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如何將Base64字符串轉換為PYTHON中的圖片

[英]How to convert Base64 String to image in PYTHON

我正在抓取亞馬遜產品圖片,但有時我得到 base64 字符串而不是圖片鏈接,所以我想將此字符串轉換為圖片。

我試過這段代碼但出錯了

代碼:

import base64
from PIL import Image
from io import BytesIO

f = open('C:\\Users\\pc\\Desktop\\base64.txt','r')
data = f.read()
im = Image.open(BytesIO(base64.b64decode(data)))
im.save('C:\\Users\\pc\\Desktop\\image.png', 'PNG')

錯誤:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\pc\AppData\Local\Programs\Python\Python39\lib\base64.py", line 87, in b64decode
    return binascii.a2b_base64(s)
binascii.Error: Invalid base64-encoded string: number of data characters (16369) cannot be 1 more than a multiple of 4

Base64:

data:image/webp;base64,UklGRrQ6AABXRUJQVlA4IKg6AAAw7ACdASo/ASwBPrFKmkekIiEU2r5ERAsE4vviiQDHoCPrX4ipDeU/JuPLgv9g5f/vXfT/5vrH/s/qE88r+7/9n1P/tb+w/vM/kr75v8p6gH9m6n30FumL/wPnoYPxqT4d+cn5bou5Z+13U778/6/rN/n/Cf5fakHuPwU9xhtf+t9BT2k+7+cn9950fxn+y9gPzQ/8fjAfiv+j7A/9V/x//h/yHu//6Xla+wfYU6ZnpANu0pY3+8S8ONu6NNpvsCTLf6sfvCGmXMtg7P4aXvlaOF3TNN8ZLla6Xpgb7dV+v1cbQa3mGtirv9YSCjpI4FqZ0HrmJJq0vdN8hQkImfY8uy/DuGgZjG5Yw/8RclGmJb1UFLmkmyMtihwG6WQ07KgsmuCLkMg4pU7Da2fVD4DvlQdD0nXJCp3uDJGRO5chKHiN9KNLDtpu2NiSRgr9xXE7JpfDTBoBwPG/+j9pwMWPKo4xTR/Om414TgAqc3s6pFmbE5UkDUt+jE/chNHYIc9woxGtvEGHvHbjKvDkaZeo4OTGNOcPOJQlDzqDctPItImJAWfaQB0fuzHan1+6gQwVu/JBHm3vq3lls+IN+M4kYz8c8NPQZobVVmccv28CRX2e2H84POP3eGm72nOiIyaTujdfiiya7shwA3ra9m/+WasB2+K6neqo+hl0NNy9w2uQvwng8wLGceJHFYxt4t5++gS/iAJDfveWuI1ily0XMbu3JUieytvi9AqeH0JIzFEXCyiR9as8zNh8ze//y8TdlmEP/GY/buTcOIKGM7AV/lXfqHouKt5E7sdTlxKQpOSf+o3WxL6BO1w8wSVLY905YdXDDTwiJOgkqFQnn6xRZtbl73qXmfJY6qSjDzKnaNHrFbsbW39JnhYQmpgWnoiLYqsTFlcpS7G80LoseB0VnYGCcMgbMzOlB6bZmJX7crWR6z2cVduJhGbP0DEhMtF5+QoZK0yIdaGxhydDML9/vbILGRhEHRsQzwlMYQjrPeUVKv65wSw8pOoMTWfJ/vnz3YqwIsj0YuIKlmG0c93TtprIPWtws7ZLZZbLS0uaYgpWzEi0xpIjo7KhlUYgHfniTzpn52Rqprn6xPkUjHMdDXaKtgB3yEzNGqiOfjuoTTZzUW+71fFQ2nclvxm9zFehnFojNvr1xZOWM3KOxpFHJOSIm+1xoGOuBX+/EWuwh2oCYEC+fCoEuI9hX32pxJxZ/42kJ2u6VXcZ5N7753qmP2RUGnoQOA6JiCd+tZMxuf7ZtWA9DGaYlecqEfJlyV90yqY9JDcQdGibDH2QkbW4bEMPvr5mncCeB44CdRzDuytGxQV34ydFs9+Q1vORJv+YxIuorlDk3hJeqzInhcbaNomjisga4JvUNyQMyL0VEGT5E0VZ9GgeyT6pjMvKh2Te+cgEDXNaQVGP6vsNOu4EGhXU+xtsJkn4P6NhbeqhfQw+6I0coiHaVsxZK3Epd6ttLxMCOhMKRp0H2mGJkWodZIhmYC00yn+yTMqSjUZP8fFkrtxo0cyt21lVOoByZgEcSotJm/EDAxX2aj7vbUk9NdWJGorrVkpQl7WlGW8pWN3Y5H2NV6KAEZNlRf/ur+D7cQZRbhIMtRBwmzkDZcnJvQ2SJrF1MmVhaud6JsXeqn91sZnmwQZpys1Rk0bUcKQ9WZfpeIkoySW3+cVWcvnOQJGLtOR92w64lsvCQqDcZpHV1bngbBSINeT2JlqSiIgLFRsD+uNG5QOsmjeZzPn5ala1ByYfUtaNVXTRkcyk+HmB/T3AF/xTU5lQMr1cgAsc6jN5aEj5bQciAWz/aqvjNtjE9WESdq0PE8sXGBCSR+/BxMI4Lf1Wppx+INjnfCYrf2fgzKD9QMrBOyOIvoipPqfA7Htl0CojzCRb85sm446Rkyo6V4G7sBHC1R6vPef4yeSrGmgCbnZ2YfpCXmmyVCUTDThsCnXX9OObUZrrez/s5K2S26790jjQ7xKFMmMvo8KKdsMzIFsOIR+fwgOAScG/OY5AiXAGDVhS3Xt2Zm5lUpqfLFdPiBntHM4Ua+gJGIAN51W/Wz1p/hxGFE9eMbSRokQkhHyFmyVH2E6z04gCWwQR2Pm1qCUXuHtbJ96quc5n52snZkNJW5/3bUODX1k03790E90oQQLypDt5ZztROol1JYKThaiyXBUDq0zArx3f0npQZSTVlkGG1Iky2bvovNog53h3C/mM8/5Qf+AmSGpybEj9DoLktvL3brnZDxVvrYdVD0KStn8zZp9VMx6ZG/qPHUDZ1dm7Gm2h1tk9hJzq5aa5wZcKLBAgZcnJhrykg7HTNan0nSvlY1SOf/evFmd62ZKA4+T4tVS+p7Sb5yvSf+jjGfUu/F7efrtp9BZ/HM6fcWxPnZlUwdeRuJXKakLpKkGe5mx5fnHwfjdtW2sWgN1cDWBI2oLp8q+3yxzIm2EwdrIWV1K886obr+53e9a7n0Bsi8Str+RRf30VqtJPaIn2QprK/gjhM0353/WYJTUm8ctLMBkPzlRbLOlOtp2V04KfQAD+5D4AF53WaKnclseH7xi2KEsdbX55NAtd7bBIHhg5AzCswEUbrHAImIyEAhbXV2cfr2mXOs89AhCHcnUNuqKCckL2/W0SboIVw5hincIrfV9dOEDmumzAl+s8hkP+l7fiyCUJx3C/ZLIBK2tXqylKsXLQv0Y0xdgxMgzxA3weP8N+QqTpPG1ow0g1vGVzUVav+Q5/1dq9Y4TIijH+A6SvYP49Wjph7jWfBOeeXtD8tRR2/J/qtttSpyb8ernjrZRas49W14+aPZznuaxMSAaYX6KsRbtOzHhExipNav7hFcWS8x3k+BajwKC/hlX806TxkJVKntXszO3O1nOirWBWilMBJWB7GFME7mTWiGaYY+252Cn9Ra8wxjcYcQhyHJuIPS1Bf77qCpNsABjbIEObRE0qlqxGBKXNRvPLUXVNet3q2hzrtUnWwJFTgSdJrJgzaKj4aUsoqoKE50LGNTZIMin5RPQYDh+ujj/kc1eqxUoQWeLFQIVqlmb9h86b6zWtsFARIQMlHzq+ixQZRJBCkC+nC/jnwAPUOs03+5ksVPhIO1fsma+hCorxCYO5LbbeDvd/3xYWKVrYYxhWx91lza9Agn6LkD1iNv4BOllGju8LQ+i/0HUtDTc5KlMrq2oCr1RiJE85+ZDgECiMSZhRsAVRfokcbxMJG3Zh3/yYY/bxqyIwsSRSeV0B2xlv25dEjvpQV9AgpZJWNTXpiZVLaptTbzupBxsIDnRL1+53vvtmURlZQ/2aAsDnAHk+vFL7+Lrubu763f/FGfhGKY+vTUneQzY1gV9W3wzTX3KaDRLtWT98frHpZJDYkOV8yB/4BatLD6Gso5e9wJ3N7EWLS+2XWmbvNYN+w5+Gc/PFwnmSHxklx5euCviVL3dqvoAIkfjQFjDO4eVIykR4TjCPfAeTdciPTt3WyBAUSnaHlsu8rD6j49cPOhU/GEIz9PkqLFXqUTznbpZAkJk6vKmJnNxpRzBipQLeWMubs1Bs0QYhmjCDGqZdpdjalOmD4EuM9H2wVh+zdanTL2LTH5xt+X5RAOYKyhBtGnIpNhjo3n2OmYOkjHy4EuyGzzHiqfv3VroRI1YC7LQQPAa7bPTseHjeK17IEdurWSD8Qc+tcMCCwJHVYe87zH6AHqI4YOfhhmJfC2yYTlqpCSPun5SMkCOZVPZMyNUW6b9ZAshnoMsXZWvTLmLhQdOYckcVeYfIL0NzyxV1lziZZ9xHskJr7KHP6mcLu1ko8GwNKLP0gJwhq0dLkjyVjcd4xfCB+p2V/053v8m8F4E4qUPOmmVf035L9snELyfm9z9kqCVW6qnUgo8sNqOrea7BkFpAegBo0n5yynfZViMhOjKzIoDBOyvKPHnCOJtVLFyIUjXcr4wkadgb1LVqOGrwl7JLLq/fYG15izcnw01x8SzCCAI5F6pgJ+h9Q4IV2LWqCvuAd2KjaoBiZSAr6Ksz44DtdFK2IvPz6xEY8XOTigIXLROslYfORAcf4+FxdQtyfkSeh/NpfA4IAJ1AGvbUckvgjVLaQIarpalQk/ZzzHRpO4n2ncP3wMHo4aMbODYcGWSxnOCHU9KBsLJXoQ150DwSAB5PJBdcPhFSFavGgsFtp+4usrHQdHHxtEyqwVxH4G2jkKhXr//xtCpT604dn055SYs92RFkD9qIVDh2dsSGPC0L71BRefN7QteWRnvwk56AyG5zRHTBfPbP5SdS3l7+G7rU56zzsNwAH15TRVdu11htxmBAmWgpMbsrcWGr81GFqYmQ7g63lwYVOhjNrAgMCXvgDpfO2c77p8y3dd8mQqz6trRwvBhM/w0edxYqMtFx8bMlDFMKuT89Svv7yM+z5sip/4HicZf0Lelop/9tatM4kOJNpqPfwfCJ39+LXmlt79UNGSWTFFWs8eP18RiEn0eL4IgLpHGQXkr/M5oyBNwXZPXAdttfFiQ1tQv9a/qgcc4+77G4mmfrtu6xQ6WMtwYVE2HffaUcDWVUGtyFMtFecSOyAXX9lymRt0JjROlSaDbGsa1IM9fGvaQMH3uzuLDm+adVZQJ7NrhJkusTqltGL+ErcLlJ8tongd/BzorSjJOCm+Dr4zg3KJeEms+rCXy8gD7mBp8K7EaGumZb/mixz09NVkfWubsKDHdA6mPQu7Vz639il7Y4J2441DxkgLOjgFZHh4KkgQKLgniPJmkzYnMCvFNxpdSuzTdmq6L/aCfxjy4Y2lRfJXjj9POfJmluBghOF/rRa7Os2qQYdmvFuh/mvoSVdaeuq5241jJz3+XrYQPJBSUnKia26uczYkjTZI2i7HLmgjhAmFWcYAzPkfq2El9PhZAX2/PNlhVwbLs86NxG/IiR5JleJOjfojRl3HX0ODSq10bHGJkMBqetnkmmwYOyzhi7vmqlKBEJoTxHQn0a2Ps3pXtd53swiH2cvqQuKE59o+QVE0ZctwSB4WUFoFRzOcqmPm2Ynxf2NXmzwiT3570a96dPbBRo3G1voaRrZgCM9IxVgd87ufmp+7xaUV4BN9COPZPGIL7IErhAc/rOIEXGw6mYv37aj9R0XPj/gtKHNk4ki1+fwaFU3BXUonQsZJFMc08mkVzXSXiE8OMmBlIzpzKrwm5BqKaAfGlkYNsvF9waT00agUBniOHLtnhu+VtAk1Cb2kiINndphPO0fRRz1fYPfsJHEr1KNK0cRSVDPYnb8PzzWyi/94SUI9AFzZEXwlM5mYw54Bm0QOtM9Z2nJCNdOoZ81qNW9Q32XuOuPUFinrjeCm5e4XYyW+e3n27vz1J78lA5IzbaAhi9hXKdWARzEpVJ5unALdEVhyBLIjnyNDv+Oh2iGo+lp+jq4ZNmO8T1jRqU496MVZntIXpQYDv7mD1vR7OSojjoy3Hx0r8Ms7WgBrWe8xnnmyWWDj0qClIjXtQOFlt7T4Zggw5L+FurCdhrlD4SsyypTNUHOoWrnFERFd0WG6cJZ0SdJvAIfqVI1HocDiYZBP0Gg89flONl431kBraLBRvjbWESYeO0JjtfScvKMB/MNfunJHaYWW57yFvwDUdMbRJRT+Du1finRGKhdZ2wqPnnPVmyiHt8hzpDaeT/eqZHdn3GNf8WR5chxBBE7GRublhf64fn3aTmnyHXHrKEOY43mn6xN8uddbXUVIgKKiW9cMFvWp8EsDXXAdZGyfutVA7aX7Nuc1HTrSO9A7G3kzB7KSdN81BBt4pYhXPRgtP8wKH/IFj2/HbSAygodfGVPFyvb599gd92huDGG6aG0gkOR3do7oUSUFx7UbuRHmLOSQw/USwimiOlkwX/3bgiF5fIHXtOuJbLkit0KzW+BtpaqI/LojFhDrGKf4CUEwetXawQL5VT/5Cb6aP8jD/d7BTptApKiVhDy+FzaYTBKkSbjlxddoZhDJjVFBOxbJfo/LbiVmJhI+UEyolet6ZuCCzZpSeOakd4DjdRHAAe+D9AZEWaS6sFwkr1tp8A0Xo6uu6/zNW1rKee3spRu3L0yto0hLsSN8ReDOHOkoth0Qy96aF9wXARXAY1J9KaO07AZeWZrZLsHEzRd8KjEdC1UkF/dIwG8ocuI9CQzjH/YGQVQLm76KPr3tF5hgtI9nScRGvj4wwDdPrVo/uUcIQQZ3ufEjaLajTrPm0LMXPGyg+8b8afaP6Nd/SeMf16O4rPrmzVMKvbuy0OqLTp/3oHs34xNw5/gTX8TsBuK6R/42SMWD95xeLGnpMNHlbp6EyYEya50wgKvYFCbvQQ3nWJBPz3hZ05vWPGSwwShjZegKAQpYe5UHMRbVXi3WX8pgS8Ojh2Bj461YeWPgs2ehOjd4fZt4ujPukA1Nd5FUUqkdQvio8gBNp+999F+DFyR819Td+x8/ghmawLmWsCOFLR7G41n+38GX+tWpQbHyfPVcHYz68dm46TZU5pNH2x+WjlXzkPEYylaWEd4jFV02+ag9f0XBxda0F4jTU5C/bFH01XvqEKsWJFBWnD6SsD6YdNR8u3Gs+cO5uh/iQDMvJpDRDLhqxOhhnOKNdDGkRxlY9KnU3yCpeR1z6FpsTSf380HGmk2puGysEp5JEWBNqNhCwjMpj8BRJJO+mROPbphb+TDr4+pDcTrc3cmu8Im126+KsnlQ8nDySrBtfr7l3GTBTeUrskmbpBqntewyclpVsWLIC8HXY5qvsbVCquG2B3RWvWbxI/y9e7weFAk3ktPYlO53X+yxd5qpUnUv3fqM3VWf4ex5kgv2/Bx7G8go6L8C7SVG1IyzU/rM8eBYif67kS/L5TtLsvpwfLV8i7CtF6fXAleumVdnIk2h+UjD8EfQrSDIcZadSOp94F7SQb9jN9eGC3CoWH6ynM/88hzF6KqY9jN1y4skfYgKl4IaR8TPEkbzKUh95VNqZVugnhv9rZ3UHuLscBaec+rCENQYRWCS1AlzfZwQU2ornXmNGjfdTAvjiiWlluV5Yo0KxpE5x6xVnXb/rBeWAoqOk6NxfU2gWoF4cBqe5YDsF59CeQBwqG/pIaDHfTB1JYwSOqB65tcPnBwMEpP3SHWdhF6CsnHv3eHqKtCz/nM1vzPEbuHGQIseCumdjzTQpJPp5bG4fPX1l+fZYl0pKMXPjtjIp0s5/Kn+3md4gE6MWKjG6pcNAbLVJLBwVyS5vQ2KjwatISK3JqKhFD5r+oy+vWSvo0XKuw61js9SaGvuXlK3aylCbjT96qG79kwu4kFI1KxBrKw3Lkr4jCkccJ76LWbKSbt2/SvwZqWs+MBSssHhRyYqTrWNugW3sZZQypoPJaHVs6a0721RjOGWO5cia5YGFz7xLW2zKxloAKvT6VkeyN7ANztmXVeJBMBHVYH6jlf9+PRMwLN4cALiMVfou+XJ2+RDA0VzCEZU6PpIkzSoKJB21EfSc+jf8lE/FqvbsTdtpWMOw7K1BQojbhzKUOwjxWqF2DpDU/OSklmzXwFKLRTkDjjR2nHryvFapygkOKn5BEYa4MnHZ/3khwd2YTFOhwirgYYOjd2JwYOtJAIuiHO/RGphZUCOoDWxZ0GYYIz1fbY3XBjv/nE8gUu3IP8NWs44Jz/eRD8I2KDWLfN40nnJYZH98z8uhtezVBTlS5nSPKj9Iga2TZzGt0CiN+Ip2suKH4sIF7IpcZgHRqNKTv01umoFEhQ2FeD6Z8XfU6Zd6FBSNLLc4VZ/RMC1Fn0arzQ7WS0WbDY704DeBGMpMGjjA9mNJBNTbz0rOhRu3e/dcVqAS7g5/uhv8WGCXJJdscqsQqSXztDpMdpqL9tpCZkh97CMfRXN82frQfp+5/Asv4sKlceQk0Dt1isKtwAC3rM9IHTsPlhUwS9V0FlBnvSsC4btg/vGGTpwg2Kejty6V6WzBfkkXo6arqL9/l6shdOB1sD7FIna9dsz/9KVqcaAPwM6ZgurZzyzhhxe+fJfXbQRfDAwTCotCgke3zmy73zOcjts+u06o4FFLJO9icweFxtnESMmkHpB2bal1dgmJ58twn/fEad0eBQGWbkYBHYtyYH40EL+qtUWuDES6kg6gdlozcl32ksxTIeJUQabqBJBkZOjmyRRYSimM77dQyf3DNUeNA1vigsNX1NT277fEpSafJGBrQitP34I0DK6fW88JF/bALjHwfWRJjulhoxsDPPYzL1GqRbHBYQhq/yrlxVEGL/9NRxaFYDDvdtdYVBz8jTur8ah0oGyrHcXS3Xal41DEKx29DqI6QxHE5a2U3ZVdECePpuf1y5HHeYXFRZqItbZgFwetM9iBwvtuiOFP0HSg3uwbnwh1SGbQQ1uEULpKfC0Wu49da8n0YGkF3nmKIhPmmzl0ZasD+JKuGipdNVNO2Pbvj2AhL3xH/RynhHk2QXLTbd5ZV7ng9gWyGJNcQMEX5Hxu83vro0h5efxPD84G+i+rPTfQPIJ1q+AtNVLe2BJWds2QXWF1RVPpjWnv7tfGU3LwtnGkV845qEokS2+cTQlws3MWE8NrY7iPFGsg8WWLIR9dHm9Mijfyp1Qh1LPw6k9liiHaYDRWsnhiQuI9WoDL9kr/7Yf9mHW042cka4C6bsl3AInCFJdhph7Oa+Qki5IIzpCf3hRe9coHqpeFmVbJeqFFhQFbkSU2ynUcAwUGXj5lI6MsVUw2uBm8SCTBAEGrqZ2XvDU2hSyG+PlNAleS/ZPkxDY6ArMGsX8hZqqCJxA1PBfSOHkyHuYEnSw6hnQEHE0VoAbpglfh37kDTbd5Tb86R7RESeTma4ym4Rmte9v6yeYuwa0FWcMQt4tjDS0FSX0MyAkfP8k578LRj7LPV1z3gOuc/kdRxr9eIP+P2jt5cA9ZTxwSUzb1ZupHWd3iNaAAOVMsRoQsvCsEYbk+hu3HQ5Jo8irz+74ZB7MDu62noHwG1nvKfRJHr8GHYBg45jJVxCBmNdrYju2mt4DU88l6yB1eCMQKyxSvwuoBIozfTTIbo7BAjBtCPoS+pn/rhxlEzcxLw51tWrqHLUf2TYP1y2hf/pBYFlJvvXyYB3JEhPy7qmTWxOYuC+RhVBVnhqkNGVaIEeKbDY8ToFUoUcMsA3f2xSCcI8vB0OtCYrWk9SkLaAikGaik7uisYMKkVOtptGzIj6j3gCVUhBOTPGHisf3wc4JoQ44Rnaf8SzAgOsBrYqw5x2xo7JjMf6Fh/nQ6K6d53Rtf19ny1FCJDHz1md0+1Jg7XEIeqCNP37qgJL+oVJ6hnIQ9Xszt/noa6g8GL51MtWADa33dS6YVoeRPpRwv5pgFJNhwrktENTOCVmYG7f3rYWI1sftbj3aeTv2gD+M11cSuXWhYK7POrvTOrvkXl5BLw5u7B9KSricSlszmgU36aY90UWiS+A+ZOtkaHcwUIBsWv1nCJi3h6VZnpKTvru7P/Lk8R0LtY3tx7HUjCtljkMNj6NbgbPVb2UFxcnQFXB2kIrrcEwhG+mHCd/CIxBu7KtdEjsltjcl1rkm/DxNl1rbtpp1QzKPj89ANGl+js9iDxQ2buLe3iD9J+ZkCxlPVyDbYv285kaCD0LyfztLsKQ8qXrmDZuGMvDz4HQWry2XZYYutfdFCLFs2txTb8Cd4sqHquxzRsCQ3v06UutlE/QMQyYFTPqm7WsYXoay/BqPYTKgjBvnQkKI7teSBMyL8RXD3ZDSSOwm8mHRcv6xzbTcK+eKVqBlcBJkV8KolRmeVeS+ALDQrO9KRcg+p089LZgz4aBC+PGeRoPhwz2KdGH0u8XMCy5BbDbg7ifS4fp3tUcsem2GHAP2/fRPd/zsSo7dqDoJ34o7SM4N3FtkBb8SoIeEqEhvcucazPX99RuUGdjko4xdYlOaBDNX7RousN8lATWMDbUwv+eVBsqWhggE7OG46Sr5e81WKm/1rAkBFO3UDS78K46aPz4kLos3yeCIKx20kBP+aSIpWfxYrdZRXzrpIPd/dOYCHqiozSw4Id2lxF6t943/v9RjmKmpId6bKMVwUFsXJ7/yTx/5NFc2GDkZLaJx1dTuKkbcVD8WHkB+W79/7DWD9P9slFlmwAiNgbSyqo1jZBrOdaGXwOHaboUs3W0arNhhhXRTupIY5Nj0HCB2Y62t3wEW1wkXfvVX6xAXJn9e17YZwOyXkO8RN0cqDyFfjSdNkmlMgZEpSRIUQAxpZXilbOqDY5cSoTFTJ/KAmQFxSwWSfz4pAE8KFOA4xN4eDMKovhdGKImpPr2p1XRAUUJT5WeuEXkyTHrpvlnIe/O08uajbLwa1xRWrBmW/b2DQgbeLsIBLotsCdrz7trQDZ7rkrnNPsXPOUCmQMjc0C0BMykmvBZ760bVHwopBLDKe74zhQve14LDzZCuQQGCHTAzw/xy6ia74fMvEQYBCVlmvT0i75stIgibLrHqRNE8DNYtpoHoX0REQuZ1A5+0GTs11kXobxkZbKtj3i6b3lQqDgoyXXNZgqSzSJ7kEqq8Tp+1iqsfe3sp3RtMUCFBeVNEIUyqLhPZK0K0ltUduSCgYRED+IQRqhZ4RRT5Bu1ta9KlXPzcy4to/vjEnrh6HFzsrggpDpVihPrJICsw2ExPGSBbupIdHkKTuayLIaB4bvxpOW0Q9f1fukv+dD+0S4ACloKGSBt3QP8XIalLjT+WHuxIJRv8uAeo+9mUapizDD7gztc7H753uS00w+QOAEbQkf3cHXaDfBNswP7a9ZC2B81TknTrGY1BNpHNZeR8FljO2+yMLtJu/xHI1GS/NZKJgI/jiyqgyN7qPwWbnyKG0XEJwREMs/5QZ1CmWix2vuAQ1QYejOQ/YYm42GLWkvYQKVyeNsg8YwuKRqffpfNgF5MN9eOal2skCNQ8W37uyOIaMusqbKUKIqdFVQhrwfc9K2noDVnsNv0mY6ean6+7PHYs1orVUDNCxQM3Nn+FRk6JdfAo/VGAQDQSoxNhPJerY4uFgnkAjq5JWFjJxxEWaFdYGxevOEzsJ3wDvo/tqd7FQeR58rnz9BrzT+wWtQTh+UMZGFc9AW9sRyeYYewlx5N3rBKbPAlt9iAk5cfuyew/qz4ybSD/Y2w0GkterFbmurxYEL3bel4V6drs74h1MAle+71WyTAnVUVErcutVeeTq8xgUUSVME3dhydj/VvfE6+LgdVIJzl4sNeGuW4lWOs1l2dG+tWQmM9deP8tcDsrwQVvHgZxjZ6vo2LOis8g9THfL9ZZeTYvVnvtQrkaoFCTeI1BzthtGthun6j9GemQQwCkDA85lc8p6JhLmBKyl3vem5eu6tLdiJuXNzYeGQ3TvKxBwGwD2eE9cKI5o23p+Gbwad0r3jAbGK4kF6x3yBupcOw4libJ7kb9BpYI9PN3iB8J6mEAj6FbPPPN73Z/hgctNZejpvibKBw0FQrf5ml2z6rFFkIKF2K3g0lxoDVO84ibvvKR52PS0VH3xHlZagjwx00Of6BpWYmOC9OqMOi1vgMM148jnyO3nxwVpO53iM3BNrvKASwuyqFBRA0sTnaxhbVreHtRFvO76H1TjIbHS98nJhogBYZn86eEOoKKsG0sJYwZg46QaoovJVtvKJLjfqNLBP7Sx5lP5Wzx2JCRsnyl9dW9OT4RatK3jjURfPtgeMAwyTyYbpioEAlKD8gSW9gVDON3woKgTL7xJJvON3aoZPYyUvCKMKMEiwYWutOlOeOUJLKRy9Y9KiwWYQxP78TfQcByeeoM4jWZ3lXsOG7zBji45De8vLVUyb+luqUFg1xDX8BqIPbZ9HVCAJhRU8mTFRqiWOBLTUFvMpgGb0ZreTAbxo9eBPZjR+h9mRGFSJlV7HzDo0AoqREuqJxdduRrQ+KMlfJ0A4T1f6O777PwrcV2Wl44gnfXnwAgboqnWgrwd4RECI22+dspu8FsW2XWKrKR32fvqt5wZF+LgnQBjMrYWJIM5o015B6yUzt0jTbg9GDNlifLYg8cBtaz/SS0qn8Mg5VCwPJURP2R6y028A+IbBVEA+EwHfTMIrXOLnzXVz6Azo1WUT7EY7r7K10nYxonEFk01wH0covtCkrQ7v3o79wxkqYX5l2QY734z4dUX/PjLY8lvXlSM3jE9gI9Ne/PLu78kQpWcqoNiuI4x2OPdPKyz9fTsO/DN8VWzKqU++/nqQXnPZzqdc5pEa0Hw0fD+9xgi95UZanwjKaErRL/Uywb7id10ErNYJuHV5385n1+R0OhZ+NuD3qhHoKVJN707/2dbyo+FWRkvYPgoqMOd3lHZHtOHvJp+yCjbsvNFUamXUBPP+GagL5dwbogvur3NMv/TJ0ytLzS3oZ+zcylfmBL8I3dGF3o9tSUsKsJsH5buSuJtY/57MmCMdjhlC4fUEF+IbKhVkKePA/Znwve2AYecrRVRDRE+jFULp+d5TXVxIbR24GkrCfw5YRvdZFXz6e/oaSVQI7/HfeDhplcQFQ12HOiPM80BhNhSTLB2DjDf+3RD0KAnp8BODcrNA/6vTU58PEnAZ7ylh/px4vPYX30imP7hwmUUWT8X77cX08XcR9xGJ8Jns5RdnD6VMCqbZhpkf+h+Dubl+Jbvl/3Er+FTm/lH3sFlxqII5Xcp/YuNgL8f4cXFDFk1NBPo7qvMokHunLfd+UIQAvZfd902QyPy/GoOzmJv8n1Kvmw8UGnUorvgR4wqOxj/DE65p8UqpUgau29cRtr4eUhv5C89D6BE4bz8sKODoXhfij6p7tcvIQbTBCXKw4qbGxinyyPcVFsNvUmfreaoB2SpUpQEHTJb5s4s1BxxNbuyMAspex8EgAZMsj5AR9EKFJqwRPopMYG4gk6LlvNt9rheaaxuK1zswsKQWk1sY864bGyrUdIFOrlgvKCgRuwjrTAQB88A7tBPuvX0qnTTQybUvKxPKVtXZ3nP3al/QdMxnq21md25QqVdkIg+9hj1QI32lBeAgCMtQUZxJQ+/6bESq1juthr4Bco3+Y5vub1VokT24y+PUvNwBCAe7qun+LYmJgjrX7kSfxRyBQOTct9M1JYuINAZDG7XPqEHEX6+DycggyG/BB6Ph6TL0zoCuQAqRh0Dbep9MztcFSfG8ZrCkuk2Z8zOCiSsUJToXucDFAz8ki9c+H77c1MQtpTpw1NjqClLFvWcK6l9b1NsH7IWV5H2tnz9BNpt+KZEHdyySgWXPCldsTG7ipGrGkpJXugjtPvfIcvxvO+ylbVBcM1VWkEYh/uLR4G8bv3RWfPNRuEIs+mmKdSuJZ4qaJsJS/ELotSRea6/fSmbU8etrO8/hIZ9f2xQqPKXuCviWbn02QfD47t/f2GuzRjmP89AnudfNe9DyvWPsxL3IymFnUcP14aoor31JGgbp1r39eiFdBqiLeKFzr/MHY+q/hJMWgnkKDaUfhSLSwRlayOkjDSsDjWKMi+CAruNDKbQjWU84krFOnWPxnEfalgWQf8YOX5xtPBLOcD9TkrlSxO6NHVFop20wR3+zzomh4WkOBdmApw0mBI2OK9dkJB+1AmS6fNKrlc3pJLuJ4534lBgiHfuKgkTnO3KK8EZ//TRX7NiSb/Q+DakjwHwaCr3bzN/CFFfHEgb1MoRfDR8wJ6wbxdvKzVF6JLZOrqSxCohbQqsNCdlRFkASmbIWIwxFpfqs3WZdlQJn39FMQLuDX81OOEkiJUKxviTzm3REIbBhP6F1SX03zpcWJ44Dwmz0ouxo6eEmwZHDgxZkGHaHtYPgvamzp9B4Szlw8EoEjosnraBFJk+v8lAFQUrCPSM9/cPnMEc9wGzkERmN3jBrN3CkM3OWRpV5LicmSKDrpWXqwmP+85udGldka3yZI63c0UZmuyt9YiJ4b/BzgpZkepcT6P13mOF5Dxe8L2xuMq4Yopkv3Qg0QuPzl8GJOMw44CjwL0RRqrEhlFZBG4FZCMPF4mAylN2Swxwb/7dU6m/uWVcqQaHkt+gkSbGAkzwaAqWWIy2gkwiePE/H1wqIAtE8WFTCbMj4edNVWsYK4dz7x3gO6JwIRbMuJ/q33FVdJqtzjQ3uoXTJXCQ20v3rrzAqeCcFQb8l5i4SM3YxL/kn+EBh7jjVv0lFCXzD22ACyoazy0pIZvLVQLJlQBY04s+oQmAoi+ue+tIIK5R1gg2ZBZ/8sXFsf0QmAcx4Wrp3+T+OVL7aX8yLMAcC5xPKYSFLgoD+VNRNymm3vtcir+UT6feed58WHuUOm4a6Yu7ym0k8xxo+MgBgGYzD4KcThJDeJWAugcp+1y4eF3oB5rn8/Oyhx7kZSFiqJZJ/L2B2dYuAVDdWfGvgdgo637/DBSqCVu/QYuQ6CMKBSja+5TNaicRzf9M4Ig6AKwSjgJquMj4npHpvQy+t8/vzNFsac9QWk+uBxFcZsf4DFV9GQxW7ST7PsuSai2LWuv5wS9XgU9frhOm5J4qmbE/xNGlBW7kSRxcJ7dx8iZLGiLdk4qSOLJ6A+/WQjOYLyI3Cc7Jkm9T855IfCU1MmiZbljsu4/j2aexTQo1IgKhNzEfWAotcUDSuH0cSC+xbtK/17OxbMilSJqXc3AVNTA2m8W/C9DCapmUF37cgs1b/AGZv7MOxtgr6AW5g6C3h4frvWOkEJf7S/I8XBbq+izTFj6vZYVKg/PfSBipps8+9gBXCXz3c/m01gaYPUXUpjHDNmy/qqBqtMAvBnJcPec1M2cLMbw6Q2sgrMZmq/7M5AoLXu2eWtVDm8gC9shfgLYJ55plDtc/7eY6kWx5jqdTD6AdC4dwde33Hd8IqHs7YVRJzVvY+HrRBcpIAIxv1prCBV0nwz+V4lL3u7qUVsSYUkiWSMNahphCfmagzbZ8Rkzht2n7AOyXCJTIco3VBI+o8U9mvOEBvRt/eBE5I637KaFzB4DHdFlnT0jRNlgGXiqSF4LmbuXUgvHGtlef6wQaazfBy6imY2isaxMWUIG2CCDO/jSuazm1MprwX3E8sZk5uPEqd9VJHMTlSmSND6T917GHhvl9HucmQNbmUIuDj0EBa2sZ6L8meK4wzDDMXxR81U7kXlBnVoy/qKjOVExCvp0GqeNb55ZhRah+mZT7TesC/ooYitPPGXMHxqqm7pHkU74gfeaj/YFpSE7RUIECcmyNYG94oA76Wgc6LNxQZYApNOxM5bRm+JublZK3S09Epab841wRc7VcvGS3CM3I8TBFLfDnmkaFyuasZa/9HF8sBB3EAA9PMcXN86YxxMk0IcsbOkdWdT8qlmICIZnqIUYjYJrwskG2kP7aub0rYj5q7HnKylA9mJqPqVShQOOwom4WhCopwZTRjq8OQ8rg6QunqaA5jbW3gJTcEF0vXDDxR30Yjf2eGbMRJ+b/46YwyvMioYMJevj5DJWm52rwXkkkr2gFdsSzp3RET3kJNQyajjMCbToave24bFSgEfTdzOw6J0KGP6jTVHJMFelh9fiTQ3yNJcKv0pwOKNOKPV9J8n3kf3ExXGq6eMh99k38Aaeb+GfnRRr+g38r5HmL/QAuEOYfXqILNEcvwG4zwIUMwkgcXK2hejd7Ejx+2EO/STZm0zEyZyKMv+KsyvrUL+tv4Inf3QEp+NQksXmDeVO4I0NmD2MP5jDHSgockt5C2H4fYoQu2nW/I0yLOQAJtMTbGkYDlUttQtmWVDlwTe4nuJfs4jz67QizBUrgxr6zy4Jt+Y7kH8NyJPmH/ouYUddI+d+oXSjj1glSxt4FyaWVSxZRfhLh9Ww/Fv1tecBKJklyw3ZCbhrzhQyRkJrAArFdomoW1cdcqncQVluF8YxytK78Wr6HfrIMo1OjjNlDqlU4MGAWKfCTPeQa+AmrvzlrI6QqrLQHewps7ipPp9xYPGrWWz3QFa0i0VtApl5LywWOgF2a0JhGpr7pzmnmQGEfBH/lbmJHpodPDIK7w+FRMjLqtPJA/SqKyevbpxrulp6PIIg/b5GyufkcW+ZAVAxGqSWbxKeev16sUjHrNdRyxAaZFstgbRT2qeaPgZPvzbTpXlaaLf/Q3GcFW6ND+oJaHU5oWaPWeqr8Rf+5tR9HO1v4QqEVD9hMq4tUawBXUvqum5LoW3wBOvyEHEQ4LOhFL9ZmyYSua03szukgZbCt+CHZffyyrJL3Pp9mB3IssyEQIMTxvzuYer2hQ27hJVOh7/h0eNGCUthVBOtfqV6u2+eda6hyk8iiBN2g0am3K/NgBSKOMEBXPxrmqmf5aQDVxLZzyiQFNkykpCoueE4GC08sK5TmziW+BaqFiFZh7kLl0hGnjJaKGBwCU6kzrp6jUaViUM/jWzUuK4gxcoYzG4jZlznl73ld8ZEah/CA66QskBB2PZEtGEcFv2JL8OiwTfWNT6zlK3Z6fwcdu41w+J1WYqv/M1qnv6+Y1hVWTwePi+BtNjhsuRU/eQPUZPFOaxWmcCoQhBuIUDUYO3NGY7mXZCmw3vzfB3XtIJToeDkzhoq9c9IBZGRTK37g8fSoPKMi6rVuuWXDKzdme5Ge55ZvLEH6bf2EkpFD/20mNb/231+xcv6m4VJAaMvbPOZtEUg5187rXnQiRV6w5UoIDYNAKtgQ7qvncgaZHf4eNa3nvqhOMIWJSoRn1Dq23SaHfxEem9GQryW525gympjEHuAIVAy9UFHJLIVpdzHI7LUy0B5yggNvJPxGWuJo1ZuR4YAe+yxuVOkiZiwbF3Cdz1TKimAyvz/2TSGrTstuK6nHL0TjQu8zvJx9VnXtuZMOLCkyhIZxsEmR5PmupmXDX0r4e976Ee2vnpvak45CRpb5pMGP3ACLWOJH5AD3bflgBVsHT+HZs1q9ElxOf/b51DKJNj1P4euGy7IE/OR5zxb6xc4ADUA390n+lgCtkmJRkE4/r8lYG92gFk9cv84Hm3nTY/+iUR9v0E3Is9JOdCvAtVHWXQWRjrMZ+Z4ojEyczuLBlniYyNpaz0fnxgDtvXZtnsqTv1K+SYioM1x+rFwCG9ia0SlpS47yzPdLsLI8Nubund1M/br0SMei4oln5LldAQlwXWHqHZd320p3rtYfX2n7k2vfMWmxkk83MgMJiGKUdSzAmWJbQzzSI6p0xW8rBrGpNYL6j7TTbp5k2G9XTXnJFx73ozSqjELFoVKbEkfrKygJUaYdTAcM4fampcdCmmr9vhmheeeLM3mXgpTKwkf7FsANGosZ4NqZgyKd4yNyfHUhGhGuW+ZNBSm2J807uSJCAw44k7rI2JS0+tl6GZkXnL4NFNL7tZVKADyXR8Eee3Vr6l5/9lI/Ajs3TjNpbUExFR1w3ppBtpynTp5w2VKOOeHlPluLN3ZmPcagldjSmvjp7cNNRpYmtWNVw8azv4ksBRR4Q2u0TK5sT+/69Woc5KpyFR0kxz43R5TE3bFT98lP8dgVWPttXM3tKBgF+iwZYAFWmRjZ3iCGlk2OmgcdgtOChKl9JHOjURbjXI0TGvia1eRZnkr+udxRVT2VzqPet2WG40dRJtRxdJNOqY9bwraDspr1TL8uRysCsma8CVw+K18NOS5FMvOZgdRt4G54kLC7xAoWuGKQ7G2ep0+IrtYsOwXSHsFCMA+sjPYLRx4RupqNfhxlgnOMl4ZyRzaxeghPWFFtkt5cUpfSA0WH5eJcCr3CUPE6zYZh17/GhuXZsvR+7+KFblEolUTB6omOAocDU6LfwRAaIJzuMm2/Pbi+W+WaOhrowWJZ5K/6CvlLkvco3xvJdFa4G8BRHT0lSzheAzacgBWwB5TnYakCWJq1q6e+OGe4GindC+J3HD9yZ+9caInBve+WXvtrKXRTbiq7qA4QBkV++sjxajB4efkL4R5hJs/MC13icyC7ZWwoLEjEleAy6vsfhwmjPp/5tJfNcrlHReB/XXrHAmRlWhRIh0z55aCry+vSbmwAEugY/23C19Ef5t6Q/bc95RU2o60ts9gs//+v9QVgdBtagdNBCLxT6eePKMw6KvqDacY4MWbXP70umUK4sDBk7QsW+6BvqGuSOY33pwejvveBncV4KdOY9XSc1cfeCj+pXNR1qQthxOfGQTPlIEF2G0tDGg0agKQj8Jl7PD6gTQY4SMUiOCjYjtL/IVTUzWUOHcrRXFT74XMl+k9K/nSPzucyRohyFExr12mZI8jcG0NBqIRs4dRQwSr6Yqo2b1gdJzJsj0dSoYiUgf/xCVXHtEz53m2qphb2lD2zvd1PCp0v6GZUPPJVGHVlQzhPGdcI7ECrg1UqRWK7RmaGHHTG+oHnlDSLbbnK1wTNH+xv8bIztPiE5/rSLvBl4NyJMKrm24O5nGn0yUDz4sfoq/MXQ65Yq2Wtjkp5cwWq4UQM9xgEXCT3tElKLVY9Lt53Jx9OEPYPpKiyj8LvXwx3lXJ3uvFUsxieFcwH9cLRSnYrMM4ZFNjn1aAVa0Y+XKVBiGDo4PyoMRZClRVqQLJ1OaE7tojH+1dVXcSyr8UsmxHnBdRqLiRMz0ZJcKIdHskbas7m910N00JrmzFFA0vXV4Obr8cvqEtcXNlYAHmrCcrr4I8+dQgkmwmD41WSahsd8aRSoX1k6J9bWly4VkxWvisYlNFPaySp2WIk30ahP88pFm8P0PvRWlJeP+e18ZoGiGZNBn63fXAhAH8Mp6pwO0PZns1tQkKC5twcDTcNw+LnCBPF520vwxi/RehaYCkGJ7zCU4hZVy58H6NY4qIRrN9kQYEFY0xUPjbCpeXjwBQ/JSsFtcOg6pv+/uaJ45lzsFanArWXtobKU4eKi/JiRHR/m8H97NGHnONTkbbILENlttHgCAk2CYqD1ydE5y1ob97K35+VEzjv7IlExozdHdDelnnBlAf0iggwaQ6S1hrb7HNH1DtPQzA23YE2uvmJmB9YyW7MCLuIsBUl5iOqdkqEEdm2hjiaKCPD/cYx/jLBFeWEtmXINrZlxt4+StG7eAXQ8Vk3McDX98SFvc9tnDuafVgtKNmtWD043oPGqVrNmgQoJxaPWaPgrsjCYojE6k7IGVDrI6rb0eK9vm1AVG4T1JFTzvc0NawTI1/TtoVao3scO58/FjVtrXlhgM2iuL1HdqbDsg018bw8tWAwPNYqKgMaIxBkMEkwXD+snhg63rGasbhzUEEUVsGuxdgZGaaxIAg6G7x/QjOUS5RhYo/StyvaGcvHRbcWdj3tDHgeXshJdRyJ0rwpdvb9uI2zmotJlS0/CgXHa/EOS/ZwopFA+PcoxBlluBNhRIqm/q9iqYNNdpCL8cx+2+QzLwpoq5W8LaWk+KWgxBawIA8vwoO7+r1oFZlftRbPCulu7/9T2vhl44p4tTi8ww7KM50r5h3jD17xSeR+rw9AaxKnxpfVhBrJ5eC+J3+TAC7AzNcrdZgeSMkVMIYp3HgM5Rt54qiMUyLMCqx3FIaTFgj86nugn98OksA7yRpkKw/5hEks52yVQj98p0235MrpmmlThaSrLozPGh37OZNdEGVAJNEUvzuuv1AbTqqM0ZXbQ1sE1a8GelGZu2vhadTVd8d0sDqDNCe9Rnhfb5eDfCQyoEz65lnWGhDJAvSIDewS4kr+WpMpNMm/sjrGOyB2qO0S9p71jBXD9tRjWWfny/YscjjtqSEeag1g5sIPqJsVk+GKoYMp99dWszRE6guyX031zsn2z28ZpVWTXBOYzul7itgP5+we/B1iHXFDmrKezWMo85XL0pgkt6bLNEMVpEH4wvw8CZLQQV+52t8kUzAvlLoZCgOtxYLFqNlXrYdkCzLhRbNyxpen9HAGGf6EIcQ7ekVJdTPsU4K5BITZ9H9Qg+saupxI2JW59vTZZhCFVLSGRmLAxmwwkYAmsiUl5GPDej1+mhrElllxtdOtP9E5pVPywG1zmQKB9nrn4Wwof4CCrJnh91LS8VstgAAJzO22meOSbfg/x4NqUJLqMZLAPa8b56F8Uacc/4wZ83YriLaIm4nasz+shx/m8IvER8DGFT2RDSbG33C2CFS53QbrueMbQ5xUxcC9N29SQftdzxtHF6kgnpeVlTHzLixgdefqHsjjxzhiJoSNo8o3p0cbV8lucaw3Ex8J6XH8LKkcVSgPcLeKw0m9ucADQ+1Mugtxz/UwPlbBEctAAAAAA

我知道有很多這樣的問題,但我沒有找到好的答案

此數據不完全是base64注意前綴數據:

data:image/webp;base64,

此數據必須先剝離,然后將其有效base64作為圖像處理。

import base64
from PIL import Image
from io import BytesIO

f = open('C:\\Users\\pc\\Desktop\\base64.txt','r')
data = f.read()
prefix = 'data:image/webp;base64,'
cut = data[len(prefix):]
im = Image.open(BytesIO(base64.b64decode(cut)))
im.save('C:\\Users\\pc\\Desktop\\image.png', 'PNG')

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