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# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
import PIL.Image
import os
os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
import cv2 # noqa
import numpy as np # noqa
from dust3r.utils.geometry import (
colmap_to_opencv_intrinsics,
opencv_to_colmap_intrinsics,
) # noqa
try:
lanczos = PIL.Image.Resampling.LANCZOS
bicubic = PIL.Image.Resampling.BICUBIC
except AttributeError:
lanczos = PIL.Image.LANCZOS
bicubic = PIL.Image.BICUBIC
class ImageList:
"""Convenience class to aply the same operation to a whole set of images."""
def __init__(self, images):
if not isinstance(images, (tuple, list, set)):
images = [images]
self.images = []
for image in images:
if not isinstance(image, PIL.Image.Image):
image = PIL.Image.fromarray(image)
self.images.append(image)
def __len__(self):
return len(self.images)
def to_pil(self):
return tuple(self.images) if len(self.images) > 1 else self.images[0]
@property
def size(self):
sizes = [im.size for im in self.images]
assert all(sizes[0] == s for s in sizes)
return sizes[0]
def resize(self, *args, **kwargs):
return ImageList(self._dispatch("resize", *args, **kwargs))
def crop(self, *args, **kwargs):
return ImageList(self._dispatch("crop", *args, **kwargs))
def _dispatch(self, func, *args, **kwargs):
return [getattr(im, func)(*args, **kwargs) for im in self.images]
def rescale_image_depthmap(
image, depthmap, camera_intrinsics, output_resolution, force=True
):
"""Jointly rescale a (image, depthmap)
so that (out_width, out_height) >= output_res
"""
image = ImageList(image)
input_resolution = np.array(image.size) # (W,H)
output_resolution = np.array(output_resolution)
if depthmap is not None:
assert tuple(depthmap.shape[:2]) == image.size[::-1]
assert output_resolution.shape == (2,)
scale_final = max(output_resolution / image.size) + 1e-8
if scale_final >= 1 and not force: # image is already smaller than what is asked
return (image.to_pil(), depthmap, camera_intrinsics)
output_resolution = np.floor(input_resolution * scale_final).astype(int)
image = image.resize(
output_resolution, resample=lanczos if scale_final < 1 else bicubic
)
if depthmap is not None:
depthmap = cv2.resize(
depthmap,
output_resolution,
fx=scale_final,
fy=scale_final,
interpolation=cv2.INTER_NEAREST,
)
camera_intrinsics = camera_matrix_of_crop(
camera_intrinsics, input_resolution, output_resolution, scaling=scale_final
)
return image.to_pil(), depthmap, camera_intrinsics
def camera_matrix_of_crop(
input_camera_matrix,
input_resolution,
output_resolution,
scaling=1,
offset_factor=0.5,
offset=None,
):
margins = np.asarray(input_resolution) * scaling - output_resolution
assert np.all(margins >= 0.0)
if offset is None:
offset = offset_factor * margins
output_camera_matrix_colmap = opencv_to_colmap_intrinsics(input_camera_matrix)
output_camera_matrix_colmap[:2, :] *= scaling
output_camera_matrix_colmap[:2, 2] -= offset
output_camera_matrix = colmap_to_opencv_intrinsics(output_camera_matrix_colmap)
return output_camera_matrix
def crop_image_depthmap(image, depthmap, camera_intrinsics, crop_bbox):
"""
Return a crop of the input view.
"""
image = ImageList(image)
l, t, r, b = crop_bbox
image = image.crop((l, t, r, b))
depthmap = depthmap[t:b, l:r]
camera_intrinsics = camera_intrinsics.copy()
camera_intrinsics[0, 2] -= l
camera_intrinsics[1, 2] -= t
return image.to_pil(), depthmap, camera_intrinsics
def bbox_from_intrinsics_in_out(
input_camera_matrix, output_camera_matrix, output_resolution
):
out_width, out_height = output_resolution
l, t = np.int32(np.round(input_camera_matrix[:2, 2] - output_camera_matrix[:2, 2]))
crop_bbox = (l, t, l + out_width, t + out_height)
return crop_bbox
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