Inferred veins and lobes on nearly 25K grapevine leaves

An inference dataset on nearly 25K aligned grapevine leaves consisting of 1) segmented rgb images, 2) Euler characteristic transform (ECT) images, and 3) inferred veins and lobes (viz). For each of the above outputs, aligned images are saved in both Cartesian and polar coordinate systems. For the inferred images (viz) segmentation classes are saved in the following true rgb colors: midvein lobe: magenta distal lobe: yellow proximal lobe: cyan midvein: blue lateral veins: green The code to run inference on this data can be found linked below. This dataset is the folder inference_pol_cart_aligned saved to ./outputs by running the following code: Prediction of veins and lobes on nearly 25K grapevine leaves: https://zenodo.org/records/18057298 The model used for inference is linked to below:Segmentation and alignment of grapevine leaf veins and lobes in a rotationally invariant space using the Euler Characteristic Transform: https://zenodo.org/records/18040082 The following shows the history of models leading to this one. The models below are primarily used to predict the petiolar junction (which only requires prediction of the full and primary vein masks), which is used as the foundational input for this inference. Segmentation of RGB leaf masks LEAF_SEGMENTATION: https://zenodo.org/records/16883916 FINAL_MASKS: https://zenodo.org/records/16883403 Segmentation of full vein mask Vein segmentation of pre-segmented grapevine leaves using the radial Euler Characteristic Transform: https://zenodo.org/records/17741713 Segmentation of primary veins Primary vein segmentation in grapevine leaves: https://zenodo.org/records/17881166 Segmentation of midvein Midvein segmentation of grapevine leaves: https://zenodo.org/records/18005383 Gaussian heatmap estimation of petiolar junction coordinate Grapevine leaf petiolar junction detection: https://zenodo.org/records/17922822 Segmentation of midvein, distal, and proximal lobes Grapevine leaf lobe detection model: https://zenodo.org/records/18004938本数据集为包含近25000张配准葡萄叶片的推理数据集，涵盖三类数据：1）分割后的RGB图像，2）欧拉特征变换（Euler Characteristic Transform，ECT）图像，3）推理得到的叶脉与叶瓣（即）。针对上述每一类输出数据，配准后的图像均同时以笛卡尔坐标系与极坐标系格式存储。 对于推理得到的图像（即前述叶脉与叶瓣），其分割类别采用以下标准RGB颜色进行标注： 中脉叶瓣：品红色 远侧叶瓣：黄色 近侧叶瓣：青色 中脉：蓝色 侧脉：绿色 可通过下述链接获取用于该数据集的推理运行代码。本数据集对应运行下述代码后保存至./outputs目录下的inference_pol_cart_aligned文件夹： 近25000张葡萄叶片的叶脉与叶瓣预测：https://zenodo.org/records/18057298 本次推理使用的模型链接如下：基于欧拉特征变换的旋转不变空间内葡萄叶片叶脉与叶瓣的分割与配准：https://zenodo.org/records/18040082 下述内容为衍生出本模型的系列模型迭代历史。下述模型主要用于预测叶柄连接处（仅需预测完整叶脉与主叶脉掩码），该预测结果将作为本次推理的基础输入数据。 RGB叶片掩码分割 LEAF_SEGMENTATION：https://zenodo.org/records/16883916 FINAL_MASKS：https://zenodo.org/records/16883403 完整叶脉掩码分割 基于径向欧拉特征变换的预分割葡萄叶片叶脉分割：https://zenodo.org/records/17741713 主叶脉分割 葡萄叶片主叶脉分割：https://zenodo.org/records/17881166 中脉分割 葡萄叶片中脉分割：https://zenodo.org/records/18005383 叶柄连接处坐标的高斯热图估计 葡萄叶片叶柄连接处检测：https://zenodo.org/records/17922822 中脉、远侧叶瓣与近侧叶瓣分割 葡萄叶片叶瓣检测模型：https://zenodo.org/records/18004938