Metadata-Version: 2.1
Name: image-comparer
Version: 1.1.1
Summary: Compares two images using siamese networks
Home-page: https://github.com/joeyism/py-image-comparer
Author: joeyism
License: UNKNOWN
Download-URL: https://github.com/joeyism/py-image-comparer/archive/1.1.1.tar.gz
Description: # py-image-comparer
        Compares two images using [Siamese Network](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf) (Machine Learning) trained from a [Pytorch Implementation](https://github.com/joeyism/siamese-pytorch)
        
        ## Installation
        To install, run
        
        ```bash
        pip install image-comparer
        ```
        
        ## Usage
        
        ### CLI
        ```bash
        image-compare
        ```
        which wil show the follow help screen
        ```
        usage: image-compare [-h] [--threshold THRESHOLD] Image1-Path Image2-Path
        ```
        
        For example, you can compare two images with
        ```bash
        image-compare tests/images/kobe.jpg tests/images/kobe2.jpg 
        ```
        which gives the result
        ```
        kobe.jpg and kobe2.jpg are not similar
        ```
        
        ### Programmatically
        With PIL
        
        ```python
        import image_comparer
        from PIL import Image
        
        image = Image.open("test/kobe.jpg")
        image2 = Image.open("test/kobe2.jpg")
        image_comparer.is_similar(image, image2, threshold=0.5)
        ```
        or with OpenCV
        
        ```python
        import image_comparer
        import cv2
        
        image = cv2.imread("test/kobe.jpg")
        image2 = cv2.imread("test/kobe2.jpg")
        image_comparer.is_similar(image, image2, threshold=0.5)
        ```
        
        ## API
        
        ### Methods
        
        `is_similar(image1: Union[Image.Image, np.ndarray], image2: Union[Image.Image, np.ndarray], threshold=0.5)`: Checks if the two images are similar based on the reshold passed
        
        
        `calculate_score(image1: Union[Image.Image, np.ndarray], image2: Union[Image.Image, np.ndarray])`: Calculates the score between the two images. The higher the score, the more closely the two images are related.
        
        
        ## Development
        
        ### Installation
        ```bash
        pip install -r requirements-test.txt
        ```
        
        ### Tests
        To run tests, run
        ```bash
        pytest
        ```
        
Keywords: pytorch,torch,machine,learning,image,compare,comparer,siamese,network,networks
Platform: UNKNOWN
Description-Content-Type: text/markdown
