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 0x7f0a853d6598>)

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.visualize module

Module contents