Base64 图像转换 - FLOATLE-Stream

Question

血液分析仪正在发送 Base64 数据以检索图像，如下所示，有人可以解释这些步骤，如何从中获取图像。我正在用 VB-6 编码，

M|1|HISTOGRAM|RBC|RBCALONGRES|FLOATLE-stream/deflate:base64^Y2AAgW5nMJVwzwVIOoCYAA==|FLOATLE-stream/deflate:base64^pdR7bFNVAIDxI/hAizoFq8LQNfLUNk4ENpHZe8+5GtRtDhCkKjpHRUFip0MhBfFSt1KZhi74WFhjGhWKMdOlIcsybZgSTbOoqTbiJtnWEGK2xkTNWDRIjF+329jMv
whNvtzX6e+c9t5WiNyrWY1vagcNITRNiJVSiCTZJ85zUojdyutodnsdNs0er9HY1xIj7eynNJ9/lGOb3tJUrCdGXHq2yq0zRlf2Ot3nb9CrfQGd8bo/fYAxET1Wdohx7Xq6rZOxx3TmYnxKd3n7ec8p3ZPM8r5RPeg8x3unSE/yUokvff6rpT89QwadN0jmkpHBEhkrmyfj4UWSeWVSLpbptmVy6MxyyRrk2GElc5/F5qmUr
Ec6bGuly+uR5YkNkrXJat8m7C3YPuwG7O3Yu3BMrgWwGrkexAsxphnzTcbtx21h7AHsdxjfin+Q9USY4z3WFGWeD1jXIeaKsbaPmO9j1tfOnB3Yceyj2J3YXdjd2AnsY9hfYB/H/mr8+w86e7G/wf4OO4X9A/aP2D9h92OfxB7AHsI+hX0a+xfsYews9q/Yv2H/gT2KPYb9J/ZZ7HPY//AeoWyeixTfs2ppmqrSbRcre/wSx
XeuIoOXqaEz05TDdgX33qZiZdNVtupK5fJepbgXKh4uUmOHr1HliWsV90UlRmaq3DOj7Ncr7pFKyhuxZ2HPxi7GnoN9E/bN2CXYDuxbsOdiz8Oej70AeyH2IuxbsW/DdmK7sG/HLsW+A3sx9p3YS7CXYi/DLsMux74Lezn2CuwK7Huw3dgato4tsRW2gX0v9n3YK7Hvx34A+0HsSuwq7Grsh7BrsFdhr8Zeg70Wex32I9jrs
T3Yj2I/hv049gbsJ7CfxH4Kuw57I7YX+2nsTdjPYD+LvRl7C/Zz2Fuxn8f2Yddjv4D9InYD9jbsl7Bfxt6OvQPbj70Texf2K9i7sV9l31TZb02O9yjX6T2cCyjf2QBzvqbiRY3M26jG5jcxd5Mqrwgyf1D51+xlDXtVYnOIdYSUMF9nLfvy/wHn8TLdQkT5zxD8tkuphkwK0xHqGv/NC9FHGRq2SlGPdb3LGpurlTqsa7n3C
J7hYjKonsLURcN0Het1UwPrf5s6aUCJnqmGMOfw/3Y323WG0LYZojbE/ruG6PnQEJlPOf6M/a85l+J6H2MHjPH/xMxJjvvpBMffM6aX7XGOPzdENM42xrmDjN/PcSNbbLOOVnGtgmMnzeLYxvZv1jPE+nqpm2LUqib+g3fSRlpNOi2lBTSbZtDlNIX+4nNmKUM/0wnqpS+pm47SJ3SE3qcItdJb9Abto4DVDtpKXqql9fQwV
ZJmtYKWkJMW0tyCcvdhJhXRdJomJ+7P79a97Zt0Xzuscvc1at3byYWtQtZzU29Vaz1L+TTr+SqhImub388lCspo/9VTUHRSppV2gYkLLPcbym/Pp/zvr/C3OPnc/14F31+p9b3W6P8C

之前我都是通过代码或者https://codebeautify.org/base64-to-image-converter帮我转换Base64数据，但是这里的格式不一样。我从这里得到了一些帮助，field6 有一些 bace64 数据，field7 有两部分数据由 // 分隔，但我不清楚如何使用它们

Answer 1

我找到了一个参考raa066aen.pdf，并设法使用Python解码数据：

解码Base64格式：

    decoded_data = base64.b64decode(coded_string)

“膨胀”
```
decoded_data
```
-使用来自
```
以下答案
```
的inflate方法将缩小的数据解压缩为字节。

    inflated_decoded_data = inflate(decoded_data)

从字节转换为浮点数（IEEE 754 Little Endian 浮点值）：

    inflated_decoded_data_as_float = np.frombuffer(inflated_decoded_data, np.float32)

代码示例：

import base64
import zlib
import numpy as np
import matplotlib.pyplot as plt

# https://stackoverflow.com/a/1089787/4926757
def inflate(data):
    decompress = zlib.decompressobj(
            -zlib.MAX_WBITS  # see above
    )
    inflated = decompress.decompress(data)
    inflated += decompress.flush()
    return inflated

coded_string = '''pdR7bFNVAIDxI/hAizoFq8LQNfLUNk4ENpHZe8+5GtRtDhCkKjpHRUFip0MhBfFSt1KZhi74WFhjGhWKMdOlIcsybZgSTbOoqTbiJtnWEGK2xkTNWDRIjF+329jMv
whNvtzX6e+c9t5WiNyrWY1vagcNITRNiJVSiCTZJ85zUojdyutodnsdNs0er9HY1xIj7eynNJ9/lGOb3tJUrCdGXHq2yq0zRlf2Ot3nb9CrfQGd8bo/fYAxET1Wdohx7Xq6rZOxx3TmYnxKd3n7ec8p3ZPM8r5RPeg8x3unSE/yUokvff6rpT89QwadN0jmkpHBEhkrmyfj4UWSeWVSLpbptmVy6MxyyRrk2GElc5/F5qmUr
Ec6bGuly+uR5YkNkrXJat8m7C3YPuwG7O3Yu3BMrgWwGrkexAsxphnzTcbtx21h7AHsdxjfin+Q9USY4z3WFGWeD1jXIeaKsbaPmO9j1tfOnB3Yceyj2J3YXdjd2AnsY9hfYB/H/mr8+w86e7G/wf4OO4X9A/aP2D9h92OfxB7AHsI+hX0a+xfsYews9q/Yv2H/gT2KPYb9J/ZZ7HPY//AeoWyeixTfs2ppmqrSbRcre/wSx
XeuIoOXqaEz05TDdgX33qZiZdNVtupK5fJepbgXKh4uUmOHr1HliWsV90UlRmaq3DOj7Ncr7pFKyhuxZ2HPxi7GnoN9E/bN2CXYDuxbsOdiz8Oej70AeyH2IuxbsW/DdmK7sG/HLsW+A3sx9p3YS7CXYi/DLsMux74Lezn2CuwK7Huw3dgato4tsRW2gX0v9n3YK7Hvx34A+0HsSuwq7Grsh7BrsFdhr8Zeg70Wex32I9jrs
T3Yj2I/hv049gbsJ7CfxH4Kuw57I7YX+2nsTdjPYD+LvRl7C/Zz2Fuxn8f2Yddjv4D9InYD9jbsl7Bfxt6OvQPbj70Texf2K9i7sV9l31TZb02O9yjX6T2cCyjf2QBzvqbiRY3M26jG5jcxd5Mqrwgyf1D51+xlDXtVYnOIdYSUMF9nLfvy/wHn8TLdQkT5zxD8tkuphkwK0xHqGv/NC9FHGRq2SlGPdb3LGpurlTqsa7n3C
J7hYjKonsLURcN0Het1UwPrf5s6aUCJnqmGMOfw/3Y323WG0LYZojbE/ruG6PnQEJlPOf6M/a85l+J6H2MHjPH/xMxJjvvpBMffM6aX7XGOPzdENM42xrmDjN/PcSNbbLOOVnGtgmMnzeLYxvZv1jPE+nqpm2LUqib+g3fSRlpNOi2lBTSbZtDlNIX+4nNmKUM/0wnqpS+pm47SJ3SE3qcItdJb9Abto4DVDtpKXqql9fQwV
ZJmtYKWkJMW0tyCcvdhJhXRdJomJ+7P79a97Zt0Xzuscvc1at3byYWtQtZzU29Vaz1L+TTr+SqhImub388lCspo/9VTUHRSppV2gYkLLPcbym/Pp/zvr/C3OPnc/14F31+p9b3W6P8C'''

decoded_data = base64.b64decode(coded_string)

inflated_decoded_data = inflate(decoded_data)

inflated_decoded_data_as_float = np.frombuffer(inflated_decoded_data, np.float32)

# Plotting the data for testing
plt.plot(np.arange(inflated_decoded_data_as_float.size), inflated_decoded_data_as_float)
plt.show()

浮点格式的值：

   0.00000000e+00, 2.78000000e+02, 0.00000000e+00, 1.77900000e+03,
   3.00000000e+00, 5.00000000e+01, 1.00000000e+02, 1.50000000e+02,
   0.00000000e+00, 2.00000000e+00, 2.54000000e+02, 1.08695650e+00,
   2.17391300e+00, 3.26086950e+00, 4.34782600e+00, 5.43478251e+00,
   6.52173901e+00, 7.60869551e+00, 8.69565201e+00, 9.78260899e+00,
   1.08695650e+01, 1.19565220e+01, 1.30434780e+01, 1.41304350e+01,
   1.52173910e+01, 1.63043480e+01, 1.73913040e+01, 1.84782600e+01,
   1.95652180e+01, 2.06521740e+01, 2.17391300e+01, 2.28260860e+01,
   2.39130440e+01, 2.50000000e+01, 2.60869560e+01, 2.71739140e+01,
   2.82608700e+01, 2.93478260e+01, 3.04347820e+01, 3.15217400e+01,
   3.26086960e+01, 3.36956520e+01, 3.47826080e+01, 3.58695641e+01,
   3.69565201e+01, 3.80434799e+01, 3.91304359e+01, 4.02173920e+01,
   4.13043480e+01, 4.23913040e+01, 4.34782600e+01, 4.45652161e+01,
   4.56521721e+01, 4.67391319e+01, 4.78260880e+01, 4.89130440e+01,
   5.00000000e+01, 5.10869560e+01, 5.21739120e+01, 5.32608681e+01,
   5.43478279e+01, 5.54347839e+01, 5.65217400e+01, 5.76086960e+01,
   5.86956520e+01, 5.97826080e+01, 6.08695641e+01, 6.19565201e+01,
   6.30434799e+01, 6.41304321e+01, 6.52173920e+01, 6.63043442e+01,
   6.73913040e+01, 6.84782639e+01, 6.95652161e+01, 7.06521759e+01,
   7.17391281e+01, 7.28260880e+01, 7.39130402e+01, 7.50000000e+01,
   7.60869598e+01, 7.71739120e+01, 7.82608719e+01, 7.93478241e+01,
   8.04347839e+01, 8.15217361e+01, 8.26086960e+01, 8.36956558e+01,
   8.47826080e+01, 8.58695679e+01, 8.69565201e+01, 8.80434799e+01,
   8.91304321e+01, 9.02173920e+01, 9.13043442e+01, 9.23913040e+01,
   9.34782639e+01, 9.45652161e+01, 9.56521759e+01, 9.67391281e+01,
   9.78260880e+01, 9.89130402e+01, 1.00000000e+02, 1.01086960e+02,
   1.02173912e+02, 1.03260872e+02, 1.04347824e+02, 1.05434784e+02,
   1.06521736e+02, 1.07608696e+02, 1.08695656e+02, 1.09782608e+02,
   1.10869568e+02, 1.11956520e+02, 1.13043480e+02, 1.14130432e+02,
   1.15217392e+02, 1.16304344e+02, 1.17391304e+02, 1.18478264e+02,
   1.19565216e+02, 1.20652176e+02, 1.21739128e+02, 1.22826088e+02,
   1.23913040e+02, 1.25000000e+02, 1.26086960e+02, 1.27173912e+02,
   1.28260864e+02, 1.29347824e+02, 1.30434784e+02, 1.31521744e+02,
   1.32608688e+02, 1.33695648e+02, 1.34782608e+02, 1.35869568e+02,
   1.36956528e+02, 1.38043472e+02, 1.39130432e+02, 1.40217392e+02,
   1.41304352e+02, 1.42391312e+02, 1.43478256e+02, 1.44565216e+02,
   1.45652176e+02, 1.46739136e+02, 1.47826080e+02, 1.48913040e+02,
   1.50000000e+02, 1.51086960e+02, 1.52173920e+02, 1.53260864e+02,
   1.54347824e+02, 1.55434784e+02, 1.56521744e+02, 1.57608688e+02,
   1.58695648e+02, 1.59782608e+02, 1.60869568e+02, 1.61956528e+02,
   1.63043472e+02, 1.64130432e+02, 1.65217392e+02, 1.66304352e+02,
   1.67391312e+02, 1.68478256e+02, 1.69565216e+02, 1.70652176e+02,
   1.71739136e+02, 1.72826080e+02, 1.73913040e+02, 1.75000000e+02,
   1.76086960e+02, 1.77173920e+02, 1.78260864e+02, 1.79347824e+02,
   1.80434784e+02, 1.81521744e+02, 1.82608688e+02, 1.83695648e+02,
   1.84782608e+02, 1.85869568e+02, 1.86956528e+02, 1.88043472e+02,
   1.89130432e+02, 1.90217392e+02, 1.91304352e+02, 1.92391312e+02,
   1.93478256e+02, 1.94565216e+02, 1.95652176e+02, 1.96739136e+02,
   1.97826080e+02, 1.98913040e+02, 2.00000000e+02, 2.01086960e+02,
   2.02173920e+02, 2.03260864e+02, 2.04347824e+02, 2.05434784e+02,
   2.06521744e+02, 2.07608688e+02, 2.08695648e+02, 2.09782608e+02,
   2.10869568e+02, 2.11956528e+02, 2.13043472e+02, 2.14130432e+02,
   2.15217392e+02, 2.16304352e+02, 2.17391312e+02, 2.18478256e+02,
   2.19565216e+02, 2.20652176e+02, 2.21739136e+02, 2.22826080e+02,
   2.23913040e+02, 2.25000000e+02, 2.26086960e+02, 2.27173920e+02,
   2.28260864e+02, 2.29347824e+02, 2.30434784e+02, 2.31521744e+02,
   2.32608688e+02, 2.33695648e+02, 2.34782608e+02, 2.35869568e+02,
   2.36956528e+02, 2.38043472e+02, 2.39130432e+02, 2.40217392e+02,
   2.41304352e+02, 2.42391312e+02, 2.43478256e+02, 2.44565216e+02,
   2.45652176e+02, 2.46739136e+02, 2.47826080e+02, 2.48913040e+02,
   2.50000000e+02, 2.51086960e+02, 2.52173920e+02, 2.53260864e+02,
   2.54347824e+02, 2.55434784e+02, 2.56521729e+02, 2.57608704e+02,
   2.58695648e+02, 2.59782623e+02, 2.60869568e+02, 2.61956512e+02,
   2.63043488e+02, 2.64130432e+02, 2.65217377e+02, 2.66304352e+02,
   2.67391296e+02, 2.68478271e+02, 2.69565216e+02, 2.70652161e+02,
   2.71739136e+02, 2.72826080e+02, 2.73913055e+02, 2.75000000e+02,
   2.76086945e+02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
   0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
   0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
   0.00000000e+00, 1.00000000e+00, 5.00000000e+00, 8.00000000e+00,
   1.10000000e+01, 1.30000000e+01, 1.60000000e+01, 1.80000000e+01,
   2.10000000e+01, 2.30000000e+01, 2.50000000e+01, 2.70000000e+01,
   2.80000000e+01, 2.90000000e+01, 2.90000000e+01, 2.60000000e+01,
   2.40000000e+01, 2.30000000e+01, 2.30000000e+01, 2.10000000e+01,
   2.10000000e+01, 1.90000000e+01, 2.20000000e+01, 2.40000000e+01,
   2.70000000e+01, 3.20000000e+01, 3.90000000e+01, 4.90000000e+01,
   6.00000000e+01, 7.20000000e+01, 9.20000000e+01, 1.16000000e+02,
   1.49000000e+02, 1.91000000e+02, 2.43000000e+02, 2.99000000e+02,
   3.65000000e+02, 4.43000000e+02, 5.27000000e+02, 6.30000000e+02,
   7.48000000e+02, 8.58000000e+02, 9.77000000e+02, 1.09100000e+03,
   1.21200000e+03, 1.31800000e+03, 1.40700000e+03, 1.49900000e+03,
   1.59000000e+03, 1.66800000e+03, 1.73000000e+03, 1.76800000e+03,
   1.77900000e+03, 1.75900000e+03, 1.73800000e+03, 1.71400000e+03,
   1.67500000e+03, 1.62200000e+03, 1.56300000e+03, 1.50600000e+03,
   1.42900000e+03, 1.33800000e+03, 1.23800000e+03, 1.14400000e+03,
   1.06100000e+03, 9.76000000e+02, 9.06000000e+02, 8.38000000e+02,
   7.57000000e+02, 6.88000000e+02, 6.16000000e+02, 5.58000000e+02,
   5.02000000e+02, 4.47000000e+02, 4.04000000e+02, 3.72000000e+02,
   3.34000000e+02, 3.04000000e+02, 2.78000000e+02, 2.51000000e+02,
   2.27000000e+02, 2.10000000e+02, 1.93000000e+02, 1.81000000e+02,
   1.67000000e+02, 1.55000000e+02, 1.47000000e+02, 1.37000000e+02,
   1.30000000e+02, 1.24000000e+02, 1.17000000e+02, 1.12000000e+02,
   1.09000000e+02, 1.07000000e+02, 1.01000000e+02, 9.70000000e+01,
   9.30000000e+01, 9.00000000e+01, 8.70000000e+01, 8.40000000e+01,
   8.10000000e+01, 7.80000000e+01, 7.60000000e+01, 7.40000000e+01,
   7.00000000e+01, 6.90000000e+01, 6.50000000e+01, 6.50000000e+01,
   6.20000000e+01, 5.90000000e+01, 5.70000000e+01, 5.60000000e+01,
   5.40000000e+01, 5.30000000e+01, 5.10000000e+01, 4.80000000e+01,
   4.80000000e+01, 4.70000000e+01, 4.50000000e+01, 4.30000000e+01,
   4.20000000e+01, 4.10000000e+01, 4.10000000e+01, 4.10000000e+01,
   3.90000000e+01, 3.70000000e+01, 3.60000000e+01, 3.50000000e+01,
   3.40000000e+01, 3.20000000e+01, 3.00000000e+01, 2.80000000e+01,
   2.70000000e+01, 2.60000000e+01, 2.40000000e+01, 2.30000000e+01,
   2.20000000e+01, 2.20000000e+01, 2.10000000e+01, 2.00000000e+01,
   1.90000000e+01, 1.90000000e+01, 1.90000000e+01, 1.90000000e+01,
   1.90000000e+01, 1.80000000e+01, 1.80000000e+01, 1.70000000e+01,
   1.60000000e+01, 1.50000000e+01, 1.50000000e+01, 1.40000000e+01,
   1.30000000e+01, 1.30000000e+01, 1.30000000e+01, 1.20000000e+01,
   1.10000000e+01, 1.00000000e+01, 9.00000000e+00, 1.00000000e+01,
   1.00000000e+01, 9.00000000e+00, 9.00000000e+00, 8.00000000e+00,
   8.00000000e+00, 8.00000000e+00, 7.00000000e+00, 7.00000000e+00,
   7.00000000e+00, 6.00000000e+00, 6.00000000e+00, 6.00000000e+00,
   5.00000000e+00, 5.00000000e+00, 5.00000000e+00, 5.00000000e+00,
   4.00000000e+00, 4.00000000e+00, 3.00000000e+00, 3.00000000e+00,
   3.00000000e+00, 3.00000000e+00, 3.00000000e+00, 3.00000000e+00,
   3.00000000e+00, 3.00000000e+00, 3.00000000e+00, 3.00000000e+00,
   3.00000000e+00, 3.00000000e+00, 3.00000000e+00, 3.00000000e+00,
   3.00000000e+00, 3.00000000e+00, 2.00000000e+00, 2.00000000e+00,
   2.00000000e+00, 2.00000000e+00, 2.00000000e+00, 2.00000000e+00,
   2.00000000e+00, 2.00000000e+00, 2.00000000e+00, 2.00000000e+00,
   2.00000000e+00, 2.00000000e+00, 2.00000000e+00, 2.00000000e+00,
   2.00000000e+00, 2.00000000e+00, 1.00000000e+00, 2.00000000e+00,
   1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
   1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
   1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
   0.00000000e+00, 1.00000000e+00, 0.00000000e+00, 0.00000000e+00,
   1.00000000e+00, 0.00000000e+00, 1.00000000e+00, 0.00000000e+00,
   0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
   0.00000000e+00, 8.00000000e+00, 9.00000000e+00, 1.00000000e+01,
   1.10000000e+01, 1.20000000e+01, 1.30000000e+01

图表：

为了获得每个元素的确切含义，我们必须更深入地研究文档并实现数据解析器...

注：

如果 PDF 链接无效，请在 Google 中搜索“RAA066AEN”。

decoded_data = base64.b64decode(coded_string)

的输出：

165,212,123,108,83,85,0,128,241,35,248,64,139,58,5,171,194,208,53,242,212,54,78,4,54,145,217,123,207,185,26,212,109,14,16,164,42,58,71,69,65,98,167,67,33,5,241,82,183,82,153,134,46,248,88,88,99,26,21,138,49,211,165,33,203,50,109,152,18,77,179,168,169,54,226,38,217,214,16,98,182,198,68,205,88,52,72,140,95,183,219,216,204,191,8,77,190,220,215,233,239,156,246,222,86,136,220,171,89,141,111,106,7,13,33,52,77,136,149,82,136,36,217,39,206,115,82,136,221,202,235,104,118,123,29,54,205,30,175,209,216,215,18,35,237,236,167,52,159,127,148,99,155,222,210,84,172,39,70,92,122,182,202,173,51,70,87,246,58,221,231,111,208,171,125,1,157,241,186,63,125,128,49,17,61,86,118,136,113,237,122,186,173,147,177,199,116,230,98,124,74,119,121,251,121,207,41,221,147,204,242,190,81,61,232,60,199,123,167,72,79,242,82,137,47,125,254,171,165,63,61,67,6,157,55,72,230,146,145,193,18,25,43,155,39,227,225,69,146,121,101,82,46,150,233,182,101,114,232,204,114,201,26,228,216,97,37,115,159,197,230,169,148,172,71,58,108,107,165,203,235,145,229,137,13,146,181,201,106,223,38,236,45,216,62,236,6,236,237,216,187,112,76,174,5,176,26,185,30,196,11,49,166,25,243,77,198,237,199,109,97,236,1,236,119,24,223,138,127,144,245,68,152,227,61,214,20,101,158,15,88,215,33,230,138,177,182,143,152,239,99,214,215,206,156,29,216,113,236,163,216,157,216,93,216,221,216,9,236,99,216,95,96,31,199,254,106,252,251,15,58,123,177,191,193,254,14,59,133,253,3,246,143,216,63,97,247,99,159,196,30,192,30,194,62,133,125,26,251,23,236,97,236,44,246,175,216,191,97,255,129,61,138,61,134,253,39,246,89,236,115,216,255,240,30,161,108,158,139,20,223,179,106,105,154,170,210,109,23,43,123,252,18,197,119,174,34,131,151,169,161,51,211,148,195,118,5,247,222,166,98,101,211,85,182,234,74,229,242,94,165,184,23,42,30,46,82,99,135,175,81,229,137,107,21,247,69,37,70,102,170,220,51,163,236,215,43,238,145,74,202,27,177,103,97,207,198,46,198,158,131,125,19,246,205,216,37,216,14,236,91,176,231,98,207,195,158,143,189,0,123,33,246,34,236,91,177,111,195,118,98,187,176,111,199,46,197,190,3,123,49,246,157,216,75,176,151,98,47,195,46,195,46,199,190,11,123,57,246,10,236,10,236,123,176,221,216,26,182,142,45,177,21,182,129,125,47,246,125,216,43,177,239,199,126,0,251,65,236,74,236,42,236,106,236,135,176,107,176,87,97,175,198,94,131,189,22,123,29,246,35,216,235,177,61,216,143,98,63,134,253,56,246,6,236,39,176,159,196,126,10,187,14,123,35,182,23,251,105,236,77,216,207,96,63,139,189,25,123,11,246,115,216,91,177,159,199,246,97,215,99,191,128,253,34,118,3,246,54,236,151,176,95,198,222,142,189,3,219,143,189,19,123,23,246,43,216,187,177,95,101,223,84,217,111,77,142,247,40,215,233,61,156,11,40,223,217,0,115,190,166,226,69,141,204,219,168,198,230,55,49,119,147,42,175,8,50,127,80,249,215,236,101,13,123,85,98,115,136,117,132,148,48,95,103,45,251,242,255,1,231,241,50,221,66,68,249,207,16,252,182,75,169,134,76,10,211,17,234,26,255,205,11,209,71,25,26,182,74,81,143,117,189,203,26,155,171,149,58,172,107,185,247,8,158,225,98,50,168,158,194,212,69,195,116,29,235,117,83,3,235,127,155,58,105,64,137,158,169,134,48,231,240,255,118,55,219,117,134,208,182,25,162,54,196,254,187,134,232,249,208,16,153,79,57,254,140,253,175,57,151,226,122,31,99,7,140,241,255,196,204,73,142,251,233,4,199,223,51,166,151,237,113,142,63,55,68,52,206,54,198,185,131,140,223,207,113,35,91,108,179,142,86,113,173,130,99,39,205,226,216,198,246,111,214,51,196,250,122,169,155,98,212,170,38,254,131,119,210,70,90,77,58,45,165,5,52,155,102,208,229,52,133,254,226,115,102,41,67,63,211,9,234,165,47,169,155,142,210,39,116,132,222,167,8,181,210,91,244,6,237,163,128,213,14,218,74,94,170,165,245,244,48,85,146,102,181,130,150,144,147,22,210,220,130,114,247,97,38,21,209,116,154,38,39,238,207,239,214,189,237,155,116,95,59,172,114,247,53,106,221,219,201,133,173,66,214,115,83,111,85,107,61,75,249,52,235,249,42,161,34,107,155,223,207,37,10,202,104,255,213,83,80,116,82,166,149,118,129,137,11,44,247,27,202,111,207,167,252,239,175,240,183,56,249,220,255,94,5,223,95,169,245,189,214,232,255,2

inflated_decoded_data = inflate(decoded_data)

的输出：

0,0,0,0,0,0,139,67,0,0,0,0,0,96,222,68,0,0,64,64,0,0,72,66,0,0,200,66,0,0,22,67,0,0,0,0,0,0,0,64,0,0,126,67,100,33,139,63,100,33,11,64,22,178,80,64,100,33,139,64,189,233,173,64,22,178,208,64,111,122,243,64,100,33,11,65,145,133,28,65,189,233,45,65,234,77,63,65,22,178,80,65,67,22,98,65,111,122,115,65,78,111,130,65,100,33,139,65,122,211,147,65,145,133,156,65,167,55,165,65,189,233,173,65,211,155,182,65,234,77,191,65,0,0,200,65,22,178,208,65,45,100,217,65,67,22,226,65,89,200,234,65,111,122,243,65,134,44,252,65,78,111,2,66,89,200,6,66,100,33,11,66,111,122,15,66,122,211,19,66,134,44,24,66,145,133,28,66,156,222,32,66,167,55,37,66,178,144,41,66,189,233,45,66,200,66,50,66,211,155,54,66,223,244,58,66,234,77,63,66,245,166,67,66,0,0,72,66,11,89,76,66,22,178,80,66,33,11,85,66,45,100,89,66,56,189,93,66,67,22,98,66,78,111,102,66,89,200,106,66,100,33,111,66,111,122,115,66,122,211,119,66,134,44,124,66,200,66,128,66,78,111,130,66,211,155,132,66,89,200,134,66,223,244,136,66,100,33,139,66,234,77,141,66,111,122,143,66,245,166,145,66,122,211,147,66,0,0,150,66,134,44,152,66,11,89,154,66,145,133,156,66,22,178,158,66,156,222,160,66,33,11,163,66,167,55,165,66,45,100,167,66,178,144,169,66,56,189,171,66,189,233,173,66,67,22,176,66,200,66,178,66,78,111,180,66,211,155,182,66,89,200,184,66,223,244,186,66,100,33,189,66,234,77,191,66,111,122,193,66,245,166,195,66,122,211,197,66,0,0,200,66,134,44,202,66,11,89,204,66,145,133,206,66,22,178,208,66,156,222,210,66,33,11,213,66,167,55,215,66,45,100,217,66,178,144,219,66,56,189,221,66,189,233,223,66,67,22,226,66,200,66,228,66,78,111,230,66,211,155,232,66,89,200,234,66,223,244,236,66,100,33,239,66,234,77,241,66,111,122,243,66,245,166,245,66,122,211,247,66,0,0,250,66,134,44,252,66,11,89,254,66,200,66,0,67,11,89,1,67,78,111,2,67,145,133,3,67,211,155,4,67,22,178,5,67,89,200,6,67,156,222,7,67,223,244,8,67,33,11,10,67,100,33,11,67,167,55,12,67,234,77,13,67,45,100,14,67,111,122,15,67,178,144,16,67,245,166,17,67,56,189,18,67,122,211,19,67,189,233,20,67,0,0,22,67,67,22,23,67,134,44,24,67,200,66,25,67,11,89,26,67,78,111,27,67,145,133,28,67,211,155,29,67,22,178,30,67,89,200,31,67,156,222,32,67,223,244,33,67,33,11,35,67,100,33,36,67,167,55,37,67,234,77,38,67,45,100,39,67,111,122,40,67,178,144,41,67,245,166,42,67,56,189,43,67,122,211,44,67,189,233,45,67,0,0,47,67,67,22,48,67,134,44,49,67,200,66,50,67,11,89,51,67,78,111,52,67,145,133,53,67,211,155,54,67,22,178,55,67,89,200,56,67,156,222,57,67,223,244,58,67,33,11,60,67,100,33,61,67,167,55,62,67,234,77,63,67,45,100,64,67,111,122,65,67,178,144,66,67,245,166,67,67,56,189,68,67,122,211,69,67,189,233,70,67,0,0,72,67,67,22,73,67,134,44,74,67,200,66,75,67,11,89,76,67,78,111,77,67,145,133,78,67,211,155,79,67,22,178,80,67,89,200,81,67,156,222,82,67,223,244,83,67,33,11,85,67,100,33,86,67,167,55,87,67,234,77,88,67,45,100,89,67,111,122,90,67,178,144,91,67,245,166,92,67,56,189,93,67,122,211,94,67,189,233,95,67,0,0,97,67,67,22,98,67,134,44,99,67,200,66,100,67,11,89,101,67,78,111,102,67,145,133,103,67,211,155,104,67,22,178,105,67,89,200,106,67,156,222,107,67,223,244,108,67,33,11,110,67,100,33,111,67,167,55,112,67,234,77,113,67,45,100,114,67,111,122,115,67,178,144,116,67,245,166,117,67,56,189,118,67,122,211,119,67,189,233,120,67,0,0,122,67,67,22,123,67,134,44,124,67,200,66,125,67,11,89,126,67,78,111,127,67,200,66,128,67,234,205,128,67,11,89,129,67,45,228,129,67,78,111,130,67,111,250,130,67,145,133,131,67,178,16,132,67,211,155,132,67,245,38,133,67,22,178,133,67,56,61,134,67,89,200,134,67,122,83,135,67,156,222,135,67,189,105,136,67,223,244,136,67,0,128,137,67,33,11,138,67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,128,63,0,0,160,64,0,0,0,65,0,0,48,65,0,0,80,65,0,0,128,65,0,0,144,65,0,0,168,65,0,0,184,65,0,0,200,65,0,0,216,65,0,0,224,65,0,0,232,65,0,0,232,65,0,0,208,65,0,0,192,65,0,0,184,65,0,0,184,65,0,0,168,65,0,0,168,65,0,0,152,65,0,0,176,65,0,0,192,65,0,0,216,65,0,0,0,66,0,0,28,66,0,0,68,66,0,0,112,66,0,0,144,66,0,0,184,66,0,0,232,66,0,0,21,67,0,0,63,67,0,0,115,67,0,128,149,67,0,128,182,67,0,128,221,67,0,192,3,68,0,128,29,68,0,0,59,68,0,128,86,68,0,64,116,68,0,96,136,68,0,128,151,68,0,192,164,68,0,224,175,68,0,96,187,68,0,192,198,68,0,128,208,68,0,64,216,68,0,0,221,68,0,96,222,68,0,224,219,68,0,64,217,68,0,64,214,68,0,96,209,68,0,192,202,68,0,96,195,68,0,64,188,68,0,160,178,68,0,64,167,68,0,192,154,68,0,0,143,68,0,160,132,68,0,0,116,68,0,128,98,68,0,128,81,68,0,64,61,68,0,0,44,68,0,0,26,68,0,128,11,68,0,0,251,67,0,128,223,67,0,0,202,67,0,0,186,67,0,0,167,67,0,0,152,67,0,0,139,67,0,0,123,67,0,0,99,67,0,0,82,67,0,0,65,67,0,0,53,67,0,0,39,67,0,0,27,67,0,0,19,67,0,0,9,67,0,0,2,67,0,0,248,66,0,0,234,66,0,0,224,66,0,0,218,66,0,0,214,66,0,0,202,66,0,0,194,66,0,0,186,66,0,0,180,66,0,0,174,66,0,0,168,66,0,0,162,66,0,0,156,66,0,0,152,66,0,0,148,66,0,0,140,66,0,0,138,66,0,0,130,66,0,0,130,66,0,0,120,66,0,0,108,66,0,0,100,66,0,0,96,66,0,0,88,66,0,0,84,66,0,0,76,66,0,0,64,66,0,0,64,66,0,0,60,66,0,0,52,66,0,0,44,66,0,0,40,66,0,0,36,66,0,0,36,66,0,0,36,66,0,0,28,66,0,0,20,66,0,0,16,66,0,0,12,66,0,0,8,66,0,0,0,66,0,0,240,65,0,0,224,65,0,0,216,65,0,0,208,65,0,0,192,65,0,0,184,65,0,0,176,65,0,0,176,65,0,0,168,65,0,0,160,65,0,0,152,65,0,0,152,65,0,0,152,65,0,0,152,65,0,0,152,65,0,0,144,65,0,0,144,65,0,0,136,65,0,0,128,65,0,0,112,65,0,0,112,65,0,0,96,65,0,0,80,65,0,0,80,65,0,0,80,65,0,0,64,65,0,0,48,65,0,0,32,65,0,0,16,65,0,0,32,65,0,0,32,65,0,0,16,65,0,0,16,65,0,0,0,65,0,0,0,65,0,0,0,65,0,0,224,64,0,0,224,64,0,0,224,64,0,0,192,64,0,0,192,64,0,0,192,64,0,0,160,64,0,0,160,64,0,0,160,64,0,0,160,64,0,0,128,64,0,0,128,64,0,0,64,64,0,0,64,64,0,0,64,64,0,0,64,64,0,0,64,64,0,0,64,64,0,0,64,64,0,0,64,64,0,0,64,64,0,0,64,64,0,0,64,64,0,0,64,64,0,0,64,64,0,0,64,64,0,0,64,64,0,0,64,64,0,0,0,64,0,0,0,64,0,0,0,64,0,0,0,64,0,0,0,64,0,0,0,64,0,0,0,64,0,0,0,64,0,0,0,64,0,0,0,64,0,0,0,64,0,0,0,64,0,0,0,64,0,0,0,64,0,0,0,64,0,0,0,64,0,0,128,63,0,0,0,64,0,0,128,63,0,0,128,63,0,0,128,63,0,0,128,63,0,0,128,63,0,0,128,63,0,0,128,63,0,0,128,63,0,0,128,63,0,0,128,63,0,0,128,63,0,0,128,63,0,0,0,0,0,0,128,63,0,0,0,0,0,0,0,0,0,0,128,63,0,0,0,0,0,0,128,63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,65,0,0,16,65,0,0,32,65,0,0,48,65,0,0,64,65,0,0,80,65

Base64 图像转换 - FLOATLE-Stream

问题描述投票：0回答：1

1个回答

最新问题

Base64 图像转换 - FLOATLE-Stream

问题描述 投票：0回答：1

1个回答

最新问题

问题描述投票：0回答：1