快速入门开发实现订单类图片识别结果抽象解析

System.load(PropertieUtil.getPropertie("这里是dll或so的完整路径");

    上限=均值+n*标准差    下限=均值-n*标准差

/**     * 利用标准差筛选     * @param values     * @return     */    private static double[] calcBestCornList(double[] values) {        // 计算标准差        StandardDeviation variance = new StandardDeviation();        double evaluate = variance.evaluate(values);        Mean mean = new Mean();        double meanValue = mean.evaluate(values);        double biggerValue = meanValue + CHOOSE_POWER * evaluate;        double smallerValue = meanValue - CHOOSE_POWER * evaluate;        List<double> selected = Lists.newArrayList();        for (double value : values) {            if (value >= smallerValue && value <= biggerValue) {                selected.add(value);            }        }        double[] selectedValue = new double[selected.size()];        for (int i = 0; i < selected.size(); i++) {            selectedValue[i] = selected.get(i);        }        logger.info("占比:{}%，筛选后角度数组：{}", (selectedValue.length / (float)values.length) * 100F, selected);        return selectedValue;    }</double>

/**     * 矫正图片，通过霍夫变换矫正     * @param oldImg 原始图片     * @param rotateParam 旋转参数     * @return     */    public static String rotateHoughLines(File oldFile, String oldImg, RotateParam rotateParam, String cid, String bankCode) throws Exception {
        Mat src= Imgcodecs.imread(oldFile.getAbsolutePath());        //读取图像到矩阵中        if(src.empty()){            throw new Exception("no file " + oldFile.getAbsolutePath());        }        // 用于计算的图片矩阵        Mat mathImg = src.clone();        // 灰度化        Imgproc.cvtColor(src, mathImg, Imgproc.COLOR_BGR2GRAY);        logger.info("二值化完成");        // 获取轮廓        Imgproc.Canny(src, mathImg, rotateParam.getCvtThreshould1(), rotateParam.getCvtThreshould2());        logger.info("轮廓完成");        // 霍夫变换获取角度，详细代码略        double corn = houghLines(mathImg, rotateParam, cid);        logger.info("霍夫变换完成，角度：{}", corn);        if(corn == 0) {            return oldImg;        }        return rotateOpenv(oldFile, corn, cid, bankCode);    }

// 按照最左上角的x坐标排序        OcrWordInfo[] sortL = NoTableParseResult.ParseUtil.bubbleSortX(ocrResponse.getPrism_wordsInfo(), false);        NoTableParseResult ntpr = new NoTableParseResult(param);        ntpr.setHeight(converImg.height());        ntpr.setWight(converImg.width());        for (int i = 0; i < sortL.length; i++) {            // 当前要比较的数据            OcrWordInfo ocrWordInfo = sortL[i];            // 处理当前列数据            ntpr.getUtil().testCurColData(ocrWordInfo);        }        // 处理最后一列        ntpr.lastCol();
        /**         * 判断是否为下一列，并处理         * @param ocrWordInfo         * @return         */        public void testCurColData(OcrWordInfo ocrWordInfo) {
            // 遍历当前列已存在的所有数据            int size = this.test.getCol().size();            if(size == 0) {                this.test.addCol(ocrWordInfo);                return;            }            for (int i = 0; i < size; i++) {                OcrWordInfo temp = this.test.getCol().get(i);                // 最右边的数据                int x1 = temp.getPos().get(1).getX();                int x2 = temp.getPos().get(2).getX();                // 当前数据最左边                int xx0 = ocrWordInfo.getPos().get(0).getX();                int xx3 = ocrWordInfo.getPos().get(3).getX();
                int threholdx = this.test.param == null ? 0 : this.test.param.getCoverColXThrehold();                if(xx0 >= (x1 - threholdx) && xx0 >= (x2 - threholdx) && xx3 >= (x1 - threholdx) && xx3 >= (x2 - threholdx)) {                    // 当前数据在右边，说明换列了!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!                    this.test.colAdd();                    this.test.addCol(ocrWordInfo);                    return;                } else {                    // 判断是否覆盖坐标                    int y0 = temp.getPos().get(0).getY();                    int y3 = temp.getPos().get(3).getY();                    int yy0 = ocrWordInfo.getPos().get(0).getY();                    int yy3 = ocrWordInfo.getPos().get(3).getY();                    int threhold = (int)Math.round((y3 - y0) * (this.test.param == null ? 0.25 : this.test.param.getCoverThrehold()));                    if(!(yy3 <= (y0 + threhold) || yy0 >= (y3 - threhold))) {                        // 当前列表数据重叠，说明换列了!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!                        this.test.colAdd();                        this.test.addCol(ocrWordInfo);                        return;                    }                }            }            // 执行到这说明没覆盖            this.test.addCol(ocrWordInfo);        }