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ungrown
V2EX  ›  Python

撸了两个 wrapper,一个 cwebp 的,一个 tesseract-ocr 的,都是包在二进制可执行文件上而非二进制库上,都支持从管道输入输出数据

  •  
  •   ungrown ·
    ungrown · 192 天前 · 915 次点击
    这是一个创建于 192 天前的主题,其中的信息可能已经有所发展或是发生改变。

    主要是自用

    https://github.com/mo-han/mo-han-toolbox/tree/master/mylib/wrapper

    下面摘自 readme

    cwebp

    • use cwebp in PATH
    • support feeding image file data via stdin pipe
    • return dict including metadata
    • set options via kwargs
    • resize with float scale (keep aspect)

    Example:

    from mylib.wrapper import cwebp
    
    cwebp.image_to_bytes
    cwebp.image_from_bytes
    
    src = 'src/image/filepath' or b'image file bytes'
    dst = '-' or 'dst/image/filepath'  # '-' get cwebp output image bytes from stdout and put it into dict
    cwebp.cwebp(src, q=50, resize=0.5, print_ssim=True)
    

    output something like this:

    {'kwargs': {'q': 50, 'print_ssim': True, 'resize': (334, 482)},
     'out': b'A LONG BYTES OF THE OUTPUT IMAGE FILE',
     'msg': ["Saving file '-'",
      'File:      -',
      'Dimension: 334 x 482',
      'Output:    8352 bytes Y-U-V-All-PSNR 39.53 99.00 99.00   41.29 dB',
      '           (0.42 bpp)',
      'block count:  intra4:        256  (39.32%)',
      '              intra16:       395  (60.68%)',
      '              skipped:       388  (59.60%)',
      'bytes used:  header:            121  (1.4%)',
      '             mode-partition:   1174  (14.1%)',
      ' Residuals bytes  |segment 1|segment 2|segment 3|segment 4|  total',
      '    macroblocks:  |       1%|      10%|      27%|      62%|     651',
      '      quantizer:  |      52 |      49 |      42 |      34 |',
      '   filter level:  |      16 |      10 |       7 |       5 |',
      'SSIM: B:19.07 G:19.07 R:19.07 A:99.00  Total:20.32'],
     'cmd': ['cwebp',
      '-q',
      '50',
      '-print_ssim',
      '-resize',
      '334',
      '482',
      '-o',
      '-',
      '--',
      '-'],
     'code': 0,
     'ok': True,
     'src': {'path': '-', 'size': 30230, 'width': 667, 'height': 964},
     'dst': {'q': 50.0,
      'scale': 0.5,
      'path': '-',
      'width': 334,
      'height': 482,
      'size': 8352,
      'psnr': {'y': 39.53, 'u': 99.0, 'v': 99.0, 'all': 41.29},
      'ssim': {'b': 19.07, 'g': 19.07, 'r': 19.07, 'a': 99.0, 'total': 20.32},
      'compress': 0.276}}
    

    tesseract_ocr

    • support feeding image via stdin pipe
    • get output data via stdout pipe
    • so no temp file created
    • set input image with filepath or file bytes or PIL Image object or nd-array
    • set options via kwargs
    • output data as dict in proper structure
    • fluent interface/api

    Example:

    from mylib.wrapper import tesseract_ocr
    
    t = tesseract_ocr.TesseractOCRCLIWrapper('path/of/tesseract/executable')
    # input image could be set via different methods for different data type
    t.set_image_array
    t.set_image_bytes
    t.set_image_object
    t.set_image_file
    
    t.set_language('chi_sim', 'eng').set_image_bytes(b'IMAGE FILE BYTES').get_ocr_tsv_to_dict_list()
    

    output something like this:

    [{'text': '名',
      'confidence': 0.91,
      'box': ((320, 10), (333, 10), (333, 23), (320, 23)),
      'page_block_paragraph_line_word_level': (1, 1, 1, 1, 1, 5)},
     {'text': '称',
      'confidence': 0.87,
      'box': ((320, 28), (333, 28), (333, 40), (320, 40)),
      'page_block_paragraph_line_word_level': (1, 1, 1, 1, 2, 5)},
     {'text': '修改',
      'confidence': 0.9,
      'box': ((320, 336), (333, 336), (333, 366), (320, 366)),
      'page_block_paragraph_line_word_level': (1, 1, 1, 1, 3, 5)},
     {'text': '日',
      'confidence': 0.96,
      'box': ((320, 372), (333, 372), (333, 379), (320, 379)),
      'page_block_paragraph_line_word_level': (1, 1, 1, 1, 4, 5)},
     {'text': '期',
      'confidence': 0.96,
      'box': ((320, 387), (333, 387), (333, 395), (320, 395)),
      'page_block_paragraph_line_word_level': (1, 1, 1, 1, 5, 5)},
     {'text': ' ',
      'confidence': 0.95,
      'box': ((0, 195), (0, 195), (0, 224), (0, 224)),
      'page_block_paragraph_line_word_level': (1, 2, 1, 1, 1, 5)},
     {'text': 'label_cn.txt',
      'confidence': 0.8,
      'box': ((283, 38), (295, 38), (295, 120), (283, 120)),
      'page_block_paragraph_line_word_level': (1, 3, 1, 1, 1, 5)},
     {'text': '2019/8/5',
      'confidence': 0.87,
      'box': ((281, 337), (294, 337), (294, 401), (281, 401)),
      'page_block_paragraph_line_word_level': (1, 3, 1, 1, 2, 5)},
     {'text': '15:24',
      'confidence': 0.95,
      'box': ((283, 408), (294, 408), (294, 446), (283, 446)),
      'page_block_paragraph_line_word_level': (1, 3, 1, 1, 3, 5)},
     {'text': '2019/8/5',
      'confidence': 0.91,
      'box': ((254, 337), (267, 337), (267, 401), (254, 401)),
      'page_block_paragraph_line_word_level': (1, 3, 1, 2, 3, 5)},
     {'text': '15:24',
      'confidence': 0.96,
      'box': ((256, 408), (267, 408), (267, 446), (256, 446)),
      'page_block_paragraph_line_word_level': (1, 3, 1, 2, 4, 5)},
     {'text': '2019/8/5',
      'confidence': 0.89,
      'box': ((227, 337), (240, 337), (240, 401), (227, 401)),
      'page_block_paragraph_line_word_level': (1, 3, 1, 3, 3, 5)},
     {'text': '15:24',
      'confidence': 0.96,
      'box': ((229, 408), (240, 408), (240, 446), (229, 446)),
      'page_block_paragraph_line_word_level': (1, 3, 1, 3, 4, 5)}]
    
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