Visualisation of pointcloud data

README.md

@@ -74,7 +74,12 @@ Preprocessed ShapeNetPart dataset (XYZ, normal and part labels) can be found <a
 See `scannet/README` and `scannet/train.py` for details.
 
 #### Visualization Tools
-We have provided a handy point cloud visualization tool under `utils`. Run `sh compile_render_balls_so.sh` to compile it and then you can try the demo with `python show3d_balls.py` The original code is from <a href="http://github.com/fanhqme/PointSetGeneration">here</a>.
+We have provided a handy point cloud visualization tool under `utils`. Run `sh compile_render_balls_so.sh` to compile it
+Then you can try the demo  with
+```bash
+python show3d_balls.py path_to_pointcloud.txt
+```
+The original code is from <a href="http://github.com/fanhqme/PointSetGeneration">here</a>.
 
 #### Prepare Your Own Data
 You can refer to <a href="https://github.com/charlesq34/3dmodel_feature/blob/master/io/write_hdf5.py">here</a> on how to prepare your own HDF5 files for either classification or segmentation. Or you can refer to `modelnet_dataset.py` on how to read raw data files and prepare mini-batches from them. A more advanced way is to use TensorFlow's dataset APIs, for which you can find more documentations <a href="https://www.tensorflow.org/programmers_guide/datasets">here</a>.

utils/show3d_balls.py

@@ -4,6 +4,8 @@
 import cv2
 import sys
 import os
+from pprint import pprint
+
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 showsz=800
 mousex,mousey=0.5,0.5
@@ -103,9 +105,9 @@ def render():
             changed=False
         cv2.imshow('show3d',show)
         if waittime==0:
-            cmd=cv2.waitKey(10)%256
+            cmd=cv2.waitKey(10) % 256
         else:
-            cmd=cv2.waitKey(waittime)%256
+            cmd=cv2.waitKey(waittime) % 256
         if cmd==ord('q'):
             break
         elif cmd==ord('Q'):
@@ -157,5 +159,20 @@ def render():
     return cmd
 if __name__=='__main__':
     np.random.seed(100)
-    showpoints(np.random.randn(2500,3))
+    if len(sys.argv) < 2:
+        exit('Enter pointcloud path')
+    path = sys.argv[1]
+
+    point_set = np.loadtxt(path ,delimiter=',').astype(np.float32)
+    random_idx = np.random.randint(point_set.shape[0], size=1024)
+    print(point_set.shape)
+    point_set = point_set[0:1024,0:3]
+
+    #point_set = point_set[random_idx,0:3]
+    #pprint(point_set)
+    #pprint(np.random.randn(2500,3))
+
+    #showpoints(np.random.randn(2500,3))
+    showpoints(point_set)
+