Metadata-Version: 2.1
Name: seg_crf
Version: 1.0.0
Summary: Conditional Random Field Implementation for segmentation models as used in Deeplab-v2
Home-page: https://github.com/Mr-TalhaIlyas/Conditional-Random-Fields-CRF
Author: Talha Ilyas
Author-email: mr.talhailyas@gmail.com
License: UNKNOWN
Description: [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) ![PyPI](https://img.shields.io/pypi/v/a)
        # Fully Connected CRF
        
        This repo implements CRF as described in Deeplab paper it takes about 0.2 seconds per image. Following image is taken form **DeepLab** paper
        
        ![alt text](https://github.com/Mr-TalhaIlyas/Conditional-Random-Fields-CRF/raw/master/screens/img1.png)
        
        ## Requirements
        
        ```
        Python <= 3.6
        pydensecrf
        cv2
        matplotlib
        gray2color
        ```
        
        It takes following arguments.
        For details visit project [page](https://github.com/Mr-TalhaIlyas/Conditional-Random-Fields-CRF).
        
        
        ```
                ⚠ Zero pixels are consdered background
                img_path : path to an image, 
                                Format [H, W, 3]; values ranging from [0, 255]
                model_op_path : path model output of the same input image.
                                Format [H, W]; values ranging from [0, num_of_classes]
                num_of_classes : number of classes in a dataset e.g. in cityscape has 30 classes
                clr_op : color the output or not a bool
                pallet2use : see https://pypi.org/project/gray2color/ for details
                img_w : for resizing image and mask to same size default is 1024
                img_h : for resizing image and mask to same size default is 512
                apperance_kernel : The PairwiseBilateral term in CRF a list of values in order [sxy, srgb, compat]  
                                    default values are [8, 164, 100]
                spatial_kernel : The PairwiseGaussian term in CRF a list of values in order [sxy, compat]  
                                    default values are [3, 10]
        ```
        
        ```python
        
        from seg_crf import Seg_CRF
        
        img_path='D:/Anaconda/Image_analysis/cat.png'
        model_op_path='D:/Anaconda/Image_analysis/mask.png'
        
        crf = Seg_CRF(img_path, model_op_path, 2, img_w=1024, img_h=512, clr_op=True, pallet2use ='cityscape')
        
        gray, rgb = crf.start()
        plt.imshow(rgb)
        
        ```
        ## Appearance and Spatial Kernel
        
        ```python
        # Default Values are
        apperance_kernel = [8, 164, 100] # PairwiseBilateral [sxy, srgb, compat]  
        spatial_kernel = [3, 10]         # PairwiseGaussian  [sxy, compat] 
        
        # or if you want to to specify seprately for each XY direction and RGB color channel then
        
        apperance_kernel = [(1.5, 1.5), (64, 64, 64), 100] # PairwiseBilateral [sxy, srgb, compat]  
        spatial_kernel = [(0.5, 0.5), 10]                  # PairwiseGaussian  [sxy, compat] 
        # Use like
        crf = Seg_CRF(img_path, model_op_path, 2, img_w=1024, img_h=512,
                         apperance_kernel=apperance_kernel, spatial_kernel=spatial_kernel,
                         clr_op=True, pallet2use ='cityscape')
        
        gray, rgb = crf.start()
        ```
        
        
Keywords: python,conditional random fields,segmentation,crf,semantic segmentation,fully connected crfdense crf,deeplabv2
Platform: UNKNOWN
Classifier: Intended Audience :: Education
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
