.scale; |
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item[2] = (item[2] - border.left) / border.scale; |
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item[5] = (item[5] - border.left) / border.scale; |
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item[7] = (item[7] - border.left) / border.scale; |
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item[9] = (item[9] - border.left) / border.scale; |
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item[11] = (item[11] - border.left) / border.scale; |
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item[1] = (item[1] - border.top) / border.scale; |
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item[3] = (item[3] - border.top) / border.scale; |
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item[6] = (item[6] - border.top) / border.scale; |
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item[8] = (item[8] - border.top) / border.scale; |
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item[10] = (item[10] - border.top) / border.scale; |
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item[12] = (item[12] - border.top) / border.scale; |
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}).collect(Collectors.toList()); |
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} |
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/** |
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* 边框数据清洗 |
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* @param value |
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* @param min |
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* @param max |
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* @return |
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*/ |
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private static int clip(double value, int min, int max){ |
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if(value > max){ |
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return max; |
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} |
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if(value < min){ |
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return min; |
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} |
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return Double.valueOf(value).intValue(); |
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} |
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} |
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@ -0,0 +1,50 @@ |
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package com.visual.open.anpr.core.models; |
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import ai.onnxruntime.OrtEnvironment; |
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import org.opencv.core.Core; |
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import org.opencv.core.Mat; |
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import org.opencv.core.Scalar; |
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import org.opencv.core.Size; |
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import org.opencv.imgcodecs.Imgcodecs; |
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import org.opencv.imgproc.Imgproc; |
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public class TestMain01 { |
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//静态加载动态链接库
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static{ nu.pattern.OpenCV.loadShared(); } |
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private OrtEnvironment env = OrtEnvironment.getEnvironment(); |
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public static void my_letter_box(Mat image, int imageWidth, int imageHeight){ |
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int w = image.width(); |
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int h = image.height(); |
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double r = Math.min(1.0 * imageHeight / h, 1.0 * imageWidth / w); |
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System.out.println(r); |
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int new_h = Double.valueOf(h*r).intValue(); |
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int new_w = Double.valueOf(w*r).intValue(); |
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int top = Double.valueOf((imageHeight - new_h) / 2.0).intValue(); |
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int left = Double.valueOf((imageWidth-new_w) / 2.0).intValue(); |
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System.out.println(top); |
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System.out.println(left); |
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int bottom = imageHeight - new_h-top ; |
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int right = imageWidth - new_w-left ; |
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System.out.println(bottom); |
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System.out.println(right); |
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Mat resizeDst = new Mat(); |
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Imgproc.resize(image, resizeDst, new Size(new_w,new_h), 0, 0, Imgproc.INTER_AREA); |
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Mat res = new Mat(); |
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Core.copyMakeBorder(resizeDst, res, top, bottom, left, right, Core.BORDER_CONSTANT, new Scalar(114,114,114)); |
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Imgcodecs.imwrite("res.jpg", res); |
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} |
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public static void main(String[] args) { |
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String imagePath = "open-anpr-core/src/test/resources/images/imagetmp.jpg"; |
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Mat image = Imgcodecs.imread(imagePath); |
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my_letter_box(image, 640, 640); |
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} |
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} |
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@ -0,0 +1,42 @@ |
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package com.visual.open.anpr.core.models; |
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import com.visual.open.anpr.core.domain.DrawImage; |
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import com.visual.open.anpr.core.domain.ImageMat; |
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import com.visual.open.anpr.core.domain.PlateInfo; |
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import java.awt.*; |
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import java.util.HashMap; |
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import java.util.List; |
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public class TorchPlateDetectionTest { |
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public static void main(String[] args) { |
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TorchPlateDetection torchPlateDetection = new TorchPlateDetection("open-anpr-core/src/main/resources/models/plate_detect.onnx", 1); |
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String imagePath = "/Users/diven/workspace/idea/gitee/open-anpr/open-anpr-core/src/test/resources/images/image003.jpg"; |
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// String imagePath = "/Users/diven/workspace/pycharm/github/Chinese_license_plate_detection_recognition/imgs3/double_yellow.jpg";
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ImageMat imageMat = ImageMat.fromImage(imagePath); |
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List<PlateInfo> plateInfos = torchPlateDetection.inference(imageMat, 0.3f,0.5f, new HashMap<>()); |
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System.out.println(plateInfos); |
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DrawImage drawImage = DrawImage.build(imagePath); |
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for(PlateInfo plateInfo : plateInfos){ |
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PlateInfo.Point [] points = plateInfo.box.toArray(); |
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for(int i =0; i< points.length; i++){ |
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if(i+1 == points.length){ |
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drawImage.drawLine( |
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new DrawImage.Point((int)points[i].x, (int)points[i].y), |
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new DrawImage.Point((int)points[0].x, (int)points[0].y), |
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2, Color.RED |
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); |
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}else{ |
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drawImage.drawLine( |
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new DrawImage.Point((int)points[i].x, (int)points[i].y), |
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new DrawImage.Point((int)points[i+1].x, (int)points[i+1].y), |
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2, Color.RED |
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); |
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} |
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} |
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} |
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ImageMat.fromCVMat(drawImage.toMat()).imShow(); |
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} |
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|
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} |
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