Replace pptk visualizer with open3D

README.md

@@ -38,7 +38,7 @@ There are two main categories of samples: **camera** and **applications**. The s
  - **visualization**
  - [**read_zdf_vis_3d**][read_zdf_vis_3d-url] - Read point cloud data from a ZDF file and visualize it.
  - **Dependencies:**
- - [pptk](https://github.com/heremaps/pptk) version 0.1.0 or newer
+ - [Open3D](http://www.open3d.org/) version 0.12.0 or newer
  - [numpy](https://numpy.org/) version 1.19.2 or newer
  - [matplotlib](https://matplotlib.org/) version 3.3.2 or newer
  - **file_formats**
@@ -71,7 +71,7 @@ There are two main categories of samples: **camera** and **applications**. The s
  - [**downsample**][downsample-url] - Downsample point cloud from a ZDF file.
  - **Dependencies:**
  - [numpy](https://numpy.org/) version 1.19.2 or newer
- - [pptk](https://github.com/heremaps/pptk) version 0.1.0 or newer
+ - [Open3D](http://www.open3d.org/) version 0.12.0 or newer
  - [**create_depth_map**][create_depth_map-url] - Read point cloud data from a ZDF file, convert it to OpenCV format, then extract and visualize depth map.
  - **Dependencies:**
  - [numpy](https://numpy.org/) version 1.19.2 or newer
@@ -80,7 +80,7 @@ There are two main categories of samples: **camera** and **applications**. The s
  - **Dependencies:**
  - [numpy](https://numpy.org/) version 1.19.2 or newer
  - [matplotlib](https://matplotlib.org/) version 3.3.2 or newer
- - [pptk](https://github.com/heremaps/pptk) version 0.1.0 or newer
+ - [Open3D](http://www.open3d.org/) version 0.12.0 or newer
  - [**gamma_correction**][gamma_correction-url] - Capture 2D image with gamma correction.
  - **Dependencies:**
  - [numpy](https://numpy.org/) version 1.19.2 or newer

requirements.txt

@@ -3,6 +3,6 @@ matplotlib
 vtk
 vtk_visualizer
 opencv-python
-pptk
+open3d
 pyyaml
 scipy

source/applications/advanced/downsample.py

@@ -4,60 +4,41 @@
 The ZDF files for this sample can be found under the main instructions for Zivid samples.
 """
 
-import math
 from pathlib import Path
 import numpy as np
-import pptk
+import open3d as o3d
 import zivid
 from sample_utils.paths import get_sample_data_path
 
 
-def _get_mid_point(xyz):
- """Calculate mid point from average of the 100 centermost points.
+def _display_pointcloud(xyz, rgb):
+ """Display point cloud.
 
  Args:
+ rgb: RGB image
  xyz: X, Y and Z images (point cloud co-ordinates)
 
- Returns:
- mid_point: Calculated mid point
+ Returns None
 
  """
- offset = 5
- xyz_center_cube = xyz[
- int(xyz.shape[0] / 2 - offset) : int(xyz.shape[0] / 2 + offset),
- int(xyz.shape[1] / 2 - offset) : int(xyz.shape[1] / 2 + offset),
- :,
- ]
- return (
- np.nanmedian(xyz_center_cube[:, :, 0]),
- np.nanmedian(xyz_center_cube[:, :, 1]),
- np.nanmedian(xyz_center_cube[:, :, 2]),
- )
+ xyz = np.nan_to_num(xyz).reshape(-1, 3)
+ rgb = rgb.reshape(-1, 3)
 
+ point_cloud_open3d = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(xyz))
+ point_cloud_open3d.colors = o3d.utility.Vector3dVector(rgb / 255)
 
-def _visualize_point_cloud(point_cloud):
- """Visualize point cloud.
+ visualizer = o3d.visualization.Visualizer() # pylint: disable=no-member
+ visualizer.create_window()
+ visualizer.add_geometry(point_cloud_open3d)
 
- Visualize the provided point cloud `xyz`, and color it with `rgb`.
+ visualizer.get_render_option().background_color = (0, 0, 0)
+ visualizer.get_render_option().point_size = 1
+ visualizer.get_render_option().show_coordinate_frame = True
+ visualizer.get_view_control().set_front([0, 0, -1])
+ visualizer.get_view_control().set_up([0, -1, 0])
 
- We take the centermost co-ordinate as 'lookat' point. We assume that camera location is at azimuth -pi/2 and
- elevation -pi/2 relative to the 'lookat' point.
-
- Args:
- point_cloud: Zivid point cloud
- """
- xyz = point_cloud.copy_data("xyz")
- rgb = point_cloud.copy_data("rgba")[:, :, 0:3]
-
- mid_point = _get_mid_point(xyz)
-
- # Setting nans to zeros
- xyz[np.isnan(xyz[:, :, 2])] = 0
-
- viewer = pptk.viewer(xyz)
- viewer.attributes(rgb.reshape(-1, 3) / 255.0)
- viewer.set(lookat=mid_point)
- viewer.set(phi=-math.pi / 2, theta=-math.pi / 2, r=mid_point[2])
+ visualizer.run()
+ visualizer.destroy_window()
 
 
 def _main():
@@ -69,17 +50,21 @@ def _main():
  frame = zivid.Frame(data_file)
 
  point_cloud = frame.point_cloud()
+ xyz = point_cloud.copy_data("xyz")
+ rgba = point_cloud.copy_data("rgba")
 
  print(f"Before downsampling: {point_cloud.width * point_cloud.height} point cloud")
 
- _visualize_point_cloud(point_cloud)
+ _display_pointcloud(xyz, rgba[:, :, 0:3])
 
  print("Downsampling point cloud")
  point_cloud.downsample(zivid.PointCloud.Downsampling.by2x2)
+ xyz_donwsampled = point_cloud.copy_data("xyz")
+ rgba_downsampled = point_cloud.copy_data("rgba")
 
  print(f"After downsampling: {point_cloud.width * point_cloud.height} point cloud")
 
- _visualize_point_cloud(point_cloud)
+ _display_pointcloud(xyz_donwsampled, rgba_downsampled[:, :, 0:3])
 
  input("Press Enter to close...")
 

source/applications/advanced/mask_point_cloud.py

@@ -4,11 +4,10 @@
 The ZDF file for this sample can be found under the main instructions for Zivid samples.
 """
 
-import math
 from pathlib import Path
 import numpy as np
 import matplotlib.pyplot as plt
-import pptk
+import open3d as o3d
 import zivid
 
 from sample_utils.paths import get_sample_data_path
@@ -51,52 +50,34 @@ def _display_depthmap(xyz):
  plt.show(block=False)
 
 
-def _get_mid_point(xyz):
- """Calculate mid point from average of the 100 centermost points.
+def _display_pointcloud(xyz, rgb):
+ """Display point cloud provided from 'xyz' with colors from 'rgb'.
 
  Args:
+ rgb: RGB image
  xyz: X, Y and Z images (point cloud co-ordinates)
 
- Returns:
- mid_point: Calculated mid point
+ Returns None
 
  """
- offset = 5
- xyz_center_cube = xyz[
- int(xyz.shape[0] / 2 - offset) : int(xyz.shape[0] / 2 + offset),
- int(xyz.shape[1] / 2 - offset) : int(xyz.shape[1] / 2 + offset),
- :,
- ]
- return (
- np.nanmedian(xyz_center_cube[:, :, 0]),
- np.nanmedian(xyz_center_cube[:, :, 1]),
- np.nanmedian(xyz_center_cube[:, :, 2]),
- )
+ xyz = np.nan_to_num(xyz).reshape(-1, 3)
+ rgb = rgb.reshape(-1, 3)
 
+ point_cloud_open3d = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(xyz))
+ point_cloud_open3d.colors = o3d.utility.Vector3dVector(rgb / 255)
 
-def _display_pointcloud(rgb, xyz):
- """Display point cloud.
+ visualizer = o3d.visualization.Visualizer() # pylint: disable=no-member
+ visualizer.create_window()
+ visualizer.add_geometry(point_cloud_open3d)
 
- Display the provided point cloud `xyz`, and color it with `rgb`.
+ visualizer.get_render_option().background_color = (0, 0, 0)
+ visualizer.get_render_option().point_size = 1
+ visualizer.get_render_option().show_coordinate_frame = True
+ visualizer.get_view_control().set_front([0, 0, -1])
+ visualizer.get_view_control().set_up([0, -1, 0])
 
- We take the centermost co-ordinate as 'lookat' point. We assume that
- camera location is at azimuth -pi/2 and elevation -pi/2 relative to
- the 'lookat' point.
-
- Args:
- rgb: RGB image
- xyz: X, Y and Z images (point cloud co-ordinates)
-
- Returns None
-
- """
- mid_point = _get_mid_point(xyz)
- point_cloud_to_view = xyz
- point_cloud_to_view[np.isnan(xyz[:, :, 2])] = 0
- viewer = pptk.viewer(point_cloud_to_view)
- viewer.attributes(rgb.reshape(-1, 3) / 255.0)
- viewer.set(lookat=mid_point)
- viewer.set(phi=-math.pi / 2, theta=-math.pi / 2, r=mid_point[2])
+ visualizer.run()
+ visualizer.destroy_window()
 
 
 def _main():
@@ -125,15 +106,15 @@ def _main():
  _display_rgb(rgba[:, :, 0:3], "RGB image")
 
  _display_depthmap(xyz)
- _display_pointcloud(rgba[:, :, 0:3], xyz)
+ _display_pointcloud(xyz, rgba[:, :, 0:3])
  input("Press Enter to continue...")
 
  print("Masking point cloud")
  xyz_masked = xyz.copy()
  xyz_masked[mask == 0] = np.nan
 
  _display_depthmap(xyz_masked)
- _display_pointcloud(rgba[:, :, 0:3], xyz_masked)
+ _display_pointcloud(xyz_masked, rgba[:, :, 0:3])
  input("Press Enter to close...")
 
 

source/applications/basic/visualization/read_zdf_vis_3d.py

@@ -4,12 +4,11 @@
 The ZDF file for this sample can be found under the main instructions for Zivid samples.
 """
 
-import math
 from pathlib import Path
 import numpy as np
 import matplotlib.pyplot as plt
-import pptk
 import zivid
+import open3d as o3d
 
 from sample_utils.paths import get_sample_data_path
 
@@ -50,51 +49,34 @@ def _display_depthmap(xyz):
  plt.show(block=False)
 
 
-def _get_mid_point(xyz):
- """Calculate mid point from average of the 100 centermost points.
+def _display_pointcloud(xyz, rgb):
+ """Display point cloud provided from 'xyz' with colors from 'rgb'.
 
  Args:
+ rgb: RGB image
  xyz: X, Y and Z images (point cloud co-ordinates)
 
- Returns:
- mid_point: Calculated mid point
+ Returns None
 
  """
- xyz_center_cube = xyz[
- int(xyz.shape[0] / 2 - 5) : int(xyz.shape[0] / 2 + 5),
- int(xyz.shape[1] / 2 - 5) : int(xyz.shape[1] / 2 + 5),
- :,
- ]
- return (
- np.nanmedian(xyz_center_cube[:, :, 0]),
- np.nanmedian(xyz_center_cube[:, :, 1]),
- np.nanmedian(xyz_center_cube[:, :, 2]),
- )
+ xyz = np.nan_to_num(xyz).reshape(-1, 3)
+ rgb = rgb.reshape(-1, 3)
 
+ point_cloud_open3d = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(xyz))
+ point_cloud_open3d.colors = o3d.utility.Vector3dVector(rgb / 255)
 
-def _display_pointcloud(rgb, xyz):
- """Display point cloud.
+ visualizer = o3d.visualization.Visualizer() # pylint: disable=no-member
+ visualizer.create_window()
+ visualizer.add_geometry(point_cloud_open3d)
 
- Display the provided point cloud `xyz`, and color it with `rgb`.
+ visualizer.get_render_option().background_color = (0, 0, 0)
+ visualizer.get_render_option().point_size = 1
+ visualizer.get_render_option().show_coordinate_frame = True
+ visualizer.get_view_control().set_front([0, 0, -1])
+ visualizer.get_view_control().set_up([0, -1, 0])
 
- We take the centermost co-ordinate as 'lookat' point. We assume that
- camera location is at azimuth -pi/2 and elevation -pi/2 relative to
- the 'lookat' point.
-
- Args:
- rgb: RGB image
- xyz: X, Y and Z images (point cloud co-ordinates)
-
- Returns None
-
- """
- mid_point = _get_mid_point(xyz)
- point_cloud_to_view = xyz
- point_cloud_to_view[np.isnan(xyz[:, :, 2])] = 0
- viewer = pptk.viewer(point_cloud_to_view)
- viewer.attributes(rgb.reshape(-1, 3) / 255.0)
- viewer.set(lookat=mid_point)
- viewer.set(phi=-math.pi / 2, theta=-math.pi / 2, r=mid_point[2])
+ visualizer.run()
+ visualizer.destroy_window()
 
 
 def _main():
@@ -114,7 +96,7 @@ def _main():
 
  _display_depthmap(xyz)
 
- _display_pointcloud(rgba[:, :, 0:3], xyz)
+ _display_pointcloud(xyz, rgba[:, :, 0:3])
 
  input("Press Enter to close...")