Merge pull request #1623 from pierotofy/class

Automated point classification and other improvements

SuperBuild/CMakeLists.txt

@@ -177,6 +177,7 @@ set(custom_libs OpenSfM
 				FPCFilter
 				PyPopsift
 				Obj2Tiles
+				OpenPointClass
 )
 
 externalproject_add(mve

SuperBuild/cmake/External-OpenPointClass.cmake

@@ -0,0 +1,33 @@
+set(_proj_name openpointclass)
+set(_SB_BINARY_DIR "${SB_BINARY_DIR}/${_proj_name}")
+
+ExternalProject_Add(${_proj_name}
+  DEPENDS           pdal eigen34
+  PREFIX            ${_SB_BINARY_DIR}
+  TMP_DIR           ${_SB_BINARY_DIR}/tmp
+  STAMP_DIR         ${_SB_BINARY_DIR}/stamp
+  #--Download step--------------
+  DOWNLOAD_DIR      ${SB_DOWNLOAD_DIR}
+  GIT_REPOSITORY    https://github.com/uav4geo/OpenPointClass
+  GIT_TAG           v1.1.3
+  #--Update/Patch step----------
+  UPDATE_COMMAND    ""
+  #--Configure step-------------
+  SOURCE_DIR        ${SB_SOURCE_DIR}/${_proj_name}
+  CMAKE_ARGS
+    -DPDAL_DIR=${SB_INSTALL_DIR}/lib/cmake/PDAL
+    -DWITH_GBT=ON
+    -DBUILD_PCTRAIN=OFF
+    -DEIGEN3_INCLUDE_DIR=${SB_SOURCE_DIR}/eigen34/
+    -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
+    -DCMAKE_INSTALL_PREFIX:PATH=${SB_INSTALL_DIR}
+    ${WIN32_CMAKE_ARGS}
+  #--Build step-----------------
+  BINARY_DIR        ${_SB_BINARY_DIR}
+  #--Install step---------------
+  INSTALL_DIR       ${SB_INSTALL_DIR}
+  #--Output logging-------------
+  LOG_DOWNLOAD      OFF
+  LOG_CONFIGURE     OFF
+  LOG_BUILD         OFF
+)

opendm/config.py

@@ -344,7 +344,7 @@ def config(argv=None, parser=None):
             action=StoreTrue,
             nargs=0,
             default=False,
-            help='Classify the point cloud outputs using a Simple Morphological Filter. '
+            help='Classify the point cloud outputs. '
             'You can control the behavior of this option by tweaking the --dem-* parameters. '
             'Default: '
             '%(default)s')

opendm/gpu.py

@@ -16,18 +16,15 @@ def has_popsift_and_can_handle_texsize(width, height):
         compute_major, compute_minor = get_cuda_compute_version(0)
         if compute_major < 3 or (compute_major == 3 and compute_minor < 5):
             # Not supported
-            log.ODM_WARNING("CUDA compute platform is not supported (detected: %s.%s but we need at least 3.5)" % (compute_major, compute_minor))
+            log.ODM_INFO("CUDA compute platform is not supported (detected: %s.%s but we need at least 3.5)" % (compute_major, compute_minor))
             return False
     except Exception as e:
-        log.ODM_WARNING("Cannot use GPU for feature extraction: %s" % str(e))
+        log.ODM_INFO("Using CPU for feature extraction: %s" % str(e))
         return False
 
     try:
         from opensfm import pypopsift
-        fits = pypopsift.fits_texture(int(width * 1.02), int(height * 1.02))
-        if not fits:
-            log.ODM_WARNING("Image size (%sx%spx) would not fit in GPU memory, try lowering --feature-quality. Falling back to CPU" % (width, height))
-        return fits
+        return pypopsift.fits_texture(int(width * 1.02), int(height * 1.02))
     except (ModuleNotFoundError, ImportError):
         return False
     except Exception as e:
@@ -91,5 +88,4 @@ def has_gpu(args):
             log.ODM_INFO("nvidia-smi detected")
             return True
         else:
-            log.ODM_INFO("nvidia-smi not found in PATH, using CPU")
             return False

opendm/opc.py

@@ -0,0 +1,31 @@
+import os
+from opendm.ai import get_model
+from opendm import log
+from opendm.system import run
+from opendm import io
+
+def classify(point_cloud):
+    tmp_output = io.related_file_path(point_cloud, postfix=".classified")
+    if os.path.isfile(tmp_output):
+        os.remove(tmp_output)
+
+    try:
+        model = get_model("openpointclass", 
+            "https://github.com/uav4geo/OpenPointClass/releases/download/v1.1.3/vehicles-vegetation-buildings.zip", 
+            "v1.0.0",
+            name="model.bin")
+
+        if model is not None:
+            run('pcclassify "%s" "%s" "%s" -u -s 2,64' % (point_cloud, tmp_output, model))
+
+            if os.path.isfile(tmp_output):
+                os.remove(point_cloud)
+                os.rename(tmp_output, point_cloud)
+            else:
+                log.ODM_WARNING("Cannot classify using OpenPointClass (no output generated)")
+        else:
+            log.ODM_WARNING("Cannot download/access model from %s" % (model_url))
+
+    except Exception as e:
+        log.ODM_WARNING("Cannot classify using OpenPointClass: %s" % str(e))
+

stages/dataset.py

@@ -330,3 +330,8 @@ def parallel_bg_filter(item):
                     log.ODM_WARNING("No omega/phi/kappa angles found in input photos (%s), switching sfm-algorithm to incremental" % p.filename)
                     args.sfm_algorithm = 'incremental'
                     break
+
+        # Rolling shutter cannot be done in non-georeferenced datasets
+        if args.rolling_shutter and not reconstruction.is_georeferenced():
+            log.ODM_WARNING("Reconstruction is not georeferenced, disabling rolling shutter correction")
+            args.rolling_shutter = False

stages/odm_dem.py

@@ -12,7 +12,7 @@
 from opendm import pseudogeo
 from opendm.tiles.tiler import generate_dem_tiles
 from opendm.cogeo import convert_to_cogeo
-
+from opendm.opc import classify
 
 class ODMDEMStage(types.ODM_Stage):
     def process(self, args, outputs):
@@ -49,14 +49,17 @@ def process(self, args, outputs):
             pc_classify_marker = os.path.join(odm_dem_root, 'pc_classify_done.txt')
 
             if not io.file_exists(pc_classify_marker) or self.rerun():
-                log.ODM_INFO("Classifying {} using Simple Morphological Filter".format(dem_input))
+                log.ODM_INFO("Classifying {} using Simple Morphological Filter (1/2)".format(dem_input))
                 commands.classify(dem_input,
                                   args.smrf_scalar, 
                                   args.smrf_slope, 
                                   args.smrf_threshold, 
                                   args.smrf_window
                                 )
 
+                log.ODM_INFO("Classifying {} using OpenPointClass (2/2)".format(dem_input))
+                classify(dem_input)
+
                 with open(pc_classify_marker, 'w') as f:
                     f.write('Classify: smrf\n')
                     f.write('Scalar: {}\n'.format(args.smrf_scalar))