Merge pull request #25 from dashidhy/master

improve installation, support remote usage

README.md

@@ -23,19 +23,29 @@ kitti
             velodyne
 ```
 
-## Requirements
-
-  - opencv
+## Install locally on a Ubuntu 16.04 PC with GUI
+- start from a new conda enviornment:
 ```
-conda install opencv -c menpo
+(base)$ conda create -n kitti_vis python=3.7 # vtk does not support python 3.8
+(base)$ conda activate kitti_vis
 ```
-  - mayavi, vtk, PIL
-
+- opencv, pillow, scipy, matplotlib
+```
+(kitti_vis)$ pip install opencv-python pillow scipy matplotlib
+```
+- install mayavi from conda-forge, this installs vtk and pyqt5 automatically
 ```
-pip install mayavi vtk pillow pyqt5
-# or
-conda install mayavi vtk pillow pyqt5
+(kitti_vis)$ conda install mayavi -c conda-forge
 ```
+- test installation
+```
+(kitti_vis)$ python kitti_object.py --show_lidar_with_depth --img_fov --const_box --vis
+```
+
+**Note: the above installation has been tested not work on MacOS.**
+
+## Install remotely
+Please refer to the [jupyter](jupyter/) folder for installing on a remote server and visulizing in Jupyter Notebook.
 
 ## Visualization
 

jupyter/README.md

@@ -0,0 +1,38 @@
+# Install remotely on a Ubuntu 16.04 server and visulize by Jupyter Notebook
+## Install mayavi
+- remote servers usually lack GUI supports, so first some libraries and backends should be installed
+```
+(base)$ sudo apt-get update && sudo apt-get install libgl1-mesa-glx xvfb
+```
+- install like locally, add [xvfbwrapper](https://github.com/enthought/mayavi/issues/477#issuecomment-477653210)
+```
+(base)$ conda create -n kitti_vis python=3.7
+(base)$ conda activate kitti_vis
+(kitti_vis)$ pip install opencv-python pillow scipy matplotlib xvfbwrapper
+(kitti_vis)$ conda install mayavi -c conda-forge
+```
+- install jupyter notebook
+```
+(kitti_vis)$ pip install notebook
+```
+- install ipywidgets and ipyevents, enable them as notebook extensions
+```
+(kitti_vis)$ pip install ipywidgets ipyevents
+(kitti_vis)$ jupyter nbextension enable --py --sys-prefix widgetsnbextension
+(kitti_vis)$ jupyter nbextension enable --py --sys-prefix ipyevents
+```
+- install and enable mayavi extension
+```
+(kitti_vis)$ jupyter nbextension install --py --sys-prefix mayavi
+(kitti_vis)$ jupyter nbextension enable --py --sys-prefix mayavi
+```
+- to test on Jupyter notebook, first set [ETS_TOOLKIT](https://github.com/enthought/mayavi/issues/439#issuecomment-251703994)
+```
+(kitti_vis)$ export ETS_TOOLKIT='null'
+```
+- then you can test mayavi intallation by [this notebook](test_mayavi.ipynb), which should show an interactive 3D curve
+
+## Use Jupyter Notebook for kitti visulization
+- Here is a simple [demo](../notebook_demo.ipynb).
+
+**Note: the above installation is tested successful on [an official PyTorch docker image](https://hub.docker.com/layers/pytorch/pytorch/1.5-cuda10.1-cudnn7-devel/images/sha256-449d5b98aa911955f55cb3ab413b81b34861ab5f4244519099328df282275690?context=explore).**

kitti_object.py

@@ -67,7 +67,7 @@ def get_image(self, idx):
         img_filename = os.path.join(self.image_dir, "%06d.png" % (idx))
         return utils.load_image(img_filename)
 
-    def get_lidar(self, idx, dtype=np.float64, n_vec=4):
+    def get_lidar(self, idx, dtype=np.float32, n_vec=4):
         assert idx < self.num_samples
         lidar_filename = os.path.join(self.lidar_dir, "%06d.bin" % (idx))
         print(lidar_filename)
@@ -170,7 +170,7 @@ def viz_kitti_video():
         video_path,
     )
     print(len(dataset))
-    for i in range(len(dataset)):
+    for _ in range(len(dataset)):
         img = dataset.get_image(0)
         pc = dataset.get_lidar(0)
         cv2.imshow("video", img)
@@ -186,7 +186,7 @@ def show_image_with_boxes(img, objects, calib, show3d=True, depth=None):
     """ Show image with 2D bounding boxes """
     img1 = np.copy(img)  # for 2d bbox
     img2 = np.copy(img)  # for 3d bbox
-    img3 = np.copy(img)  # for 3d bbox
+    #img3 = np.copy(img)  # for 3d bbox
     for obj in objects:
         if obj.type == "DontCare":
             continue
@@ -197,7 +197,7 @@ def show_image_with_boxes(img, objects, calib, show3d=True, depth=None):
             (0, 255, 0),
             2,
         )
-        box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
+        box3d_pts_2d, _ = utils.compute_box_3d(obj, calib.P)
         img2 = utils.draw_projected_box3d(img2, box3d_pts_2d)
 
         # project
@@ -215,6 +215,8 @@ def show_image_with_boxes(img, objects, calib, show3d=True, depth=None):
         cv2.imshow("3dbox", img2)
     if depth is not None:
         cv2.imshow("depth", depth)
+
+    return img1, img2
 
 
 def show_image_with_boxes_3type(img, objects, calib, objects2d, name, objects_pred):
@@ -357,6 +359,8 @@ def show_lidar_with_depth(
 ):
     """ Show all LiDAR points.
         Draw 3d box in LiDAR point cloud (in velo coord system) """
+    if "mlab" not in sys.modules:
+        import mayavi.mlab as mlab
     from viz_util import draw_lidar_simple, draw_lidar, draw_gt_boxes3d
 
     print(("All point num: ", pc_velo.shape[0]))
@@ -394,7 +398,7 @@ def show_lidar_with_depth(
         if obj.type == "DontCare":
             continue
         # Draw 3d bounding box
-        box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
+        _, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
         box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
         print("box3d_pts_3d_velo:")
         print(box3d_pts_3d_velo)
@@ -407,13 +411,13 @@ def show_lidar_with_depth(
             if obj.type == "DontCare":
                 continue
             # Draw 3d bounding box
-            box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
+            _, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
             box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
             print("box3d_pts_3d_velo:")
             print(box3d_pts_3d_velo)
             draw_gt_boxes3d([box3d_pts_3d_velo], fig=fig, color=color)
             # Draw heading arrow
-            ori3d_pts_2d, ori3d_pts_3d = utils.compute_orientation_3d(obj, calib.P)
+            _, ori3d_pts_3d = utils.compute_orientation_3d(obj, calib.P)
             ori3d_pts_3d_velo = calib.project_rect_to_velo(ori3d_pts_3d)
             x1, y1, z1 = ori3d_pts_3d_velo[0, :]
             x2, y2, z2 = ori3d_pts_3d_velo[1, :]
@@ -530,7 +534,7 @@ def show_lidar_with_boxes(
         if obj.type == "DontCare":
             continue
         # Draw 3d bounding box
-        box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
+        _, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
         box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
         print("box3d_pts_3d_velo:")
         print(box3d_pts_3d_velo)
@@ -550,10 +554,9 @@ def show_lidar_with_boxes(
             print("dep_pc_velo:", dep_pc_velo)
 
             draw_lidar(dep_pc_velo, fig=fig, pts_color=(1, 1, 1))
-        #
 
         # Draw heading arrow
-        ori3d_pts_2d, ori3d_pts_3d = utils.compute_orientation_3d(obj, calib.P)
+        _, ori3d_pts_3d = utils.compute_orientation_3d(obj, calib.P)
         ori3d_pts_3d_velo = calib.project_rect_to_velo(ori3d_pts_3d)
         x1, y1, z1 = ori3d_pts_3d_velo[0, :]
         x2, y2, z2 = ori3d_pts_3d_velo[1, :]
@@ -572,13 +575,13 @@ def show_lidar_with_boxes(
             if obj.type == "DontCare":
                 continue
             # Draw 3d bounding box
-            box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
+            _, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
             box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
             print("box3d_pts_3d_velo:")
             print(box3d_pts_3d_velo)
             draw_gt_boxes3d([box3d_pts_3d_velo], fig=fig, color=color)
             # Draw heading arrow
-            ori3d_pts_2d, ori3d_pts_3d = utils.compute_orientation_3d(obj, calib.P)
+            _, ori3d_pts_3d = utils.compute_orientation_3d(obj, calib.P)
             ori3d_pts_3d_velo = calib.project_rect_to_velo(ori3d_pts_3d)
             x1, y1, z1 = ori3d_pts_3d_velo[0, :]
             x2, y2, z2 = ori3d_pts_3d_velo[1, :]
@@ -629,14 +632,15 @@ def stat_lidar_with_boxes(pc_velo, objects, calib):
     for obj in objects:
         if obj.type == "DontCare":
             continue
-        box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
+        _, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
         box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
         v_l, v_w, v_h, _ = get_velo_whl(box3d_pts_3d_velo, pc_velo)
         print("%.4f %.4f %.4f %s" % (v_w, v_h, v_l, obj.type))
 
 
 def show_lidar_on_image(pc_velo, img, calib, img_width, img_height):
     """ Project LiDAR points to image """
+    img =  np.copy(img)
     imgfov_pc_velo, pts_2d, fov_inds = get_lidar_in_image_fov(
         pc_velo, calib, 0, 0, img_width, img_height, True
     )
@@ -671,7 +675,7 @@ def show_lidar_topview_with_boxes(pc_velo, objects, calib, objects_pred=None):
     # gt
 
     def bbox3d(obj):
-        box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
+        _, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
         box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
         return box3d_pts_3d_velo
 
@@ -692,12 +696,13 @@ def bbox3d(obj):
         )
 
     cv2.imshow("top_image", top_image)
+    return top_image
 
 
 def dataset_viz(root_dir, args):
     dataset = kitti_object(root_dir, split=args.split, args=args)
     ## load 2d detection results
-    objects2ds = read_det_file("box2d.list")
+    #objects2ds = read_det_file("box2d.list")
 
     if args.show_lidar_with_depth:
         import mayavi.mlab as mlab
@@ -713,7 +718,7 @@ def dataset_viz(root_dir, args):
             objects = dataset.get_label_objects(data_idx)
         else:
             objects = []
-        objects2d = objects2ds[data_idx]
+        #objects2d = objects2ds[data_idx]
 
         objects_pred = None
         if args.pred:
@@ -736,11 +741,11 @@ def dataset_viz(root_dir, args):
         pc_velo = dataset.get_lidar(data_idx, dtype, n_vec)[:, 0:n_vec]
         calib = dataset.get_calibration(data_idx)
         img = dataset.get_image(data_idx)
-        img_height, img_width, img_channel = img.shape
+        img_height, img_width, _ = img.shape
         print(data_idx, "image shape: ", img.shape)
         print(data_idx, "velo  shape: ", pc_velo.shape)
         if args.depth:
-            depth, is_exist = dataset.get_depth(data_idx)
+            depth, _ = dataset.get_depth(data_idx)
             print(data_idx, "depth shape: ", depth.shape)
         else:
             depth = None
@@ -806,9 +811,9 @@ def depth_to_lidar_format(root_dir, args):
 
         pc_velo = dataset.get_lidar(data_idx)[:, 0:4]
         calib = dataset.get_calibration(data_idx)
-        depth, is_exist = dataset.get_depth(data_idx)
+        depth, _ = dataset.get_depth(data_idx)
         img = dataset.get_image(data_idx)
-        img_height, img_width, img_channel = img.shape
+        img_height, img_width, _ = img.shape
         print(data_idx, "image shape: ", img.shape)
         print(data_idx, "velo  shape: ", pc_velo.shape)
         print(data_idx, "depth shape: ", depth.shape)
@@ -826,12 +831,12 @@ def depth_to_lidar_format(root_dir, args):
             depth,
             constraint_box=args.const_box,
         )
-        input_str = raw_input()
+        #input_str = raw_input()
 
 
 def read_det_file(det_filename):
     """ Parse lines in 2D detection output files """
-    det_id2str = {1: "Pedestrian", 2: "Car", 3: "Cyclist"}
+    #det_id2str = {1: "Pedestrian", 2: "Car", 3: "Cyclist"}
     objects = {}
     with open(det_filename, "r") as f:
         for line in f.readlines():

kitti_util.py

@@ -414,17 +414,14 @@ def load_velo_scan(velo_filename, dtype=np.float32, n_vec=4):
     return scan
 
 
-def lidar_to_top_coords(x, y, z=None):
-    if 0:
-        return x, y
-    else:
-        # print("TOP_X_MAX-TOP_X_MIN:",TOP_X_MAX,TOP_X_MIN)
-        X0, Xn = 0, int((TOP_X_MAX - TOP_X_MIN) // TOP_X_DIVISION) + 1
-        Y0, Yn = 0, int((TOP_Y_MAX - TOP_Y_MIN) // TOP_Y_DIVISION) + 1
-        xx = Yn - int((y - TOP_Y_MIN) // TOP_Y_DIVISION)
-        yy = Xn - int((x - TOP_X_MIN) // TOP_X_DIVISION)
+def lidar_to_top_coords(x, y):
+    # print("TOP_X_MAX-TOP_X_MIN:",TOP_X_MAX,TOP_X_MIN)
+    Xn = int((TOP_X_MAX - TOP_X_MIN) // TOP_X_DIVISION) + 1
+    Yn = int((TOP_Y_MAX - TOP_Y_MIN) // TOP_Y_DIVISION) + 1
+    xx = Yn - int((y - TOP_Y_MIN) // TOP_Y_DIVISION)
+    yy = Xn - int((x - TOP_X_MIN) // TOP_X_DIVISION)
 
-        return xx, yy
+    return xx, yy
 
 
 def lidar_to_top(lidar):
@@ -789,7 +786,7 @@ def linear_regression(train_x, train_y, test_x):
 
     # dump fit result
     dump_fit_func(w_fit)
-    fit_cost = dump_fit_cost(w_fit, train_x, train_y)
+    dump_fit_cost(w_fit, train_x, train_y)
 
     # test set
     # test_x = np.array(np.arange(train_x.min(), train_x.max(), 1.0))

viz_util.py

@@ -57,7 +57,7 @@ def rotation_matrix_numpy0(axis, theta, dtype=None):
 
 
 def rotation_matrix_numpy(axis, theta):
-    mat = np.eye(3, 3)
+
     axis = axis / np.sqrt(np.dot(axis, axis))
     a = np.cos(theta / 2.0)
     b, c, d = -axis * np.sin(theta / 2.0)
@@ -261,8 +261,8 @@ def draw_lidar(
     TOP_Y_MAX = 20
     TOP_X_MIN = 0
     TOP_X_MAX = 40
-    TOP_Z_MIN = -2.0
-    TOP_Z_MAX = 0.4
+    #TOP_Z_MIN = -2.0
+    #TOP_Z_MAX = 0.4
 
     x1 = TOP_X_MIN
     x2 = TOP_X_MAX