iscc-core 0.1.8

ISCC - Core Algorithms

iscc-core - ISCC Core Algorithms

iscc-core is a Python library that implements the core algorithms of the ISCC (International Standard Content Code)

NOTE: This is a low level reference implementation. iscc-core does not support content/metadata detection, extraction or preprocessing. For easy generation of ISCC codes see: iscc-cli

What is ISCC

The ISCC (International Standard Content Code) is an identifier for digital media assets.

An ISCC is derived algorithmically from the digital content itself, just like cryptographic hashes. However, instead of using a single cryptographic hash function to identify data only, the ISCC uses a variety of algorithms to create a composite identifier that exhibits similarity-preserving properties (soft hash).

The component-based structure of the ISCC identifies content at multiple levels of abstraction. Each component is self-describing, modular and can be used separately or in conjunction with others to aid in various content identification tasks.

The algorithmic design supports scenarios that require content deduplication, database synchronisation and indexing, integrity verification, timestamping, versioning, data provenance, similarity clustering, anomaly detection, usage tracking, allocation of royalties, fact-checking and general digital asset management use-cases.

What is iscc-core

iscc-core is the python based library of the core algorithms to create standard compliant ISCC codes. It also serves as a reference for porting ISCC to other programming languages.

ISCC Architecture

Installation

Use the package manager pip to install iscc-core.

pip install iscc-core

Quick Start

import iscc_core


meta_code = iscc_core.gen_meta_code(title="ISCC Test Document!")

print(f"Meta-Code:     ISCC:{meta_code.iscc}")
print(f"Structure:     {meta_code.code_obj.explain}\n")

# Extract text from file
with open('demo.txt', "rt", encoding='utf-8') as stream:
    text = stream.read()
    text_code = iscc_core.gen_text_code_v0(text)
    print(f"Text-Code:     ISCC:{text_code.iscc}")
    print(f"Structure:     {text_code.code_obj.explain}\n")

# Process raw bytes of textfile
with open('demo.txt', "rb") as stream:
    data_code = iscc_core.gen_data_code(stream)
    print(f"Data-Code:     ISCC:{data_code.iscc}")
    print(f"Structure:     {data_code.code_obj.explain}\n")

    stream.seek(0)
    instance_code = iscc_core.gen_instance_code(stream)
    print(f"Instance-Code: ISCC:{instance_code.iscc}")
    print(f"Structure:     {instance_code.code_obj.explain}\n")

iscc_code = iscc_core.gen_iscc_code(
    (meta_code.iscc, text_code.iscc, data_code.iscc, instance_code.iscc)
)
print(f"ISCC-CODE:     ISCC:{iscc_code.iscc}")
print(f"Structure:     {iscc_code.code_obj.explain}\n")

iscc_id = iscc_core.gen_iscc_id(chain=1, iscc_code=iscc_code.iscc, uc=7)
print(f"ISCC-ID:       ISCC:{iscc_id.iscc}")
print(f"Structure:     ISCC:{iscc_id.code_obj.explain}")

The output of this example is as follows:

Meta-Code:     ISCC:AAA3MGR7CSJ3O3D3
Structure:     META-NONE-V0-64-b61a3f1493b76c7b

Text-Code:     ISCC:EAASS2POFOWX6KDJ
Structure:     CONTENT-TEXT-V0-64-2969ee2bad7f2869

Data-Code:     ISCC:GAAZ5SQ47ZQ34A3V
Structure:     DATA-NONE-V0-64-9eca1cfe61be0375

Instance-Code: ISCC:IAASQF7FY2TLVFRC
Structure:     INSTANCE-NONE-V0-64-2817e5c6a6ba9622

ISCC-CODE:     ISCC:KAD3MGR7CSJ3O3D3FFU64K5NP4UGTHWKDT7GDPQDOUUBPZOGU25JMIQ
Structure:     ISCC-TEXT-V0-256-b61a3f1493b76c7b2969ee2bad7f28699eca1cfe61be03752817e5c6a6ba9622

ISCC-ID:       ISCC:MEASBPSKHY7KDPZIA4
Structure:     ISCC:ID-BITCOIN-V0-72-20be4a3e3ea1bf28-7

Documentation

https://iscc-core.iscc.codes

Project Status

ISCC is in the process of being standardized within ISO/TC 46/SC 9.

Maintainers

@titusz

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate.

You may also want join our developer chat on Telegram at https://t.me/iscc_dev.

Changelog

[0.1.8] - 2021-12-12

• Added conformance tests for all top level functions
• Added conformance tests to source dir
• Added conformance module with selftest function
• Changed gen_image_code to accept normalized pixels instead of stream
• Changed opts to core_opts
• Removed image pre-processing and Pillow dependency
• Fixed readability of conformance tests
• Fixed soft_hash_video_v0 to accept non-tuple sequences
• Updated example code

[0.1.7] - 2021-12-09

• Add dotenv for enviroment based configuration
• Cleanup package toplevel imports
• Return schema objects for iscc_code and iscc_id
• Exclude unset and none values from result dicts
• Add support for multiple code combinations for ISCC-CODE
• Add support for ISCC-ID based on singular Instance-Code
• Add initial conformance test system

[0.1.6] - 2021-11-29

• Show counter for ISCC-ID in Code.explain

[0.1.5] - 2021-11-28

• Fix documentation
• Change metahash creation logic
• Refactor models
• Add Content-Code-Mixed
• Add ISCC-ID
• Refactor compose to gen_iscc_code
• Refactor models to schema

[0.1.4] - 2021-11-17

• Simplified options
• Optimize video WTA-hash for use with 64-bit granular features

[0.1.3] - 2021-11-15

• Try to compile Cython/C accelerator modules when installing via pip
• Simplify soft_hash api return values
• Add .code() method to InstanceHasher, DataHasher
• Remove granular fingerprint calculation
• Add more top-level imports

[0.1.2] - 2021-11-14

• Export more functions to toplevel
• Return schema driven objects from ISCC code generators.

[0.1.1] - 2021-11-14

• Fix packaging problems

[0.1.0] - 2021-11-13

• Initial release