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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.images.2019.nir
    41. 

  41.       data/processed.masks.2019  data/dataset  --source_dim 8192 --tile_size 512 --format
    42. 

  42.       PNG
    43. 

  43.     deps:
    44. 

  44.     - path: data/processed.images.2019
    45. 

  45.       md5: a3c48148cd75adc9b069207edf27b637.dir
    46. 

  46.       size: 169321666330
    47. 

  47.       nfiles: 1925
    48. 

  48.     - path: data/processed.masks.2019
    49. 

  49.       md5: bcdadbbf7cb31f3a2a4f437290dcbd28.dir
    50. 

  50.       size: 3288758774
    51. 

  51.       nfiles: 49
    52. 

  52.     params:
    53. 

  53.       params.yaml:
    54. 

  54.         createdataset.tile_size: 512
    55. 

  55.         file_type: PNG
    56. 

  56.         source_dim: 8192
    57. 

  57.     outs:
    58. 

  58.     - path: data/dataset/stats.csv
    59. 

  59.       md5: 06da46443d403f1fd39a2c75131ec3b4
    60. 

  60.       size: 387162
    61. 

  61.     - path: data/dataset/train
    62. 

  62.       md5: f5da4d9fd3c2ed89b22183f7ff85a7f3.dir
    63. 

  63.       size: 6324981760
    64. 

  64.       nfiles: 23
    65. 

  65.   createbalanced:
    66. 

  66.     cmd: python scripts/createbalanced.py  data/dataset/stats.csv data/dataset/train  data/dataset/train_balanced  data/dataset/train_balanced_short
    67. 

  67.       --format PNG --tmp-dir ./tmp
    68. 

  68.     deps:
    69. 

  69.     - path: data/dataset/stats.csv
    70. 

  70.       md5: 06da46443d403f1fd39a2c75131ec3b4
    71. 

  71.       size: 387162
    72. 

  72.     - path: data/dataset/train
    73. 

  73.       md5: f5da4d9fd3c2ed89b22183f7ff85a7f3.dir
    74. 

  74.       size: 6324981760
    75. 

  75.       nfiles: 23
    76. 

  76.     params:
    77. 

  77.       params.yaml:
    78. 

  78.         file_type: PNG
    79. 

  79.     outs:
    80. 

  80.     - path: data/dataset/train_balanced
    81. 

  81.       md5: b7b1bf1ad26274dc9c8eb731dc394357.dir
    82. 

  82.       size: 6279290880
    83. 

  83.       nfiles: 44
    84. 

  84.     - path: data/dataset/train_balanced_short
    85. 

  85.       md5: 019b83384ae120b28a085c5f4946948c.dir
    86. 

  86.       size: 416327680
    87. 

  87.       nfiles: 11
    88. 

  88.   createtiles@2017:
    89. 

  89.     cmd: mkdir -p data/processed.images.2017; gdal_retile.py  -csv locations.csv  -v
    90. 

  90.       -ps 8192 8192 -co "TILED=YES" -co "COMPRESS=LZW"  -targetDir data/processed.images.2017  data/raw/ortho_2017_ESPG3044.tif
    91. 

  91.     deps:
    92. 

  92.     - path: data/raw/ortho_2017_ESPG3044.tif
    93. 

  93.       md5: a3dbc3a39bd6bb1c932ee887af198901
    94. 

  94.       size: 381359748231
    95. 

  95.     params:
    96. 

  96.       params.yaml:
    97. 

  97.         source_dim: 8192
    98. 

  98.     outs:
    99. 

  99.     - path: data/processed.images.2017
    100. 

  100.       md5: 78c52e327b8dcd7dfbb5d43790326eb3.dir
    101. 

  101.       size: 143381387397
    102. 

  102.       nfiles: 1925
    103. 

  103.   inference@2017:
    104. 

  104.     cmd: mkdir -p data/predicted.2017; mkdir -p data/predicted.2017_preview; stdbuf
    105. 

  105.       -i0 -o0 -e0 python deadtrees/deployment/tiler.py --all -o data/predicted.2017
    106. 

  106.       data/processed.images.2017; gdal_merge.py  -co 'COMPRESS=LZW'  -co 'TILED=YES'  -o
    107. 

  107.       data/predicted_mosaic_2017.tif  data/predicted.2017/ortho_2017_ESPG3044_*
    108. 

  108.     deps:
    109. 

  109.     - path: data/processed.images.2017
    110. 

  110.       md5: 78c52e327b8dcd7dfbb5d43790326eb3.dir
    111. 

  111.       size: 143381387397
    112. 

  112.       nfiles: 1925
    113. 

  113.     outs:
    114. 

  114.     - path: data/predicted.2017
    115. 

  115.       md5: 03accabe790944174dfe0d5b59f78a9d.dir
    116. 

  116.       size: 625167771
    117. 

  117.       nfiles: 1395
    118. 

  118.     - path: data/predicted.2017_preview
    119. 

  119.       md5: fdd75c06b1c558994e9bf43e5c1d46ce.dir
    120. 

  120.       size: 92731291854
    121. 

  121.       nfiles: 1395
    122. 

  122.     - path: data/predicted_mosaic_2017.tif
    123. 

  123.       md5: c48627137e12a9b3be38d07564f0a212
    124. 

  124.       size: 852005583
    125. 

  125.   inference@2019:
    126. 

  126.     cmd: mkdir -p data/predicted.2019; mkdir -p data/predicted.2019_preview; stdbuf
    127. 

  127.       -i0 -o0 -e0 python deadtrees/deployment/tiler.py --all -o data/predicted.2019
    128. 

  128.       data/processed.images.2019; gdal_merge.py  -co 'COMPRESS=LZW'  -co 'TILED=YES'  -o
    129. 

  129.       data/predicted_mosaic_2019.tif  data/predicted.2019/ortho_2019_ESPG3044_*
    130. 

  130.     deps:
    131. 

  131.     - path: data/processed.images.2019
    132. 

  132.       md5: a3c48148cd75adc9b069207edf27b637.dir
    133. 

  133.       size: 169321666330
    134. 

  134.       nfiles: 1925
    135. 

  135.     outs:
    136. 

  136.     - path: data/predicted.2019
    137. 

  137.       md5: c11c8b07d66915a7d0f134a86da83522.dir
    138. 

  138.       size: 504032171
    139. 

  139.       nfiles: 1089
    140. 

  140.     - path: data/predicted.2019_preview
    141. 

  141.       md5: bc98ec08d56fc2fb6638385f3dbf4b8f.dir
    142. 

  142.       size: 73081685754
    143. 

  143.       nfiles: 1089
    144. 

  144.     - path: data/predicted_mosaic_2019.tif
    145. 

  145.       md5: 8f61777c5f40aa8be9f516a611debb27
    146. 

  146.       size: 811495979
    147.
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