iobjectspy.ml.vision package

Module contents

class iobjectspy.ml.vision.DataPreparation

Bases: object

Image data preparation process entry

static create_training_data(input_data, input_label, label_class_field, output_path, output_name, training_data_format, tile_format='jpg', tile_size_x=1024, tile_size_y=1024, tile_offset_x=512, tile_offset_y=512, tile_start_index=0, save_nolabel_tiles=False, input_compare_data=None, **kwargs)

Training data generation

Generate the training sample image tiles in specified size from the input imagery with labeled vector data.

The output includes pictures, annotations, and meta-information. The pictures and annotations name one-to-one correspondence.

Parameters:

• input_data (str) – input image data, support image file
• input_label (str or DatasetVector) – input vector label data, support vector dataset
• label_class_field (str or None) – the field name represents the label categpries. If ‘None’ is specified, all labels are of the same category.
• output_path (str) – output training data storage path
• training_data_format (str) – output training data format, support four different formates: ‘VOC’, ‘MULTI_C’, ‘BINARY_C’, ‘SCENE_C’.
• tile_format (str) – image tile format, support ‘tif’, ‘jpg’, ‘png’, and ‘origin formates’
• tile_size_x (int) – tile size in x direction
• tile_size_y (int) – tile size in y direction
• tile_offset_x (int) – tile offset in the x direction
• tile_offset_y (int) – tile offset in y direction
• tile_start_index (int) – the initial index value for naming the tiles. The default is 0 and set to -1 when using this function to process multiple images.
• save_nolabel_tiles (bool) – whether to save tiles without labels

Returns:

None

VOC format:

./VOC

./VOC/Annotations/000000001.xml label tiles

./VOC/Images/000000001.jpg image tiles

./VOC/ImageSets/Main/train.txt, val.txt, test.txt, trainval.txt training dataset tile name, validation dataset tile name, test dataset tile name, training dataset and validation dataset tile name

./VOC/VOC.sda training data configuration file

MULTI_C format:

./MULTI_C

./MULTI_C/Images/00000000.tif image tiles

./MULTI_C/Masks/00000000.png label tiles

./MULTI_C/MULTI_C.sda training data configuration file

BINARY_C format:

./BINARY_C

./BINARY_C/Images/00000000.tif image tiles

./BINARY_C/Masks/00000000.png label tiles

./BINARY_C/BINARY_C.sda training data configuration file

SCENE_C format:

./SCENE_C

./SCENE_C/0/00000000.tif image tiles

./SCENE_C/1/00000000.png image tiles

./SCENE_C/2/00000000.tif image tiles

….

./SCENE_C/scene_classification.csv mapping the relationship between saved image file path and the categories.

./SCENE_C/SCENE_C.sda Training data configuration file

class iobjectspy.ml.vision.ImageryEvaluation

Bases: object

static binary_classification(inference_data, ground_truth_data, inference_class_value_field=None, ground_truth_class_value_field=None, metric_type=None, out_data='', out_data_name='metric')

影像二元分类模型评估接口，可基于输入的真实标签数据和预测标签数据计算结果，支持影像和影像数据计算，矢量和矢量数据计算。

Parameters:

• inference_data (str or DatasetVector) – 必选参数。推理结果数据集，输入的矢量面数据集来自于模型推理object_detect_infer
• ground_truth_data (str or DatasetVector) – 必选参数。真实标签数据集，输入的矢量面数据集来自于真实的标签数据集
• inference_class_value_field (str or None) – 可选参数。推理结果数据包含类别字段名。如果指定的字段为None，则默认去找’value’字段，若字段不存在，则所有记录都被认定为是同一个类
• ground_truth_class_value_field (str or None) – 可选参数。真实数据类别字段名。如果指定的字段为None，则默认去找’value’字段，若字段不存在，则所有记录都被认定为是同一个类
• metric_type (str or None) – 可选参数。待计算的指标名称。默认为None，为None时输出该功能全部指标。支持的metric_type为：PA,IoU,F1,Kappa
• out_data (str or Datasource or DatasourceConnectionInfo) – 可选参数。输出文件（或数据源）路径
• out_data_name (str) – 可选参数。输出文件（或数据集）名称

Returns:

tuple (metric_type ,[dict, dict, …])

static general_change_detection(inference_data, ground_truth_data, inference_class_value_field=None, ground_truth_class_value_field=None, metric_type=None, out_data='', out_data_name='metric')

影像通用变化检测模型评估接口，可基于输入的真实标签数据和预测标签数据计算结果，支持影像和影像数据计算，矢量和矢量数据计算。

Parameters:

• inference_data (str or DatasetVector) – 必选参数。推理结果数据集，输入的矢量面数据集来自于模型推理object_detect_infer
• ground_truth_data (str or DatasetVector) – 必选参数。真实标签数据集，输入的矢量面数据集来自于真实的标签数据集
• inference_class_value_field (str or None) – 可选参数。推理结果数据包含类别字段名。如果指定的字段为None，则默认去找’value’字段，若字段不存在，则所有记录都被认定为是同一个类
• ground_truth_class_value_field (str or None) – 可选参数。真实数据类别字段名。如果指定的字段为None，则默认去找’value’字段，若字段不存在，则所有记录都被认定为是同一个类
• metric_type (str or None) – 可选参数。待计算的指标名称。默认为None，为None时输出该功能全部指标。支持的metric_type为：PA,IoU,F1,Kappa
• out_data (str or Datasource or DatasourceConnectionInfo) – 可选参数。输出文件（或数据源）路径
• out_data_name (str) – 可选参数。输出文件（或数据集）名称

Returns:

tuple (metric_type ,[dict, dict, …])

static multi_classification(inference_data, ground_truth_data, inference_class_value_field=None, ground_truth_class_value_field=None, metric_type=None, out_data='', out_data_name='metric')

影像地物分类模型评估接口，可基于输入的真实标签数据和预测标签数据计算结果，支持影像和影像数据计算，矢量和矢量数据计算。

Parameters:

• inference_data (str or DatasetVector) – 必选参数。推理结果数据集，输入的矢量面数据集来自于模型推理object_detect_infer
• ground_truth_data (str or DatasetVector) – 必选参数。真实标签数据集，输入的矢量面数据集来自于真实的标签数据集
• inference_class_value_field (str or None) – 可选参数。推理结果数据包含类别字段名。如果指定的字段为None，则默认去找’value’字段，若字段不存在，则所有记录都被认定为是同一个类
• ground_truth_class_value_field (str or None) – 可选参数。真实数据类别字段名。如果指定的字段为None，则默认去找’value’字段，若字段不存在，则所有记录都被认定为是同一个类
• metric_type (str or None) – 可选参数。待计算的指标名称。默认为None，为None时输出该功能全部指标。支持的metric_type为：F1,CPA,IoU,Kappa,mPA,mIoU
• out_data (str or Datasource or DatasourceConnectionInfo) – 可选参数。输出文件（或数据源）路径
• out_data_name (str) – 可选参数。输出文件（或数据集）名称

Returns:

tuple (metric_type ,[dict, dict, …])

static object_detection(inference_data, ground_truth_data, inference_class_value_field=None, ground_truth_class_value_field='category', metric_type=None, out_data='', out_data_name='metric', iou_thr='')

影像目标检测模型评估接口，可基于输入的预测标签数据集与真实标签数据集计算结果计算指标（只支持规则正矩形数据）

Parameters:

• inference_data (str or DatasetVector) – 必选参数。推理结果数据集，输入的矢量面数据集来自于模型推理object_detect_infer
• ground_truth_data (str or DatasetVector) – 必选参数。真实标签数据集，输入的矢量面数据集来自于真实的标签数据集
• inference_class_value_field (str or None) – 可选参数。推理结果数据包含类别字段名。如果指定的字段为None，则默认去找’value’字段，若字段不存在，则所有记录都被认定为是同一个类。
• ground_truth_class_value_field (str or None) – 可选参数。真实数据类别字段名。如果指定的字段为None，则默认去找’value’字段，若字段不存在，则所有记录都被认定为是同一个类。
• metric_type (str or None) – 可选参数。待计算的指标名称。默认为None，为None时输出该功能全部指标。支持的metric_type为：F1、Recall、Precision、AP、mAP。
• out_data (str or Datasource or DatasourceConnectionInfo) – 可选参数。输出文件（或数据源）路径
• out_data_name (str) – 可选参数。输出文件（或数据集）名称
• iou_thr (float) – 可选参数。IoU(Intersection over Union)用作评估目标检测模型准确性的阈值的交集与并集的比率.分子是推理边界框和真实边界框之间的重叠区域,分母是两个边界框合并的区域。IoU值应在0到1的范围内,[0,1] 示例：0.5

Returns:

tuple (metric_type ,[dict, dict, …])

static object_extraction(inference_data, ground_truth_data, inference_class_value_field=None, ground_truth_class_value_field='category', metric_type=None, out_data='', out_data_name='metric', iou_thr='')

影像对象提取模型评估接口，可基于输入的预测标签数据集与真实标签数据集计算结果计算指标

Parameters:

• inference_data (str or DatasetVector) – 必选参数。推理结果数据集，输入的矢量面数据集来自于模型推理object_detect_infer
• ground_truth_data (str or DatasetVector) – 必选参数。真实标签数据集，输入的矢量面数据集来自于真实的标签数据集
• inference_class_value_field (str or None) – 可选参数。推理结果数据包含类别字段名。如果指定的字段为None，则默认去找’value’字段，若字段不存在，则所有记录都被认定为是同一个类。
• ground_truth_class_value_field (str or None) – 可选参数。真实数据类别字段名。如果指定的字段为None，则默认去找’value’字段，若字段不存在，则所有记录都被认定为是同一个类。
• metric_type (str or None) – 可选参数。待计算的指标名称。默认为None，为None时输出该功能全部指标。支持的metric_type为：F1、Recall、Precision、AP、mAP。
• out_data (str or Datasource or DatasourceConnectionInfo) – 可选参数。输出文件（或数据源）路径
• out_data_name (str) – 可选参数。输出文件（或数据集）名称
• iou_thr (float) – 可选参数。IoU(Intersection over Union)用作评估目标检测模型准确性的阈值的交集与并集的比率.分子是推理边界框和真实边界框之间的重叠区域,分母是两个边界框合并的区域。IoU值应在0到1的范围内,[0,1] 示例：0.5

Returns:

tuple (metric_type ,[dict, dict, …])

super_resolution(*args, **kwargs)

class iobjectspy.ml.vision.ImageryTrainer(train_data_path, config, epoch, batch_size, lr, output_model_path, output_model_name, backbone_name, backbone_weight_path=None, log_path='./', reload_model=False, pretrained_model_path=None, gpus=[0], init_data=True, **kwargs)

Bases: object

Model training function entrance for pictures.

Parameters:

• train_data_path (str) – training data path
• config (str) – configuration file path
• epoch (int) – number of iterations
• batch_size (int) – batch size
• lr (float) – learning rate
• output_model_path (str) – output model file path
• output_model_name (str) – output model file name
• backbone_name (str) – backtrunk network name
• backbone_weight_path (str or None) – path to the trunk network model file. If it is None, the model weight is initialized randomly
• log_path (str) – log and checkpoint output paths
• reload_model (bool) – whether to reload the checkpoint model trained previously
• pretrained_model_path (str or None) – pre-trained model path (optional)

binary_classify_train()

Function entry for binary classification model training. The build model will be stored under the ‘output_model_path’ folder :return: None

general_change_detection_train()

通用变化检测模型训练功能

生成模型将存储在输入的 ‘output_model_path’ 路径下

Returns:None

multi_classify_train()

Function entry for ground object classification model training. The build model will be stored under the ‘output_model_path’ folder :return: None

object_detect_train()

Function entry for object detection model training. The build model will be stored under the ‘output_model_path’ folder :return: None

object_extract_train()

Function entry for objects classification model training. The build model will be stored under the ‘output_model_path’ folder :return: None

scene_classify_train()

Function entry for scene classification model training. The build model will be stored under the ‘output_model_path’ folder :return: None

super_resolution_train()

class iobjectspy.ml.vision.PictureTrainer(train_data_path, config, epoch, batch_size, lr, output_model_path, output_model_name, backbone_name, backbone_weight_path=None, log_path='./', reload_model=False, pretrained_model_path=None, gpus=[0], init_data=True, **kwargs)

Bases: object

Function entry for picture data model training.

Parameters:

• train_data_path (str) – training data path
• config (str) – configuration file path
• epoch (int) – number of iterations
• batch_size (int) – batch size
• lr (float) – learning rate
• output_model_path (str) – output model file path
• output_model_name (str) – output model file name
• backbone_name (str) – backtrunk network name
• backbone_weight_path (str or None) – path to the trunk network model file. If it is None, the model weight is initialized randomly
• log_path (str) – log and checkpoint output paths
• reload_model (bool) – whether to reload the checkpoint model trained previously
• pretrained_model_path (str or None) – pre-trained model path (optional)

object_detect_train()

Function entry for object detection model training. The build model will be stored under the ‘output_model_path’ folder :return: None

object_extraction_train()

目标检测模型训练功能

生成模型将存储在输入的 ‘output_model_path’ 路径下

Returns:None

picture_classify_train()

Function entry for picture classification model training. The build model will be stored under the ‘output_model_path’ folder :return: None

picture_object_extraction_train()

图片分割训练功能：基于Mask-rcnn

生成模型将存储在输入的 ‘output_model_path’ 路径下

Returns:None

class iobjectspy.ml.vision.ImageryInference(model_path, gpus=[0], batch_size=1, **kwargs)

Bases: object

Image data model inference function entry

Parameters:model_path (str) – saved model path

binary_classify_infer(*args, **kwargs)

close()

Closed the loaded model

general_changedet_infer(*args, **kwargs)

multi_classify_infer(*args, **kwargs)

object_classify_infer(input_data, input_region, field_name='class_type', **kwargs)

影像对象分类 支持tif影像数据，对应的影像区域矢量数据集

Parameters:

• input_data (str) – 待推理的数据
• input_region (DatasetVector) – 对应的矢量面数据集
• field_name (str) – 输出类别的字段名称

Returns:

数据集名称

object_detect_infer(*args, **kwargs)

object_extract_infer(*args, **kwargs)

prompt_segmentation_infer(*args, **kwargs)

scene_classify_infer(*args, **kwargs)

super_resolution_infer(*args, **kwargs)

class iobjectspy.ml.vision.PictureInference(model_path, gpus=[0], batch_size=1, **kwargs)

Bases: object

Model inference function entry for imagery data

Parameters:model_path (str) – model saved path

close()

Close the loaded model

object_detect_infer(*args, **kwargs)

object_extract_infer(*args, **kwargs)

picture_classify_infer(*args, **kwargs)

class iobjectspy.ml.vision.ModelConverter(model_path, output_model_path, **kwargs)

Bases: object

模型转换功能入口 支持输出torchscript, onnx等中间模型 支持Libtorch，ONNX Runtime，TensorRT，Ncnn，Openppl，OpenVINO等多个推理引擎

Parameters:

• model_path (str) – 模型存储路径
• output_model_path (str) – 模型输出路径

pytorch2onnx()

onnx模型转换 :return:

pytorch2torchscript()

torchscript模型转换 :return: