Crop JPEG via Python Cloud API
Crop JPEG using native Python Cloud APIs without needing any image editor or 3rd-party libraries.
Get StartedHow to Crop JPEG Using Python Cloud API
Aspose.Imaging.Cloud for Python API which is a feature-rich, powerful and easy to use image manipulation and conversion Cloud API for Python platform. You can download its latest version directly from pypi.org or install it from pip command line.
Command Prompt
> pip install aspose-imaging-cloud
Steps to Crop JPEG via Python Cloud API
Developers can easily load & crop JPEG files in just a few lines of code.
- Load JPEG file as stream
- Create & set the instance of CreateCroppedImageRequest
- Set start position and crop area size
- Call the CreateCroppedImage method
- Get cropped image from response stream
System Requirements
Aspose.Imaging Cloud for Python is supported on all major operating systems. Just make sure that you have the following prerequisites.
- Python 2.7 or later.
Crop JPEG - Cloud
About Aspose.Imaging Cloud API for Python
Aspose.Imaging Cloud API is an image processing solution to process images (photos) within your cloud or web applications. It offers: cross-platform Image processing, including but not limited to conversions between various image formats (including uniform multi-page or multi-frame image processing), transformations (resize, crop, flip&rotate, grayscale, adjust), advanced image manipulation features (filtering, deskewing), AI features (i.e. object detection and reverse image search). It’s a Cloud API and does not depend on any software for image operations. One can easily add high-performance image conversion features with Cloud APIs within projects. Flexible integrations options including SDKs for various languages (Python, Ruby, .NET, Java, NodeJS, PHP) and the use of the REST API allow to make the integration easy.
Crop JPEGs via Online App
Crop JPEG documents by visiting our Live Demos website. The live demo has the following benefits:
JPEG What is JPEG File Format
A JPEG is a type of image format that is saved using the method of lossy compression. The output image, as result of compression, is a trade-off between storage size and image quality. Users can adjust the compression level to achieve the desired quality level while at the same time reduce the storage size. Image quality is negligibly affected if 10:1 compression is applied to the image. The higher the compression value, the higher the degradation in image quality.
Read MoreOther Supported Conversions
Using Python Cloud API, one can easily crop different formats including: