Metadata-Version: 1.1
Name: gif2numpy
Version: 1.3
Summary: Convert single and multiple frame gif images to numpy images or to OpenCV without PIL or pillow
Home-page: https://github.com/bunkahle/gif2numpy
Author: Andreas Bunkahle
Author-email: abunkahle@t-online.de
License: MIT
Description: # gif2numpy Version 1.3
        Python library to convert single oder multiple frame gif images to numpy images or to OpenCV without PIL or pillow. OpenCV does not support gif images.
        
        Install it with 
        
            setup.py install
            
        or with
        
            pip install gif2numpy
            
        # Usage
        
        You can use the library this way:
        
            from __future__ import print_function
            import gif2numpy
            import cv2
            
            images = "Images/Rotating_earth.gif", "Images/hopper.gif", "Images/audrey.gif", "Images/testcolors.gif"
            for image in images:
                frames, exts, image_specs = gif2numpy.convert(image)
                print()
                print("Image:", image)
                print()
                print("len frames", len(frames))
                print("len exts", len(exts))
                print("exts:", exts)
                print("image_specs:", image_specs)
                for i in range(len(frames)):
                    cv2.imshow("np_image", frames[i])
                    k = cv2.waitKey(0)
                    if k == 27:
                        break
                cv2.destroyWindow("np_image")
            
        There is also the class Gif inside the module which can be used to determine Gif features inside the image.
        The general features are given in the dictionary image_specs.
        If multiple frames are saved in the gif you can retrieve them in the list of frames. The list of exts with the same index number as in frames gives you the specifications of each frame (block_size, flags, delay_time, transparent_idx, terminator, lzw_min, 
        top, left, width, height, has_color_table, local_color_table).
        
        By default this module was designed for the connection with cv2 which has the BGR(A) color mapping sequence for the color tuples. In case you are not using opencv and its color mapping you should set the color conversion flag BGR2RGB to False so that no color conversion takes place. So use the module like that:
        
            from __future__ import print_function
            import gif2numpy
            import cv2
            
            images = "Images/Rotating_earth.gif", "Images/hopper.gif", "Images/audrey.gif", "Images/testcolors.gif"
            for image in images:
                frames, exts, image_specs = gif2numpy.convert(image, BGR2RGB=False)
                print()
                print("Image:", image)
                print()
                print("len frames", len(frames))
                print("len exts", len(exts))
                print("exts:", exts)
                print("image_specs:", image_specs)
                for i in range(len(frames)):
                    cv2.imshow("np_image", frames[i])
                    k = cv2.waitKey(0)
                    if k == 27:
                        break
                cv2.destroyWindow("np_image")
        
        # Version history
        
        1.3: Additional flag for BGR2RGB conversion, by default this flag is set and a BGR2RGB color conversion takes place, better time optimization of color table mapping
        
        1.2: Bug fix for multiple frame gif images, pixel error in frames fixed
        
        1.1: single frame and multiple frame gif images are now supported
        
        1.0: first release just for still single images
        
        # Dependencies
        
        You need to install numpy and kaitaistruct by:
        
            pip install numpy kaitaistruct
        
Keywords: GIF Converter Numpy
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
