Metadata-Version: 2.1
Name: imgcrop
Version: 0.1.0
Summary: Simple image augmentation library focusing on random geometric cropping.
Home-page: https://github.com/yu4u/imgcrop
Author: Yusuke Uchida
Author-email: ren4yu@gmail.com
License: MIT
Description: # imgcrop
        Simple image augmentation library focusing on random geometric cropping.
        Different from pipeline-based augmentation libraries, this library efficiently performs cropping and geometric transformations at once.
        As image processing functions such as adding Gaussian noise, blurring, and contrast adjustment are not provided,
        please use the other great libraries like [imgaug](https://github.com/aleju/imgaug) [1], [Augmentor](https://github.com/mdbloice/Augmentor) [2], and [albumentations](https://github.com/albu/albumentations) [3]
        to further transform images cropped by this library.
        
        ![](fig/all1.png?raw=true)
        
        ## Features
        
        - Simple API, easy to use
        - Efficient; cropping and all geometric transformations are performed at once (by a single perspective transformation)
        - Guarantee that all pixels in cropped image are taken from inside the original image (if margin is not used)
        
        
        ## Installation
        
        ```bash
        pip install imgcrop
        ```
        
        ## APIs
        
        ### imgcrop.get_cropper
        ```python
        get_cropper(patch_size=128, scale=(1.0, 1.0), rotate=(0, 0), distort=0.0, flip=0.0, margin=0)
        ```
        
        #### parameters
        - **patch_size**: *int, default 128*
          - output patch size (in pixel)
        - **scale**: *tuple of float, default (1.0, 1.0)*
          - sampling scale range
        - **rotate**: *tuple of int, default (0, 0)*
          - sampling rotation range (in degree)
        - **distort**: *float, default 0.0*
          - distortion strength in perspective transformation (ratio to output image scale)
        - **flip**: *float, default 0.0*
          - horizontal flip probability
        - **margin**: *int, default 0*
          - margin in cropping around original image
        
        #### returns
        - **random_crop**: function
        
        
        ### random_crop
        ```python
        random_crop(img, points=None) -> cropped_img[, output_points], src_points, m
        ```
        
        #### parameters
        - **img**: *numpy array (single image) or list of numpy arrays (multiple images)*
          - input image(s) to be cropped with the same geometric transformation
        - **points**: *numpy array with the shape (point_num, 2), default None*
          - input points to be transformed with the same transformation matrix as input image(s)
        
        #### returns
        - **cropped_img**: *numpy array or list of numpy arrays)*
          - cropped output image(s)
        - **output_points**: *numpy array with the shape (point_num, 2)*
          - output points if input points are given
        - **src_points**: *numpy array*
          - points defining the cropped region in the input image(s)
        - **m**: *numpy array*
          - 3x3 perspective transformation matrix from the input image(s) to the output image(s)
        
        ## Example
        
        Pleaes run or refer to [the example script](example/example.py) to see how this library works:
        
        ```bash
        python example/example.py
        ```
        
        You can easily try different parameters by arguments:
        
        ```bash
        optional arguments:
          -h, --help            show this help message and exit
          --patch_size PATCH_SIZE
                                output patch image size (default: 256)
          --scale SCALE SCALE   scale range in sampling (default: [0.8, 1.2])
          --rotate ROTATE ROTATE
                                rotation range in sampling (default: [-60, 60])
          --distort DISTORT     distortion strength for perspective transformation
                                (default: 0.2)
          --flip FLIP           horizontal flip probability (default: 0.5)
          --margin MARGIN       margin around original image (default: 0)
        ```
        
        | parameters | cropping results (input, cropped, mask, keypoints) |
        | --- | --- |
        | scaling | ![](fig/scale1.png?raw=true) |
        |  | ![](fig/scale2.png?raw=true) |
        |  | ![](fig/scale3.png?raw=true) |
        | rotation + scaling | ![](fig/rotate1.png?raw=true) |
        |  | ![](fig/rotate2.png?raw=true) |
        |  | ![](fig/rotate3.png?raw=true) |
        | distortion + scaling | ![](fig/distort1.png?raw=true) |
        |  | ![](fig/distort2.png?raw=true) |
        |  | ![](fig/distort3.png?raw=true) |
        | flip + rotation | ![](fig/flip1.png?raw=true) |
        |  | ![](fig/flip2.png?raw=true) |
        |  | ![](fig/flip3.png?raw=true) |
        | margin + scaling | ![](fig/margin1.png?raw=true) |
        |  | ![](fig/margin2.png?raw=true) |
        |  | ![](fig/margin3.png?raw=true) |
        | all | ![](fig/all1.png?raw=true) |
        |  | ![](fig/all2.png?raw=true) |
        |  | ![](fig/all3.png?raw=true) |
        
        ## Algorithm
        Patch region in the original image is defined by a set of four points.
        These points are randomly transformed according to transformation parameters.
        The transformation matrix from input image to output image is then calculated using these points.
        Finally, cropping is performed by applying perspective transformation.
        
        ![](fig/algorithm.png?raw=true)
        
        ## References
        1. imgaug, https://github.com/aleju/imgaug
        2. Augmentor, https://github.com/mdbloice/Augmentor
        3. albumentations, https://github.com/albu/albumentations
        
Keywords: augmentation,image,deep learning,neural network,machine learning
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
