RameshKamath · February 25, 2020 00:01 · Apr 27, 2018 · Mar 15, 2018 · Mar 15, 2018 · Mar 15, 2018
diff --git a/kitti_lidar.py b/kitti_lidar.py
@@ -41,7 +41,7 @@
 
 
 else:
-    # Plot Using Matplotlib - Much slower than mayavi and visuals are not as goog as mayavi.
+    # Plot Using Matplotlib - Much slower than mayavi and visuals are not as good as mayavi.
     import matplotlib.pyplot as plt
     from mpl_toolkits.mplot3d import Axes3D
     fig = plt.figure()

diff --git a/kitti_lidar.py b/kitti_lidar.py
@@ -45,7 +45,6 @@
     import matplotlib.pyplot as plt
     from mpl_toolkits.mplot3d import Axes3D
     fig = plt.figure()
-    fig.plot(range(10))
 
 # Raw Data directory information
 basedir = '/home/ramesh/Documents/kitti_data'

diff --git a/kitti_lidar.py b/kitti_lidar.py
@@ -27,7 +27,7 @@
 if VISLIB == "mayavi":
     # Plot using mayavi -Much faster and smoother than matplotlib
     # is built for 3D scientific data visualization and plotting
-    import mayavi.mlab
+    from mayavi import mlab
     """
     I like to use python3 so i am giving installtion help for python3 (python2 might be similar):
     
@@ -44,6 +44,8 @@
     # Plot Using Matplotlib - Much slower than mayavi and visuals are not as goog as mayavi.
     import matplotlib.pyplot as plt
     from mpl_toolkits.mplot3d import Axes3D
+    fig = plt.figure()
+    fig.plot(range(10))
 
 # Raw Data directory information
 basedir = '/home/ramesh/Documents/kitti_data'
@@ -53,44 +55,46 @@
 # Optionally, specify the frame range to load
 # since we are only visualizing one frame, we will restrict what we load
 # Set to None to use all the data
-frame_range = range(150, 151, 1)
+frame_range = range(0, 151, 1)
 
 # Load the data
 dataset = pykitti.raw(basedir, date, drive, frame = frame_range)
 
+l=True #to loop or not
+n = 1  #keeping track of frames
 
-n = 1                        #keeping track of frames
 # Plot only the ith frame (out of what has been loaded)
 i = 3
 
 #same as "velo = dataset.velo[i]"
-for velo in dataset.velo:
-    if n == i:
+for velo in dataset.velo:  #velo=[[x y z reflectance][....][....]....]
+    if l:
+       i=5  # changing limit of looped frame
+    if n == i or l:
         if VISLIB == "mayavi":
-            fig = mayavi.mlab.figure(bgcolor=(0, 0, 0), size=(640, 360))
-            mayavi.mlab.points3d(
+            fig = mlab.figure(bgcolor=(0, 0, 0), size=(640, 360))            
+            mlab.points3d(
                 velo[:, 0],   # x
-                velo[:, 1],   # y
-                velo[:, 2],   # z
+                velo[:, 1],   # y 
+                velo[:, 2],   # z (height)
                 velo[:, 2],   # Height data used for shading
+                #velo[:, 3], # reflectance values
                 mode="point", # How to render each point {'point', 'sphere' , 'cube' }
-                colormap='spectral',  # 'bone', 'copper',
+                colormap='spectral',  #'bone', 'copper','spectral','hsv','hot','CMRmap','Blues'
+
                 #color=(0, 1, 0),     # Used a fixed (r,g,b) color instead of colormap
                 scale_factor=100,     # scale of the points
                 line_width=10,        # Scale of the line, if any
                 figure=fig,
             )
-            # velo[:, 3], # reflectance values
-            mayavi.mlab.show()
+            mlab.show()
             if i==n:
                 break
             n=n+1
         else:
             # NOTE: Only 1 out of every 100 points are plotted using the matplotlib
             #       version to prevent crashing the computer
             skip = 50 # plot one in every `skip` points
-
-            fig = plt.figure()
             ax = fig.add_subplot(111, projection='3d')
             velo_range = range(0, velo.shape[0], skip) # skip points to prevent crash
             ax.scatter(velo[velo_range, 0],   # x

diff --git a/kitti_lidar.py b/kitti_lidar.py
@@ -14,17 +14,39 @@
  - Mayavi     - Much faster, and looks nicer.
               - Preserves actual scale along each axes so items look
                 recognizable
+
 """
 import pykitti  # install using pip install pykitti
 import os
 import numpy as np
 
 # Chose which visualization library to use:  "mayavi" or "matplotlib"
-# VISLIB = "mayavi"
-VISLIB = "matplotlib"
+VISLIB = "mayavi"
+#VISLIB = "matplotlib"
+
+if VISLIB == "mayavi":
+    # Plot using mayavi -Much faster and smoother than matplotlib
+    # is built for 3D scientific data visualization and plotting
+    import mayavi.mlab
+    """
+    I like to use python3 so i am giving installtion help for python3 (python2 might be similar):
+    
+    pip3 install mayavi
+   
+    sudo apt-get install build-essential git cmake libqt4-dev libphonon-dev python3-dev libxml2-dev libxslt1-dev qtmobility-dev libqtwebkit-dev
+
+    sudo apt-get install python3-pyqt4 (mayavi needs pyqt4 to work)
+    """
+
+
+
+else:
+    # Plot Using Matplotlib - Much slower than mayavi and visuals are not as goog as mayavi.
+    import matplotlib.pyplot as plt
+    from mpl_toolkits.mplot3d import Axes3D
 
 # Raw Data directory information
-basedir = '/home/dodo_brain/kitti_data/'
+basedir = '/home/ramesh/Documents/kitti_data'
 date = '2011_09_26'
 drive = '0005'
 
@@ -34,52 +56,53 @@
 frame_range = range(150, 151, 1)
 
 # Load the data
-dataset = pykitti.raw(basedir, date, drive, frame_range)
+dataset = pykitti.raw(basedir, date, drive, frame = frame_range)
 
-# Load Lidar Data
-dataset.load_velo()         # Each scan is a Nx4 array of [x,y,z,reflectance]
 
+n = 1                        #keeping track of frames
 # Plot only the ith frame (out of what has been loaded)
-i = 0
-velo = dataset.velo[i]
-
-if VISLIB == "mayavi":
-    # Plot using mayavi -Much faster and smoother than matplotlib
-    import mayavi.mlab
-
-    fig = mayavi.mlab.figure(bgcolor=(0, 0, 0), size=(640, 360))
-    mayavi.mlab.points3d(
-        velo[:, 0],   # x
-        velo[:, 1],   # y
-        velo[:, 2],   # z
-        velo[:, 2],   # Height data used for shading
-        mode="point", # How to render each point {'point', 'sphere' , 'cube' }
-        colormap='spectral',  # 'bone', 'copper',
-        #color=(0, 1, 0),     # Used a fixed (r,g,b) color instead of colormap
-        scale_factor=100,     # scale of the points
-        line_width=10,        # Scale of the line, if any
-        figure=fig,
-    )
-    # velo[:, 3], # reflectance values
-    mayavi.mlab.show()
-
-else:
-    # Plot Using Matplotlib - Much slower than mayavi.
-    # NOTE: Only 1 out of every 100 points are plotted using the matplotlib
-    #       version to prevent crashing the computer
-    import matplotlib.pyplot as plt
-    from mpl_toolkits.mplot3d import Axes3D
-
-    skip = 100 # plot one in every `skip` points
-
-    fig = plt.figure()
-    ax = fig.add_subplot(111, projection='3d')
-    velo_range = range(0, velo.shape[0], skip) # skip points to prevent crash
-    ax.scatter(velo[velo_range, 0],   # x
-               velo[velo_range, 1],   # y
-               velo[velo_range, 2],   # z
-               c=velo[velo_range, 3], # reflectance
-               cmap='gray')
-    ax.set_title('Lidar scan (subsampled)')
-    plt.show()
+i = 3
+
+#same as "velo = dataset.velo[i]"
+for velo in dataset.velo:
+    if n == i:
+        if VISLIB == "mayavi":
+            fig = mayavi.mlab.figure(bgcolor=(0, 0, 0), size=(640, 360))
+            mayavi.mlab.points3d(
+                velo[:, 0],   # x
+                velo[:, 1],   # y
+                velo[:, 2],   # z
+                velo[:, 2],   # Height data used for shading
+                mode="point", # How to render each point {'point', 'sphere' , 'cube' }
+                colormap='spectral',  # 'bone', 'copper',
+                #color=(0, 1, 0),     # Used a fixed (r,g,b) color instead of colormap
+                scale_factor=100,     # scale of the points
+                line_width=10,        # Scale of the line, if any
+                figure=fig,
+            )
+            # velo[:, 3], # reflectance values
+            mayavi.mlab.show()
+            if i==n:
+                break
+            n=n+1
+        else:
+            # NOTE: Only 1 out of every 100 points are plotted using the matplotlib
+            #       version to prevent crashing the computer
+            skip = 50 # plot one in every `skip` points
 
+            fig = plt.figure()
+            ax = fig.add_subplot(111, projection='3d')
+            velo_range = range(0, velo.shape[0], skip) # skip points to prevent crash
+            ax.scatter(velo[velo_range, 0],   # x
+                       velo[velo_range, 1],   # y
+                       velo[velo_range, 2],   # z
+                       c=velo[velo_range, 3], # reflectance
+                       cmap='gray')
+            ax.set_title('Lidar scan (subsampled)')
+            plt.show()
+
+            if i==n:
+                break
+            n=n+1
+    else:
+        n=n+1
diff --git a/kitti_lidar.py b/kitti_lidar.py
@@ -1,4 +1,6 @@
 """
+VISUALISE THE LIDAR DATA FROM THE KITTI DATASET
+
 Based on the sample code from
     https://github.com/utiasSTARS/pykitti/blob/master/demos/demo_raw.py
 And:
@@ -22,9 +24,9 @@
 VISLIB = "matplotlib"
 
 # Raw Data directory information
-basedir = '/home/dodo_brain/data/kitti_data'
-date = '2011_09_26' # Date of the rawdata trial 
-drive = '0005'      # Drive number of the raw data trial
+basedir = '/home/dodo_brain/kitti_data/'
+date = '2011_09_26'
+drive = '0005'
 
 # Optionally, specify the frame range to load
 # since we are only visualizing one frame, we will restrict what we load
@@ -45,17 +47,22 @@
     # Plot using mayavi -Much faster and smoother than matplotlib
     import mayavi.mlab
 
+    fig = mayavi.mlab.figure(bgcolor=(0, 0, 0), size=(640, 360))
     mayavi.mlab.points3d(
-        velo[:, 0], # x
-        velo[:, 1], # y
-        velo[:, 2], # z
-        mode="cube",
-        color=(0, 1, 0),
-        scale_factor=0.4
+        velo[:, 0],   # x
+        velo[:, 1],   # y
+        velo[:, 2],   # z
+        velo[:, 2],   # Height data used for shading
+        mode="point", # How to render each point {'point', 'sphere' , 'cube' }
+        colormap='spectral',  # 'bone', 'copper',
+        #color=(0, 1, 0),     # Used a fixed (r,g,b) color instead of colormap
+        scale_factor=100,     # scale of the points
+        line_width=10,        # Scale of the line, if any
+        figure=fig,
     )
-    # velo[velo_range, 3] # reflectance data
-
+    # velo[:, 3], # reflectance values
     mayavi.mlab.show()
+
 else:
     # Plot Using Matplotlib - Much slower than mayavi.
     # NOTE: Only 1 out of every 100 points are plotted using the matplotlib
@@ -75,3 +82,4 @@
                cmap='gray')
     ax.set_title('Lidar scan (subsampled)')
     plt.show()
+
diff --git a/kitti_lidar.py b/kitti_lidar.py
@@ -0,0 +1,77 @@
+"""
+Based on the sample code from
+    https://github.com/utiasSTARS/pykitti/blob/master/demos/demo_raw.py
+And:
+    http://stackoverflow.com/a/37863912
+
+Contains two methods of visualizing lidar data interactively.
+ - Matplotlib - very slow, and likely to crash, so only 1 out of every 100
+                points are plotted.
+              - Also, data looks VERY distorted due to auto scaling along
+                each axis. (this could potentially be edited)
+ - Mayavi     - Much faster, and looks nicer.
+              - Preserves actual scale along each axes so items look
+                recognizable
+"""
+import pykitti  # install using pip install pykitti
+import os
+import numpy as np
+
+# Chose which visualization library to use:  "mayavi" or "matplotlib"
+# VISLIB = "mayavi"
+VISLIB = "matplotlib"
+
+# Raw Data directory information
+basedir = '/home/dodo_brain/data/kitti_data'
+date = '2011_09_26' # Date of the rawdata trial 
+drive = '0005'      # Drive number of the raw data trial
+
+# Optionally, specify the frame range to load
+# since we are only visualizing one frame, we will restrict what we load
+# Set to None to use all the data
+frame_range = range(150, 151, 1)
+
+# Load the data
+dataset = pykitti.raw(basedir, date, drive, frame_range)
+
+# Load Lidar Data
+dataset.load_velo()         # Each scan is a Nx4 array of [x,y,z,reflectance]
+
+# Plot only the ith frame (out of what has been loaded)
+i = 0
+velo = dataset.velo[i]
+
+if VISLIB == "mayavi":
+    # Plot using mayavi -Much faster and smoother than matplotlib
+    import mayavi.mlab
+
+    mayavi.mlab.points3d(
+        velo[:, 0], # x
+        velo[:, 1], # y
+        velo[:, 2], # z
+        mode="cube",
+        color=(0, 1, 0),
+        scale_factor=0.4
+    )
+    # velo[velo_range, 3] # reflectance data
+
+    mayavi.mlab.show()
+else:
+    # Plot Using Matplotlib - Much slower than mayavi.
+    # NOTE: Only 1 out of every 100 points are plotted using the matplotlib
+    #       version to prevent crashing the computer
+    import matplotlib.pyplot as plt
+    from mpl_toolkits.mplot3d import Axes3D
+
+    skip = 100 # plot one in every `skip` points
+
+    fig = plt.figure()
+    ax = fig.add_subplot(111, projection='3d')
+    velo_range = range(0, velo.shape[0], skip) # skip points to prevent crash
+    ax.scatter(velo[velo_range, 0],   # x
+               velo[velo_range, 1],   # y
+               velo[velo_range, 2],   # z
+               c=velo[velo_range, 3], # reflectance
+               cmap='gray')
+    ax.set_title('Lidar scan (subsampled)')
+    plt.show()