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Bug: Polygon must have at least 3 points #1769

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cinjon opened this issue Jun 21, 2020 · 13 comments
Closed

Bug: Polygon must have at least 3 points #1769

cinjon opened this issue Jun 21, 2020 · 13 comments
Labels
bug Something isn't working
Milestone
1.1.0-release

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@cinjon
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cinjon commented Jun 21, 2020

I am using cvat.org to test out the service. I used the MaskRCNN feature to annotate a video. After completion, I went into the job and found that I could not open it. I get this error in the console:

cvat-app.tsx:192 Error: Polygon must have at least 3 points, but got 2

I also see the same error in the window:

Error: Polygon must have at least 3 points, but got 2

However, I can open it in the Old UI no problem. This seems like a bug.
@azhavoro azhavoro added the bug Something isn't working label Jun 22, 2020
@bsekachev
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@cinjon

Could you provide annotations data that you get from server to client?
I suspicious there are wrong polygons.

@cinjon
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cinjon commented Jun 24, 2020

I just tried dumping as COCO 1.0 Annotations and got the error:

Could not dump annotations for the task 1690
Error: Request failed with status code 500. "AssertionError: Wrong polygon points: [395.0, 85.5, 395.0, 85.5]\n".

@bsekachev
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@cinjon

It seems there is a bug in MaskRCNN. Polygon mustn't have less then 3 points. But the algorithm have returned the wrong result.
Another idea is instead of the error we might output a warning and continue working in UI.

Let's look into this.

@bsekachev
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Speaking about dumping. @zhiltsov-max what do you think? Should we have the assert?

@zhiltsov-max
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We have wrong annotations, assert is here, and it fires. Moreover, these annotations are incompatible with the official COCO python API.

@savan77
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savan77 commented Jul 9, 2020 •
edited
Loading

I am also facing the same issue. The annotations were generated by the MaskRCNN model. But when click on that job, it throws the same error and I can't see any images in that job. Did you guys find any workaround? @zhiltsov-max @bsekachev

UPDATE: I added a logline to print the result from auto segmentation (MaskRCNN). I then checked how many predictions have a length less than 3. To my surprise, there wasn't a single prediction with less than 3 points. Below is the output dictionary I got. The minimum length that I can see is 4. This function might be relevant: https://github.com/cocodataset/cocoapi/blob/8c9bcc3cf640524c4c20a9c40e89cb6a2f2fa0e9/PythonAPI/pycocotools/_mask.pyx#L288

s="{36: [[0, [237.0, 652.5, 226.0, 648.5, 216.5, 638.0, 225.0, 636.5, 236.0, 641.5, 239.5, 645.0, 237.0, 652.5]], [0, [37.0, 651.5, 10.0, 650.5, 3.0, 648.5, 2.5, 645.0, 10.0, 641.5, 50.5, 644.0, 48.0, 648.5, 37.0, 651.5]], [0, [1389.0, 758.5, 1383.0, 757.5, 1374.5, 748.0, 1375.0, 741.5, 1385.0, 743.5, 1392.5, 751.0, 1389.0, 758.5]], [0, [163.0, 605.5, 138.5, 591.0, 149.0, 589.5, 162.0, 596.5, 163.0, 605.5]], [0, [222.0, 620.5, 213.5, 614.0, 231.0, 609.5, 249.5, 611.0, 222.0, 620.5]], [1, [1388.0, 758.5, 1373.5, 747.0, 1375.0, 741.5, 1384.0, 743.5, 1390.5, 750.0, 1388.0, 758.5]], [1, [235.0, 651.5, 226.0, 649.5, 214.5, 638.0, 224.0, 636.5, 234.0, 641.5, 238.5, 646.0, 235.0, 651.5]], [1, [24.0, 653.5, 7.5, 646.0, 13.0, 642.5, 45.0, 642.5, 53.5, 646.0, 46.0, 651.5, 24.0, 653.5]], [1, [226.0, 620.5, 213.5, 617.0, 219.0, 612.5, 233.0, 610.5, 250.5, 612.0, 226.0, 620.5]], [1, [163.0, 607.5, 141.0, 596.5, 130.0, 596.5, 129.0, 600.5, 122.5, 597.0, 135.0, 590.5, 149.0, 591.5, 163.5, 600.0, 163.0, 607.5]], [2, [35.0, 653.5, 16.0, 652.5, 8.5, 645.0, 13.0, 641.5, 48.0, 641.5, 51.5, 645.0, 35.0, 653.5]], [2, [159.0, 603.5, 143.0, 598.5, 124.0, 602.5, 121.5, 597.0, 132.0, 591.5, 148.0, 591.5, 159.5, 597.0, 159.0, 603.5]], [2, [234.0, 652.5, 223.0, 648.5, 212.5, 637.0, 221.0, 635.5, 233.0, 641.5, 234.0, 652.5]], [2, [224.0, 620.5, 213.0, 619.5, 211.5, 616.0, 229.0, 609.5, 248.5, 611.0, 224.0, 620.5]], [2, [1385.0, 758.5, 1372.5, 749.0, 1374.0, 741.5, 1387.5, 751.0, 1385.0, 758.5]], [3, [160.0, 602.5, 142.0, 598.5, 126.0, 601.5, 121.5, 597.0, 128.0, 591.5, 144.0, 589.5, 160.0, 595.5, 160.0, 602.5]], [3, [31.0, 653.5, 9.0, 650.5, 7.5, 645.0, 14.0, 640.5, 45.0, 640.5, 51.5, 644.0, 47.0, 650.5, 31.0, 653.5]], [3, [224.0, 620.5, 213.0, 620.5, 210.5, 616.0, 218.0, 610.5, 248.5, 610.0, 224.0, 620.5]], [3, [236.0, 654.5, 222.0, 649.5, 210.5, 638.0, 220.0, 635.5, 232.0, 641.5, 236.5, 647.0, 236.0, 654.5]], [3, [1385.0, 757.5, 1374.5, 751.0, 1374.0, 741.5, 1385.5, 747.0, 1385.0, 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639.5, 235.5, 646.0, 234.0, 652.5]], [6, [35.0, 653.5, 5.0, 650.5, 1.5, 645.0, 9.0, 641.5, 46.0, 641.5, 52.5, 645.0, 35.0, 653.5]], [6, [163.0, 605.5, 139.0, 598.5, 128.0, 599.5, 126.5, 596.0, 135.0, 590.5, 147.0, 590.5, 161.0, 597.5, 163.0, 605.5]], [6, [1388.0, 758.5, 1374.5, 750.0, 1376.0, 742.5, 1387.5, 748.0, 1390.5, 753.0, 1388.0, 758.5]], [6, [223.0, 620.5, 215.0, 619.5, 215.5, 614.0, 245.5, 612.0, 223.0, 620.5]], [6, [174.0, 593.5, 170.5, 593.0, 173.0, 573.5, 182.5, 583.0, 182.5, 588.0, 174.0, 593.5]], [6, [234.0, 650.5, 223.0, 647.5, 212.5, 638.0, 227.0, 638.5, 236.5, 645.0, 234.0, 650.5]], [7, [38.0, 653.5, 6.0, 652.5, 1.5, 646.0, 10.0, 641.5, 55.5, 643.0, 55.0, 647.5, 38.0, 653.5]], [7, [227.0, 619.5, 214.0, 619.5, 212.5, 616.0, 231.0, 609.5, 248.5, 611.0, 227.0, 619.5]], [7, [162.0, 603.5, 127.5, 596.0, 136.0, 590.5, 147.0, 590.5, 160.0, 596.5, 162.0, 603.5]], [7, [1389.0, 758.5, 1374.5, 749.0, 1374.0, 742.5, 1381.0, 742.5, 1388.5, 749.0, 1391.5, 754.0, 1389.0, 758.5]], [7, 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620.5]], [9, [28.0, 654.5, 15.0, 651.5, 9.5, 646.0, 15.0, 641.5, 51.5, 645.0, 46.0, 650.5, 28.0, 654.5]], [9, [234.0, 654.5, 220.0, 648.5, 210.5, 639.0, 220.0, 637.5, 241.5, 649.0, 234.0, 654.5]], [10, [34.0, 654.5, 15.0, 653.5, 7.5, 649.0, 7.5, 645.0, 14.0, 641.5, 45.0, 641.5, 52.5, 645.0, 49.0, 650.5, 34.0, 654.5]], [10, [222.0, 619.5, 210.5, 618.0, 215.0, 611.5, 244.5, 611.0, 222.0, 619.5]], [10, [1389.0, 758.5, 1372.0, 749.5, 1371.5, 743.0, 1385.0, 744.5, 1391.5, 752.0, 1389.0, 758.5]], [10, [232.0, 653.5, 209.5, 639.0, 219.0, 636.5, 227.0, 639.5, 236.5, 647.0, 232.0, 653.5]], [10, [156.0, 602.5, 128.0, 599.5, 126.5, 595.0, 135.0, 589.5, 147.0, 589.5, 160.5, 598.0, 156.0, 602.5]], [11, [23.0, 654.5, 5.5, 646.0, 12.0, 641.5, 48.0, 641.5, 56.5, 646.0, 45.0, 652.5, 23.0, 654.5]], [11, [225.0, 621.5, 209.5, 619.0, 221.0, 611.5, 247.5, 612.0, 225.0, 621.5]], [11, [231.0, 653.5, 207.5, 638.0, 220.0, 636.5, 239.5, 649.0, 231.0, 653.5]], [11, [128.0, 604.5, 123.5, 602.0, 125.0, 596.5, 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649.5, 209.5, 637.0, 219.0, 635.5, 232.0, 641.5, 239.5, 647.0, 235.0, 653.5]], [16, [72.0, 602.5, 57.0, 584.5, 72.5, 593.0, 72.0, 602.5]], [17, [221.0, 621.5, 212.0, 620.5, 211.5, 614.0, 230.0, 609.5, 248.5, 611.0, 221.0, 621.5]], [17, [0.0, 645.5, 9.0, 641.5, 41.0, 641.5, 52.5, 646.0, 37.0, 653.5, 13.0, 652.5, 0.0, 647.5]], [17, [160.0, 604.5, 129.5, 598.0, 138.0, 591.5, 147.0, 591.5, 158.0, 596.5, 160.0, 604.5]], [17, [234.0, 652.5, 225.0, 650.5, 208.5, 638.0, 217.0, 635.5, 226.0, 638.5, 239.5, 648.0, 234.0, 652.5]], [18, [221.0, 619.5, 212.0, 618.5, 210.5, 615.0, 231.0, 608.5, 247.5, 610.0, 221.0, 619.5]], [18, [37.0, 653.5, 7.0, 650.5, 3.5, 645.0, 11.0, 641.5, 43.0, 641.5, 51.5, 646.0, 37.0, 653.5]], [18, [159.0, 603.5, 127.5, 598.0, 137.0, 590.5, 148.0, 590.5, 161.5, 598.0, 159.0, 603.5]], [18, [234.0, 652.5, 210.5, 638.0, 219.0, 635.5, 227.0, 638.5, 240.5, 648.0, 234.0, 652.5]], [19, [40.0, 653.5, 6.0, 652.5, 1.5, 650.0, 1.5, 645.0, 11.0, 641.5, 45.0, 641.5, 57.5, 647.0, 40.0, 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412.0, 160.0, 414.5]], [272, [69.0, 599.5, 15.0, 581.5, 0.5, 568.0, 38.0, 574.5, 85.5, 591.0, 69.0, 599.5]], [273, [66.0, 428.5, 24.0, 419.5, 8.5, 409.0, 4.5, 394.0, 6.0, 362.5, 40.0, 375.5, 75.5, 402.0, 76.5, 423.0, 66.0, 428.5]], [274, [1883.0, 82.5, 1870.5, 78.0, 1875.5, 58.0, 1889.0, 50.5, 1897.0, 50.5, 1900.5, 56.0, 1897.5, 71.0, 1891.0, 80.5, 1883.0, 82.5]], [275, [209.0, 420.5, 188.0, 418.5, 168.5, 407.0, 171.5, 386.0, 169.0, 379.5, 224.0, 373.5, 241.5, 380.0, 241.5, 395.0, 226.5, 414.0, 219.0, 419.5, 209.0, 420.5]], [281, [1575.0, 523.5, 1542.0, 518.5, 1532.0, 510.5, 1513.0, 506.5, 1504.5, 502.0, 1505.5, 494.0, 1533.0, 493.5, 1578.0, 506.5, 1586.5, 513.0, 1580.0, 522.5, 1575.0, 523.5]], [281, [1815.0, 799.5, 1790.0, 792.5, 1785.5, 788.0, 1786.5, 761.0, 1800.0, 745.5, 1812.5, 765.0, 1815.0, 799.5]], [283, [1849.0, 385.5, 1775.0, 382.5, 1755.0, 374.5, 1744.5, 360.0, 1749.0, 355.5, 1768.0, 355.5, 1815.0, 362.5, 1852.0, 376.5, 1855.5, 382.0, 1849.0, 385.5]], [283, [1678.0, 831.5, 1664.5, 817.0, 1663.5, 808.0, 1686.0, 791.5, 1689.5, 795.0, 1691.5, 813.0, 1678.0, 831.5]], [284, [1799.0, 211.5, 1783.0, 210.5, 1778.5, 205.0, 1818.0, 179.5, 1850.0, 177.5, 1857.5, 181.0, 1855.5, 195.0, 1845.0, 204.5, 1839.0, 207.5, 1819.0, 205.5, 1799.0, 211.5]], [285, [1828.0, 704.5, 1828.0, 704.5]], [292, [1282.0, 643.5, 1277.0, 643.5, 1274.5, 636.0, 1285.0, 624.5, 1302.0, 623.5, 1309.5, 627.0, 1307.0, 631.5, 1282.0, 643.5]], [292, [1400.0, 666.5, 1387.0, 661.5, 1382.5, 653.0, 1405.0, 638.5, 1414.5, 649.0, 1400.0, 666.5]], [293, [421.0, 810.5, 409.0, 810.5, 402.5, 805.0, 420.0, 804.5, 421.0, 810.5]], [293, [410.0, 807.5, 400.5, 805.0, 405.0, 800.5, 415.5, 804.0, 410.0, 807.5]], [295, [451.0, 813.5, 436.0, 809.5, 434.5, 805.0, 450.0, 806.5, 451.0, 813.5]], [295, [431.0, 798.5, 433.0, 792.5, 439.5, 793.0, 431.0, 798.5]], [296, [467.0, 815.5, 448.5, 808.0, 461.0, 806.5, 466.5, 810.0, 467.0, 815.5]], [296, [466.0, 810.5, 446.5, 807.0, 458.0, 804.5, 466.0, 810.5]], [297, [266.0, 802.5, 253.0, 802.5, 252.5, 799.0, 263.0, 795.5, 268.5, 799.0, 266.0, 802.5]], [297, [485.0, 815.5, 465.5, 807.0, 479.0, 806.5, 487.5, 812.0, 485.0, 815.5]], [298, [298.0, 802.5, 285.0, 802.5, 284.5, 798.0, 295.0, 795.5, 300.5, 799.0, 298.0, 802.5]], [298, [514.0, 816.5, 497.5, 807.0, 515.0, 809.5, 518.5, 814.0, 514.0, 816.5]], [299, [320.0, 799.5, 314.0, 799.5, 311.5, 794.0, 328.5, 794.0, 320.0, 799.5]], [299, [545.0, 811.5, 529.0, 807.5, 524.5, 802.0, 539.0, 802.5, 545.0, 811.5]], [299, [530.0, 796.5, 523.5, 795.0, 527.0, 789.5, 544.5, 788.0, 530.0, 796.5]], [299, [210.0, 775.5, 210.0, 775.5]], [299, [327.0, 795.5, 313.5, 794.0, 324.0, 789.5, 327.0, 795.5]], [299, [317.0, 793.5, 310.5, 792.0, 320.0, 788.5, 321.5, 791.0, 317.0, 793.5]], [300, [46.0, 568.5, 36.0, 567.5, 32.5, 557.0, 33.5, 532.0, 42.5, 525.0, 44.5, 516.0, 99.0, 511.5, 101.5, 517.0, 91.5, 536.0, 63.0, 562.5, 46.0, 568.5]], [300, [366.0, 796.5, 357.0, 796.5, 356.5, 793.0, 372.5, 791.0, 366.0, 796.5]], [300, [588.0, 809.5, 579.0, 809.5, 567.5, 801.0, 581.0, 801.5, 588.0, 809.5]], [300, [573.0, 794.5, 567.5, 794.0, 571.0, 788.5, 588.5, 788.0, 573.0, 794.5]], [300, [589.0, 817.5, 571.5, 804.0, 582.0, 802.5, 587.0, 805.5, 590.5, 811.0, 589.0, 817.5]]]}"

You can convert this string into dictionary as follows:

import ast
data = ast.literal_eval(s)

UPDATE 2: I manually removed points with length = 4, then uploaded annotation to CVAT and it works. So probably, it's the points with a length of 4 are creating an issue for me.

@zhiltsov-max
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@savan77, there is 7 annotations with less than 6 values: 52, 100, 154, 176, 184, 285, 299. You're right about the function in COCO API.

@bsekachev bsekachev assigned bsekachev and unassigned bsekachev Aug 16, 2020
@bsekachev bsekachev added this to the 1.1.0-release milestone Aug 16, 2020
@rushtehrani
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rushtehrani commented Aug 21, 2020 •
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Is there a PR for this already? Looked through the PRs but couldn’t find anything. Our workaround right now is to remove the check in UI code.

@savan77
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savan77 commented Aug 21, 2020

@zhiltsov-max we looked at MaskRCNN output of two videos. It turns out those polygons with 4 values can be removed. If you look at those values both points are same.
Take a look at this example:

point 4 [523.0, 338.5, 523.0, 338.5]
point 4 [268.0, 340.5, 268.0, 340.5]
point 4 [208.0, 324.5, 208.0, 324.5]
point 4 [1137.0, 246.5, 1137.0, 246.5]
point 4 [269.0, 280.5, 269.0, 280.5]

I also looked at some points with 4 values in the result that attached above. Same thing.

I think we can just filter out those polygons in auto_segmentation/views.py. I understand we will be having lambda functions. But in the interim, this maybe the solution. We can send a PR if you want.

@zhiltsov-max
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@savan77, actually, lambda functions have already come. It would be great, if you could prepare a PR with this fix. I also think that it can be interesting to check for small area polygons (e.g. < 1 px^2), instead of just 4-value arrays.

@savan77
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savan77 commented Aug 28, 2020

@zhiltsov-max That's great. I think it will be easier to just check for length not only logically but also computationally. Let me know what do you think, happy to send a pr.

@zhiltsov-max
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@savan77, it will be enough.

@savan77 savan77 mentioned this issue Aug 28, 2020
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@bsekachev
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Can this issue be closed now?

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@cinjon @rushtehrani @zhiltsov-max @savan77 @bsekachev @azhavoro