Merge https://github.com/argoai/argoverse-api into upgrade-numpy

README.md

@@ -75,6 +75,14 @@ Make sure that you can run `python -c "import argoverse"` in python, and you are
 ### (optional) Install mayavi
 * Some visualizations may require `mayavi`. See instructions on how to install Mayavi [here](https://docs.enthought.com/mayavi/mayavi/installation.html).
 
+### (optional) Stereo tutorial dependencies
+* You will need to install four dependencies to run the [stereo tutorial](https://github.com/argoai/argoverse-api/blob/master/demo_usage/competition_stereo_tutorial.ipynb):
+
+    * **Open3D**: See instructions on how to install [here](https://github.com/intel-isl/Open3D).
+    * **OpenCV contrib**: See instructions on how to install [here](https://pypi.org/project/opencv-contrib-python).
+    * **Plotly**: See instructions on how to install [here](https://github.com/plotly/plotly.py).
+    * **Numba**: See instructions on how to install [here](http://numba.pydata.org/).
+
 ### (optional) Remake the object-oriented label folders
 * The `track_labels_amodal` folders contains object-oriented labels (in contrast to per-frame labels in `per_sweep_annotations_amodal` folders. Run following script to remake `track_labels_amodal` folders and fix existing issues:
 

argoverse/evaluation/stereo/utils.py

@@ -5,10 +5,10 @@
 from typing import List
 
 import cv2
-import disparity_interpolation
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+from numba import njit
 
 from argoverse.evaluation.stereo.constants import (
     DEFAULT_ABS_ERROR_THRESHOLDS,
@@ -128,7 +128,8 @@ def accumulate_stereo_metrics(abs_error_thresholds: List[int]) -> pd.DataFrame:
     return pd.DataFrame([[0] * num_fields], columns=columns)
 
 
-def interpolate_disparity(disp: np.ndarray) -> np.ndarray:
+@njit(nogil=True)  # type: ignore
+def interpolate_disparity(disparity: np.ndarray) -> np.ndarray:
     """Interpolate disparity image to inpaint holes.
 
     The predicted disparity map might contain holes which need to be interpolated for a dense result.
@@ -140,15 +141,64 @@ def interpolate_disparity(disp: np.ndarray) -> np.ndarray:
     The expected run time for the Argoverse stereo image with 2056 × 2464 pixels is ~50 ms.
 
     Args:
-        disp: Array of shape (M, N) representing a float32 single-channel disparity map.
+        disparity: Array of shape (M, N) representing a float32 single-channel disparity map.
 
     Returns:
-        disp_interp: Array of shape (M, N) representing a float32 single-channel interpolated disparity map.
+        disparity: Array of shape (M, N) representing a float32 single-channel interpolated disparity map.
     """
-    disp[disp == 0] = -1
-    disp_interp = disparity_interpolation.disparity_interpolator(disp)
-
-    return disp_interp
+    height, width = disparity.shape
+
+    disparity = np.where(disparity == 0, -1, disparity)  # Set invalid disparities to -1
+
+    for v in range(height):
+        count = 0
+        for u in range(width):
+            # If disparity is valid
+            if disparity[v, u] >= 0:
+                #  At least one pixel requires interpolation
+                if count >= 1:
+                    # First and last value for interpolation
+                    u1 = u - count
+                    u2 = u - 1
+                    # Set pixel to min disparity
+                    if u1 > 0 and u2 < width - 1:
+                        d_ipol = min(disparity[v, u1 - 1], disparity[v, u2 + 1])
+                        for u_curr in range(u1, u2 + 1):
+                            disparity[v, u_curr] = d_ipol
+                count = 0
+            else:
+                count += 1
+
+        # Extrapolate to the left
+        for u in range(width):
+            if disparity[v, u] >= 0:
+                for u2 in range(u):
+                    disparity[v, u2] = disparity[v, u]
+                break
+
+        # Extrapolate to the right
+        for u in range(width - 1, -1, -1):
+            if disparity[v, u] >= 0:
+                for u2 in range(u + 1, width):
+                    disparity[v, u2] = disparity[v, u]
+                break
+
+    for u in range(0, width):
+        # Extrapolate to the top
+        for v in range(0, height):
+            if disparity[v, u] >= 0:
+                for v2 in range(0, v):
+                    disparity[v2, u] = disparity[v, u]
+                break
+
+        # Extrapolate to the bottom
+        for v in range(height - 1, -1, -1):
+            if disparity[v, u] >= 0:
+                for v2 in range(v + 1, height):
+                    disparity[v2, u] = disparity[v, u]
+                break
+
+    return disparity
 
 
 def write_disparity_error_image(

argoverse/sensor_dataset_config.py

@@ -69,4 +69,6 @@ class ArgoverseConfig(SensorDatasetConfig):
         ring_rear_right=SensorConfig(RING_CAMERA_HEIGHT, RING_CAMERA_WIDTH, "ring_rear_right"),
         stereo_front_left=SensorConfig(STEREO_CAMERA_HEIGHT, STEREO_CAMERA_WIDTH, "stereo_front_left"),
         stereo_front_right=SensorConfig(STEREO_CAMERA_HEIGHT, STEREO_CAMERA_WIDTH, "stereo_front_right"),
+        stereo_front_left_rect=SensorConfig(STEREO_CAMERA_HEIGHT, STEREO_CAMERA_WIDTH, "stereo_front_left_rect"),
+        stereo_front_right_rect=SensorConfig(STEREO_CAMERA_HEIGHT, STEREO_CAMERA_WIDTH, "stereo_front_right_rect"),
     )