hyperpose.Dataset.mpii_dataset package¶

Submodules¶

hyperpose.Dataset.mpii_dataset.dataset module¶

class hyperpose.Dataset.mpii_dataset.dataset.MPII_dataset(config, input_kpt_cvter=None, output_kpt_cvter=None, dataset_filter=None)¶

Bases: hyperpose.Dataset.base_dataset.Base_dataset

a dataset class specified for mpii dataset, provides uniform APIs

Methods

`get_eval_dataset`(self[, in_list])	provide uniform tensorflow dataset for evaluating
`get_train_dataset`(self[, in_list, …])	provide uniform tensorflow dataset for training
`official_eval`(self, pd_anns[, eval_dir])	providing official evaluation of MPII dataset
`prepare_dataset`(self)	download,extract, and reformat the dataset the official dataset is in .mat format, format it into json format automaticly.
`visualize`(self[, vis_num])	visualize annotations of the train dataset

generate_eval_data
generate_test_data
generate_train_data
get_colors
get_dataset_type
get_eval_datasize
get_input_kpt_cvter
get_output_kpt_cvter
get_parts
get_test_dataset
get_test_datasize
get_train_datasize
official_test
set_dataset_version
set_input_kpt_cvter
set_output_kpt_cvter

generate_eval_data(self)¶

generate_test_data(self)¶

generate_train_data(self)¶

get_colors(self)¶

get_dataset_type(self)¶

get_input_kpt_cvter(self)¶

get_output_kpt_cvter(self)¶

get_parts(self)¶

official_eval(self, pd_anns, eval_dir='./eval_dir')¶

providing official evaluation of MPII dataset

output model metrics of PCHs on mpii evaluation dataset(split automaticly)

Parameters

arg1String: A string path of the json file in the same format of cocoeval annotation file(person_keypoints_val2017.json) which contains predicted results. one can refer the evaluation pipeline of models for generation procedure of this json file.
arg2String: A string path indicates where the result json file which contains MPII PCH metrics of various keypoint saves.

Returns

None

official_test(self, pd_anns, test_dir='./test_dir')¶

prepare_dataset(self)¶

download,extract, and reformat the dataset the official dataset is in .mat format, format it into json format automaticly.

Parameters

None

Returns

None

set_input_kpt_cvter(self, input_kpt_cvter)¶

set_output_kpt_cvter(self, output_kpt_cvter)¶

visualize(self, vis_num=10)¶

visualize annotations of the train dataset

visualize the annotation points in the image to help understand and check annotation the visualized image will be saved in the “data_vis_dir” of the corresponding model directory(specified by model name). the visualized annotations are from the train dataset.

Parameters

arg1Int: An integer indicates how many images with their annotations are going to be visualized.

Returns

None

hyperpose.Dataset.mpii_dataset.dataset.init_dataset(config)¶

hyperpose.Dataset.mpii_dataset.define module¶

class hyperpose.Dataset.mpii_dataset.define.MpiiPart(value)¶

Bases: enum.Enum

An enumeration.

Headtop = 9¶

LAnkle = 5¶

LElbow = 14¶

LHip = 3¶

LKnee = 4¶

LShoulder = 13¶

LWrist = 15¶

Pelvis = 6¶

RAnkle = 0¶

RElbow = 11¶

RHip = 2¶

RKnee = 1¶

RShoulder = 12¶

RWrist = 10¶

Thorax = 7¶

UpperNeck = 8¶

static from_coco(human)¶

hyperpose.Dataset.mpii_dataset.define.opps_input_converter(mpii_kpts)¶

hyperpose.Dataset.mpii_dataset.define.opps_output_converter(kpt_list)¶

hyperpose.Dataset.mpii_dataset.define.ppn_input_converter(coco_kpts)¶

hyperpose.Dataset.mpii_dataset.define.ppn_output_converter(kpt_list)¶

hyperpose.Dataset.mpii_dataset.format module¶

class hyperpose.Dataset.mpii_dataset.format.MPIIMeta(image_path, annos_list)¶

Bases: object

Methods

to_anns_list

to_anns_list(self)¶

class hyperpose.Dataset.mpii_dataset.format.PoseInfo(image_dir, annos_path, dataset_filter=None)¶

Bases: object

Methods

get_center_list
get_headbbx_list
get_image_annos
get_image_id_list
get_image_list
get_kpt_list
get_scale_list

get_center_list(self)¶

get_headbbx_list(self)¶

get_image_annos(self)¶

get_image_id_list(self)¶

get_image_list(self)¶

get_kpt_list(self)¶

get_scale_list(self)¶

hyperpose.Dataset.mpii_dataset.format.generate_json(mat_path, is_test=False)¶

hyperpose.Dataset.mpii_dataset.generate module¶

hyperpose.Dataset.mpii_dataset.generate.generate_eval_data(eval_images_path, eval_annos_path, dataset_filter=None)¶

hyperpose.Dataset.mpii_dataset.generate.generate_test_data(test_images_path, test_annos_path)¶

hyperpose.Dataset.mpii_dataset.generate.generate_train_data(train_images_path, train_annos_path, dataset_filter=None, input_kpt_cvter=<function <lambda> at 0x7f2d22b98ea0>)¶

hyperpose.Dataset.mpii_dataset.prepare module¶

hyperpose.Dataset.mpii_dataset.prepare.prepare_dataset(dataset_path)¶

hyperpose.Dataset.mpii_dataset.utils module¶

hyperpose.Dataset.mpii_dataset.utils.affine_transform(pt, t)¶

hyperpose.Dataset.mpii_dataset.utils.get_3rd_point(a, b)¶

hyperpose.Dataset.mpii_dataset.utils.get_affine_transform(center, scale, rot, output_size, shift=array([0.0, 0.0], dtype=float32), inv=0)¶

hyperpose.Dataset.mpii_dataset.utils.get_dir(src_point, rot_rad)¶

hyperpose.Dataset.mpii_dataset package¶

Submodules¶

hyperpose.Dataset.mpii_dataset.dataset module¶

hyperpose.Dataset.mpii_dataset.define module¶

hyperpose.Dataset.mpii_dataset.format module¶

hyperpose.Dataset.mpii_dataset.generate module¶

hyperpose.Dataset.mpii_dataset.prepare module¶

hyperpose.Dataset.mpii_dataset.utils module¶

hyperpose.Dataset.mpii_dataset.visualize module¶

Module contents¶