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2 Commits
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8d891b420e
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xiazongji
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8d891b420e | |
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xiazongji
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68e3076f6a |
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*.log
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tmp/
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tmp/
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datas/
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imgs/
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download.zip
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download.zip
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fast_api_run.py
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fast_api_run.py
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A,V,FC,C,H,N,S,O,H/C,O/C,N/C,Rt,Hr,dp,T,Char
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16.6457787398883,62.0967741935484,37.9032258064516,68.38,5.6,1.12,0.42,24.05,0.982743492249196,0.263783269961977,0.0140391927464171,0.3,5.0,2.0,650,48.44904
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8.38943552563439,42.4194460146976,57.5805539853024,82.63,4.08,1.09,0.33,18.56,0.592520876195087,0.16846181774174,0.011306858456804,20.0,5.0,0.2,490,78.69
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||||
13.5,54.1,45.9,71.6,5.8,1.4,0.9,20.3,0.972067039106145,0.212639664804469,0.0167597765363128,30.0,10.0,0.84,600,57.0752
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||||
17.44,39.81,60.19,78.08,3.95,0.65,2.87,14.45,0.607069672131148,0.138799948770492,0.0071355386416861,20.0,5.0,0.2,510,79.9
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||||
24.2816545626776,49.402279677509,50.597720322491,73.99,5.66,1.14,0.49,18.72,0.917961886741452,0.189755372347615,0.0132064178556948,15.0,5.0,0.2,600,66.820533
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||||
21.14,35.73,64.27,77.41,4.39,1.62,0.51,16.08,0.68053223097791,0.155793825087198,0.0179378817797627,15.0,5.0,0.08,600,78.5678276838
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||||
4.77944939105516,41.9496990541703,58.0503009458297,75.09,4.79,3.56,0.32,19.22,0.765481422293248,0.191969636436276,0.0406369499457793,20.0,5.0,0.2,510,84.31
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||||
8.24,38.05,61.95,82.3,4.73,0.92,1.32,12.05,0.689671931956258,0.109811664641555,0.0095816698489845,60.0,10.0,0.2,650,73.39
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||||
5.35137948984904,33.22,66.78,80.23,5.17,1.08,0.24,13.28,0.773276829116291,0.124143088620217,0.0115382560851837,10.0,30.0,0.07,600,73.314965
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||||
43.585255354201,50.5566709253513,49.4433290746487,64.68,5.18,0.11,4.89,25.28,0.961038961038961,0.293135435992579,0.0014577259475218,30.0,10.0,1.0,800,59.675632
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A,V,FC,C,H,N,S,O,H/C,O/C,N/C,Rt,Hr,dp,T,Char
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16.6457787398883,62.0967741935484,37.9032258064516,68.38,5.6,1.12,0.42,24.05,0.982743492249196,0.263783269961977,0.0140391927464171,0.3,5.0,2.0,650,48.44904
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||||
8.38943552563439,42.4194460146976,57.5805539853024,82.63,4.08,1.09,0.33,18.56,0.592520876195087,0.16846181774174,0.011306858456804,20.0,5.0,0.2,490,78.69
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||||
13.5,54.1,45.9,71.6,5.8,1.4,0.9,20.3,0.972067039106145,0.212639664804469,0.0167597765363128,30.0,10.0,0.84,600,57.0752
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||||
17.44,39.81,60.19,78.08,3.95,0.65,2.87,14.45,0.607069672131148,0.138799948770492,0.0071355386416861,20.0,5.0,0.2,510,79.9
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||||
24.2816545626776,49.402279677509,50.597720322491,73.99,5.66,1.14,0.49,18.72,0.917961886741452,0.189755372347615,0.0132064178556948,15.0,5.0,0.2,600,66.820533
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21.14,35.73,64.27,77.41,4.39,1.62,0.51,16.08,0.68053223097791,0.155793825087198,0.0179378817797627,15.0,5.0,0.08,600,78.5678276838
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4.77944939105516,41.9496990541703,58.0503009458297,75.09,4.79,3.56,0.32,19.22,0.765481422293248,0.191969636436276,0.0406369499457793,20.0,5.0,0.2,510,84.31
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8.24,38.05,61.95,82.3,4.73,0.92,1.32,12.05,0.689671931956258,0.109811664641555,0.0095816698489845,60.0,10.0,0.2,650,73.39
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5.35137948984904,33.22,66.78,80.23,5.17,1.08,0.24,13.28,0.773276829116291,0.124143088620217,0.0115382560851837,10.0,30.0,0.07,600,73.314965
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43.585255354201,50.5566709253513,49.4433290746487,64.68,5.18,0.11,4.89,25.28,0.961038961038961,0.293135435992579,0.0014577259475218,30.0,10.0,1.0,800,59.675632
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@ -1,11 +1,11 @@
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A,V,FC,C,H,N,S,O,H/C,O/C,N/C,Rt,Hr,dp,T,Gas
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5.78,37.71,62.29,76.25,4.37,0.89,0.46,12.25,0.687737704918033,0.120491803278689,0.0100046838407494,30.0,10.0,13.0,650,6.1
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25.57,45.86,54.14,65.71,4.92,1.26,2.42,25.69,0.898493380003044,0.293220210013697,0.0164358545122508,20.0,5.0,0.15,600,15.38
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5.5005500550055,31.5483119906868,68.4516880093132,83.09,4.62,1.07,0.48,10.74,0.667228306655434,0.0969430737754242,0.0110379450853635,15.0,5.0,0.25,600,7.97193
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||||
16.6457787398883,62.0967741935484,37.9032258064516,68.38,5.6,1.12,0.42,24.05,0.982743492249196,0.263783269961977,0.0140391927464171,0.3,5.0,2.0,750,10.88348
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||||
9.79,48.99,51.01,73.91,3.98,0.88,0.49,20.75,0.646191313759978,0.210560140711676,0.0102054622417226,20.0,10.0,3.0,550,16.68
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||||
5.44,41.2,58.8,82.0,4.79,1.51,0.58,11.1,0.7009756097560976,0.1015243902439024,0.0157839721254355,15.0,5.0,0.2,600,11.3
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38.7730061349693,47.15,52.85,74.47,4.8,1.41,0.98,18.34,0.773465825164496,0.184705250436417,0.0162289704387193,60.0,10.0,1.0,700,8.05
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||||
15.85645,36.5599621123663,63.4400378876337,80.0704032260806,5.75550432943082,1.22339222461332,0.44301627527664,12.5076839445986,0.8625665560614234,0.1171564345937181,0.013096248608248,15.0,5.0,0.2,600,8.4
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||||
30.38,59.81,40.19,67.36,4.54,1.67,1.12,25.31,0.808788598574822,0.281806710213777,0.0212504241601629,15.0,5.0,0.08,1100,25.8819005672
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||||
11.2165120400292,34.8127274862041,65.1872725137959,80.57,5.39,1.01,0.49,12.54,0.802780191138141,0.116730793099168,0.0107448713629674,30.0,30.0,0.14,450,18.87111
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||||
A,V,FC,C,H,N,S,O,H/C,O/C,N/C,Rt,Hr,dp,T,Gas
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||||
5.78,37.71,62.29,76.25,4.37,0.89,0.46,12.25,0.687737704918033,0.120491803278689,0.0100046838407494,30.0,10.0,13.0,650,6.1
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||||
25.57,45.86,54.14,65.71,4.92,1.26,2.42,25.69,0.898493380003044,0.293220210013697,0.0164358545122508,20.0,5.0,0.15,600,15.38
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||||
5.5005500550055,31.5483119906868,68.4516880093132,83.09,4.62,1.07,0.48,10.74,0.667228306655434,0.0969430737754242,0.0110379450853635,15.0,5.0,0.25,600,7.97193
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||||
16.6457787398883,62.0967741935484,37.9032258064516,68.38,5.6,1.12,0.42,24.05,0.982743492249196,0.263783269961977,0.0140391927464171,0.3,5.0,2.0,750,10.88348
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||||
9.79,48.99,51.01,73.91,3.98,0.88,0.49,20.75,0.646191313759978,0.210560140711676,0.0102054622417226,20.0,10.0,3.0,550,16.68
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||||
5.44,41.2,58.8,82.0,4.79,1.51,0.58,11.1,0.7009756097560976,0.1015243902439024,0.0157839721254355,15.0,5.0,0.2,600,11.3
|
||||
38.7730061349693,47.15,52.85,74.47,4.8,1.41,0.98,18.34,0.773465825164496,0.184705250436417,0.0162289704387193,60.0,10.0,1.0,700,8.05
|
||||
15.85645,36.5599621123663,63.4400378876337,80.0704032260806,5.75550432943082,1.22339222461332,0.44301627527664,12.5076839445986,0.8625665560614234,0.1171564345937181,0.013096248608248,15.0,5.0,0.2,600,8.4
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||||
30.38,59.81,40.19,67.36,4.54,1.67,1.12,25.31,0.808788598574822,0.281806710213777,0.0212504241601629,15.0,5.0,0.08,1100,25.8819005672
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||||
11.2165120400292,34.8127274862041,65.1872725137959,80.57,5.39,1.01,0.49,12.54,0.802780191138141,0.116730793099168,0.0107448713629674,30.0,30.0,0.14,450,18.87111
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@ -0,0 +1,11 @@
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A,V,FC,C,H,N,S,O,H/C,O/C,N/C,Rt,Hr,dp,T,Tar
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13.5,54.1,45.9,71.6,5.8,1.4,0.9,20.3,0.972067039106145,0.212639664804469,0.0167597765363128,30.0,10.0,0.84,450,6.2108384
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||||
14.270205066345,50.41,49.59,58.69,3.14,1.24,0.21,18.12,0.642017379451355,0.231555631283012,0.0181096804030864,120.0,5.0,12.0,650,9.38428
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||||
11.29,47.12,52.88,65.21,9.71,2.01,0.7,22.37,1.78684250881767,0.257284158871339,0.026420137139352,20.0,15.0,6.0,480,10.14648
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39.4,53.71,46.29,77.46,6.64,1.57,0.57,20.61,1.0286599535244,0.199554608830364,0.0173730220205821,15.0,30.5,5.0,600,9.6968
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||||
11.29,47.12,52.88,65.21,9.71,2.01,0.7,22.37,1.78684250881767,0.257284158871339,0.026420137139352,10.0,15.0,6.0,480,10.07076
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||||
25.56,45.86,54.14,65.71,4.92,1.26,2.42,25.69,0.898493380003044,0.293220210013697,0.0164358545122508,15.0,5.0,0.15,600,8.87
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||||
11.2165120400292,34.8127274862041,65.1872725137959,80.57,5.39,1.01,0.49,12.54,0.802780191138141,0.116730793099168,0.0107448713629674,30.0,30.0,0.14,650,9.53456
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||||
17.44,39.81,60.19,78.08,3.95,0.65,2.87,14.45,0.607069672131148,0.138799948770492,0.0071355386416861,20.0,40.0,6.0,500,6.1334
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||||
15.84092126406,50.41,49.59,58.69,3.14,1.24,0.21,18.12,0.642017379451355,0.231555631283012,0.0181096804030864,120.0,5.0,6.0,650,6.504628
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6.17,46.5,53.5,76.14,3.06,1.06,0.24,19.5,0.482269503546099,0.192080378250591,0.0119329055499268,30.0,10.0,0.07,650,8.585445
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@ -1,11 +1,11 @@
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A,V,FC,C,H,N,S,O,H/C,O/C,N/C,Rt,Hr,dp,T,Water
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4.54,47.35,52.65,70.41,6.95,1.15,0.43,21.06,1.18449083936941,0.224328930549638,0.0139996347921359,30.0,20.0,2.0,600,10.046517999999995
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13.5,54.1,45.9,71.6,5.8,1.4,0.9,20.3,0.972067039106145,0.212639664804469,0.0167597765363128,30.0,10.0,0.84,550,8.1107936
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11.3260262196432,54.9321376635967,45.0678623364033,63.75,4.39,1.25,0.55,30.11,0.826352941176471,0.354235294117647,0.0168067226890756,20.0,5.0,3.0,376,5.59
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10.5,42.36,57.64,82.1381852776787,5.01843263170413,0.820549411344988,0.249732429539779,30.9311452015698,0.733169248588389,0.282430867236138,0.0085627417319903,20.0,5.0,0.2,510,6.15
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10.5,42.36,57.64,82.1381852776787,5.01843263170413,0.820549411344988,0.249732429539779,30.9311452015698,0.733169248588389,0.282430867236138,0.0085627417319903,60.0,10.0,0.15,650,5.44968
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8.8735776177054,38.1,61.9,78.54,5.28,1.2,0.39,14.59,0.80672268907563,0.139323911382735,0.0130961475499291,20.0,5.0,0.07,600,4.368024
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24.2816545626776,49.402279677509,50.597720322491,73.99,5.66,1.14,0.49,18.72,0.917961886741452,0.189755372347615,0.0132064178556948,15.0,5.0,0.2,600,7.078245
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16.6326530612245,36.82,63.18,83.04,5.39,1.48,0.64,9.45,0.778901734104046,0.0853504335260115,0.0152766308835673,60.0,10.0,1.0,500,5.58
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4.4152621238755,29.6624837732583,70.3375162267417,81.78,4.79,1.1,0.38,11.95,0.702861335289802,0.10959280997799,0.0115291898123886,30.0,20.0,0.85,700,12.071072
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11.9159836065574,51.1573804815633,48.8426195184367,83.22,3.89,2.72,0.45,20.21,0.560922855082913,0.182137707281903,0.0280152435884231,30.0,5.0,0.2,510,1.1399999999999952
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A,V,FC,C,H,N,S,O,H/C,O/C,N/C,Rt,Hr,dp,T,Water
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4.54,47.35,52.65,70.41,6.95,1.15,0.43,21.06,1.18449083936941,0.224328930549638,0.0139996347921359,30.0,20.0,2.0,600,10.046517999999995
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13.5,54.1,45.9,71.6,5.8,1.4,0.9,20.3,0.972067039106145,0.212639664804469,0.0167597765363128,30.0,10.0,0.84,550,8.1107936
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||||
11.3260262196432,54.9321376635967,45.0678623364033,63.75,4.39,1.25,0.55,30.11,0.826352941176471,0.354235294117647,0.0168067226890756,20.0,5.0,3.0,376,5.59
|
||||
10.5,42.36,57.64,82.1381852776787,5.01843263170413,0.820549411344988,0.249732429539779,30.9311452015698,0.733169248588389,0.282430867236138,0.0085627417319903,20.0,5.0,0.2,510,6.15
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10.5,42.36,57.64,82.1381852776787,5.01843263170413,0.820549411344988,0.249732429539779,30.9311452015698,0.733169248588389,0.282430867236138,0.0085627417319903,60.0,10.0,0.15,650,5.44968
|
||||
8.8735776177054,38.1,61.9,78.54,5.28,1.2,0.39,14.59,0.80672268907563,0.139323911382735,0.0130961475499291,20.0,5.0,0.07,600,4.368024
|
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24.2816545626776,49.402279677509,50.597720322491,73.99,5.66,1.14,0.49,18.72,0.917961886741452,0.189755372347615,0.0132064178556948,15.0,5.0,0.2,600,7.078245
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||||
16.6326530612245,36.82,63.18,83.04,5.39,1.48,0.64,9.45,0.778901734104046,0.0853504335260115,0.0152766308835673,60.0,10.0,1.0,500,5.58
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||||
4.4152621238755,29.6624837732583,70.3375162267417,81.78,4.79,1.1,0.38,11.95,0.702861335289802,0.10959280997799,0.0115291898123886,30.0,20.0,0.85,700,12.071072
|
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11.9159836065574,51.1573804815633,48.8426195184367,83.22,3.89,2.72,0.45,20.21,0.560922855082913,0.182137707281903,0.0280152435884231,30.0,5.0,0.2,510,1.1399999999999952
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@ -1,6 +1,6 @@
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import pandas as pd
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# 读取Excel文件
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file_path = "D:\\project\\ai_station\\meirejie\\data\\char_data.csv" # 替换为你的Excel文件路径
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file_path = "/home/xiazj/ai-station-code/meirejie/data/tar_data.csv" # 替换为你的Excel文件路径
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df = pd.read_csv(file_path)
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# 随机抽取10条数据
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test_set = df.sample(n=10, random_state=1) # random_state保证每次抽样结果相同
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@ -8,45 +8,45 @@ test_set = df.sample(n=10, random_state=1) # random_state保证每次抽样结
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columns = ['A', 'V', 'FC', 'C', 'H', 'N', 'S', 'O', 'H/C', 'O/C', 'N/C', 'Rt','Hr', 'dp', 'T','Tar']
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test_set = test_set[columns]
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# 保存测试集到新的Excel文件
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test_set.to_csv('D:\\project\\ai_station\\meirejie\\data\\char_data_test.csv', index=False) # 保存为test_set.xlsx，不保存索引
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test_set.to_csv('/home/xiazj/ai-station-code/meirejie/data/tar_data_test.csv', index=False) # 保存为test_set.xlsx，不保存索引
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print("测试集已保存为 char_data_test.csv")
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file_path = "D:\\project\\ai_station\\meirejie\\data\\gas_data.csv" # 替换为你的Excel文件路径
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file_path = "/home/xiazj/ai-station-code/meirejie/data/gas_data.csv" # 替换为你的Excel文件路径
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df = pd.read_csv(file_path)
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# 随机抽取10条数据
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test_set = df.sample(n=10, random_state=1) # random_state保证每次抽样结果相同
|
||||
columns = ['A', 'V', 'FC', 'C', 'H', 'N', 'S', 'O', 'H/C', 'O/C', 'N/C', 'Rt','Hr', 'dp', 'T','Gas']
|
||||
test_set = test_set[columns]
|
||||
# 保存测试集到新的Excel文件
|
||||
test_set.to_csv('D:\\project\\ai_station\\meirejie\\data\\gas_data_test.csv', index=False) # 保存为test_set.xlsx，不保存索引
|
||||
test_set.to_csv('/home/xiazj/ai-station-code/meirejie/data/gas_data_test.csv', index=False) # 保存为test_set.xlsx，不保存索引
|
||||
|
||||
print("测试集已保存为 gas_data_test.csv")
|
||||
|
||||
|
||||
|
||||
|
||||
file_path = "D:\\project\\ai_station\\meirejie\\data\\water_data.csv" # 替换为你的Excel文件路径
|
||||
file_path = "/home/xiazj/ai-station-code/meirejie/data/water_data.csv" # 替换为你的Excel文件路径
|
||||
df = pd.read_csv(file_path)
|
||||
# 随机抽取10条数据
|
||||
test_set = df.sample(n=10, random_state=1) # random_state保证每次抽样结果相同
|
||||
columns = ['A', 'V', 'FC', 'C', 'H', 'N', 'S', 'O', 'H/C', 'O/C', 'N/C', 'Rt','Hr', 'dp', 'T','Water']
|
||||
test_set = test_set[columns]
|
||||
# 保存测试集到新的Excel文件
|
||||
test_set.to_csv('D:\\project\\ai_station\\meirejie\\data\\water_data_test.csv', index=False) # 保存为test_set.xlsx，不保存索引
|
||||
test_set.to_csv('/home/xiazj/ai-station-code/meirejie/data/water_data_test.csv', index=False) # 保存为test_set.xlsx，不保存索引
|
||||
|
||||
print("测试集已保存为 water_data_test.csv")
|
||||
|
||||
|
||||
|
||||
file_path = "D:\\project\\ai_station\\meirejie\\data\\char_data.csv" # 替换为你的Excel文件路径
|
||||
file_path = "/home/xiazj/ai-station-code/meirejie/data/char_data.csv" # 替换为你的Excel文件路径
|
||||
df = pd.read_csv(file_path)
|
||||
# 随机抽取10条数据
|
||||
test_set = df.sample(n=10, random_state=1) # random_state保证每次抽样结果相同
|
||||
columns = ['A', 'V', 'FC', 'C', 'H', 'N', 'S', 'O', 'H/C', 'O/C', 'N/C', 'Rt','Hr', 'dp', 'T','Char']
|
||||
test_set = test_set[columns]
|
||||
# 保存测试集到新的Excel文件
|
||||
test_set.to_csv('D:\\project\\ai_station\\meirejie\\data\\char_data_test.csv', index=False) # 保存为test_set.xlsx，不保存索引
|
||||
test_set.to_csv('/home/xiazj/ai-station-code/meirejie/data/char_data_test.csv', index=False) # 保存为test_set.xlsx，不保存索引
|
||||
|
||||
print("测试集已保存为 char_data_test.csv")
|
||||
830
run.py
830
run.py
|
|
@ -1,18 +1,22 @@
|
|||
import sys
|
||||
|
||||
import io
|
||||
from fastapi import FastAPI,File, UploadFile,Form,Query
|
||||
from fastapi.staticfiles import StaticFiles
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from fastapi.responses import FileResponse
|
||||
from fastapi.responses import FileResponse,JSONResponse
|
||||
import sys
|
||||
import os
|
||||
import shutil
|
||||
from pydantic import BaseModel, validator
|
||||
from typing import List
|
||||
from typing import List, Optional
|
||||
import asyncio
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
import pickle
|
||||
import cv2
|
||||
import copy
|
||||
import base64
|
||||
# 获取当前脚本所在目录
|
||||
print("Current working directory:", os.getcwd())
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
|
|
@ -25,7 +29,7 @@ from wudingpv.taihuyuan_roof.manet.model.resunet import resUnetpamcarb as roof_r
|
|||
from wudingpv.predictandeval_util import segmentation
|
||||
from guangfufadian import model_base as guangfufadian_model_base
|
||||
from fenglifadian import model_base as fenglifadian_model_base
|
||||
from work_util import prepare_data,model_deal,params,data_util,post_model
|
||||
from work_util import prepare_data,model_deal,params,data_util,post_model,sam_deal
|
||||
from work_util.logger import logger
|
||||
import joblib
|
||||
import mysql.connector
|
||||
|
|
@ -33,7 +37,10 @@ import uuid
|
|||
import json
|
||||
import zipfile
|
||||
from pathlib import Path
|
||||
|
||||
from segment_anything_model import sam_annotator
|
||||
from segment_anything_model.sam_config import sam_config, sam_api_config
|
||||
from segment_anything_model.segment_anything import sam_model_registry, SamPredictor
|
||||
import traceback
|
||||
version = f"{sys.version_info.major}.{sys.version_info.minor}"
|
||||
|
||||
app = FastAPI()
|
||||
|
|
@ -77,6 +84,17 @@ windfd_args = fenglifadian_model_base.fenglifadian_Args()
|
|||
windfd_model_path = os.path.join(windfd_args.checkpoints,'Crossformer_Wind_farm_il192_ol12_sl6_win2_fa10_dm256_nh4_el3_itr0/checkpoint.pth') # 修改为实际模型路径
|
||||
windfd_model = fenglifadian_model_base.ModelInference(windfd_model_path, windfd_args)
|
||||
|
||||
|
||||
# 模型加载
|
||||
checkpoint_path = os.path.join(current_dir,'segment_anything_model/weights/vit_b.pth')
|
||||
sam = sam_model_registry["vit_b"](checkpoint=checkpoint_path)
|
||||
device = "cuda" if cv2.cuda.getCudaEnabledDeviceCount() > 0 else "cpu"
|
||||
_ = sam.to(device=device)
|
||||
sam_predictor = SamPredictor(sam)
|
||||
print(f"SAM模型已加载，使用设备: {device}")
|
||||
|
||||
|
||||
|
||||
# 将 /root/app 目录挂载为静态文件
|
||||
app.mount("/files", StaticFiles(directory="/root/app"), name="files")
|
||||
|
||||
|
|
@ -86,6 +104,25 @@ async def read_root():
|
|||
return {"message": message}
|
||||
|
||||
|
||||
# 首页
|
||||
# 获取数据界面资源信息
|
||||
@app.get("/ai-station-api/index/show")
|
||||
async def get_source_index_info():
|
||||
sql = "SELECT id,application_name, describe_data, img_url FROM app_shouye"
|
||||
data = data_util.fetch_data(sql)
|
||||
if data is None:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":"获取信息列表失败",
|
||||
"data":None
|
||||
}
|
||||
else:
|
||||
return {"success":True,
|
||||
"msg":"获取信息成功",
|
||||
"data":data}
|
||||
|
||||
|
||||
|
||||
|
||||
# 获取数据界面资源信息
|
||||
@app.get("/ai-station-api/data_source/show")
|
||||
|
|
@ -188,7 +225,7 @@ type = {tar,char,gas,water}
|
|||
"""
|
||||
@app.get("/ai-station-api/mrj_feature/show")
|
||||
async def get_mrj_feature_info(type:str = None):
|
||||
sql = "SELECT type, chinese_name, col_name, data_type, unit, data_scale FROM meirejie_features where use_type = %s;"
|
||||
sql = "SELECT type, chinese_name, col_name, data_type, unit, data_scale,best_data FROM meijitancailiao_features where use_type = %s;"
|
||||
data = data_util.fetch_data_with_param(sql,(type,))
|
||||
if data is None:
|
||||
return {
|
||||
|
|
@ -235,7 +272,7 @@ async def mjt_models_predict_ssa(content: post_model.Zongbiaomianji):
|
|||
new_order = ["A", "VM", "K/C", "MM", "AT", "At", "Rt"]
|
||||
meijiegou_test_content = meijiegou_test_content.reindex(columns=new_order)
|
||||
ssa_result = model_deal.pred_single_ssa(meijiegou_test_content)
|
||||
logger.info("Root endpoint was accessed")
|
||||
# logger.info("Root endpoint was accessed")
|
||||
if ssa_result is None:
|
||||
return {
|
||||
"success":False,
|
||||
|
|
@ -273,9 +310,14 @@ async def upload_file(file: UploadFile = File(...),type: str = Form(...), ):
|
|||
@app.get("/ai-station-api/mjt_multi_ssa/predict")
|
||||
async def mjt_multi_ssa_pred(model:str = None, path:str = None):
|
||||
data = model_deal.get_excel_ssa(model, path)
|
||||
return {"success":True,
|
||||
if data['status'] == True:
|
||||
return {"success":True,
|
||||
"msg":"获取信息成功",
|
||||
"data":data}
|
||||
"data":data['reason']}
|
||||
else:
|
||||
return {"success":False,
|
||||
"msg":data['reason'],
|
||||
"data":None}
|
||||
|
||||
|
||||
|
||||
|
|
@ -308,9 +350,14 @@ async def mjt_models_predict_tpv(content: post_model.Zongbiaomianji):
|
|||
@app.get("/ai-station-api/mjt_multi_tpv/predict")
|
||||
async def mjt_multi_tpv_pred(model:str = None, path:str = None):
|
||||
data = model_deal.get_excel_tpv(model, path)
|
||||
return {"success":True,
|
||||
if data['status'] == True:
|
||||
return {"success":True,
|
||||
"msg":"获取信息成功",
|
||||
"data":data}
|
||||
"data":data['reason']}
|
||||
else:
|
||||
return {"success":False,
|
||||
"msg":data['reason'],
|
||||
"data":None}
|
||||
|
||||
#==========================煤基碳材料-煤炭材料应用细节接口==================================
|
||||
@app.post("/ai-station-api/mjt_models_meitan/predict")
|
||||
|
|
@ -339,9 +386,14 @@ async def mjt_models_predict_meitan(content: post_model.Meitan):
|
|||
@app.get("/ai-station-api/mjt_multi_meitan/predict")
|
||||
async def mjt_multi_meitan_pred(model:str = None, path:str = None):
|
||||
data = model_deal.get_excel_meitan(model, path)
|
||||
return {"success":True,
|
||||
if data['status'] == True:
|
||||
return {"success":True,
|
||||
"msg":"获取信息成功",
|
||||
"data":data}
|
||||
"data":data['reason']}
|
||||
else:
|
||||
return {"success":False,
|
||||
"msg":data['reason'],
|
||||
"data":None}
|
||||
|
||||
|
||||
#==========================煤基碳材料-煤沥青应用细节接口==================================
|
||||
|
|
@ -371,10 +423,14 @@ async def mjt_models_predict_meiliqing(content: post_model.Meitan):
|
|||
@app.get("/ai-station-api/mjt_multi_meiliqing/predict")
|
||||
async def mjt_multi_meiliqing_pred(model:str = None, path:str = None):
|
||||
data = model_deal.get_excel_meiliqing(model, path)
|
||||
return {"success":True,
|
||||
if data['status'] == True:
|
||||
return {"success":True,
|
||||
"msg":"获取信息成功",
|
||||
"data":data}
|
||||
|
||||
"data":data['reason']}
|
||||
else:
|
||||
return {"success":False,
|
||||
"msg":data['reason'],
|
||||
"data":None}
|
||||
|
||||
|
||||
|
||||
|
|
@ -428,6 +484,176 @@ async def mjt_multi_meiliqing_pred(model:str = None, path:str = None, type:int =
|
|||
}
|
||||
|
||||
|
||||
#===============================煤热解-tar =======================================================
|
||||
"""
|
||||
模型综合分析
|
||||
"""
|
||||
@app.post("/ai-station-api/mrj_models_tar/predict")
|
||||
async def mrj_models_predict_tar(content: post_model.Meirejie):
|
||||
# 处理接收到的字典数据
|
||||
test_content = pd.DataFrame([content.model_dump()])
|
||||
test_content= test_content.rename(columns={"H_C":"H/C"})
|
||||
test_content= test_content.rename(columns={"O_C":"O/C"})
|
||||
test_content= test_content.rename(columns={"N_C":"N/C"})
|
||||
new_order = ["A", "V", "FC", "C", "H", "N" ,"S" ,"O" ,"H/C" ,"O/C" ,"N/C" ,"Rt" ,"Hr", "dp","T"]
|
||||
test_content = test_content.reindex(columns=new_order)
|
||||
tmp_result = model_deal.pred_single_tar(test_content)
|
||||
if tmp_result is None:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":"获取信息列表失败",
|
||||
"data":None
|
||||
}
|
||||
else:
|
||||
return {"success":True,
|
||||
"msg":"获取信息成功",
|
||||
"data":tmp_result}
|
||||
|
||||
|
||||
|
||||
"""
|
||||
批量预测接口
|
||||
"""
|
||||
@app.get("/ai-station-api/mrj_multi_tar/predict")
|
||||
async def mrj_multi_tar_pred(model:str = None, path:str = None):
|
||||
data = model_deal.get_excel_tar(model, path)
|
||||
if data['status'] == True:
|
||||
return {"success":True,
|
||||
"msg":"获取信息成功",
|
||||
"data":data['reason']}
|
||||
else:
|
||||
return {"success":False,
|
||||
"msg":data['reason'],
|
||||
"data":None}
|
||||
|
||||
|
||||
|
||||
|
||||
#===============================煤热解-char =======================================================
|
||||
"""
|
||||
模型综合分析
|
||||
"""
|
||||
@app.post("/ai-station-api/mrj_models_char/predict")
|
||||
async def mrj_models_predict_char(content: post_model.Meirejie):
|
||||
# 处理接收到的字典数据
|
||||
test_content = pd.DataFrame([content.model_dump()])
|
||||
test_content= test_content.rename(columns={"H_C":"H/C"})
|
||||
test_content= test_content.rename(columns={"O_C":"O/C"})
|
||||
test_content= test_content.rename(columns={"N_C":"N/C"})
|
||||
new_order = ["A", "V", "FC", "C", "H", "N" ,"S" ,"O" ,"H/C" ,"O/C" ,"N/C" ,"Rt" ,"Hr", "dp","T"]
|
||||
test_content = test_content.reindex(columns=new_order)
|
||||
tmp_result = model_deal.pred_single_char(test_content)
|
||||
if tmp_result is None:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":"获取信息列表失败",
|
||||
"data":None
|
||||
}
|
||||
else:
|
||||
return {"success":True,
|
||||
"msg":"获取信息成功",
|
||||
"data":tmp_result}
|
||||
|
||||
"""
|
||||
批量预测接口
|
||||
"""
|
||||
@app.get("/ai-station-api/mrj_multi_char/predict")
|
||||
async def mrj_multi_char_pred(model:str = None, path:str = None):
|
||||
data = model_deal.get_excel_char(model, path)
|
||||
if data['status'] == True:
|
||||
return {"success":True,
|
||||
"msg":"获取信息成功",
|
||||
"data":data['reason']}
|
||||
else:
|
||||
return {"success":False,
|
||||
"msg":data['reason'],
|
||||
"data":None}
|
||||
|
||||
|
||||
#===============================煤热解-water =======================================================
|
||||
"""
|
||||
模型综合分析
|
||||
"""
|
||||
@app.post("/ai-station-api/mrj_models_water/predict")
|
||||
async def mrj_models_predict_water(content: post_model.Meirejie):
|
||||
# 处理接收到的字典数据
|
||||
test_content = pd.DataFrame([content.model_dump()])
|
||||
test_content= test_content.rename(columns={"H_C":"H/C"})
|
||||
test_content= test_content.rename(columns={"O_C":"O/C"})
|
||||
test_content= test_content.rename(columns={"N_C":"N/C"})
|
||||
new_order = ["A", "V", "FC", "C", "H", "N" ,"S" ,"O" ,"H/C" ,"O/C" ,"N/C" ,"Rt" ,"Hr", "dp","T"]
|
||||
test_content = test_content.reindex(columns=new_order)
|
||||
tmp_result = model_deal.pred_single_water(test_content)
|
||||
if tmp_result is None:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":"获取信息列表失败",
|
||||
"data":None
|
||||
}
|
||||
else:
|
||||
return {"success":True,
|
||||
"msg":"获取信息成功",
|
||||
"data":tmp_result}
|
||||
|
||||
"""
|
||||
批量预测接口
|
||||
"""
|
||||
@app.get("/ai-station-api/mrj_multi_water/predict")
|
||||
async def mrj_multi_water_pred(model:str = None, path:str = None):
|
||||
data = model_deal.get_excel_water(model, path)
|
||||
if data['status'] == True:
|
||||
return {"success":True,
|
||||
"msg":"获取信息成功",
|
||||
"data":data['reason']}
|
||||
else:
|
||||
return {"success":False,
|
||||
"msg":data['reason'],
|
||||
"data":None}
|
||||
|
||||
|
||||
#===============================煤热解-gas =======================================================
|
||||
"""
|
||||
模型综合分析
|
||||
"""
|
||||
@app.post("/ai-station-api/mrj_models_gas/predict")
|
||||
async def mrj_models_predict_gas(content: post_model.Meirejie):
|
||||
# 处理接收到的字典数据
|
||||
test_content = pd.DataFrame([content.model_dump()])
|
||||
test_content= test_content.rename(columns={"H_C":"H/C"})
|
||||
test_content= test_content.rename(columns={"O_C":"O/C"})
|
||||
test_content= test_content.rename(columns={"N_C":"N/C"})
|
||||
new_order = ["A", "V", "FC", "C", "H", "N" ,"S" ,"O" ,"H/C" ,"O/C" ,"N/C" ,"Rt" ,"Hr", "dp","T"]
|
||||
test_content = test_content.reindex(columns=new_order)
|
||||
tmp_result = model_deal.pred_single_gas(test_content)
|
||||
if tmp_result is None:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":"获取信息列表失败",
|
||||
"data":None
|
||||
}
|
||||
else:
|
||||
return {"success":True,
|
||||
"msg":"获取信息成功",
|
||||
"data":tmp_result}
|
||||
|
||||
"""
|
||||
批量预测接口
|
||||
"""
|
||||
@app.get("/ai-station-api/mrj_multi_gas/predict")
|
||||
async def mrj_multi_gas_pred(model:str = None, path:str = None):
|
||||
data = model_deal.get_excel_gas(model, path)
|
||||
if data['status'] == True:
|
||||
return {"success":True,
|
||||
"msg":"获取信息成功",
|
||||
"data":data['reason']}
|
||||
else:
|
||||
return {"success":False,
|
||||
"msg":data['reason'],
|
||||
"data":None}
|
||||
|
||||
|
||||
|
||||
|
||||
#================================ 地貌识别 ====================================================
|
||||
"""
|
||||
上传图片 , type = dimaoshibie
|
||||
|
|
@ -458,6 +684,7 @@ async def upload_image(file: UploadFile = File(...),type: str = Form(...), ):
|
|||
"msg":"获取信息成功",
|
||||
"data":{"location": file_location}}
|
||||
|
||||
|
||||
"""
|
||||
图片地貌识别
|
||||
"""
|
||||
|
|
@ -753,7 +980,6 @@ async def get_ch4_data(path:str = None, page: int = Query(1, ge=1), page_size: i
|
|||
csv上传
|
||||
"""
|
||||
# @app.post("/ai-station-api/document/upload") type = "ch4"
|
||||
|
||||
"""
|
||||
预测, type
|
||||
"""
|
||||
|
|
@ -771,7 +997,6 @@ async def get_ch4_predict(path:str=None,start_time:str=None, end_time:str = None
|
|||
"msg":"获取信息成功",
|
||||
"data":data['reason']}
|
||||
|
||||
|
||||
#========================================光伏预测==========================================================
|
||||
"""
|
||||
返回显示列表
|
||||
|
|
@ -982,6 +1207,8 @@ csv上传
|
|||
"""
|
||||
# @app.post("/ai-station-api/document/upload") type = "wind_electric"
|
||||
|
||||
|
||||
|
||||
"""
|
||||
预测, type
|
||||
"""
|
||||
|
|
@ -1000,9 +1227,576 @@ async def get_wind_electri_predict(path:str=None,start_time:str=None, end_time:s
|
|||
"data":data['reason']}
|
||||
|
||||
|
||||
#======================== SAM =============================================================
|
||||
"""
|
||||
文件上传
|
||||
1、 创建inputs目录，outputs目录
|
||||
2、 将图片上传到iuputs目录
|
||||
4、 加载当前图像
|
||||
5、 返回当前图像路径给前端
|
||||
"""
|
||||
@app.post("/ai-station-api/sam-image/upload")
|
||||
async def upload_sam_image(file: UploadFile = File(...),type: str = Form(...), ):
|
||||
if not data_util.allowed_file(file.filename):
|
||||
return {
|
||||
"success":False,
|
||||
"msg":"图片必须以 '.jpg', '.jpeg', '.png', '.tif' 结尾",
|
||||
"data":None
|
||||
}
|
||||
if file.size > param.MAX_FILE_SAM_SIZE:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":"图片大小不能大于10MB",
|
||||
"data":None
|
||||
}
|
||||
upload_dir = os.path.join(current_dir,'tmp',type, str(uuid.uuid4()))
|
||||
if not os.path.exists(upload_dir):
|
||||
os.makedirs(upload_dir )
|
||||
input_dir = os.path.join(upload_dir,'input')
|
||||
os.makedirs(input_dir)
|
||||
output_dir = os.path.join(upload_dir,'output')
|
||||
os.makedirs(output_dir)
|
||||
temp_dir = os.path.join(upload_dir,'temp')
|
||||
os.makedirs(temp_dir)
|
||||
# 将文件保存到指定目录
|
||||
file_location = os.path.join(input_dir , file.filename)
|
||||
with open(file_location, "wb") as buffer:
|
||||
shutil.copyfileobj(file.file, buffer)
|
||||
|
||||
config_path = os.path.join(upload_dir,'model_params.pickle')
|
||||
# # 初始化配置 , 每次上传图片时，会创建一个新的配置文件
|
||||
config = copy.deepcopy(sam_config)
|
||||
api_config = copy.deepcopy(sam_api_config)
|
||||
|
||||
config['input_dir'] = input_dir
|
||||
config['output_dir'] = output_dir
|
||||
config['image_files'] = [f for f in os.listdir(input_dir) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.tiff'))]
|
||||
config['current_index'] = 0
|
||||
config['filename'] = config['image_files'][config['current_index']]
|
||||
image_path = os.path.join(config['input_dir'], config['filename'])
|
||||
config['image'] = cv2.imread(image_path)
|
||||
config['image_rgb'] = cv2.cvtColor(config['image'].copy(), cv2.COLOR_BGR2RGB)
|
||||
# 配置类获取参数信息
|
||||
api_config['output_dir'] = output_dir
|
||||
# # 重置pickle中的信息
|
||||
# 重置API中的class_annotations，保留类别但清除掩码和点
|
||||
config, api_config = sam_deal.reset_class_annotations(config,api_config)
|
||||
config = sam_deal.reset_annotation(config)
|
||||
|
||||
save_data = (config,api_config)
|
||||
with open(config_path, 'wb') as file:
|
||||
pickle.dump(save_data, file)
|
||||
# 将图片拷贝到temp目录下
|
||||
pil_img = Image.fromarray(config['image_rgb'])
|
||||
tmp_path = os.path.join(temp_dir,'output_image.jpg')
|
||||
pil_img.save(tmp_path)
|
||||
encoded_string = sam_deal.load_tmp_image(tmp_path)
|
||||
return {"success":True,
|
||||
"msg":"获取信息成功",
|
||||
"image": JSONResponse(content={"image_data": encoded_string}),
|
||||
#"input_dir": input_dir,
|
||||
#'output_dir': output_dir,
|
||||
#'temp_dir': temp_dir,
|
||||
'file_path': upload_dir}
|
||||
|
||||
"""
|
||||
添加分类
|
||||
添加分类，并将分类设置为最新的添加的分类;
|
||||
|
||||
要求：针对返回current_index,将列表默认成选择current_index对应的分类
|
||||
"""
|
||||
@app.get("/ai-station-api/sam_class/create")
|
||||
async def sam_class_set(
|
||||
class_name: str = None,
|
||||
color: Optional[List[int]] = Query(None, description="list of RGB color"),
|
||||
path: str = None
|
||||
):
|
||||
loaded_data,api_config = sam_deal.load_model(path)
|
||||
result = sam_deal.add_class(loaded_data,class_name,color)
|
||||
if result['status'] == True:
|
||||
loaded_data = result['reason']
|
||||
else:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":result['reason'],
|
||||
"data":None
|
||||
}
|
||||
loaded_data['class_index'] = loaded_data['class_names'].index(class_name)
|
||||
r, g, b = [int(c) for c in color]
|
||||
bgr_color = (b, g, r)
|
||||
result, api_config = sam_deal.set_current_class(loaded_data, api_config, loaded_data['class_index'], color=bgr_color)
|
||||
# 更新配置内容
|
||||
sam_deal.save_model(loaded_data,api_config,path)
|
||||
tmp_path = os.path.join(path,'temp/output_image.jpg')
|
||||
encoded_string = sam_deal.load_tmp_image(tmp_path)
|
||||
return {"success":True,
|
||||
"msg":f"已添加类别: {class_name}, 颜色: {color}",
|
||||
"image": JSONResponse(content={"image_data": encoded_string}),
|
||||
"data":{"class_name_list": loaded_data['class_names'],
|
||||
"current_index": loaded_data['class_index'],
|
||||
"class_dict":loaded_data['class_colors'],
|
||||
}}
|
||||
|
||||
|
||||
|
||||
"""
|
||||
选择颜色,
|
||||
current_index : 下拉列表中的分类索引
|
||||
rgb_color :
|
||||
"""
|
||||
@app.get("/ai-station-api/sam_color/select")
|
||||
async def set_sam_color(
|
||||
current_index: int = None,
|
||||
rgb_color: List[int] = Query(None, description="list of RGB color"),
|
||||
path: str = None
|
||||
):
|
||||
loaded_data,api_config = sam_deal.load_model(path)
|
||||
r, g, b = [int(c) for c in rgb_color]
|
||||
bgr_color = (b, g, r)
|
||||
data, api = sam_deal.set_class_color(loaded_data, api_config, current_index, bgr_color)
|
||||
result, api_config = sam_deal.set_current_class(data, api, current_index, color=bgr_color)
|
||||
sam_deal.save_model(data,api,path)
|
||||
img = sam_deal.refresh_image(data,api,path)
|
||||
if img['status'] == True:
|
||||
encoded_string = sam_deal.load_tmp_image(img['reason'])
|
||||
return {
|
||||
"success":True,
|
||||
"msg":"",
|
||||
"image": JSONResponse(content={"image_data": encoded_string}),
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":img['reason'],
|
||||
"image":None
|
||||
}
|
||||
|
||||
|
||||
"""
|
||||
选择分类
|
||||
"""
|
||||
@app.get("/ai-station-api/sam_classs/change")
|
||||
async def on_class_selected(class_index : int = None,path: str = None):
|
||||
# 加载配置内容
|
||||
loaded_data,api_config = sam_deal.load_model(path)
|
||||
result, api_config = sam_deal.set_current_class(loaded_data, api_config, class_index, color=None)
|
||||
# loaded_data['class_index'] = class_index
|
||||
sam_deal.save_model(loaded_data,api_config,path)
|
||||
if result:
|
||||
img = sam_deal.refresh_image(loaded_data,api_config,path)
|
||||
if img['status'] == True:
|
||||
encoded_string = sam_deal.load_tmp_image(img['reason'])
|
||||
return {
|
||||
"success":True,
|
||||
"msg":"",
|
||||
"image":JSONResponse(content={"image_data": encoded_string})
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":img['reason'],
|
||||
"image":None
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":"分类标签识别错误",
|
||||
"image":None
|
||||
}
|
||||
|
||||
|
||||
"""
|
||||
删除分类, 前端跳转为normal ，index=0
|
||||
"""
|
||||
@app.get("/ai-station-api/sam_class/delete")
|
||||
async def sam_remove_class(path:str=None,select_index:int=None):
|
||||
loaded_data,api_config = sam_deal.load_model(path)
|
||||
class_name = loaded_data['class_names'][select_index]
|
||||
loaded_data,api_config = sam_deal.remove_class(loaded_data,api_config,class_name)
|
||||
sam_deal.save_model(loaded_data,api_config,path)
|
||||
img = sam_deal.refresh_image(loaded_data,api_config,path)
|
||||
if img['status'] == True:
|
||||
encoded_string = sam_deal.load_tmp_image(img['reason'])
|
||||
return {
|
||||
"success":True,
|
||||
"msg":"",
|
||||
"image":JSONResponse(content={"image_data": encoded_string})
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":img['reason'],
|
||||
"image":None
|
||||
}
|
||||
|
||||
"""
|
||||
添加标注点 -左键
|
||||
"""
|
||||
@app.get("/ai-station-api/sam_point/left_add")
|
||||
async def left_mouse_down(x:int=None,y:int=None,path:str=None):
|
||||
loaded_data,api_config = sam_deal.load_model(path)
|
||||
if not api_config['current_class']:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":"请先选择一个分类，在添加标点之前",
|
||||
"image":None
|
||||
}
|
||||
is_foreground = True
|
||||
result = sam_deal.add_annotation_point(api_config,x,y,is_foreground)
|
||||
if result['status']== False:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":result['reason'],
|
||||
"image":None
|
||||
}
|
||||
api_config = result['api']
|
||||
sam_deal.save_model(loaded_data,api_config,path)
|
||||
img = sam_deal.refresh_image(loaded_data,api_config,path)
|
||||
if img['status'] == True:
|
||||
encoded_string = sam_deal.load_tmp_image(img['reason'])
|
||||
return {
|
||||
"success":True,
|
||||
"msg":"",
|
||||
"image":JSONResponse(content={"image_data": encoded_string})
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":img['reason'],
|
||||
"image":None
|
||||
}
|
||||
|
||||
"""
|
||||
添加标注点 -右键
|
||||
"""
|
||||
@app.get("/ai-station-api/sam_point/right_add")
|
||||
async def right_mouse_down(x:int=None,y:int=None,path:str=None):
|
||||
loaded_data,api_config = sam_deal.load_model(path)
|
||||
if not api_config['current_class']:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":"请先选择一个分类，在添加标点之前",
|
||||
"image":None
|
||||
}
|
||||
is_foreground = False
|
||||
result = sam_deal.add_annotation_point(api_config,x,y,is_foreground)
|
||||
if result['status']== False:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":result['reason'],
|
||||
"image":None
|
||||
}
|
||||
api_config = result['api']
|
||||
sam_deal.save_model(loaded_data,api_config,path)
|
||||
img = sam_deal.refresh_image(loaded_data,api_config,path)
|
||||
if img['status'] == True:
|
||||
encoded_string = sam_deal.load_tmp_image(img['reason'])
|
||||
return {
|
||||
"success":True,
|
||||
"msg":"",
|
||||
"image":JSONResponse(content={"image_data": encoded_string})
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":img['reason'],
|
||||
"image":None
|
||||
}
|
||||
|
||||
"""
|
||||
删除上一个点
|
||||
"""
|
||||
@app.get("/ai-station-api/sam_point/delete_last")
|
||||
async def sam_delete_last_point(path:str=None):
|
||||
loaded_data,api_config = sam_deal.load_model(path)
|
||||
result = sam_deal.delete_last_point(api_config)
|
||||
if result['status'] == True:
|
||||
api_config = result['reason']
|
||||
sam_deal.save_model(loaded_data,api_config,path)
|
||||
img = sam_deal.refresh_image(loaded_data,api_config,path)
|
||||
if img['status'] == True:
|
||||
encoded_string = sam_deal.load_tmp_image(img['reason'])
|
||||
return {
|
||||
"success":True,
|
||||
"msg":"",
|
||||
"image":JSONResponse(content={"image_data": encoded_string})
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":img['reason'],
|
||||
"data":None
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":result['reason'],
|
||||
"data":None
|
||||
}
|
||||
|
||||
|
||||
"""
|
||||
删除所有点
|
||||
"""
|
||||
@app.get("/ai-station-api/sam_point/delete_all")
|
||||
async def sam_clear_all_point(path:str=None):
|
||||
loaded_data,api_config = sam_deal.load_model(path)
|
||||
result = sam_deal.reset_current_class_points(api_config)
|
||||
if result['status'] == True:
|
||||
api_config = result['reason']
|
||||
sam_deal.save_model(loaded_data,api_config,path)
|
||||
img = sam_deal.refresh_image(loaded_data,api_config,path)
|
||||
if img['status'] == True:
|
||||
encoded_string = sam_deal.load_tmp_image(img['reason'])
|
||||
return {
|
||||
"success":True,
|
||||
"msg":"",
|
||||
"image":JSONResponse(content={"image_data": encoded_string})
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":img['reason'],
|
||||
"image":None
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":result['reason'],
|
||||
"image":None
|
||||
}
|
||||
|
||||
|
||||
"""
|
||||
模型预测
|
||||
"""
|
||||
@app.get("/ai-station-api/sam_model/predict")
|
||||
async def sam_predict_mask(path:str=None):
|
||||
loaded_data,api_config = sam_deal.load_model(path)
|
||||
class_data = api_config['class_annotations'].get(api_config['current_class'], {})
|
||||
if not class_data.get('points'):
|
||||
return {
|
||||
"success":False,
|
||||
"msg":"请在预测前添加至少一个预测样本点",
|
||||
"data":None
|
||||
}
|
||||
else:
|
||||
loaded_data = sam_deal.reset_annotation(loaded_data)
|
||||
# 将标注点添加到loaded_data 中
|
||||
for i, (x, y) in enumerate(class_data['points']):
|
||||
is_foreground = class_data['point_types'][i]
|
||||
loaded_data = sam_deal.add_point(loaded_data, x, y, is_foreground=is_foreground)
|
||||
try:
|
||||
result = sam_deal.predict_mask(loaded_data,sam_predictor)
|
||||
if result['status'] == False:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":result['reason'],
|
||||
"data":None}
|
||||
|
||||
result = result['reason']
|
||||
loaded_data = result['data']
|
||||
class_data['masks'] = [np.array(mask, dtype=np.uint8) for mask in result['masks']]
|
||||
class_data['scores'] = result['scores']
|
||||
class_data['selected_mask_index'] = result['selected_index']
|
||||
if result['selected_index'] >= 0:
|
||||
class_data['selected_mask'] = class_data['masks'][result['selected_index']]
|
||||
logger.info(f"predict: Predicted {len(result['masks'])} masks, selected index: {result['selected_index']}")
|
||||
sam_deal.save_model(loaded_data,api_config,path)
|
||||
img = sam_deal.refresh_image(loaded_data,api_config,path)
|
||||
|
||||
if img['status'] == True:
|
||||
encoded_string = sam_deal.load_tmp_image(img['reason'])
|
||||
return {
|
||||
"success":True,
|
||||
"msg":"",
|
||||
"image":JSONResponse(content={"image_data": encoded_string})
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":img['reason'],
|
||||
"image":None
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"predict: Error during prediction: {str(e)}")
|
||||
traceback.print_exc()
|
||||
return {
|
||||
"success":False,
|
||||
"msg":f"predict: Error during prediction: {str(e)}",
|
||||
"data":None}
|
||||
|
||||
|
||||
"""
|
||||
清除所有信息
|
||||
"""
|
||||
@app.get("/ai-station-api/sam_model/clear_all")
|
||||
async def sam_reset_annotation(path:str=None):
|
||||
loaded_data,api_config = sam_deal.load_model(path)
|
||||
loaded_data,api_config = sam_deal.reset_annotation_all(loaded_data,api_config)
|
||||
img = sam_deal.refresh_image(loaded_data,api_config,path)
|
||||
if img['status'] == True:
|
||||
encoded_string = sam_deal.load_tmp_image(img['reason'])
|
||||
return {
|
||||
"success":True,
|
||||
"msg":"",
|
||||
"image":JSONResponse(content={"image_data": encoded_string})
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":img['reason'],
|
||||
"image":None
|
||||
}
|
||||
|
||||
|
||||
"""
|
||||
保存预测分类
|
||||
"""
|
||||
@app.get("/ai-station-api/sam_model/save_stage")
|
||||
async def sam_add_to_class(path:str=None,class_index:int=None):
|
||||
loaded_data,api_config = sam_deal.load_model(path)
|
||||
class_name = loaded_data['class_names'][class_index]
|
||||
result = sam_deal.add_to_class(api_config,class_name)
|
||||
if result['status'] == True:
|
||||
api_config = result['reason']
|
||||
sam_deal.save_model(loaded_data,api_config,path)
|
||||
img = sam_deal.refresh_image(loaded_data,api_config,path)
|
||||
if img['status'] == True:
|
||||
encoded_string = sam_deal.load_tmp_image(img['reason'])
|
||||
return {
|
||||
"success":True,
|
||||
"msg":"",
|
||||
"image":JSONResponse(content={"image_data": encoded_string})
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":img['reason'],
|
||||
"image":None
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"success":False,
|
||||
"msg":result['reason'],
|
||||
"image":None
|
||||
}
|
||||
|
||||
|
||||
|
||||
"""
|
||||
保存结果
|
||||
"""
|
||||
@app.get("/ai-station-api/sam_model/save")
|
||||
async def sam_save_annotation(path:str=None):
|
||||
loaded_data,api_config = sam_deal.load_model(path)
|
||||
|
||||
if not api_config['output_dir']:
|
||||
logger.info("save_annotation: Output directory not set")
|
||||
return {
|
||||
"success":False,
|
||||
"msg":"save_annotation: Output directory not set",
|
||||
"data":None
|
||||
}
|
||||
has_annotations = False
|
||||
for class_name, class_data in api_config['class_annotations'].items():
|
||||
if 'final_mask' in class_data and class_data['final_mask'] is not None:
|
||||
has_annotations = True
|
||||
break
|
||||
if not has_annotations:
|
||||
logger.info("save_annotation: No final masks to save")
|
||||
return {
|
||||
"success":False,
|
||||
"msg":"save_annotation: No final masks to save",
|
||||
"data":None
|
||||
}
|
||||
image_info = sam_deal.get_image_info(loaded_data)
|
||||
if not image_info:
|
||||
logger.info("save_annotation: No image info available")
|
||||
return {
|
||||
"success":False,
|
||||
"msg":"save_annotation: No image info available",
|
||||
"data":None
|
||||
}
|
||||
image_basename = os.path.splitext(image_info['filename'])[0]
|
||||
annotation_dir = os.path.join(api_config['output_dir'], image_basename)
|
||||
os.makedirs(annotation_dir, exist_ok=True)
|
||||
saved_files = []
|
||||
orig_img = loaded_data['image']
|
||||
original_img_path = os.path.join(annotation_dir, f"{image_basename}.jpg")
|
||||
cv2.imwrite(original_img_path, orig_img)
|
||||
saved_files.append(original_img_path)
|
||||
vis_img = orig_img.copy()
|
||||
img_height, img_width = orig_img.shape[:2]
|
||||
labelme_data = {
|
||||
"version": "5.1.1",
|
||||
"flags": {},
|
||||
"shapes": [],
|
||||
"imagePath": f"{image_basename}.jpg",
|
||||
"imageData": None,
|
||||
"imageHeight": img_height,
|
||||
"imageWidth": img_width
|
||||
}
|
||||
for class_name, class_data in api_config['class_annotations'].items():
|
||||
if 'final_mask' in class_data and class_data['final_mask'] is not None:
|
||||
color = api_config['class_colors'].get(class_name, (0, 255, 0))
|
||||
vis_mask = class_data['final_mask'].copy()
|
||||
color_mask = np.zeros_like(vis_img)
|
||||
color_mask[vis_mask > 0] = color
|
||||
vis_img = cv2.addWeighted(vis_img, 1.0, color_mask, 0.5, 0)
|
||||
binary_mask = (class_data['final_mask'] > 0).astype(np.uint8)
|
||||
contours, _ = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||||
for contour in contours:
|
||||
epsilon = 0.0001 * cv2.arcLength(contour, True)
|
||||
approx_contour = cv2.approxPolyDP(contour, epsilon, True)
|
||||
points = [[float(point[0][0]), float(point[0][1])] for point in approx_contour]
|
||||
if len(points) >= 3:
|
||||
shape_data = {
|
||||
"label": class_name,
|
||||
"points": points,
|
||||
"group_id": None,
|
||||
"shape_type": "polygon",
|
||||
"flags": {}
|
||||
}
|
||||
labelme_data["shapes"].append(shape_data)
|
||||
vis_path = os.path.join(annotation_dir, f"{image_basename}_mask.jpg")
|
||||
cv2.imwrite(vis_path, vis_img)
|
||||
saved_files.append(vis_path)
|
||||
try:
|
||||
is_success, buffer = cv2.imencode(".jpg", orig_img)
|
||||
if is_success:
|
||||
img_bytes = io.BytesIO(buffer).getvalue()
|
||||
labelme_data["imageData"] = base64.b64encode(img_bytes).decode('utf-8')
|
||||
else:
|
||||
print("save_annotation: Failed to encode image data")
|
||||
labelme_data["imageData"] = ""
|
||||
except Exception as e:
|
||||
logger.error(f"save_annotation: Could not encode image data: {str(e)}")
|
||||
labelme_data["imageData"] = ""
|
||||
json_path = os.path.join(annotation_dir, f"{image_basename}.json")
|
||||
with open(json_path, 'w') as f:
|
||||
json.dump(labelme_data, f, indent=2)
|
||||
saved_files.append(json_path)
|
||||
logger.info(f"save_annotation: Annotation saved to {annotation_dir}")
|
||||
|
||||
# 将其打包
|
||||
zip_filename = "download.zip"
|
||||
zip_filepath = Path(zip_filename)
|
||||
dir_path = os.path.dirname(annotation_dir)
|
||||
# 创建 ZIP 文件
|
||||
try:
|
||||
with zipfile.ZipFile(zip_filepath, 'w') as zip_file:
|
||||
# 遍历文件夹中的所有文件
|
||||
for foldername, subfolders, filenames in os.walk(dir_path):
|
||||
for filename in filenames:
|
||||
file_path = os.path.join(foldername, filename)
|
||||
# 将文件写入 ZIP 文件
|
||||
zip_file.write(file_path, os.path.relpath(file_path, dir_path))
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=f"Error creating zip file: {str(e)}")
|
||||
|
||||
# 返回 ZIP 文件作为响应
|
||||
return FileResponse(zip_filepath, media_type='application/zip', filename=zip_filename)
|
||||
|
||||
|
||||
# @app.post("/ai-station-api/items/")
|
||||
|
|
|
|||
|
|
@ -0,0 +1 @@
|
|||
Subproject commit 473f843be79dcab0e8155a4eda08f1149b697457
|
||||
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|
|
@ -129,6 +129,11 @@ def allowed_file(filename: str) -> bool:
|
|||
param = params.ModelParams()
|
||||
return any(filename.endswith(ext) for ext in param.ALLOWED_EXTENSIONS)
|
||||
|
||||
|
||||
|
||||
####################SAM###################################
|
||||
|
||||
|
||||
# data_info ={
|
||||
# "result": [
|
||||
# {
|
||||
|
|
|
|||
|
|
@ -13,10 +13,10 @@ import cv2 as cv
|
|||
import torch.nn.functional as F
|
||||
from joblib import dump, load
|
||||
from fastapi import HTTPException
|
||||
|
||||
import pickle
|
||||
param = params.ModelParams()
|
||||
|
||||
|
||||
import cv2
|
||||
import traceback
|
||||
|
||||
|
||||
################################################### 图像类函数调用###########################################################################
|
||||
|
|
@ -579,6 +579,7 @@ def pred_single_tar(test_content):
|
|||
|
||||
|
||||
|
||||
|
||||
def pred_single_gas(test_content):
|
||||
gas_pred = []
|
||||
current_directory = os.getcwd()
|
||||
|
|
@ -587,7 +588,7 @@ def pred_single_gas(test_content):
|
|||
gas_model = load(model_path)
|
||||
pred = gas_model.predict(test_content)
|
||||
gas_pred.append(pred[0])
|
||||
result = [param.meirejie_tar_mae, param.meirejie_tar_r2,gas_pred]
|
||||
result = [param.meirejie_gas_mae, param.meirejie_gas_r2,gas_pred]
|
||||
# 创建 DataFrame
|
||||
df = pd.DataFrame(result, index=param.index, columns=param.columns)
|
||||
result = df.to_json(orient='index')
|
||||
|
|
@ -602,7 +603,7 @@ def pred_single_water(test_content):
|
|||
water_model = load(model_path)
|
||||
pred = water_model.predict(test_content)
|
||||
water_pred.append(pred[0])
|
||||
result = [param.meirejie_tar_mae, param.meirejie_tar_r2,water_pred]
|
||||
result = [param.meirejie_water_mae, param.meirejie_water_r2,water_pred]
|
||||
# 创建 DataFrame
|
||||
df = pd.DataFrame(result, index=param.index, columns=param.columns)
|
||||
result = df.to_json(orient='index')
|
||||
|
|
@ -616,7 +617,7 @@ def pred_single_char(test_content):
|
|||
char_model = load(model_path)
|
||||
pred = char_model.predict(test_content)
|
||||
char_pred.append(pred[0])
|
||||
result = [param.meirejie_tar_mae, param.meirejie_tar_r2,char_pred]
|
||||
result = [param.meirejie_char_mae, param.meirejie_char_r2,char_pred]
|
||||
# 创建 DataFrame
|
||||
df = pd.DataFrame(result, index=param.index, columns=param.columns)
|
||||
result = df.to_json(orient='index')
|
||||
|
|
@ -624,50 +625,65 @@ def pred_single_char(test_content):
|
|||
|
||||
|
||||
|
||||
def choose_model(name,data):
|
||||
def choose_model_meirejie(name,data):
|
||||
current_directory = os.getcwd()
|
||||
model_path = os.path.join(current_directory,'meirejie',param.meirejie_model_dict[name])
|
||||
model = load(model_path)
|
||||
pred = model.predict(data)
|
||||
return pred
|
||||
|
||||
|
||||
def get_excel_tar(model_name):
|
||||
data_name = param.meirejie_test_data['tar']
|
||||
current_directory = os.getcwd()
|
||||
data_path = os.path.join(current_directory,"tmp","meirejie",data_name)
|
||||
test_data = pd.read_csv(data_path)
|
||||
pred = choose_model(model_name,test_data)
|
||||
def get_excel_tar(model_name,file_path):
|
||||
if not file_path.endswith('.csv'):
|
||||
return {"status":False, "reason":"上传文件类型有误，必须是 CSV 文件"}
|
||||
test_data = pd.read_csv(file_path)
|
||||
expected_columns = ["A", "V", "FC", "C", "H", "N" ,"S" ,"O" ,"H/C" ,"O/C" ,"N/C" ,"Rt" ,"Hr", "dp","T","Tar"]
|
||||
if list(test_data.columns) != expected_columns:
|
||||
return {"status":False, "reason":f"文件列名不匹配，预期列名为: {expected_columns}"}
|
||||
# raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||||
del test_data['Tar']
|
||||
pred = choose_model_meirejie(model_name,test_data)
|
||||
test_data['tar_pred'] = pred
|
||||
return test_data.to_json(orient='records', lines=True)
|
||||
return {"status":True, "reason":test_data.to_dict(orient='records')}
|
||||
|
||||
|
||||
def get_excel_gas(model_name):
|
||||
data_name = param.meirejie_test_data['gas']
|
||||
current_directory = os.getcwd()
|
||||
data_path = os.path.join(current_directory,"tmp","meirejie",data_name)
|
||||
test_data = pd.read_csv(data_path)
|
||||
pred = choose_model(model_name,test_data)
|
||||
def get_excel_gas(model_name,file_path):
|
||||
if not file_path.endswith('.csv'):
|
||||
return {"status":False, "reason":"上传文件类型有误，必须是 CSV 文件"}
|
||||
test_data = pd.read_csv(file_path)
|
||||
expected_columns = ["A", "V", "FC", "C", "H", "N" ,"S" ,"O" ,"H/C" ,"O/C" ,"N/C" ,"Rt" ,"Hr", "dp","T","Gas"]
|
||||
if list(test_data.columns) != expected_columns:
|
||||
return {"status":False, "reason":f"文件列名不匹配，预期列名为: {expected_columns}"}
|
||||
# raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||||
del test_data['Gas']
|
||||
pred = choose_model_meirejie(model_name,test_data)
|
||||
test_data['gas_pred'] = pred
|
||||
return test_data.to_json(orient='records', lines=True)
|
||||
return {"status":True, "reason":test_data.to_dict(orient='records')}
|
||||
|
||||
def get_excel_char(model_name):
|
||||
data_name = param.meirejie_test_data['char']
|
||||
current_directory = os.getcwd()
|
||||
data_path = os.path.join(current_directory,"tmp","meirejie",data_name)
|
||||
test_data = pd.read_csv(data_path)
|
||||
pred = choose_model(model_name,test_data)
|
||||
def get_excel_char(model_name,file_path):
|
||||
if not file_path.endswith('.csv'):
|
||||
return {"status":False, "reason":"上传文件类型有误，必须是 CSV 文件"}
|
||||
test_data = pd.read_csv(file_path)
|
||||
expected_columns = ["A", "V", "FC", "C", "H", "N" ,"S" ,"O" ,"H/C" ,"O/C" ,"N/C" ,"Rt" ,"Hr", "dp","T","Char"]
|
||||
if list(test_data.columns) != expected_columns:
|
||||
return {"status":False, "reason":f"文件列名不匹配，预期列名为: {expected_columns}"}
|
||||
# raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||||
del test_data['Char']
|
||||
pred = choose_model_meirejie(model_name,test_data)
|
||||
test_data['char_pred'] = pred
|
||||
return test_data.to_json(orient='records', lines=True)
|
||||
return {"status":True, "reason":test_data.to_dict(orient='records')}
|
||||
|
||||
def get_excel_water(model_name):
|
||||
data_name = param.meirejie_test_data['water']
|
||||
current_directory = os.getcwd()
|
||||
data_path = os.path.join(current_directory,"tmp","meirejie",data_name)
|
||||
test_data = pd.read_csv(data_path)
|
||||
pred = choose_model(model_name,test_data)
|
||||
def get_excel_water(model_name,file_path):
|
||||
if not file_path.endswith('.csv'):
|
||||
return {"status":False, "reason":"上传文件类型有误，必须是 CSV 文件"}
|
||||
test_data = pd.read_csv(file_path)
|
||||
expected_columns = ["A", "V", "FC", "C", "H", "N" ,"S" ,"O" ,"H/C" ,"O/C" ,"N/C" ,"Rt" ,"Hr", "dp","T","Water"]
|
||||
if list(test_data.columns) != expected_columns:
|
||||
return {"status":False, "reason":f"文件列名不匹配，预期列名为: {expected_columns}"}
|
||||
# raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||||
del test_data['Water']
|
||||
pred = choose_model_meirejie(model_name,test_data)
|
||||
test_data['water_pred'] = pred
|
||||
return test_data.to_json(orient='records', lines=True)
|
||||
return {"status":True, "reason":test_data.to_dict(orient='records')}
|
||||
|
||||
|
||||
|
||||
|
|
@ -741,52 +757,59 @@ def choose_model_meijitancailiao(name,data):
|
|||
|
||||
def get_excel_ssa(model_name,file_path):
|
||||
if not file_path.endswith('.csv'):
|
||||
raise HTTPException(status_code=400, detail="文件类型有误，必须是 CSV 文件")
|
||||
return {"status":False, "reason":"上传文件类型有误，必须是 CSV 文件"}
|
||||
# raise HTTPException(status_code=400, detail="文件类型有误，必须是 CSV 文件")
|
||||
test_data = pd.read_csv(file_path)
|
||||
expected_columns = ["A", "VM", "K/C", "MM", "AT", "At", "Rt", "SSA"]
|
||||
if list(test_data.columns) != expected_columns:
|
||||
raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||||
return {"status":False, "reason":f"文件列名不匹配，预期列名为: {expected_columns}"}
|
||||
# raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||||
del test_data['SSA']
|
||||
pred = choose_model_meijitancailiao(model_name,test_data)
|
||||
test_data['ssa_pred'] = pred
|
||||
return test_data.to_json(orient='records')
|
||||
return {"status":True, "reason":test_data.to_dict(orient='records')}
|
||||
|
||||
def get_excel_tpv(model_name,file_path):
|
||||
if not file_path.endswith('.csv'):
|
||||
raise HTTPException(status_code=400, detail="文件类型有误，必须是 CSV 文件")
|
||||
return {"status":False, "reason":"上传文件类型有误，必须是 CSV 文件"}
|
||||
# raise HTTPException(status_code=400, detail="文件类型有误，必须是 CSV 文件")
|
||||
test_data = pd.read_csv(file_path)
|
||||
expected_columns = ["A", "VM", "K/C", "MM", "AT", "At", "Rt", "TPV"]
|
||||
if list(test_data.columns) != expected_columns:
|
||||
raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||||
# raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||||
return {"status":False, "reason":f"文件列名不匹配，预期列名为: {expected_columns}"}
|
||||
del test_data['TPV']
|
||||
pred = choose_model_meijitancailiao(model_name,test_data)
|
||||
test_data['tpv_pred'] = pred
|
||||
return test_data.to_json(orient='records')
|
||||
return {"status":True, "reason":test_data.to_dict(orient='records')}
|
||||
|
||||
|
||||
def get_excel_meitan(model_name,file_path):
|
||||
if not file_path.endswith('.csv'):
|
||||
raise HTTPException(status_code=400, detail="文件类型有误，必须是 CSV 文件")
|
||||
return {"status":False, "reason":"上传文件类型有误，必须是 CSV 文件"}
|
||||
# raise HTTPException(status_code=400, detail="文件类型有误，必须是 CSV 文件")
|
||||
test_data = pd.read_csv(file_path)
|
||||
expected_columns = ["SSA", "TPV", "N", "O", "ID/IG", "J","C"]
|
||||
if list(test_data.columns) != expected_columns:
|
||||
raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||||
# raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||||
return {"status":False, "reason":f"文件列名不匹配，预期列名为: {expected_columns}"}
|
||||
del test_data['C']
|
||||
pred = choose_model_meijitancailiao(model_name,test_data)
|
||||
test_data['C_pred'] = pred
|
||||
return test_data.to_json(orient='records', lines=True)
|
||||
# return test_data.to_dict(orient='records')
|
||||
return {"status":True, "reason":test_data.to_dict(orient='records')}
|
||||
|
||||
def get_excel_meiliqing(model_name,file_path):
|
||||
if not file_path.endswith('.csv'):
|
||||
raise HTTPException(status_code=400, detail="文件类型有误，必须是 CSV 文件")
|
||||
return {"status":False, "reason":"上传文件类型有误，必须是 CSV 文件"}
|
||||
test_data = pd.read_csv(file_path)
|
||||
expected_columns = ["SSA", "TPV", "N", "O", "ID/IG", "J", "C"]
|
||||
if list(test_data.columns) != expected_columns:
|
||||
raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||||
return {"status":False, "reason":f"文件列名不匹配，预期列名为: {expected_columns}"}
|
||||
del test_data['C']
|
||||
pred = choose_model_meijitancailiao(model_name,test_data)
|
||||
test_data['C_pred'] = pred
|
||||
return test_data.to_json(orient='records', lines=True)
|
||||
return {"status":True, "reason":test_data.to_dict(orient='records')}
|
||||
|
||||
|
||||
def pred_func(func_name, pred_data):
|
||||
|
|
@ -834,4 +857,7 @@ def get_pic_path(url):
|
|||
|
||||
# 3. 添加本地根目录
|
||||
local_path = f"/root/app{relative_path}"
|
||||
return local_path
|
||||
return local_path
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -140,9 +140,18 @@ class ModelParams():
|
|||
}
|
||||
|
||||
index = ['mae', 'r2', 'result']
|
||||
columns = ['XGBoost', 'Linear Regression', 'Ridge Regression', 'Gaussian Process Regression',
|
||||
'ElasticNet Regression', 'K-Nearest Neighbors', 'Support Vector Regression',
|
||||
'Decision Tree Regression', 'Random Forest Regression', 'AdaBoost Regression']
|
||||
columns = [
|
||||
'XGBoost(极端梯度提升)',
|
||||
'Linear Regression(线性回归)',
|
||||
'Ridge Regression(岭回归)',
|
||||
'Gaussian Process Regression(高斯过程回归)',
|
||||
'ElasticNet Regression(弹性网回归)',
|
||||
'K-Nearest Neighbors(K最近邻)',
|
||||
'Support Vector Regression(支持向量回归)',
|
||||
'Decision Tree Regression(决策树回归)',
|
||||
'Random Forest Regression(随机森林回归)',
|
||||
'AdaBoost Regression(AdaBoost回归)'
|
||||
]
|
||||
meirejie_model_list_gas = ['xgb_gas','lr_gas','ridge_gas','gp_gas','en_gas','kn_gas','svr_gas','dtr_gas','rfr_gas','adb_gas']
|
||||
meirejie_model_list_char = ['xgb_char','lr_char','ridge_char','gp_char','en_char','kn_char','svr_char','dtr_char','rfr_char','adb_char']
|
||||
meirejie_model_list_water = ['xgb_water','lr_water','ridge_water','gp_water','en_water','kn_water','svr_water','dtr_water','rfr_water','adb_water']
|
||||
|
|
@ -224,29 +233,65 @@ class ModelParams():
|
|||
"xgb_meiliqing":"model/meiliqing_XGB.joblib",
|
||||
|
||||
}
|
||||
columns_ssa = ['XGBoost', 'Linear Regression', 'Ridge Regression', 'Gaussian Process Regression',
|
||||
'ElasticNet Regression', 'K-Nearest Neighbors', 'Support Vector Regression',
|
||||
'Decision Tree Regression', 'Random Forest Regression', 'AdaBoost Regression']
|
||||
columns_ssa = [
|
||||
'XGBoost(极端梯度提升)',
|
||||
'Linear Regression(线性回归)',
|
||||
'Ridge Regression(岭回归)',
|
||||
'Gaussian Process Regression(高斯过程回归)',
|
||||
'ElasticNet Regression(弹性网回归)',
|
||||
'K-Nearest Neighbors(最近邻居算法)',
|
||||
'Support Vector Regression(支持向量回归)',
|
||||
'Decision Tree Regression(决策树回归)',
|
||||
'Random Forest Regression(随机森林回归)',
|
||||
'AdaBoost Regression(自适应提升回归)'
|
||||
]
|
||||
meijitancailiao_model_list_ssa = ['xgb_ssa','lr_ssa','ridge_ssa','gp_ssa','en_ssa','kn_ssa','svr_ssa','dtr_ssa','rfr_ssa','adb_ssa']
|
||||
meijitancailiao_ssa_mae = [258, 407,408 ,282 ,411 ,389, 405, 288,193, 330]
|
||||
meijitancailiao_ssa_r2 = [0.92,0.82,0.82,0.89,0.81,0.82,0.87,0.88,0.95,0.88]
|
||||
columns_tpv = ['XGBoost', 'Linear Regression', 'Ridge Regression', 'Gaussian Process Regression',
|
||||
'ElasticNet Regression', 'Gradient Boosting Regression', 'Support Vector Regression',
|
||||
'Decision Tree Regression', 'Random Forest Regression', 'AdaBoost Regression']
|
||||
columns_tpv = [
|
||||
'XGBoost(极端梯度提升)',
|
||||
'Linear Regression(线性回归)',
|
||||
'Ridge Regression(岭回归)',
|
||||
'Gaussian Process Regression(高斯过程回归)',
|
||||
'ElasticNet Regression(弹性网回归)',
|
||||
'Gradient Boosting Regression(梯度提升回归)',
|
||||
'Support Vector Regression(支持向量回归)',
|
||||
'Decision Tree Regression(决策树回归)',
|
||||
'Random Forest Regression(随机森林回归)',
|
||||
'AdaBoost Regression(自适应提升回归)'
|
||||
]
|
||||
meijitancailiao_model_list_tpv = ['xgb_tpv', 'lr_tpv', 'ridge_tpv', 'gp_tpv', 'en_tpv', 'gdbt_tpv', 'svr_tpv', 'dtr_tpv', 'rfr_tpv', 'adb_tpv']
|
||||
meijitancailiao_tpv_mae = [0.2, 0.2, 0.2, 0.2, 0.2, 0.23, 0.23, 0.21, 0.16, 0.21]
|
||||
meijitancailiao_tpv_r2 = [0.81, 0.81, 0.81, 0.8, 0.82, 0.80, 0.78, 0.73, 0.85, 0.84]
|
||||
|
||||
columns_meitan = ['XGBoost', 'Linear Regression', 'Ridge Regression', 'Gaussian Process Regression',
|
||||
'ElasticNet Regression', 'Gradient Boosting Regression', 'Support Vector Regression',
|
||||
'Decision Tree Regression', 'Random Forest Regression', 'AdaBoost Regression']
|
||||
columns_meitan = [
|
||||
'XGBoost(极端梯度提升)',
|
||||
'Linear Regression(线性回归)',
|
||||
'Ridge Regression(岭回归)',
|
||||
'Gaussian Process Regression(高斯过程回归)',
|
||||
'ElasticNet Regression(弹性网回归)',
|
||||
'Gradient Boosting Regression(梯度提升回归)',
|
||||
'Support Vector Regression(支持向量回归)',
|
||||
'Decision Tree Regression(决策树回归)',
|
||||
'Random Forest Regression(随机森林回归)',
|
||||
'AdaBoost Regression(自适应提升回归)'
|
||||
]
|
||||
meijitancailiao_model_list_meitan = ['xgb_meitan', 'lr_meitan', 'ridge_meitan', 'gp_meitan', 'en_meitan', 'gdbt_meitan', 'svr_meitan', 'dtr_meitan', 'rfr_meitan', 'adb_meitan']
|
||||
meijitancailiao_meitan_mae = [8.17, 37.61, 37.66, 13.41, 20.96, 8.03, 14.89, 19.48, 12.53, 15.6]
|
||||
meijitancailiao_meitan_r2 = [0.96, 0.19, 0.19, 0.91, 0.8, 0.96, 0.88, 0.86, 0.91, 0.91]
|
||||
|
||||
columns_meiliqing = ['XGBoost', 'Linear Regression', 'Ridge Regression', 'Gaussian Process Regression',
|
||||
'ElasticNet Regression', 'Gradient Boosting Regression', 'Support Vector Regression',
|
||||
'Decision Tree Regression', 'Random Forest Regression', 'AdaBoost Regression']
|
||||
columns_meiliqing = [
|
||||
'XGBoost(极端梯度提升)',
|
||||
'Linear Regression(线性回归)',
|
||||
'Ridge Regression(岭回归)',
|
||||
'Gaussian Process Regression(高斯过程回归)',
|
||||
'ElasticNet Regression(弹性网回归)',
|
||||
'Gradient Boosting Regression(梯度提升回归)',
|
||||
'Support Vector Regression(支持向量回归)',
|
||||
'Decision Tree Regression(决策树回归)',
|
||||
'Random Forest Regression(随机森林回归)',
|
||||
'AdaBoost Regression(自适应提升回归)'
|
||||
]
|
||||
meijitancailiao_model_list_meiliqing = ['xgb_meiliqing', 'lr_meiliqing', 'ridge_meiliqing', 'gp_meiliqing', 'en_meiliqing', 'gdbt_meiliqing', 'svr_meiliqing', 'dtr_meiliqing', 'rfr_meiliqing', 'adb_meiliqing']
|
||||
meijitancailiao_meiliqing_mae = [8.38, 35.02, 35.1, 11.02, 13.58, 7.04, 13.13, 13.13, 11.25, 9.99]
|
||||
meijitancailiao_meiliqing_r2 = [0.95, 0.33, 0.33, 0.94, 0.91, 0.97, 0.88, 0.89, 0.92, 0.94]
|
||||
|
|
@ -276,4 +321,12 @@ class ModelParams():
|
|||
}
|
||||
|
||||
ALLOWED_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.tif'}
|
||||
MAX_FILE_SIZE = 100 * 1024 * 1024 # 100 MB
|
||||
MAX_FILE_SIZE = 100 * 1024 * 1024 # 100 MB
|
||||
MAX_FILE_SAM_SIZE = 10 * 1024 * 1024
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
DEFAULT_MODEL_PATH = r"/home/xiazj/ai-station-code/segment_anything_model/weights/vit_b.pth"
|
||||
|
||||
|
|
@ -21,6 +21,26 @@ class Meitan(BaseModel):
|
|||
J: float
|
||||
|
||||
|
||||
class Meirejie(BaseModel):
|
||||
A: float
|
||||
V: float
|
||||
FC: float
|
||||
C: float
|
||||
H: float
|
||||
N: float
|
||||
S: float
|
||||
O: float
|
||||
H_C: float
|
||||
O_C: float
|
||||
N_C: float
|
||||
Rt: float
|
||||
Hr: float
|
||||
dp: float
|
||||
T: float
|
||||
|
||||
|
||||
|
||||
|
||||
class FormData(BaseModel):
|
||||
A_min: Optional[float] = None
|
||||
A_max: Optional[float] = None
|
||||
|
|
|
|||
|
|
@ -0,0 +1,398 @@
|
|||
import cv2
|
||||
import os
|
||||
import numpy as np
|
||||
import json
|
||||
import shutil
|
||||
from segment_anything_model.segment_anything import sam_model_registry, SamPredictor
|
||||
import random
|
||||
import traceback
|
||||
import base64
|
||||
import io
|
||||
from work_util.logger import logger
|
||||
import pickle
|
||||
from PIL import Image
|
||||
|
||||
|
||||
|
||||
def get_display_image_ori(loaded_data):
|
||||
if loaded_data['image_rgb'] is None:
|
||||
logger.info("get_display_image: No image loaded")
|
||||
return {"status": False,"reason":"无图片加载"}
|
||||
display_image = loaded_data['image_rgb'].copy()
|
||||
try:
|
||||
for point, label in zip(loaded_data['input_point'], loaded_data['input_label']):
|
||||
color = (0, 255, 0) if label == 1 else (0, 0, 255)
|
||||
cv2.circle(display_image, tuple(point), 5, color, -1)
|
||||
if loaded_data['selected_mask'] is not None:
|
||||
class_name = loaded_data['class_names'][loaded_data['class_index']]
|
||||
color = loaded_data['class_colors'].get(class_name, (0, 0, 128))
|
||||
display_image = sam_apply_mask(display_image, loaded_data['selected_mask'], color)
|
||||
logger.info(f"get_display_image: Returning image with shape {display_image.shape}")
|
||||
return {"status" : True, "reason":display_image}
|
||||
except Exception as e:
|
||||
logger.info(f"get_display_image: Error processing image: {str(e)}")
|
||||
traceback.print_exc()
|
||||
return {"status": False,"reason":f"get_display_image: Error processing image: {str(e)}"}
|
||||
|
||||
def get_all_classes(data):
|
||||
return {
|
||||
"classes": data['class_names'],
|
||||
"colors": {name: color for name, color in data['class_colors'].items()}
|
||||
}
|
||||
|
||||
def get_classes(data):
|
||||
return get_all_classes(data)
|
||||
|
||||
|
||||
def reset_annotation(loaded_data):
|
||||
loaded_data['input_point'] = []
|
||||
loaded_data['input_label'] = []
|
||||
loaded_data['selected_mask'] = None
|
||||
loaded_data['logit_input'] = None
|
||||
loaded_data['masks'] = {}
|
||||
logger.info("已重置标注状态")
|
||||
return loaded_data
|
||||
|
||||
|
||||
def remove_class(data,api,class_name):
|
||||
if class_name in api['class_annotations']:
|
||||
del api['class_annotations'][class_name]
|
||||
|
||||
if class_name == data['class_names'][data['class_index']]:
|
||||
data['class_index'] = 0
|
||||
data['class_names'].remove(class_name)
|
||||
if class_name in data['class_colors']:
|
||||
del data['class_colors'][class_name]
|
||||
if class_name in data['masks']:
|
||||
del data['masks'][class_name]
|
||||
return data,api
|
||||
|
||||
|
||||
def reset_annotation_all(data,api):
|
||||
data = reset_annotation(data)
|
||||
for class_data in api['class_annotations'].values():
|
||||
class_data['points'] = []
|
||||
class_data['point_types'] = []
|
||||
class_data['masks'] = []
|
||||
class_data['scores'] = []
|
||||
class_data['selected_mask_index'] = -1
|
||||
if 'selected_mask' in class_data:
|
||||
del class_data['selected_mask']
|
||||
return data,api
|
||||
|
||||
|
||||
def reset_class_annotations(data,api):
|
||||
"""重置class_annotations，保留类别但清除掩码和点"""
|
||||
classes = get_classes(data).get('classes', [])
|
||||
new_annotations = {}
|
||||
for class_name in classes:
|
||||
new_annotations[class_name] = {
|
||||
'points': [],
|
||||
'point_types': [],
|
||||
'masks': [],
|
||||
'selected_mask_index': -1
|
||||
}
|
||||
api['class_annotations'] = new_annotations
|
||||
logger.info("已重置class_annotations，保留类别但清除掩码和点")
|
||||
return data,api
|
||||
|
||||
|
||||
def add_class(data,class_name, color=None):
|
||||
if class_name in data['class_names']:
|
||||
logger.info(f"类别 '{class_name}' 已存在")
|
||||
return {'status':False, 'reason':f"类别 '{class_name}' 已存在"}
|
||||
data['class_names'].append(class_name)
|
||||
if color is None:
|
||||
color = tuple(np.random.randint(100, 256, 3).tolist())
|
||||
r, g, b = [int(c) for c in color]
|
||||
bgr_color = (b, g, r)
|
||||
data['class_colors'][class_name] = tuple(bgr_color)
|
||||
logger.info(f"已添加类别: {class_name}, 颜色: {tuple(color)}")
|
||||
return {'status':True, 'reason':data}
|
||||
|
||||
|
||||
def set_current_class(data, api, class_index, color=None):
|
||||
classes = get_classes(data)
|
||||
if 'classes' in classes and class_index < len(classes['classes']):
|
||||
class_name = classes['classes'][class_index]
|
||||
api['current_class'] = class_name
|
||||
if class_name not in api['class_annotations']:
|
||||
api['class_annotations'][class_name] = {
|
||||
'points': [],
|
||||
'point_types': [],
|
||||
'masks': [],
|
||||
'selected_mask_index': -1
|
||||
}
|
||||
color = data['class_colors'][class_name]
|
||||
if color:
|
||||
api['class_colors'][class_name] = color
|
||||
elif class_name not in api['class_colors']:
|
||||
predefined_colors = [
|
||||
(255, 0, 0), (0, 255, 0), (0, 0, 255),
|
||||
(255, 255, 0), (255, 0, 255), (0, 255, 255)
|
||||
]
|
||||
color_index = len(api['class_colors']) % len(predefined_colors)
|
||||
api['class_colors'][class_name] = predefined_colors[color_index]
|
||||
return class_name,api
|
||||
return None,api
|
||||
|
||||
|
||||
def add_point(data, x, y, is_foreground=True):
|
||||
data['input_point'].append([x, y])
|
||||
data['input_label'].append(1 if is_foreground else 0)
|
||||
logger.info(f"添加{'前景' if is_foreground else '背景'}点: ({x}, {y})")
|
||||
return data
|
||||
|
||||
|
||||
|
||||
def load_model(path):
|
||||
# 加载配置内容
|
||||
config_path = os.path.join(path,'model_params.pickle')
|
||||
with open(config_path, 'rb') as file:
|
||||
loaded_data,api_config = pickle.load(file)
|
||||
return loaded_data,api_config
|
||||
|
||||
def save_model(loaded_data,api_config,path):
|
||||
config_path = os.path.join(path,'model_params.pickle')
|
||||
save_data = (loaded_data,api_config)
|
||||
with open(config_path, 'wb') as file:
|
||||
pickle.dump(save_data, file)
|
||||
|
||||
|
||||
def sam_apply_mask(image, mask, color, alpha=0.5):
|
||||
masked_image = image.copy()
|
||||
for c in range(3):
|
||||
masked_image[:, :, c] = np.where(
|
||||
mask == 1,
|
||||
image[:, :, c] * (1 - alpha) + alpha * color[c],
|
||||
image[:, :, c]
|
||||
)
|
||||
return masked_image
|
||||
|
||||
|
||||
def apply_mask_overlay(image, mask, color, alpha=0.5):
|
||||
colored_mask = np.zeros_like(image)
|
||||
colored_mask[mask > 0] = color
|
||||
return cv2.addWeighted(image, 1, colored_mask, alpha, 0)
|
||||
|
||||
def load_tmp_image(path):
|
||||
with open(path, "rb") as image_file:
|
||||
# 将图片文件读取为二进制数据并进行 Base64 编码
|
||||
encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
|
||||
return encoded_string
|
||||
|
||||
def refresh_image(data,api,path):
|
||||
result = get_image_display(data,api)
|
||||
if result['status'] == False:
|
||||
return {
|
||||
"status" : False,
|
||||
"reason" : result['reason']
|
||||
}
|
||||
else:
|
||||
img = result['reason']
|
||||
display_img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
||||
pil_img = Image.fromarray(display_img_rgb)
|
||||
tmp_path = os.path.join(path,'temp/output_image.jpg')
|
||||
pil_img.save(tmp_path)
|
||||
return {
|
||||
"status" : True,
|
||||
"reason" : tmp_path
|
||||
}
|
||||
|
||||
|
||||
def set_class_color(data,api,index,color):
|
||||
class_name = data['class_names'][index]
|
||||
api['class_colors'][class_name] = color
|
||||
data['class_colors'][class_name] = color
|
||||
return data,api
|
||||
|
||||
|
||||
|
||||
def add_to_class(api,class_name):
|
||||
if class_name is None:
|
||||
class_name = api['current_class']
|
||||
if not class_name or class_name not in api['class_annotations']:
|
||||
return {
|
||||
'status':False,
|
||||
'reason': "该分类没有标注点"
|
||||
}
|
||||
class_data = api['class_annotations'][class_name]
|
||||
if 'selected_mask' not in class_data or class_data['selected_mask'] is None:
|
||||
return {
|
||||
'status':False,
|
||||
'reason': "该分类没有进行预测"
|
||||
}
|
||||
class_data['final_mask'] = class_data['selected_mask'].copy()
|
||||
class_data['points'] = []
|
||||
class_data['point_types'] = []
|
||||
class_data['masks'] = []
|
||||
class_data['scores'] = []
|
||||
class_data['selected_mask_index'] = -1
|
||||
return {
|
||||
'status':True,
|
||||
'reason': api
|
||||
}
|
||||
|
||||
|
||||
|
||||
def get_image_display(data,api):
|
||||
|
||||
if data['image'] is None:
|
||||
logger.info("get_display_image: No image loaded")
|
||||
return {"status":False, "reason":"获取图像：没有图像加载"}
|
||||
display_image = data['image'].copy()
|
||||
try:
|
||||
for point, label in zip(data['input_point'], data['input_label']):
|
||||
color = (0, 255, 0) if label == 1 else (0, 0, 255)
|
||||
cv2.circle(display_image, tuple(point), 5, color, -1)
|
||||
if data['selected_mask'] is not None:
|
||||
class_name = data['class_names'][data['class_index']]
|
||||
color = data['class_colors'].get(class_name, (0, 0, 128))
|
||||
display_image = sam_apply_mask(display_image, data['selected_mask'], color)
|
||||
logger.info(f"get_display_image: Returning image with shape {display_image.shape}")
|
||||
img = display_image
|
||||
if not isinstance(img, np.ndarray) or img.size == 0:
|
||||
logger.info(f"get_image_display: Invalid image array, shape: {img.shape if isinstance(img, np.ndarray) else 'None'}")
|
||||
return {"status":False, "reason":f"get_image_display: Invalid image array, shape: {display_image.shape if isinstance(display_image, np.ndarray) else 'None'}"}
|
||||
# 仅应用当前图片的final_mask
|
||||
for class_name, class_data in api['class_annotations'].items():
|
||||
if 'final_mask' in class_data and class_data['final_mask'] is not None:
|
||||
color = api['class_colors'].get(class_name, (0, 255, 0))
|
||||
mask = class_data['final_mask']
|
||||
if isinstance(mask, list):
|
||||
mask = np.array(mask, dtype=np.uint8)
|
||||
logger.info(f"Applying mask for class {class_name}, shape: {mask.shape}")
|
||||
img = apply_mask_overlay(img, mask, color, alpha=0.5)
|
||||
elif 'selected_mask' in class_data and class_data['selected_mask'] is not None:
|
||||
color = api['class_colors'].get(class_name, (0, 255, 0))
|
||||
mask = class_data['selected_mask']
|
||||
if isinstance(mask, list):
|
||||
mask = np.array(mask, dtype=np.uint8)
|
||||
logger.info(f"Applying mask for class {class_name}, shape: {mask.shape}")
|
||||
img = apply_mask_overlay(img, mask, color, alpha=0.5)
|
||||
if api['current_class'] and api['current_class'] in api['class_annotations']:
|
||||
class_data = api['class_annotations'][api['current_class']]
|
||||
for i, (x, y) in enumerate(class_data['points']):
|
||||
is_fg = class_data['point_types'][i]
|
||||
color = (0, 255, 0) if is_fg else (0, 0, 255)
|
||||
print(f"Drawing point at ({x}, {y}), type: {'foreground' if is_fg else 'background'}")
|
||||
cv2.circle(img, (int(x), int(y)), 5, color, -1)
|
||||
logger.info(f"get_image_display: Returning image with shape {img.shape}")
|
||||
return {"status":True, "reason":img}
|
||||
except Exception as e:
|
||||
logger.error(f"get_display_image: Error processing image: {str(e)}")
|
||||
traceback.print_exc()
|
||||
return {"status":False, "reason":f"get_display_image: Error processing image: {str(e)}"}
|
||||
|
||||
|
||||
def add_annotation_point(api, x, y, is_foreground=True):
|
||||
if not api['current_class'] or api['current_class'] not in api['class_annotations']:
|
||||
return {
|
||||
"status": False,
|
||||
"reason": "请选择或新建分类"
|
||||
}
|
||||
class_data = api['class_annotations'][api['current_class']]
|
||||
|
||||
class_data['points'].append((x, y))
|
||||
class_data['point_types'].append(is_foreground)
|
||||
return {
|
||||
'status' : True,
|
||||
'reason' : {
|
||||
'points': class_data['points'],
|
||||
'types': class_data['point_types']
|
||||
},
|
||||
'api':api
|
||||
}
|
||||
|
||||
|
||||
|
||||
def delete_last_point(api):
|
||||
if not api['current_class'] or api['current_class'] not in api['class_annotations']:
|
||||
return {
|
||||
"status": False,
|
||||
"reason": "当前类没有点需要删除"
|
||||
}
|
||||
class_data = api['class_annotations'][api['current_class']]
|
||||
if not class_data['points']:
|
||||
return {
|
||||
"status": False,
|
||||
"reason": "当前类没有点需要删除"
|
||||
}
|
||||
class_data['points'].pop()
|
||||
class_data['point_types'].pop()
|
||||
return {
|
||||
"status": True,
|
||||
"reason": api
|
||||
}
|
||||
|
||||
|
||||
|
||||
def reset_current_class_points(api):
|
||||
if not api['current_class'] or api['current_class'] not in api['class_annotations']:
|
||||
return {
|
||||
"status": False,
|
||||
"reason": "当前类没有点需要删除"
|
||||
}
|
||||
class_data = api['class_annotations'][api['current_class']]
|
||||
class_data['points'] = []
|
||||
class_data['point_types'] = []
|
||||
return {
|
||||
"status": True,
|
||||
"reason": api
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
def predict_mask(data,sam_predictor):
|
||||
if data['image_rgb'] is None:
|
||||
logger.info("predict_mask: No image loaded")
|
||||
return {"status":False, "reason":"预测掩码：没有图像加载"}
|
||||
if len(data['input_point']) == 0:
|
||||
logger.info("predict_mask: No points added")
|
||||
return {"status":False, "reason":"预测掩码：没有进行点标注"}
|
||||
try:
|
||||
sam_predictor.set_image(data['image_rgb'])
|
||||
except Exception as e:
|
||||
logger.error(f"predict_mask: Error setting image: {str(e)}")
|
||||
return {"status":False, "reason":f"predict_mask: Error setting image: {str(e)}"}
|
||||
input_point_np = np.array(data['input_point'])
|
||||
input_label_np = np.array(data['input_label'])
|
||||
try:
|
||||
masks_pred, scores, logits = sam_predictor.predict(
|
||||
point_coords=input_point_np,
|
||||
point_labels=input_label_np,
|
||||
mask_input=data['logit_input'][None, :, :] if data['logit_input'] is not None else None,
|
||||
multimask_output=True,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"predict_mask: Error during prediction: {str(e)}")
|
||||
traceback.print_exc()
|
||||
return {"status":False, "reason":f"predict_mask: Error during prediction: {str(e)}"}
|
||||
data['masks_pred'] = masks_pred
|
||||
data['scores'] = scores
|
||||
data['logits'] = logits
|
||||
best_mask_idx = np.argmax(scores)
|
||||
data['selected_mask'] = masks_pred[best_mask_idx]
|
||||
data['logit_input'] = logits[best_mask_idx, :, :]
|
||||
logger.info(f"predict_mask: Predicted {len(masks_pred)} masks, best score: {scores[best_mask_idx]:.4f}")
|
||||
return {
|
||||
"status":True,
|
||||
"reason":{
|
||||
"masks": [mask.tolist() for mask in masks_pred],
|
||||
"scores": scores.tolist(),
|
||||
"selected_index": int(best_mask_idx),
|
||||
"data":data
|
||||
}}
|
||||
|
||||
|
||||
def get_image_info(data):
|
||||
|
||||
return {
|
||||
"filename": data['filename'],
|
||||
"index": data['current_index'],
|
||||
"total": len(data['image_files']),
|
||||
"width": data['image'].shape[1],
|
||||
"height": data['image'].shape[0]
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue