ImageKit.io Python SDK

Python SDK for ImageKit implements the new APIs and interface for different file operations.

ImageKit is complete media storage, optimization, and transformation solution that comes with an image and video CDN. It can be integrated with your existing infrastructure - storage like AWS S3, web servers, your CDN, and custom domain names, allowing you to deliver optimized images in minutes with minimal code changes.

Supported Python Versions: >=3.6

Table of contents -

• Installation
• Initialization
• Change Log
• Usage
  • URL Generation
  • File Upload
  • File Management
  • Utility Functions
• Handling errors
• Development
  • Tests
  • Sample
• Support
• Links

Installation

Go to your terminal and type the following command.

pip install imagekitio

Initialization

from imagekitio import ImageKit

imagekit = ImageKit(
    private_key='your_private_key',
    public_key='your_public_key',
    url_endpoint='your_url_endpoint'
)

Change log

This document presents a list of changes that break the existing functionality of previous versions. We try to minimize these disruptions, but they are sometimes unavoidable, especially in significant updates. Therefore, versions are marked semantically and tagged as major upgrades whenever such breaking changes occur.

Breaking History:

Changes from 3.2.0 -> 4.0.0 are listed below

1. Overlay syntax update

• In version 4.0.0, we've removed the old overlay syntax parameters for transformations, such as oi, ot, obg, and more. These parameters are deprecated and will start returning errors when used in URLs. Please migrate to the new layers syntax that supports overlay nesting, provides better positional control, and allows more transformations at the layer level. You can start with examples to learn quickly.
• You can migrate to the new layers syntax using the raw transformation parameter.

Changes from 2.2.8 -> 3.0.0 are listed below

1. Throw an Error:

What changed

• Before the upgrade, an error dict was coming in the return object of any function call. Now, SDK throws an exception in case of an error.

Who is affected?

• This affects any development in your software that calls APIs from ImageKit IO and handles errors based on what's returned.

How should I update my code?

• To avoid failures in an application, you could handle errors as documented here

Usage

You can use this Python SDK for three different kinds of methods:

• URL Generation
• File Upload
• File Management
• Utility Functions

URL Generation

1. Using Image path and endpoint (hostname)

This method allows you to create a URL using the relative file path where the image exists and the URL endpoint(url_endpoint) you want to use to access the image. You can refer to the documentation here to read more about URL endpoints in ImageKit and the section about image origins to understand about paths with different kinds of origins.

The file can be an image, video, or any other static file supported by ImageKit.

imagekit_url = imagekit.url({
    "path": "/default-image.jpg",
    "url_endpoint": "https://ik.imagekit.io/your_imagekit_id/endpoint/",
    "transformation": [{
        "height": "300",
        "width": "400",
        "raw": "ar-4-3,q-40"
    }],
})

Sample Result URL -

https://ik.imagekit.io/your_imagekit_id/endpoint/tr:h-300,w-400,ar-4-3,q-40/default-image.jpg

2. Using full image URL

This method allows you to add transformation parameters to an absolute URL using the src parameter. This method should be used if you have the complete image URL stored in your database.

image_url = imagekit.url({
    "src": "https://ik.imagekit.io/your_imagekit_id/endpoint/default-image.jpg",
    "transformation": [{
        "height": "300",
        "width": "400",
        "raw": "ar-4-3,q-40"
    }]
})

Sample Result URL -

https://ik.imagekit.io/your_imagekit_id/endpoint/default-image.jpg?tr=h-300%2Cw-400%2Car-4-3%2Cq-40

The .url() method accepts the following parameters.

Examples of generating URLs

1. Chained Transformations as a query parameter

image_url = imagekit.url({
    "path": "/default-image.jpg",
    "url_endpoint": "https://ik.imagekit.io/your_imagekit_id/endpoint/",
    "transformation": [
        {
            "height": "300",
            "width": "400"
        },
        {
            "rotation": 90
        }
    ],
    "transformation_position": "query"
})

Sample Result URL -

https://ik.imagekit.io/your_imagekit_id/endpoint/default-image.jpg?tr=h-300%2Cw-400%3Art-90

2. Sharpening, contrast transform and progressive JPG image

Add transformations like Sharpening to the URL with or without any other value. To use such transforms without specifying a value, set it as "-" in the transformation object. Otherwise, use the value that one wants to add to this transformation.

image_url = imagekit.url({
    "src": "https://ik.imagekit.io/your_imagekit_id/endpoint/default-image.jpg",
    "transformation": [{
        "format": "jpg",
        "progressive": "true",
        "effect_sharpen": "-",
        "effect_contrast": "1"
    }]
})

Sample Result URL -

# Note that because the `src` parameter is in effect, the transformation string gets added as a query parameter `tr`

https://ik.imagekit.io/your_imagekit_id/endpoint/default-image.jpg?tr=f-jpg%2Cpr-true%2Ce-sharpen%2Ce-contrast-1

3. Signed URL that expires in 300 seconds with the default URL endpoint and other query parameters

image_url = imagekit.url({
    "path": "/default-image.jpg",
    "query_parameters": {
        "p1": "123",
        "p2": "345"
    },
    "transformation": [{
        "height": "300",
        "width": "400"
    }],
    "signed": True,
    "expire_seconds": 300
})

Sample Result URL -

https://ik.imagekit.io/your_imagekit_id/tr:h-300,w-400/default-image.jpg?p1=123&p2=345&ik-t=1658899345&ik-s=8f03aca28432d4e87f697a48143efb4497bbed9e

4. Adding overlays

ImageKit.io enables you to apply overlays to images and videos using the raw parameter with the concept of layers. The raw parameter facilitates incorporating transformations directly in the URL. A layer is a distinct type of transformation that allows you to define an asset to serve as an overlay, along with its positioning and additional transformations.

Text as overlays

You can add any text string over a base video or image using a text layer (l-text).

For example:

image_url = imagekit.url({
    "path": "/default-image",
    "url_endpoint": "https://ik.imagekit.io/your_imagekit_id/endpoint/",
    "transformation": [{
        "height": "300",
        "width": "400",
        "raw": "l-text,i-Imagekit,fs-50,l-end"
    }],
})

Sample Result URL

https://ik.imagekit.io/your_imagekit_id/tr:h-300,w-400,l-text,i-Imagekit,fs-50,l-end/default-image.jpg

Image as overlays

You can add an image over a base video or image using an image layer (l-image).

For example:

image_url = imagekit.url({
    "path": "/default-image",
    "url_endpoint": "https://ik.imagekit.io/your_imagekit_id/endpoint/",
    "transformation": [{
        "height": "300",
        "width": "400",
        "raw": "l-image,i-default-image.jpg,w-100,b-10_CDDC39,l-end"
    }],
})

Sample Result URL

https://ik.imagekit.io/your_imagekit_id/tr:h-300,w-400,l-image,i-default-image.jpg,w-100,b-10_CDDC39,l-end/default-image.jpg

Solid color blocks as overlays

You can add solid color blocks over a base video or image using an image layer (l-image).

For example:

image_url = imagekit.url({
    "path": "/img/sample-video",
    "url_endpoint": "https://ik.imagekit.io/your_imagekit_id/endpoint/",
    "transformation": [{
        "height": "300",
        "width": "400",
        "raw": "l-image,i-ik_canvas,bg-FF0000,w-300,h-100,l-end"
    }],
})

Sample Result URL

https://ik.imagekit.io/your_imagekit_id/tr:h-300,w-400,l-image,i-ik_canvas,bg-FF0000,w-300,h-100,l-end/img/sample-video.mp4

5. Arithmetic expressions in transformations

ImageKit allows use of arithmetic expressions in certain dimension and position-related parameters, making media transformations more flexible and dynamic.

For example:

image_url = imagekit.url({
    "path": "/default-image.jpg",
    "url_endpoint": "https://ik.imagekit.io/your_imagekit_id/endpoint/",
    "transformation": [{
        "height": "ih_div_2",
        "width": "iw_div_4",
        "border": "cw_mul_0.05_yellow"
    }],
})

Sample Result URL

https://ik.imagekit.io/your_imagekit_id/default-image.jpg?tr=w-iw_div_4,h-ih_div_2,b-cw_mul_0.05_yellow

List of transformations

The complete list of transformations supported and their usage in ImageKit is available here. The SDK gives a name to each transformation parameter, making the code simpler, more straightforward, and readable. If a transformation is supported in ImageKit, though it cannot be found in the table below, then use the transformation code from ImageKit docs as the name when using the URL function.

If you want to generate transformations in your application and add them to the URL as it is, use the raw parameter.

File Upload

The SDK provides a simple interface using the .upload_file() method to upload files to the ImageKit Media library. It accepts all the parameters supported by the ImageKit Upload API.

The upload_file() method requires at least the file as (URL/Base64/Binary) and the file_name parameter to upload a file. The method returns a dict data in case of success, or it will throw a custom exception in case of failure. Use the options parameter to pass other parameters supported by the ImageKit Upload API. Use the same parameter name as specified in the upload API documentation.

Simple usage

from imagekitio.models.UploadFileRequestOptions import UploadFileRequestOptions

extensions = [
    {
        'name': 'remove-bg',
        'options': {
            'add_shadow': True,
            'bg_color': 'pink'
        }
    },
    {
        'name': 'google-auto-tagging',
        'minConfidence': 80,
        'maxTags': 10
    }
]

transformation = {
    'pre': 'l-text,i-Imagekit,fs-50,l-end', 
    'post': [
        {
            'type': 'transformation', 
            'value': 'w-100'
        }
    ]
}

options = UploadFileRequestOptions(
    use_unique_file_name=False,
    tags=['abc', 'def'],
    folder='/testing-python-folder/',
    is_private_file=False,
    custom_coordinates='10,10,20,20',
    response_fields=['tags', 'custom_coordinates', 'is_private_file',
                     'embedded_metadata', 'custom_metadata'],
    extensions=extensions,
    webhook_url='https://webhook.site/c78d617f-33bc-40d9-9e61-608999721e2e',
    overwrite_file=True,
    overwrite_ai_tags=False,
    overwrite_tags=False,
    overwrite_custom_metadata=True,
    custom_metadata={'testss': 12},
    transformation=transformation
)

result = imagekit.upload_file(file='<url|base_64|binary>', # required
                              file_name='my_file_name.jpg', # required
                              options=options)

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print that uploaded file's ID
print(result.file_id)

If the upload succeeds, the result will be the UploadFileResult class.

If the upload fails, the custom exception will be thrown with:

• response_help for any kind of help
• response_metadata with raw, http_status_code and headers
• message can be called to get the error message received from ImageKit's servers.

File Management

The SDK provides a simple interface for all the media APIs mentioned here to manage your files. This also returns result.

1. List & Search Files

Accepts an object specifying the parameters used to list and search files. All parameters specified in the documentation here can be passed with the correct values to get the results.

from imagekitio.models.ListAndSearchFileRequestOptions import ListAndSearchFileRequestOptions

options = ListAndSearchFileRequestOptions(
    type='file',
    sort='ASC_CREATED',
    path='/',
    search_query="created_at >= '2d' OR size < '2mb' OR format='png'",
    file_type='all',
    limit=5,
    skip=0,
    tags='Software, Developer, Engineer',
)

result = imagekit.list_files(options=options)

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print the first file's ID
print(result.list[0].file_id)

2. Get File Details

Accepts the file ID and fetches the details as per the API documentation here

file_id = "your_file_id"
result = imagekit.get_file_details(file_id=file_id)  # file_id required

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print that file's id
print(result.file_id)

3. Get File Versions

Accepts the file ID and fetches the details as per the API documentation here

file_id = "your_file_id"
result = imagekit.get_file_versions(file_id=file_id)  # file_id required


# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print that file's version id
print(result.list[0].version_info.id)

4. Get File Version details

Accepts the file_id and version_id and fetches the details as per the API documentation here

result = imagekit.get_file_version_details(
    file_id='file_id',
    version_id='version_id'
)

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print that file's id
print(result.file_id)

# print that file's version id
print(result.version_info.id)

5. Update File Details

Accepts all the parameters as per the API documentation here. The first argument to the update_file_details() method is the file ID, and a second argument is an object with the parameters to be updated.

from imagekitio.models.UpdateFileRequestOptions import UpdateFileRequestOptions

extensions = [
    {
        'name': 'remove-bg',
        'options': {
            'add_shadow': True,
            'bg_color': 'red'
        }
    },
    {
        'name': 'google-auto-tagging',
        'minConfidence': 80,
        'maxTags': 10
    }
]

options = UpdateFileRequestOptions(
    remove_ai_tags=['remove-ai-tag-1', 'remove-ai-tag-2'],
    webhook_url='url',
    extensions=extensions,
    tags=['tag-1', 'tag-2'],
    custom_coordinates='10,10,100,100',
    custom_metadata={'test': 11},
)

result = imagekit.update_file_details(file_id='62cfd39819ca454d82a07182'
        , options=options)  # required

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print that file's id
print(result.file_id)

6. Add tags

Accepts a list of file_ids and tags as a parameter to be used to add tags. All parameters specified in the API documentation here can be passed to the .add_tags() functions to get the results.

result = imagekit.add_tags(file_ids=['file-id-1', 'file-id-2'], tags=['add-tag-1', 'add-tag-2'])

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# list successfully updated file ids
print(result.successfully_updated_file_ids)

# print the first file's id
print(result.successfully_updated_file_ids[0])

7. Remove tags

Accepts a list of file_ids and tags as a parameter to be used to remove tags. All parameters specified in the API documentation here can be passed to the .remove_tags() functions to get the results.

result = imagekit.remove_tags(file_ids=['file-id-1', 'file-id-2'], tags=['remove-tag-1', 'remove-tag-2'])

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# list successfully updated file ids
print(result.successfully_updated_file_ids)

# print the first file's id
print(result.successfully_updated_file_ids[0])

8. Remove AI tags

Accepts a list of file_ids and ai_tags as a parameter to remove AI tags. All parameters specified in the API documentation here can be passed to the .remove_ai_tags() functions to get the results.

result = imagekit.remove_ai_tags(file_ids=['file-id-1', 'file-id-2'], ai_tags=['remove-ai-tag-1', 'remove-ai-tag-2'])

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# list successfully updated file ids
print(result.successfully_updated_file_ids)

# print the first file's id
print(result.successfully_updated_file_ids[0])

9. Delete File

Delete a file according to the API documentation here. It accepts the file ID of the File that has to be deleted.

file_id = "file_id"
result = imagekit.delete_file(file_id=file_id)

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

10. Delete FileVersion

Delete a file version as per the API documentation here. The method accepts the file_id and particular version id of the file that has to be deleted.

result = imagekit.delete_file_version(file_id="file_id", version_id="version_id")

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

11. Bulk File Delete by IDs

Delete a file as per the API documentation here. The method accepts a list of file IDs that have to be deleted.

result = imagekit.bulk_file_delete(file_ids=["file_id1", "file_id2"])

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# list successfully deleted file ids
print(result.successfully_deleted_file_ids)

# print the first file's id
print(result.successfully_deleted_file_ids[0])

12. Copy file

Copy a file according to the API documentation here. The method accepts source_file_path, destination_path, and include_file_versions of the file that has to be copied.

from imagekitio.models.CopyFileRequestOptions import CopyFileRequestOptions

options = \
    CopyFileRequestOptions(source_file_path='/source_file_path.jpg',
                           destination_path='/destination_path',
                           include_file_versions=True)
result = imagekit.copy_file(options=options)

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

13. Move File

Move a file as per the API documentation here. The method accepts source_file_path and destination_path of the file that has to be moved.

from imagekitio.models.MoveFileRequestOptions import MoveFileRequestOptions

options = \
    MoveFileRequestOptions(source_file_path='/source_file_path.jpg',
                           destination_path='/destination_path')
result = imagekit.move_file(options=options)

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

14. Rename File

Rename a file per the API documentation here. The method accepts the file_path, new_file_name, and purge_cache boolean that has to be renamed.

from imagekitio.models.RenameFileRequestOptions import RenameFileRequestOptions

options = RenameFileRequestOptions(file_path='/file_path.jpg',
                                   new_file_name='new_file_name.jpg',
                                   purge_cache=True)
result = imagekit.rename_file(options=options)

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print the purge request id
print(result.purge_request_id)

15. Restore file Version

Restore a file as per the API documentation here. The method accepts the file_id and version_id of the file that has to be restored.

result = imagekit.restore_file_version(file_id="file_id", version_id="version_id")

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print that file's id
print(result.file_id)

16. Create Folder

Create a folder per the API documentation here. The method accepts folder_name and parent_folder_path as options that must be created.

from imagekitio.models.CreateFolderRequestOptions import CreateFolderRequestOptions

options = CreateFolderRequestOptions(folder_name='test',
                                     parent_folder_path='/')
result = imagekit.create_folder(options=options)

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

17. Delete Folder

Delete a folder as per the API documentation here. The method accepts folder_path as an option that must be deleted.

from imagekitio.models.DeleteFolderRequestOptions import DeleteFolderRequestOptions

options = DeleteFolderRequestOptions(folder_path='/test/demo')
result = imagekit.delete_folder(options=options)

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

18. Copy Folder

Copy a folder as per the API documentation here. The method accepts the source_folder_path, destination_path, and include_file_versions boolean as options that have to be copied.

from imagekitio.models.CopyFolderRequestOptions import CopyFolderRequestOptions
options = \
    CopyFolderRequestOptions(source_folder_path='/source_folder_path',
                             destination_path='/destination/path',
                             include_file_versions=True)
result = imagekit.copy_folder(options=options)

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print the job's id
print(result.job_id)

19. Move Folder

Move a folder as per the API documentation here. The method accepts the source_folder_path and destination_path of a folder as options that must be moved.

from imagekitio.models.MoveFolderRequestOptions import MoveFolderRequestOptions
options = \
    MoveFolderRequestOptions(source_folder_path='/source_folder_path',
                             destination_path='/destination_path')
result = imagekit.move_folder(options=options)
# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print the job's id
print(result.job_id)

20. Get Bulk Job Status

Accepts the job_id to get bulk job status as per the API documentation here. The method takes only jobId.

result = imagekit.get_bulk_job_status(job_id="job_id")

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print the job's id
print(result.job_id)

# print the status
print(result.status)

21. Purge Cache

Programmatically issue an explicit cache request as per the API documentation here. Accepts the full URL of the File for which the cache has to be cleared.

result = imagekit.purge_file_cache(file_url="full_url_of_file")

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print the purge file cache request id
print(result.request_id)

22. Purge Cache Status

Get the purge cache request status using the cache_request_id returned when a purge cache request gets submitted as per the API documentation here

result = imagekit.get_purge_file_cache_status(purge_cache_id="cache_request_id")

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print the purge file cache status
print(result.status)

23. Get File Metadata

Accepts the file_id and fetches the metadata as per the API documentation here

result = imagekit.get_file_metadata(file_id="file_id")

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print the file metadata fields
print(result.width)
print(result.exif.image.x_resolution)

24. Get File Metadata from remote URL

Accepts the remote_file_url and fetches the metadata as per the API documentation here

result = imagekit.get_remote_file_url_metadata(remote_file_url="remote_file_url")

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print the file metadata fields
print(result.width)
print(result.exif.image.x_resolution)

25. Create CustomMetaDataFields

Accepts an option specifying the parameters used to create custom metadata fields. All parameters specified in the API documentation here can be passed as it is with the correct values to get the results.

Check for the allowed values in the schema.

Example:

# Example for the type number

from imagekitio.models.CreateCustomMetadataFieldsRequestOptions import CreateCustomMetadataFieldsRequestOptions
from imagekitio.models.CustomMetadataFieldsSchema import CustomMetadataFieldsSchema
from imagekitio.models.CustomMetaDataTypeEnum import CustomMetaDataTypeEnum
schema = CustomMetadataFieldsSchema(type=CustomMetaDataTypeEnum.Number,
                                    min_value=100,
                                    max_value=200)
options = CreateCustomMetadataFieldsRequestOptions(name='test',
                                                   label='test',
                                                   schema=schema)
result = imagekit.create_custom_metadata_fields(options=options)

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print the id of created custom metadata fields
print(result.id)

# print the schema's type of created custom metadata fields
print(result.schema.type)

# MultiSelect type Example

from imagekitio.models.CreateCustomMetadataFieldsRequestOptions import CreateCustomMetadataFieldsRequestOptions
from imagekitio.models.CustomMetadataFieldsSchema import CustomMetadataFieldsSchema
from imagekitio.models.CustomMetaDataTypeEnum import CustomMetaDataTypeEnum

schema = \
    CustomMetadataFieldsSchema(type=CustomMetaDataTypeEnum.MultiSelect,
                               is_value_required=True,
                               default_value=['small', 30, True],
                               select_options=[
                                    'small',
                                    'medium',
                                    'large',
                                    30,
                                    40,
                                    True,
                                ])
options = \
    CreateCustomMetadataFieldsRequestOptions(name='test-MultiSelect',
        label='test-MultiSelect', schema=schema)
result = imagekit.create_custom_metadata_fields(options=options)

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print the name of created custom metadata fields
print(result.name)

# print the schema's select options of created custom metadata fields
print(result.schema.select_options)

# Date type Example

from imagekitio.models.CreateCustomMetadataFieldsRequestOptions import CreateCustomMetadataFieldsRequestOptions
from imagekitio.models.CustomMetadataFieldsSchema import CustomMetadataFieldsSchema
from imagekitio.models.CustomMetaDataTypeEnum import CustomMetaDataTypeEnum

schema = CustomMetadataFieldsSchema(type=CustomMetaDataTypeEnum.Date,
                                    min_value='2022-11-29T10:11:10+00:00',
                                    max_value='2022-11-30T10:11:10+00:00')
options = CreateCustomMetadataFieldsRequestOptions(name='test-date',
                                                   label='test-date',
                                                   schema=schema)
result = imagekit.create_custom_metadata_fields(options=options)

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print the label of created custom metadata fields
print(result.label)

# print the schema's min value of created custom metadata fields
print(result.schema.min_value)

26. Get CustomMetaDataFields

Accepts the include_deleted boolean as the initial parameter and fetches the metadata as per the API documentation here .

result = imagekit.get_custom_metadata_fields()  # in this case, it will consider includeDeleted as a False

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print the first customMetadataField's id
print(result.list[0].id)

# print the first customMetadataField schema's type
print(result.list[0].schema.type)

result = imagekit.get_custom_metadata_fields(include_deleted=True)

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print the first customMetadataField's name
print(result.list[0].name)

# print the first customMetadataField schema's default value
print(result.list[0].schema.default_value)

27. Update CustomMetaDataFields

Accepts a field_id and options for specifying the parameters to be used to edit custom metadata fields as per the API documentation here .

from imagekitio.models.CustomMetadataFieldsSchema import CustomMetadataFieldsSchema
from imagekitio.models.UpdateCustomMetadataFieldsRequestOptions import UpdateCustomMetadataFieldsRequestOptions

schema = CustomMetadataFieldsSchema(min_value=100, max_value=200)
options = UpdateCustomMetadataFieldsRequestOptions(
    label='test-update',
    schema=schema
)
result = imagekit.update_custom_metadata_fields(
    field_id='id_of_custom_metadata_field',
    options=options
)

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

# print the label of updated custom metadata fields
print(result.label)

# print the schema's min value of updated custom metadata fields
print(result.schema.min_value)

28. Delete CustomMetaDataFields

Accepts the id to delete the custom metadata fields as per the API documentation here .

result = imagekit.delete_custom_metadata_field(field_id="id_of_custom_metadata_field")

# Final Result
print(result)

# Raw Response
print(result.response_metadata.raw)

Utility functions

We have included the following commonly used utility functions in this package.

Authentication parameter generation

Suppose one wants to implement client-side file upload. In that case, one will need a token, expiry timestamp, and a valid signature for that upload. The SDK provides a simple method that one can use in their code to generate these authentication parameters.

Note: Any client-side code should never expose The Private API Key. One must always generate these authentications parameters on the server-side

authentication

authentication_parameters = imagekit.get_authentication_parameters(token, expire)

Returns

{
    "token": "unique_token",
    "expire": "valid_expiry_timestamp",
    "signature": "generated_signature"
}

Both the token and expire parameters are optional. If not specified, the SDK uses the UUID to generate a random token and internally generates a valid expiry timestamp. The token and expire used to generate signature is part of a response returned by the server.

Distance calculation between two pHash values

Perceptual hashing allows you to construct a has value that uniquely identifies an input image based on the contents of an image. imagekit.io metadata API returns the pHash value of an image in the response. You can use this value to find a duplicate or similar image by calculating the distance between the two images.

This SDK exposes the phash_distance function to calculate the distance between two pHash values. It accepts two pHash hexadecimal strings and returns a numeric value indicative of the difference between the two images.

def calculate_distance():
    # fetch metadata of two uploaded image files
    ...
    # extract pHash strings from both: say 'first_hash' and 'second_hash'
    ...
    # calculate the distance between them:

    distance = imagekit.phash_distance(first_hash, second_hash)
    return distance

Distance calculation examples

imagekit.phash_distance('f06830ca9f1e3e90', 'f06830ca9f1e3e90')
# output: 0 (same image)

imagekit.phash_distance('2d5ad3936d2e015b', '2d6ed293db36a4fb')
# output: 17 (similar images)

imagekit.phash_distance('a4a65595ac94518b', '7838873e791f8400')
# output: 37 (dissimilar images)

HTTP response metadata of Internal API

HTTP response metadata of the internal API call can be accessed using the _response_metadata on the Result object. Example:

result = imagekit.upload_file(
    file="<url|base_64|binary>",
    file_name="my_file_name.jpg",
)

# Final Result
print(result)
print(result.response_metadata.raw)
print(result.response_metadata.http_status_code)
print(result.response_metadata.headers)

Sample Code Instruction

To run sample code go to the code samples here are hosted on GitHub - https://github.com/imagekit-samples/quickstart/tree/master/python and run.

python sample.py

Handling errors

Catch and respond to invalid data, internal problems, and more.

ImageKit Python SDK raises exceptions for many reasons, such as not found, invalid parameters, authentication, and internal server errors. Therefore, we recommend writing code that gracefully handles all possible API exceptions.

Example:

from imagekitio.exceptions.BadRequestException import BadRequestException
from imagekitio.exceptions.UnauthorizedException import UnauthorizedException
from imagekitio.exceptions.ForbiddenException import ForbiddenException
from imagekitio.exceptions.TooManyRequestsException import TooManyRequestsException
from imagekitio.exceptions.InternalServerException import InternalServerException
from imagekitio.exceptions.PartialSuccessException import PartialSuccessException
from imagekitio.exceptions.NotFoundException import NotFoundException
from imagekitio.exceptions.UnknownException import UnknownException

try:

    # Use ImageKit's SDK to make requests...
    print('Run image kit api')
except BadRequestException, e:
    # Missing or Invalid parameters were supplied to Imagekit.io's API
    print('Status is: ' + e.response_metadata.http_status_code)
    print('Message is: ' + e.message)
    print('Headers are: ' + e.response_metadata.headers)
    print('Raw body is: ' + e.response_metadata.raw)
except UnauthorizedException, e:
    print(e)
except ForbiddenException, e:
    # No valid API key was provided.
    print(e)
except TooManyRequestsException, e:
    # Can be for the following reasons:
    # ImageKit could not authenticate your account with the keys provided.
    # An expired key (public or private) was used with the request.
    # The account is disabled.
    # If you use the upload API, the total storage limit (or upload limit) is exceeded.
    print(e)
except InternalServerException, e:
    # Too many requests made to the API too quickly
    print(e)
except PartialSuccessException, e:
    # Something went wrong with ImageKit.io API.
    print(e)
except NotFoundException, e:
    # Error cases on partial success.
    print(e)
except UnknownException, e:
    # If any of the field or parameter is not found in the data
    print(e)

# Something else happened, which can be unrelated to ImageKit; the reason will be indicated in the message field

Development

Tests

Tests are powered by Tox.

$ git clone https://github.com/imagekit-developer/imagekit-python && cd imagekit-python
$ pip install tox
$ tox

Sample

Get & Install local ImageKit Python SDK

$ git clone https://github.com/imagekit-developer/imagekit-python && cd imagekit-python
$ pip install -e .

Get samples

To integrate ImageKit Samples in the Python, the code samples covered here are hosted on GitHub - https://github.com/imagekit-samples/quickstart/tree/master/python.

Open the python/sample.py file and replace placeholder credentials with actual values. You can get the value of URL-endpoint from your ImageKit dashboard. API keys can be obtained from the developer section in your ImageKit dashboard.

In the python/sample.py file, set the following parameters for authentication:

from imagekitio import ImageKit
imagekit = ImageKit(
    private_key='your private_key',
    public_key='your public_key',
    url_endpoint = 'your url_endpoint'
)

To install dependencies that are in the python/requirements.txt file, can fire this command to install them:

pip install -r python/requirements.txt

Now run python/sample.py. If you are using CLI Tool (Terminal/Command prompt), open the project in CLI and execute it.

# if not installed already
pip install imagekitio

# if installing local sdk
pip install -e <path_to_local_sdk>

# to run sample.py file
python3 python/sample.py

Support

For any feedback or to report any issues or general implementation support, please reach out to support@imagekit.io

Links

• Documentation

• Main Website

License

Released under the MIT license.