Metadata-Version: 2.1
Name: mmkit-features
Version: 0.0.1a4
Summary: A multimodal architecture to build multimodal knowledge graphs with flexible multimodal feature extraction and dynamic multimodal concept generation.
Home-page: https://github.com/dhchenx/mmkit-features
Author: Donghua Chen
Author-email: douglaschan@126.com
License: MIT
Project-URL: Bug Reports, https://github.com/dhchenx/mmkit-features/issues
Project-URL: Source, https://github.com/dhchenx/mmkit-features
Description: # MMKit-Features: Multimodal Feature Extraction Toolkit
        
        Traditional knowledge graphs (KGs) are usually comprised of entities, relationships, and attributes. However, they are not designed to effectively store or represent multimodal data. This limitation prevents them from capturing and integrating information from different modes of data, such as text, images, and audio, in a meaningful and holistic way.
        
        The `MMKit-Features` project proposes a multimodal architecture to build multimodal knowledge graphs with flexible multimodal feature extraction and dynamic multimodal concept generation. 
        
        ## Project Goal
        - To extract, store, and fuse various multimodal features from multimodal datasets efficiently;
        - To achieve generative adversarial network(GAN)-based multimodal knowledge representation dynamically in multimodal knowledge graphs;
        - To provide a common deep learning-based architecture to enhance multimodal knowledge reasoning in real life. 
        
        ## Installation
        
        You can install this toolkit using our [PyPi](https://pypi.org/project/mmkit-features/) package. 
        
        ```
            pip install mmkit-features
        ```
        
        ## Design Science Framework
        
        ![Multimodal Computational Sequence](https://dhchenx.github.io/projects/mmk-features/images/multimodal-computational-sequence.jpg)
        
        Figure 1: Multimodal Computational Sequence
        
        ![GAN-based Multimodal Concept Generation](https://dhchenx.github.io/projects/mmk-features/images/gan-based-cross-modal-generation.jpg)
        
        Figure 2: GAN-based Multimodal Concept Generation
        
        ## Modalities
        
        1. Text/Language modality
        2. Image modality
        3. Video modality
        4. Audio modality
        5. Cross-modality among above
        
        ## Usage
        A toy example showing how to build a multimodal feature (MMF) library is here:
        
        ```python
        from mmkfeatures.fusion.mm_features_lib import MMFeaturesLib
        from mmkfeatures.fusion.mm_features_node import MMFeaturesNode
        import numpy as np
        if __name__ == "__main__":
            # 1. create an empty multimodal features library with root and dataset names
            feature_lib = MMFeaturesLib(root_name="test features",dataset_name = "test_features")
            # 2. set short names for each dimension for convenience
            feature_lib.set_features_name(["feature1","feature2","feature3"])
            # 3. set a  list of content IDs
            content_ids = ["content1","content2","content3"]
            # 4. according to IDs, assign a group of features with interval to corresponding content ID
            features_dict = {}
            for id in content_ids:
                mmf_node = MMFeaturesNode(id)
                mmf_node.set_item("name",str(id))
                mmf_node.set_item("features",np.array([[1,2,3]]))
                mmf_node.set_item("intervals",np.array([[0,1]]))
                features_dict[id] = mmf_node
            # 5. set the library's data
            feature_lib.set_data(features_dict)
            # 6. save the features to disk for future use
            feature_lib.save_data("test6_feature.csd")
            # 7. check structure of lib file with the format of h5py
            feature_lib.show_structure("test6_feature.csd")
            # 8. have a glance of features content within the dataset
            feature_lib.show_sample_data("test6_feature.csd")
            # 9. Finally, we construct a simple multimodal knowledge base. 
        ```
        
        Further instructions on the toolkit refers to [here](https://github.com/dhchenx/mmkit-features/tree/main/doc). 
        
        
        ## Applications
        
        Here are some examples of using our work in real life with codes and documents. 
        
        ### 1. Multimodal Features Extractors
        
        - [Text Features Extraction](doc/text_features_extraction.md)
        - [Speech Features Extraction](doc/speech_features_extraction.md)
        - [Image Features Extractoin](doc/image_features_extraction.md)
        - [Video Features Extraction](doc/video_features_extraction.md)
        - [Transformer-based Features Extraction](src/mmkfeatures/transformer/README.md)
        
        ### 2. Multimodal Feature Library (MMFLib)
        
        - [Basic Computational Sequence](doc/simple_computational_seq_use.md)
        - [Core use of MMFLib](doc/multimodal_features_library.md)
        
        ### 3. Multimodal Knowledge Bases
        
        - [Multimodal Birds Feature Library](doc/example_bird_library.md)
        - [Multimodal Disease Coding Feature Library](doc/example_icd11_library.md)
        - [Multimodal ROCO Feature Library](examples/roco_lib/step1_create_lib_roco.py)
        
        ### 4. Multimodal Indexing and Querying
        
        - [Brute Force Indexing](examples/birds_features_lib/step3_use_index.py)
        - [Inverted Indexing](examples/birds_features_lib/step3_use_index.py)
        - [Positional Indexing](examples/birds_features_lib/step3_use_index.py)
        - [Multimodal Indexing and querying](examples/birds_features_lib/evaluate/)
        
        ## Credits
        
        The project includes some source codes from various open-source contributors. Here is a list of their contributions. 
        
        1. [A2Zadeh/CMU-MultimodalSDK](https://github.com/A2Zadeh/CMU-MultimodalSDK)
        2. [aishoot/Speech_Feature_Extraction](https://github.com/aishoot/Speech_Feature_Extraction)
        3. [antoine77340/video_feature_extractor](https://github.com/antoine77340/video_feature_extractor)
        4. [jgoodman8/py-image-features-extractor](https://github.com/jgoodman8/py-image-features-extractor)
        5. [v-iashin/Video Features](https://v-iashin.github.io/video_features/)
        
        ## License
        
        The `mmkit-features` project is provided by [Donghua Chen](https://github.com/dhchenx) with MIT license. 
        
        ## Citation
        
        Please cite our project if the project is used in your research. 
        
        Chen, D. (2023). MMKit-Features: Multimodal Features Extraction Toolkit (Version 0.0.2) [Computer software]
        
        
Keywords: multimodal features,multimodal data,knowledge base
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.6, <4
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: test
