Wpf5jdZddlmZddlmZddlmZddlZddlmcmZ dZ d3dZ dZ d4d Z d4d Zd4d Zd4d Zd4dZd4dZd4dZd4dZd4dZd4dZd4dZd4dZd4dZd4dZd4dZd4dZd4dZd4dZd4dZd4dZ d4dZ!d4dZ"d4d Z#d4d!Z$d4d"Z%d4d#Z&d4d$Z'd4d%Z(d4d&Z)d5d'Z*d5d(Z+d5d)Z,d5d*Z-d5d+Z.d5d,Z/d5d-Z0d5d.Z1d5d/Z2d5d0Z3d5d1Z4d5d2Z5dS)6aCopyright 2019 The MediaPipe Authors. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. Utilities for creating MediaSequence functions. A set of lightweight functions to simplify access to tensorflow SequenceExample features and functions to create getters and setters for common types. )absolute_import)division)print_functionNc\tj|j|j|j|f|jS)z5Creates a new function with a default last parameter.)types FunctionType__code__ __globals____name__ __closure__)fdefaults k/var/www/html/nettyfy-visnx/env/lib/python3.11/site-packages/mediapipe/util/sequence/media_sequence_util.pyfunction_with_defaultrs,  AJ qz$J  7 77cF|t}|D] }||||<dS)aAdds functions to another module. Args: function_dict: a list of names and function references. The functions will be accessible in the new module by the name. module_dict: The results of globals() for the current module or a_module.__dict__() call. Adding values to these dicts makes the functions accessible from the module. If not specified, globals() is used. N)globals) function_dict module_dictnames radd_functions_to_moduler#s>))K,,d%d+K,,rc8|rd||fS|S)N/)joinprefixkeys r merge_prefixr3s%  88VSM " "" Jrc:t|||jjvSN)rcontextfeaturersequencers r has_contextr&:s fc " "h&6&> >>rc<|jjt||=dSr!)r"r#rr$s r clear_contextr(>s |FC88999rcf|f|jjt||jjdd<dSr!r"r#r float_listvaluerr,r%rs rset_context_floatr.Bs7 M(<445@FqqqIIIrcb|jjt||jjdSNrr*r$s rget_context_floatr1Gs2   !, c## uQ ((rcf|f|jjt||jjdd<dSr!r"r#r bytes_listr,r-s rset_context_bytesr5LB+0( < cuQQQ(((rcb|jjt||jjdSr0r3r$s rget_context_bytesr8Q(   !,vs";"; < G Ma PPrcf|f|jjt||jjdd<dSr!r"r#r int64_listr,r-s rset_context_intr=Ur6rcb|jjt||jjdSr0r;r$s rget_context_intr?Zr9rcd||jjt||jjdd<dSr!r*r-s rset_context_float_listrA^@*/ < cuQQQ(((rcV|jjt||jjSr!r*r$s rget_context_float_listrDc#   !,vs";"; < G MMrcd||jjt||jjdd<dSr!r3r-s rset_context_bytes_listrGgrBrcV|jjt||jjSr!r3r$s rget_context_bytes_listrIlrErcd||jjt||jjdd<dSr!r;r-s rset_context_int_listrKprBrcV|jjt||jjSr!r;r$s rget_context_int_listrMurErc:t|||jjvSr!)r feature_lists feature_listr$s rhas_feature_listrQys fc " "h&<&I IIrctt|||r2t|jjt||jSdSr0)rQrlenrOrPr#r$s rget_feature_list_sizerT}sXl63//:: x%2<44   1rc<|jjt||=dSr!)rOrPrr$s rclear_feature_listrVs ),vs*C*CDDDrcl|jjt||j|jjSr!rOrPrr#r+r,rindexr%rs rget_float_list_atr[/   ,\&#-F-F G O  rcl|jjt||j|jjSr!rOrPrr#r<r,rYs rget_int_list_atr_r\rcl|jjt||j|jjSr!rOrPrr#r4r,rYs rget_bytes_list_atrbr\rc||jjt||jjjdd<dSr!rOrPrr#addr+r,rvaluesr%rs radd_float_listrhL  %l63&?&?@HLLuQQQrc||jjt||jjjdd<dSr!rOrPrr#rer4r,rfs radd_bytes_listrlrirc||jjt||jjjdd<dSr!rOrPrr#rer<r,rfs r add_int_listrorircx|jjt||j|jjdSr0rXrYs r get_float_atrq6   ,\&#-F-F G O  a !!rcx|jjt||j|jjdSr0r^rYs r get_int_atrtrrrcx|jjt||j|jjdSr0rarYs r get_bytes_atrvrrrc|f|jjt||jjjdd<dSr!rdr-s r add_floatrxN ( %l63&?&?@HLLuQQQrc|f|jjt||jjjdd<dSr!rkr-s r add_bytesr{ryrc|f|jjt||jjjdd<dSr!rnr-s radd_intr}ryrc |ffd }|ffd }|ffd }|ffd }|ffd }d} d|z|d|z|d |z|d |z|d|zd z|d|zd z| i} t| |d S)aCreates accessor functions for list of bytes features. The provided functions are has_${NAME}, get_${NAME}, set_${NAME} and clear_${NAME}. Example: example = tensorflow.train.SequenceExample() set_clip_label_string(["dog", "cat"], example) if has_clip_label_string(example): clip_label_string = get_clip_label_string(example) clear_clip_label_string(example) Args: name: the name of the feature to use in function names. key: the key for this feature in the SequenceExample. prefix: a prefix to append to the key in the SequenceExample module_dict: adds the functions to the corresponding module dict. c(t||SN)rr&sequence_examplerrs r_hasz/create_bytes_list_context_feature.._has s,V < < <._get !#'7 G G GGrc,t||dSrr(rs r_clearz1create_bytes_list_context_feature.._clear#'777777rc.t|||dSr)rGr,rrrs r_setz/create_bytes_list_context_feature.._set!3'7GGGGGGrc$t|Sr!rrs r_get_keyz3create_bytes_list_context_feature.._get_key  $ $$rcTtjtjSr!tfio VarLenFeaturestringrr_get_default_parserz>create_bytes_list_context_feature.._get_default_parser 5  ry ) ))rhas_get_clear_set__key_default_parserNr rrrrrrrrrrrs ` r!create_bytes_list_context_featurers0&%+======%+HHHHHH'-888888,2HHHHHH%%%%%%*** tmT tmTov tmT tmfh tm'')< --55555rc |ffd }|ffd }|ffd }|ffd }|ffd }d} d|z|d|z|d |z|d |z|d|zd z|d|zd z| i} t| |d S)aCreates accessor functions for list of floats features. The provided functions are has_${NAME}, get_${NAME}, set_${NAME} and clear_${NAME}. Example: example = tensorflow.train.SequenceExample() set_segment_label_confidence([0.47, 0.49], example) if has_segment_label_confidence(example): confidences = get_segment_label_confidence(example) clear_segment_label_confidence(example) Args: name: the name of the feature to use in function names. key: the key for this feature in the SequenceExample. prefix: a prefix to append to the key in the SequenceExample module_dict: adds the functions to the corresponding module dict. c(t||Srrrs rrz/create_float_list_context_feature.._hasrrc(t||Sr)rDrs rrz/create_float_list_context_feature.._get rrc,t||dSrrrs rrz1create_float_list_context_feature.._clearrrc.t|||dSr)rArs rrz/create_float_list_context_feature.._setrrc$t|Sr!rrs rrz3create_float_list_context_feature.._get_keyrrcTtjtjSr!rrrfloat32rrrrz>create_float_list_context_feature.._get_default_parser 5  rz * **rrrrrrrNrrs ` r!create_float_list_context_featurers0&%+======%+HHHHHH'-888888,2HHHHHH%%%%%%+++ tmT tmTov tmT tmfh tm'')< --55555rc |ffd }|ffd }|ffd }|ffd }|ffd }d} d|z|d|z|d |z|d |z|d|zd z|d|zd z| i} t| |d S)aCreates accessor functions for list of int64 features. The provided functions are has_${NAME}, get_${NAME}, set_${NAME} and clear_${NAME}. Example: example = tensorflow.train.SequenceExample() set_clip_label_index([0, 1, 2, 3], example) if has_clip_label_index(example): clip_label_index = get_clip_label_index(example) clear_clip_label_index Args: name: the name of the feature to use in function names. key: the key for this feature in the SequenceExample. prefix: a prefix to append to the key in the SequenceExample module_dict: adds the functions to the corresponding module dict. c(t||Srrrs rrz-create_int_list_context_feature.._has8rrc(t||Sr)rMrs rrz-create_int_list_context_feature.._get;s %5f E E EErc,t||dSrrrs rrz/create_int_list_context_feature.._clear>rrc.t|||dSr)rKrs rrz-create_int_list_context_feature.._setAs!e%5fEEEEEErc$t|Sr!rrs rrz1create_int_list_context_feature.._get_keyDrrcTtjtjSr!rrrint64rrrrz._get_default_parserG 5  rx ( ((rrrrrrrNrrs ` rcreate_int_list_context_featurer%s0&%+======%+FFFFFF'-888888,2FFFFFF%%%%%%))) tmT tmTov tmT tmfh tm'')< --55555rc |ffd }|ffd }|ffd }|ffd }|ffd }d} d|z|d|z|d |z|d |z|d|zd z|d|zd z| i} t| |d S)acCreates accessor functions for single bytes features. The provided functions are has_${NAME}, get_${NAME}, set_${NAME} and clear_${NAME}. Example: example = tensorflow.train.SequenceExample() set_data_path("path_to_a_file", example) if has_data_path(example): path = get_data_path(example) clear_data_path(example) Args: name: the name of the feature to use in function names. key: the key for this feature in the SequenceExample. prefix: a prefix to append to the key in the SequenceExample module_dict: adds the functions to the corresponding module dict. c(t||Srrrs rrz*create_bytes_context_feature.._hashrrc(t||Sr)r8rs rrz*create_bytes_context_feature.._getk S"26 B B BBrc,t||dSrrrs rrz,create_bytes_context_feature.._clearnrrc.t|||dSr)r5rs rrz*create_bytes_context_feature.._setq!c5"26BBBBBBrc$t|Sr!rrs rrz.create_bytes_context_feature.._get_keytrrcVtjdtjSNr)rrFixedLenFeaturerrrrrz9create_bytes_context_feature.._get_default_parserws 5 RY / //rrrrrrrNrrs ` rcreate_bytes_context_featurerUs0&%+======%+CCCCCC'-888888,2CCCCCC%%%%%%000 tmT tmTov tmT tmfh tm'')< --55555rc |ffd }|ffd }|ffd }|ffd }|ffd }d} d|z|d|z|d |z|d |z|d|zd z|d|zd z| i} t| |d S)aCreates accessor functions for single float features. The provided functions are has_${NAME}, get_${NAME}, set_${NAME} and clear_${NAME}. Example: example = tensorflow.train.SequenceExample() set_image_frame_rate(0.47, example) if has_image_frame_rate(example): image_frame_rate = get_image_frame_rate(example) clear_image_frame_rate(example) Args: name: the name of the feature to use in function names. key: the key for this feature in the SequenceExample. prefix: a prefix to append to the key in the SequenceExample module_dict: adds the functions to the corresponding module dict. c(t||Srrrs rrz*create_float_context_feature.._hasrrc(t||Sr)r1rs rrz*create_float_context_feature.._getrrc,t||dSrrrs rrz,create_float_context_feature.._clearrrc.t|||dSr)r.rs rrz*create_float_context_feature.._setrrc$t|Sr!rrs rrz.create_float_context_feature.._get_keyrrcVtjdtjSr)rrrrrrrrz9create_float_context_feature.._get_default_parsers 5 RZ 0 00rrrrrrrNrrs ` rcreate_float_context_featurers0&%+======%+CCCCCC'-888888,2CCCCCC%%%%%%111 tmT tmTov tmT tmfh tm'')< --55555rc |ffd }|ffd }|ffd }|ffd }|ffd }d} d|z|d|z|d |z|d |z|d|zd z|d|zd z| i} t| |d S)adCreates accessor functions for single int64 features. The provided functions are has_${NAME}, get_${NAME}, set_${NAME} and clear_${NAME}. Example: example = tensorflow.train.SequenceExample() set_clip_frames(47, example) if has_clip_frames(example): clip_frames = get_clip_frames(example) clear_clip_frames(example) Args: name: the name of the feature to use in function names. key: the key for this feature in the SequenceExample. prefix: a prefix to append to the key in the SequenceExample module_dict: adds the functions to the corresponding module dict. c(t||Srrrs rrz(create_int_context_feature.._hasrrc(t||Sr)r?rs rrz(create_int_context_feature.._gets 3 0 @ @ @@rc,t||dSrrrs rrz*create_int_context_feature.._clearrrc.t|||dSr)r=rs rrz(create_int_context_feature.._sets!C 0@@@@@@rc$t|Sr!rrs rrz,create_int_context_feature.._get_keyrrcVtjdtjSr)rrrrrrrrz7create_int_context_feature.._get_default_parsers 5 RX . ..rrrrrrrNrrs ` rcreate_int_context_featurers0&%+======%+AAAAAA'-888888,2AAAAAA%%%%%%/// tmT tmTov tmT tmfh tm'')< --55555rc|ffd }|ffd }|ffd }|ffd }|ffd }|ffd } d} d|z|d |zd z|d |zd z|d |z|d |z|d |zdz| d |zdz| i} t| |dS)aCreates accessor functions for bytes feature lists. The provided functions are has_${NAME}, get_${NAME}_size, get_${NAME}_at, clear_${NAME}, and add_${NAME}. example = tensorflow.train.SequenceExample() add_image_encoded(1000000, example) add_image_encoded(2000000, example) if has_image_encoded: for i in range(get_image_encoded_size(example): image_encoded = get_image_encoded_at(i, example) clear_image_encoded(example) Args: name: the name of the feature to use in function names. key: the key for this feature in the SequenceExample. prefix: a prefix to append to the key in the SequenceExample module_dict: adds the functions to the corresponding module dict. c(t||SrrQrs rrz'create_bytes_feature_list.._has C!1& A A AArc(t||SrrTrs r _get_sizez,create_bytes_feature_list.._get_size &6v F F FFrc*t|||Sr)rvrZrrrs r_get_atz*create_bytes_feature_list.._get_at U$4V D D DDrc,t||dSrrVrs rrz)create_bytes_feature_list.._clears,V<<<<<._add! c5*6::::::rc$t|Sr!rrs rrz+create_bytes_feature_list.._get_keyrrcVtjdtjSr)rrFixedLenSequenceFeaturerrrrrz6create_bytes_feature_list.._get_default_parser s 5 ( (RY 7 77rrr_size_atradd_rrNr rrrrrrrrrrrrs ` rcreate_bytes_feature_listrsl(%+BBBBBB*0GGGGGG/5EEEEEE'-======,2;;;;;;%%%%%%888 tmT tmgy tmeWov tmT tmfh tm'')<--55555rc|ffd }|ffd }|ffd }|ffd }|ffd }|ffd } d} d|z|d |zd z|d |zd z|d |z|d |z|d |zdz| d |zdz| i} t| |dS)aCreates accessor functions for float feature lists. The provided functions are has_${NAME}, get_${NAME}_size, get_${NAME}_at, clear_${NAME}, and add_${NAME}. example = tensorflow.train.SequenceExample() add_confidence(0.47, example) add_confidence(0.49, example) if has_confidence: for i in range(get_confidence_size(example): confidence = get_confidence_at(i, example) clear_confidence(example) Args: name: the name of the feature to use in function names. key: the key for this feature in the SequenceExample. prefix: a prefix to append to the key in the SequenceExample module_dict: adds the functions to the corresponding module dict. c(t||Srrrs rrz'create_float_feature_list.._has.rrc(t||Srrrs rrz,create_float_feature_list.._get_size1rrc*t|||Sr)rqrs rrz*create_float_feature_list.._get_at4rrc,t||dSrrrs rrz)create_float_feature_list.._clear7rrc.t|||dSr)rxrs rrz'create_float_feature_list.._add:rrc$t|Sr!rrs rrz+create_float_feature_list.._get_key=rrcVtjdtjSr)rrrrrrrrz6create_float_feature_list.._get_default_parser@s 5 ( (RZ 8 88rrrrrrrrrNrrs ` rcreate_float_feature_listrsl(%+BBBBBB*0GGGGGG/5EEEEEE'-======,2;;;;;;%%%%%%999 tmT tmgy tmeWov tmT tmfh tm'')<--55555rc|ffd }|ffd }|ffd }|ffd }|ffd }|ffd } d} d|z|d |zd z|d |zd z|d |z|d |z|d |zdz| d |zdz| i} t| |dS)aCreates accessor functions for bytes feature lists. The provided functions are has_${NAME}, get_${NAME}_size, get_${NAME}_at, clear_${NAME}, and add_${NAME}. example = tensorflow.train.SequenceExample() add_image_timestamp(1000000, example) add_image_timestamp(2000000, example) if has_image_timestamp: for i in range(get_image_timestamp_size(example): timestamp = get_image_timestamp_at(i, example) clear_image_timestamp(example) Args: name: the name of the feature to use in function names. key: the key for this feature in the SequenceExample. prefix: a prefix to append to the key in the SequenceExample module_dict: adds the functions to the corresponding module dict. c(t||Srrrs rrz%create_int_feature_list.._hascrrc(t||Srrrs rrz*create_int_feature_list.._get_sizefrrc*t|||Sr)rtrs rrz(create_int_feature_list.._get_atis c5"26 B B BBrc,t||dSrrrs rrz'create_int_feature_list.._clearlrrc.t|||dSr)r}rs rrz%create_int_feature_list.._addos! C(888888rc$t|Sr!rrs rrz)create_int_feature_list.._get_keyrrrcVtjdtjSr)rrrrrrrrz4create_int_feature_list.._get_default_parserus 5 ( (RX 6 66rrrrrrrrrNrrs ` rcreate_int_feature_listrOsl(%+BBBBBB*0GGGGGG/5CCCCCC'-======,2999999%%%%%%777 tmT tmgy tmeWov tmT tmfh tm'')<--55555rc|ffd }|ffd }|ffd }|ffd }|ffd }|ffd } d} d|z|d |zd z|d |zd z|d |z|d |z|d |zdz| d |zdz| i} t| |dS)aCreates accessor functions for list of bytes feature lists. The provided functions are has_${NAME}, get_${NAME}_size, get_${NAME}_at, clear_${NAME}, and add_${NAME}. example = tensorflow.train.SequenceExample() add_bbox_label_string(["dog", "cat"], example) add_bbox_label_string(["cat", "dog"], example) if has_bbox_label_string: for i in range(get_bbox_label_string_size(example): timestamp = get_bbox_label_string_at(i, example) clear_bbox_label_string(example) Args: name: the name of the feature to use in function names. key: the key for this feature in the SequenceExample. prefix: a prefix to append to the key in the SequenceExample module_dict: adds the functions to the corresponding module dict. c(t||Srrrs rrz,create_bytes_list_feature_list.._hasrrc(t||Srrrs rrz1create_bytes_list_feature_list.._get_sizerrc*t|||Sr)rbrs rrz/create_bytes_list_feature_list.._get_at S%)9& I I IIrc,t||dSrrrs rrz.create_bytes_list_feature_list.._clearrrc.t|||dSr)rlrs rrz,create_bytes_list_feature_list.._add!3/??????rc$t|Sr!rrs rrz0create_bytes_list_feature_list.._get_keyrrcTtjtjSr!rrrrrz;create_bytes_list_feature_list.._get_default_parserrrrrrrrrrrNrrs ` rcreate_bytes_list_feature_listrsr(%+BBBBBB*0GGGGGG/5JJJJJJ'-======,2@@@@@@%%%%%%*** tmT tmgy tmeWov tmT tmfh tm'')<--55555rc|ffd }|ffd }|ffd }|ffd }|ffd }|ffd } d} d|z|d |zd z|d |zd z|d |z|d |z|d |zdz| d |zdz| i} t| |dS)aCreates accessor functions for list of float feature lists. The provided functions are has_${NAME}, get_${NAME}_size, get_${NAME}_at, clear_${NAME}, and add_${NAME}. example = tensorflow.train.SequenceExample() add_bbox_ymin([0.47, 0.49], example) add_bbox_ymin([0.49, 0.47], example) if has_bbox_ymin: for i in range(get_bbox_ymin_size(example): bbox_ymin = get_bbox_ymin_at(i, example) clear_bbox_ymin(example) Args: name: the name of the feature to use in function names. key: the key for this feature in the SequenceExample. prefix: a prefix to append to the key in the SequenceExample module_dict: adds the functions to the corresponding module dict. c(t||Srrrs rrz,create_float_list_feature_list.._hasrrc(t||Srrrs rrz1create_float_list_feature_list.._get_sizerrc*t|||Sr)r[rs rrz/create_float_list_feature_list.._get_atr rc,t||dSrrrs rrz.create_float_list_feature_list.._clearrrc.t|||dSr)rhrs rrz,create_float_list_feature_list.._addr rc$t|Sr!rrs rrz0create_float_list_feature_list.._get_keyrrcTtjtjSr!rrrrrz;create_float_list_feature_list.._get_default_parserrrrrrrrrrrNrrs ` rcreate_float_list_feature_listrsr(%+BBBBBB*0GGGGGG/5JJJJJJ'-======,2@@@@@@%%%%%%+++ tmT tmgy tmeWov tmT tmfh tm'')<--55555rc|ffd }|ffd }|ffd }|ffd }|ffd }|ffd } d} d|z|d |zd z|d |zd z|d |z|d |z|d |zdz| d |zdz| i} t| |dS)aCreates accessor functions for list of int64 feature lists. The provided functions are has_${NAME}, get_${NAME}_size, get_${NAME}_at, clear_${NAME}, and add_${NAME}. example = tensorflow.train.SequenceExample() add_bbox_label_index([47, 49], example) add_bbox_label_index([49, 47], example) if has_bbox_label_index: for i in range(get_bbox_label_index_size(example): bbox_label_index = get_bbox_label_index_at(i, example) clear_bbox_label_index(example) Args: name: the name of the feature to use in function names. key: the key for this feature in the SequenceExample. prefix: a prefix to append to the key in the SequenceExample module_dict: adds the functions to the corresponding module dict. c(t||Srrrs rrz*create_int_list_feature_list.._hasrrc(t||Srrrs rrz/create_int_list_feature_list.._get_sizerrc*t|||Sr)r_rs rrz-create_int_list_feature_list.._get_ats 3'7 G G GGrc,t||dSrrrs rrz,create_int_list_feature_list.._clear rrc.t|||dSr)rors rrz*create_int_list_feature_list.._adds!e-f======rc$t|Sr!rrs rrz.create_int_list_feature_list.._get_keyrrcTtjtjSr!rrrrrz9create_int_list_feature_list.._get_default_parserrrrrrrrrrrNrrs ` rcreate_int_list_feature_listr!sl(%+BBBBBB*0GGGGGG/5HHHHHH'-======,2>>>>>>%%%%%%))) tmT tmgy tmeWov tmT tmfh tm'')<--55555rr!)r)rN)6__doc__ __future__rrrrtensorflow.compat.v1compatv1rrrrr&r(r.r1r5r8r=r?rArDrGrIrKrMrQrTrVr[r_rbrhrlrorqrtrvrxr{r}rrrrrrrrrrrr!rrrr's('&&&&&%%%%%% !!!!!!!!!777 , , , , ????:::: (((( 3333 QQQQ3333 QQQQ0000 NNNN0000 NNNN0000 NNNNJJJJ    EEEE   !!!! !!!! !!!! !!!! !!!! !!!! #### #### #### -6-6-6-6`-6-6-6-6`-6-6-6-6`-6-6-6-6`-6-6-6-6`-6-6-6-6`26262626j26262626j26262626j26262626j26262626j262626262626r