new: support image scaling for ct.project.depth_to_point_cloud() (#54)

camtools/image.py

@@ -412,7 +412,7 @@ def recover_rotated_pixels(dst_pixels, src_wh, ccw_degrees):
  return src_pixels
 
 
-def resize(im, shape_wh, aspect_ratio_fill=None):
+def resize(im, shape_wh, aspect_ratio_fill=None, interpolation=cv2.INTER_LINEAR):
  """
  Resize image to shape_wh = (width, height).
  In numpy, the resulting shape is (height, width) or (height, width, 3).
@@ -426,6 +426,7 @@ def resize(im, shape_wh, aspect_ratio_fill=None):
  - If not None, the number of elements must match the channel size,
  1 or 3. The dtype and range must also match the input image.
  These value(s) will be filled to maintain the aspect ratio.
+ interpolation: OpenCV interpolation method, e.g., cv2.INTER_LINEAR.
 
  Returns:
  im_resized: image of shape (height, width) or (height, width, 3).
@@ -484,7 +485,7 @@ def resize(im, shape_wh, aspect_ratio_fill=None):
  assert tmp_w <= dst_w and tmp_h <= dst_h
 
  # Resize.
- im_tmp = cv2.resize(im, (tmp_w, tmp_h))
+ im_tmp = cv2.resize(im, dsize=(tmp_w, tmp_h), interpolation=interpolation)
 
  # Pad if necessary.
  if tmp_w == dst_w and tmp_h == dst_h:

camtools/project.py

@@ -2,9 +2,10 @@
 Functions for projecting 2D->3D or 3D->2D.
 """
 
+import cv2
 import numpy as np
-from . import sanity
-from . import convert
+
+from . import convert, image, sanity
 
 
 def point_cloud_to_pixel(points, K, T):
@@ -58,6 +59,7 @@ def depth_to_point_cloud(
  im_color: np.ndarray = None,
  return_as_image: bool = False,
  ignore_invalid: bool = True,
+ scale_factor: float = 1.0,
 ):
  """
  Convert a depth image to a point cloud, optionally including color information.
@@ -74,6 +76,9 @@ def depth_to_point_cloud(
  invalid depths are not removed. If False, returns a sparse point cloud
  of shape (N, 3) while respecting ignore_invalid flag.
  ignore_invalid: If True, ignores invalid depths (<= 0 or >= inf).
+ scale_factor: scale the im_depth (and optionally im_color) images before
+ projecting to 3D points. When scale_factor == 0.5, the image size
+ is reduced to half.
 
  Returns:
  - im_color == None, as_image == False:
@@ -110,6 +115,34 @@ def depth_to_point_cloud(
  print("Warning: ignore_invalid is ignored when return_as_image is True.")
  ignore_invalid = False
 
+ # Make copies as K may be modified inplace
+ K = np.copy(K)
+ T = np.copy(T)
+
+ if scale_factor != 1.0:
+ # Calculate new dimensions
+ new_width = int(im_depth.shape[1] * scale_factor)
+ new_height = int(im_depth.shape[0] * scale_factor)
+
+ # Resize images
+ im_depth = image.resize(
+ im_depth,
+ shape_wh=(new_width, new_height),
+ interpolation=cv2.INTER_NEAREST,
+ )
+ if im_color is not None:
+ im_color = image.resize(
+ im_color,
+ shape_wh=(new_width, new_height),
+ interpolation=cv2.INTER_LINEAR,
+ )
+
+ # Adjust the intrinsic matrix K for the new image dimensions
+ K[0, 0] *= scale_factor
+ K[1, 1] *= scale_factor
+ K[0, 2] *= scale_factor
+ K[1, 2] *= scale_factor
+
  height, width = im_depth.shape
  pose = convert.T_to_pose(T)