hyperpose.Model.openpose package¶
Subpackages¶
Submodules¶
hyperpose.Model.openpose.define module¶
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class
hyperpose.Model.openpose.define.CocoPart(value)¶ Bases:
enum.EnumAn enumeration.
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Background= 18¶
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LAnkle= 13¶
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LEar= 17¶
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LElbow= 6¶
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LEye= 15¶
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LHip= 11¶
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LKnee= 12¶
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LShoulder= 5¶
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LWrist= 7¶
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Neck= 1¶
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Nose= 0¶
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RAnkle= 10¶
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REar= 16¶
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RElbow= 3¶
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REye= 14¶
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RHip= 8¶
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RKnee= 9¶
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RShoulder= 2¶
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RWrist= 4¶
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class
hyperpose.Model.openpose.define.MpiiPart(value)¶ Bases:
enum.EnumAn enumeration.
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Background= 15¶
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Center= 14¶
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Headtop= 0¶
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LAnkle= 13¶
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LElbow= 6¶
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LHip= 11¶
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LKnee= 12¶
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LShoulder= 5¶
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LWrist= 7¶
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Neck= 1¶
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RAnkle= 10¶
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RElbow= 3¶
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RHip= 8¶
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RKnee= 9¶
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RShoulder= 2¶
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RWrist= 4¶
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hyperpose.Model.openpose.define.get_coco_flip_list()¶
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hyperpose.Model.openpose.define.get_mpii_flip_list()¶
hyperpose.Model.openpose.eval module¶
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hyperpose.Model.openpose.eval.evaluate(model, dataset, config, vis_num=30, total_eval_num=10000, enable_multiscale_search=True)¶ evaluate pipeline of Openpose class models
input model and dataset, the evaluate pipeline will start automaticly the evaluate pipeline will: 1.loading newest model at path ./save_dir/model_name/model_dir/newest_model.npz 2.perform inference and parsing over the chosen evaluate dataset 3.visualize model output in evaluation in directory ./save_dir/model_name/eval_vis_dir 4.output model metrics by calling dataset.official_eval()
- Parameters
- arg1tensorlayer.models.MODEL
a preset or user defined model object, obtained by Model.get_model() function
- arg2dataset
a constructed dataset object, obtained by Dataset.get_dataset() function
- arg3Int
an Integer indicates how many model output should be visualized
- arg4Int
an Integer indicates how many images should be evaluated
- Returns
- None
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hyperpose.Model.openpose.eval.infer_one_img(model, post_processor, img, img_id=- 1, enable_multiscale_search=False, is_visual=False, save_dir='./vis_dir')¶
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hyperpose.Model.openpose.eval.multiscale_search(img, model)¶
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hyperpose.Model.openpose.eval.test(model, dataset, config, vis_num=30, total_test_num=10000, enable_multiscale_search=True)¶ evaluate pipeline of Openpose class models
input model and dataset, the evaluate pipeline will start automaticly the evaluate pipeline will: 1.loading newest model at path ./save_dir/model_name/model_dir/newest_model.npz 2.perform inference and parsing over the chosen evaluate dataset 3.visualize model output in evaluation in directory ./save_dir/model_name/eval_vis_dir 4.output model metrics by calling dataset.official_eval()
- Parameters
- arg1tensorlayer.models.MODEL
a preset or user defined model object, obtained by Model.get_model() function
- arg2dataset
a constructed dataset object, obtained by Dataset.get_dataset() function
- arg3Int
an Integer indicates how many model output should be visualized
- arg4Int
an Integer indicates how many images should be evaluated
- Returns
- None
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hyperpose.Model.openpose.eval.visualize(img, img_id, humans, conf_map, paf_map, save_dir)¶
hyperpose.Model.openpose.infer module¶
hyperpose.Model.openpose.train module¶
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hyperpose.Model.openpose.train.get_paramed_map_fn(augmentor, preprocessor, data_format='channels_first')¶
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hyperpose.Model.openpose.train.parallel_train(train_model, dataset, config)¶ Parallel train pipeline of openpose class models
input model and dataset, the train pipeline will start automaticly the train pipeline will: 1.store and restore ckpt in directory ./save_dir/model_name/model_dir 2.log loss information in directory ./save_dir/model_name/log.txt 3.visualize model output periodly during training in directory ./save_dir/model_name/train_vis_dir the newest model is at path ./save_dir/model_name/model_dir/newest_model.npz
- Parameters
- arg1tensorlayer.models.MODEL
a preset or user defined model object, obtained by Model.get_model() function
- arg2dataset
a constructed dataset object, obtained by Dataset.get_dataset() function
- Returns
- None
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hyperpose.Model.openpose.train.single_train(train_model, dataset, config)¶ Single train pipeline of Openpose class models
input model and dataset, the train pipeline will start automaticly the train pipeline will: 1.store and restore ckpt in directory ./save_dir/model_name/model_dir 2.log loss information in directory ./save_dir/model_name/log.txt 3.visualize model output periodly during training in directory ./save_dir/model_name/train_vis_dir the newest model is at path ./save_dir/model_name/model_dir/newest_model.npz
- Parameters
- arg1tensorlayer.models.MODEL
a preset or user defined model object, obtained by Model.get_model() function
- arg2dataset
a constructed dataset object, obtained by Dataset.get_dataset() function
- Returns
- None
hyperpose.Model.openpose.utils module¶
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hyperpose.Model.openpose.utils.cal_vectormap_fast(vectormap, countmap, i, v_start, v_end)¶
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hyperpose.Model.openpose.utils.cal_vectormap_ori(vectormap, countmap, i, v_start, v_end)¶
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hyperpose.Model.openpose.utils.draw_results(images, heats_ground, heats_result, pafs_ground, pafs_result, masks, save_dir, name='', data_format='channels_first')¶ Save results for debugging.
- Parameters
- imagesa list of RGB images
- heats_grounda list of keypoint heat maps or None
- heats_resulta list of keypoint heat maps or None
- pafs_grounda list of paf vector maps or None
- pafs_resulta list of paf vector maps or None
- masksa list of mask for people
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hyperpose.Model.openpose.utils.get_colors(dataset_type)¶
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hyperpose.Model.openpose.utils.get_flip_list(dataset_type)¶
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hyperpose.Model.openpose.utils.get_heatmap(annos, height, width, hout, wout, parts, limbs, data_format='channels_first')¶
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hyperpose.Model.openpose.utils.get_limbs(dataset_type)¶
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hyperpose.Model.openpose.utils.get_parts(dataset_type)¶
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hyperpose.Model.openpose.utils.get_vectormap(annos, height, width, hout, wout, parts, limbs, data_format='channels_first')¶
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hyperpose.Model.openpose.utils.postprocess(conf_map, paf_map, img_h, img_w, parts, limbs, data_format='channels_first', colors=None)¶ postprocess function of openpose class models
take model predicted feature maps, output parsed human objects, each one contains all detected keypoints of the person
- Parameters
- arg1numpy array
model predicted conf_map, heatmaps of keypoints, shape C*H*W(channels_first) or H*W*C(channels_last)
- arg2numpy array
model predicted paf_map, heatmaps of limbs, shape C*H*W(channels_first) or H*W*C(channels_last)
- arg3Config.DATA
an enum value of enum class Config.DATA width of the model output, will be the width of the generated maps
- arg4string
data format speficied for channel order available input: ‘channels_first’: data_shape C*H*W ‘channels_last’: data_shape H*W*C
- Returns
- list
contain object of humans,see Model.Human for detail information of Human object
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hyperpose.Model.openpose.utils.preprocess(annos, img_height, img_width, model_hout, model_wout, parts, limbs, data_format='channels_first')¶ preprocess function of openpose class models
take keypoints annotations, image height and width, model input height and width, and dataset type, return the constructed conf_map and paf_map
- Parameters
- arg1list
a list of annotation, each annotation is a list of keypoints that belongs to a person, each keypoint follows the format (x,y), and x<0 or y<0 if the keypoint is not visible or not annotated. the annotations must from a known dataset_type, other wise the keypoint and limbs order will not be correct.
- arg2Int
height of the input image, need this to make use of keypoint annotation
- arg3Int
width of the input image, need this to make use of keypoint annotation
- arg4Int
height of the model output, will be the height of the generated maps
- arg5Int
width of the model output, will be the width of the generated maps
- arg6Config.DATA
a enum value of enum class Config.DATA dataset_type where the input annotation list from, because to generate correct conf_map and paf_map, the order of keypoints and limbs should be awared.
- arg7string
data format speficied for channel order available input: ‘channels_first’: data_shape C*H*W ‘channels_last’: data_shape H*W*C
- Returns
- list
including two element conf_map: heatmaps of keypoints, shape C*H*W(channels_first) or H*W*C(channels_last) paf_map: heatmaps of limbs, shape C*H*W(channels_first) or H*W*C(channels_last)
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hyperpose.Model.openpose.utils.put_heatmap(heatmap, plane_idx, center, stride, sigma)¶
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hyperpose.Model.openpose.utils.vis_annos(image, annos, save_dir, name='')¶ Save results for debugging.
- Parameters
- imagessingle RGB image
- annosannotation, list of lists
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hyperpose.Model.openpose.utils.visualize(img, conf_map, paf_map, save_name='maps', save_dir='./save_dir/vis_dir', data_format='channels_first', save_tofile=True)¶ visualize function of openpose class models
take model predict feature maps, output visualized image. the image will be saved at ‘save_dir’/’save_name’_visualize.png
- Parameters
- arg1numpy array
image
- arg2numpy array
model output conf_map, heatmaps of keypoints, shape C*H*W(channels_first) or H*W*C(channels_last)
- arg3numpy array
model output paf_map, heatmaps of limbs, shape C*H*W(channels_first) or H*W*C(channels_last)
- arg4String
specify output image name to distinguish.
- arg5String
specify which directory to save the visualized image.
- arg6string
data format speficied for channel order available input: ‘channels_first’: data_shape C*H*W ‘channels_last’: data_shape H*W*C
- Returns
- None