在Python或java代码中将十六进制文本文件转换为原始图像（JPEG）

Question

我有hex JPEG图像格式的FFD8...........FFD9表示数据，如何将此十六进制数据转换回原始图像（viewable）

这是我的数据

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

Answer 1

这是实际图片

我做了以下事情：

import binascii
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55CC0DB799F41EF82242036558603C2B6729FF002E67297F31A6B0A7942EED986E0730A753CDAF50F147AB228136B728D7FEB01A72B87A926C9075609650DC73546BD58D8D22666D04A568F94F3729D1347050A0E370588B561C331447F6D7507C3CAF955FA5D3AD8A4D0D2C624A96951144B1A8504B36C430B0D754BED500209405072ACB0049143A1D2FBFD08164B98164050658A3802A9B1615B17396FE2EC14C536C92E411D487760CE0804B6EF76A3FAC25EAEEC480546F6524825C1A575A03A180A965614C429C6E48DAC4338208208FE609072D699280DA8F62C547A166AD46A444A62D131002C8CE2895900661B28AAA0834A9DABA9125A952C90C68E1ABA5D2454540B6ADEE053F0E12A2C282A00D0588709A80D7EC62FF00EA9F2A55D085151B067079853ADC7681264D05A64BB6A0DC757B91A3DC307B3C5985C666A06777CA0DCB3B8E6BEBEFD1C00370F88605241503D1258D8B78AE9A1162C2E20D9452AE57BB805C9F22091F50E2A432A32C4A530080A582680024EE52D94971FB3EB04216841573A4819478E849AB976208E6A8482157EA0D36499649A31D4B020B5C38BD287BF61689E32D1C817049A8D48626A1BE5B08B158D9374CC494D9EE4167639402E48A0D74A8200CAC5C94B2C2CA8120948964E6CAA19B22D441CC017617A5600E0B2405275465528E4248A2413998B8E514AD1E2AC2F115D41767A9E67490E3405AF5F2EC6DE0065E2B112244ACC94CE0415AAA97095A949C8F71945091424D4313E852FD16C92ACCAC42CF642410CECC493514B82295101C14C9B98B29490142B415D9409624EADAB11A46671352485CB9A5AAF2CDD0A15252A02BA8652598A9F5AFAFE17D1AE0517F5AB724B2A6307376C8137A53A4747C2B8361F0E929952D2906F751346A9539FF00701E79E80943D4E2824929F592CB11549295387D6C3E80C7A8987CA05A90C4C0343FCE22F0F009E13C2311300F0A1B34454B03580778903102613C07CC93A5E5635522AC52D99049BB9AD4EFA862C62495292A7A3915BE5989ECC03B7BBA3B434280B4A12CE9728D8D4A0EC6A69B1B8F782964A4852494AC36D5A50E82BB58E95850A01C730CC917A901C949ADAEE877A5D2E748A15434040AEC1C6BB31D76F94285015CC05C39A1A39AD350A0F76F93BEA2C44EAE5582CCC6E140DBA740FF00C328500B1618BA09297E55307ECABF37D3C85E388AE6154D2B0A2FCE898EACFB9B00D405DC104F9C28500262A50354394FE5BA803A5FBF7AB55D20592A6292C14C680804137A86AB83F3DA14280365ADC005370ECC1F6CC397BC1582E138A5974C89AA04BE644B531356344D1437F7B83550A037257E08E2135DF0EA4120D4B25CD19401506A876EA5A0C93E8C3882C02AF552D4198AA6394BF8BC282F5D2C42B421CA8501A89F44739442978A421440CE10852813B87525BFDE91B186F45387A7AC9F356D4A042411B5412DA8AB8D08148785007E0FD1970E97696B5549AAC8A9041F065D0C09F8E3F0C60E470F9C64C89685A7214A99CA55EB054125C788DB73BC34280F1938F295ACD466242824B0AB8228CCF566A1B5C318AB0E329287524DC51EB7B6B6234240235014280CDC1CC507218B508350A4EC46DF7A53530612B4A86998509A826B7FFEDFBB9868501D0FA39414714C355B3296FB2C89732E1A8AA3BD1D8D8B83F4123E10F0A02461A1E140278630A1403343428500E62261428067DE0270A7EE7E10F0A02F945C758B40850A03FFD9"
data=data.strip()
data=data.replace(' ', '')
data=data.replace('\n', '')
data = binascii.a2b_hex(data)
with open('image.jpg', 'wb') as image_file:
    image_file.write(data)

注意：确保您的图片十六进制字符串没有换行符，空格或任何特殊字符，您可以通过添加data=data.replace('\n', '')类似行中的代码来删除特殊字符。