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cwerner
/
deadtrees
connected to https://github.com/cwerner/deadtrees.git

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Issues 14 Pull Requests 2 Discussions 0 Invite collaborators

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  1. schema: '2.0'
    2. 

  2. stages:
    3. 

  3.   createtiles@2019:
    4. 

  4.     cmd: mkdir data/processed; gdal_retile.py  -csv locations.csv  -v -ps 8192 8192
    5. 

  5.       -co "TILED=YES" -co "COMPRESS=LZW"  -targetDir data/processed.images.2019  data/raw/ortho_2019_ESPG3044.tif
    6. 

  6.     deps:
    7. 

  7.     - path: data/raw/ortho_2019_ESPG3044.tif
    8. 

  8.       md5: 746e66cb751ca75c9497920f53cdc0b8
    9. 

  9.       size: 381359748231
    10. 

  10.     params:
    11. 

  11.       params.yaml:
    12. 

  12.         source_dim: 8192
    13. 

  13.     outs:
    14. 

  14.     - path: data/processed.images.2019
    15. 

  15.       md5: a3c48148cd75adc9b069207edf27b637.dir
    16. 

  16.       size: 169321666330
    17. 

  17.       nfiles: 1925
    18. 

  18.   createmasks:
    19. 

  19.     cmd: python scripts/createmasks.py  --bbox data/raw/shapefiles/deadtrees_area/deadtrees_area.shp
    20. 

  20.       data/processed.images.2019  data/processed.masks.2019  data/raw/shapefiles/deadtrees/deadtrees.shp
    21. 

  21.     deps:
    22. 

  22.     - path: data/processed.images.2019
    23. 

  23.       md5: a3c48148cd75adc9b069207edf27b637.dir
    24. 

  24.       size: 169321666330
    25. 

  25.       nfiles: 1925
    26. 

  26.     - path: data/raw/shapefiles/deadtrees
    27. 

  27.       md5: fdc094c879c6588fe71d19a7177eda48.dir
    28. 

  28.       size: 1665350
    29. 

  29.       nfiles: 5
    30. 

  30.     - path: data/raw/shapefiles/deadtrees_area
    31. 

  31.       md5: f098a36246abde3d3e0c0efa6183093a.dir
    32. 

  32.       size: 1756
    33. 

  33.       nfiles: 5
    34. 

  34.     outs:
    35. 

  35.     - path: data/processed.masks.2019
    36. 

  36.       md5: bcdadbbf7cb31f3a2a4f437290dcbd28.dir
    37. 

  37.       size: 3288758774
    38. 

  38.       nfiles: 49
    39. 

  39.   createdataset:
    40. 

  40.     cmd: python scripts/createdataset.py data/processed.images.2019 data/processed.masks.2019
    41. 

  41.       data/dataset  --source_dim 8192 --tile_size 512 --format PNG
    42. 

  42.     deps:
    43. 

  43.     - path: data/processed.images.2019
    44. 

  44.       md5: a3c48148cd75adc9b069207edf27b637.dir
    45. 

  45.       size: 169321666330
    46. 

  46.       nfiles: 1925
    47. 

  47.     - path: data/processed.masks.2019
    48. 

  48.       md5: bcdadbbf7cb31f3a2a4f437290dcbd28.dir
    49. 

  49.       size: 3288758774
    50. 

  50.       nfiles: 49
    51. 

  51.     params:
    52. 

  52.       params.yaml:
    53. 

  53.         createdataset.tile_size: 512
    54. 

  54.         file_type: PNG
    55. 

  55.         source_dim: 8192
    56. 

  56.     outs:
    57. 

  57.     - path: data/dataset/stats.csv
    58. 

  58.       md5: 06da46443d403f1fd39a2c75131ec3b4
    59. 

  59.       size: 387162
    60. 

  60.     - path: data/dataset/train
    61. 

  61.       md5: ea28c53f1d87e38f519f9674e7728dc7.dir
    62. 

  62.       size: 4677847040
    63. 

  63.       nfiles: 23
    64. 

  64.   createbalanced:
    65. 

  65.     cmd: python scripts/createbalanced.py  data/dataset/stats.csv data/dataset/train  data/dataset/train_balanced  data/dataset/train_balanced_short
    66. 

  66.       --format PNG --tmp-dir ./tmp
    67. 

  67.     deps:
    68. 

  68.     - path: data/dataset/stats.csv
    69. 

  69.       md5: 06da46443d403f1fd39a2c75131ec3b4
    70. 

  70.       size: 387162
    71. 

  71.     - path: data/dataset/train
    72. 

  72.       md5: ea28c53f1d87e38f519f9674e7728dc7.dir
    73. 

  73.       size: 4677847040
    74. 

  74.       nfiles: 23
    75. 

  75.     params:
    76. 

  76.       params.yaml:
    77. 

  77.         file_type: PNG
    78. 

  78.     outs:
    79. 

  79.     - path: data/dataset/train_balanced
    80. 

  80.       md5: 7df9d2814d314cca795889d257d9d281.dir
    81. 

  81.       size: 4644628480
    82. 

  82.       nfiles: 44
    83. 

  83.     - path: data/dataset/train_balanced_short
    84. 

  84.       md5: f0bb5fb08c815bf826684b7d542f355d.dir
    85. 

  85.       size: 308807680
    86. 

  86.       nfiles: 11
    87. 

  87.   createtiles@2017:
    88. 

  88.     cmd: mkdir -p data/processed.images.2017; gdal_retile.py  -csv locations.csv  -v
    89. 

  89.       -ps 8192 8192 -co "TILED=YES" -co "COMPRESS=LZW"  -targetDir data/processed.images.2017  data/raw/ortho_2017_ESPG3044.tif
    90. 

  90.     deps:
    91. 

  91.     - path: data/raw/ortho_2017_ESPG3044.tif
    92. 

  92.       md5: a3dbc3a39bd6bb1c932ee887af198901
    93. 

  93.       size: 381359748231
    94. 

  94.     params:
    95. 

  95.       params.yaml:
    96. 

  96.         source_dim: 8192
    97. 

  97.     outs:
    98. 

  98.     - path: data/processed.images.2017
    99. 

  99.       md5: 78c52e327b8dcd7dfbb5d43790326eb3.dir
    100. 

  100.       size: 143381387397
    101. 

  101.       nfiles: 1925
    102. 

  102.   inference@2017:
    103. 

  103.     cmd: mkdir -p data/predicted.2017; mkdir -p data/predicted.2017_preview; stdbuf
    104. 

  104.       -i0 -o0 -e0 python deadtrees/deployment/tiler.py --all -o data/predicted.2017
    105. 

  105.       data/processed.images.2017; gdal_merge.py  -co 'COMPRESS=LZW'  -co 'TILED=YES'  -o
    106. 

  106.       data/predicted_mosaic_2017.tif  data/predicted.2017/ortho_2017_ESPG3044_*
    107. 

  107.     deps:
    108. 

  108.     - path: data/processed.images.2017
    109. 

  109.       md5: 78c52e327b8dcd7dfbb5d43790326eb3.dir
    110. 

  110.       size: 143381387397
    111. 

  111.       nfiles: 1925
    112. 

  112.     outs:
    113. 

  113.     - path: data/predicted.2017
    114. 

  114.       md5: 08eb29659a4aa6386d9777e3d591754b.dir
    115. 

  115.       size: 635081579
    116. 

  116.       nfiles: 1395
    117. 

  117.     - path: data/predicted.2017_preview
    118. 

  118.       md5: 7d274667139834382d3371fd87a271d8.dir
    119. 

  119.       size: 92731291854
    120. 

  120.       nfiles: 1395
    121. 

  121.     - path: data/predicted_mosaic_2017.tif
    122. 

  122.       md5: da018740476c189d48ba1c4da4c96821
    123. 

  123.       size: 861791051
    124. 

  124.   inference@2019:
    125. 

  125.     cmd: mkdir -p data/predicted.2019; mkdir -p data/predicted.2019_preview; stdbuf
    126. 

  126.       -i0 -o0 -e0 python deadtrees/deployment/tiler.py --all -o data/predicted.2019
    127. 

  127.       data/processed.images.2019; gdal_merge.py  -co 'COMPRESS=LZW'  -co 'TILED=YES'  -o
    128. 

  128.       data/predicted_mosaic_2019.tif  data/predicted.2019/ortho_2019_ESPG3044_*
    129. 

  129.     deps:
    130. 

  130.     - path: data/processed.images.2019
    131. 

  131.       md5: a3c48148cd75adc9b069207edf27b637.dir
    132. 

  132.       size: 169321666330
    133. 

  133.       nfiles: 1925
    134. 

  134.     outs:
    135. 

  135.     - path: data/predicted.2019
    136. 

  136.       md5: 86282b7012c3167d2ad854bb46d4bfcd.dir
    137. 

  137.       size: 503364215
    138. 

  138.       nfiles: 1089
    139. 

  139.     - path: data/predicted.2019_preview
    140. 

  140.       md5: 6ac0071571c104fd004436ea364c1dbd.dir
    141. 

  141.       size: 73081685754
    142. 

  142.       nfiles: 1089
    143. 

  143.     - path: data/predicted_mosaic_2019.tif
    144. 

  144.       md5: c7bc54286b30358278ba867e41d616a2
    145. 

  145.       size: 810727835
    146.
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