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
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generate_eval_data
(self)¶
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generate_test_data
(self)¶
-
generate_train_data
(self)¶
-
get_colors
(self)¶
-
get_dataset_type
(self)¶
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get_input_kpt_cvter
(self)¶
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get_output_kpt_cvter
(self)¶
-
get_parts
(self)¶
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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)¶
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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
-
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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¶
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LAnkle
= 5¶
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LElbow
= 14¶
-
LHip
= 3¶
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LKnee
= 4¶
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LShoulder
= 13¶
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LWrist
= 15¶
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Pelvis
= 6¶
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RAnkle
= 0¶
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RElbow
= 11¶
-
RHip
= 2¶
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RKnee
= 1¶
-
RShoulder
= 12¶
-
RWrist
= 10¶
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Thorax
= 7¶
-
UpperNeck
= 8¶
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static
from_coco
(human)¶
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hyperpose.Dataset.mpii_dataset.define.
opps_input_converter
(mpii_kpts)¶
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hyperpose.Dataset.mpii_dataset.define.
opps_output_converter
(kpt_list)¶
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hyperpose.Dataset.mpii_dataset.define.
ppn_input_converter
(coco_kpts)¶
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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
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get_center_list
(self)¶
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get_headbbx_list
(self)¶
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get_image_annos
(self)¶
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get_image_id_list
(self)¶
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get_image_list
(self)¶
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get_kpt_list
(self)¶
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get_scale_list
(self)¶
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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)¶
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hyperpose.Dataset.mpii_dataset.generate.
generate_test_data
(test_images_path, test_annos_path)¶
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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¶
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hyperpose.Dataset.mpii_dataset.prepare.
prepare_dataset
(dataset_path)¶
hyperpose.Dataset.mpii_dataset.utils module¶
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hyperpose.Dataset.mpii_dataset.utils.
affine_transform
(pt, t)¶
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hyperpose.Dataset.mpii_dataset.utils.
get_3rd_point
(a, b)¶
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hyperpose.Dataset.mpii_dataset.utils.
get_affine_transform
(center, scale, rot, output_size, shift=array([0.0, 0.0], dtype=float32), inv=0)¶
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hyperpose.Dataset.mpii_dataset.utils.
get_dir
(src_point, rot_rad)¶