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Python library to handle 360° environment maps

License: GNU Lesser General Public License v3.0

Python 100.00%

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Non-uniform functionality for cube projections

The functions cube2world and world2cube have non-uniform operating characteristics to other projection functions. Input/Output should match, as either floats or arrays respectively.

cube2world processes only arrays:

>>> env.projections.cube2world(np.array([0.5]),np.array([0.5]))
(array([0.]), array([-0.70710678]), array([-0.70710678]), array([ True]))

cube2world fails to parse float input:

>>> env.projections.cube2world(0,0)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/iamaq/.local/lib/python3.10/site-packages/envmap/projections.py", line 232, in cube2world
    x = np.zeros(u.shape)

world2cube returns arrays for float inputs:

>>> env.projections.world2cube(0.5,.5,0.5)
(array([1.]), array([0.25]))

Unintented universal offset?

It needs to be verified that the 90 degree orientation offset is universal (as implemented) or specific to skydb. If it's the later, the code should be altered to accept a desired offset.

skylibs/hdrtools/sunutils.py

Line 100 in adb148d

elevation, azimuth = np.deg2rad(elevation), np.deg2rad(90 + azimuth)

Wrong description

https://github.com/soravux/skylibs/blob/f9bbf0ab30a61a4cb8963a779d379c1b94f022d0/hdrtools/sunutils.py#L96C1-L103C8

Depricated function names

sunutils.sunPosFromEnvmap() and sunutils.sunPosFromCoord() are depricated function names:

skylibs/skydb/__init__.py

Line 163 in b39bc34

return sunutils.sunPosFromCoord(latitude, longitude, d, elevation=elevation)

skylibs/skydb/__init__.py

Line 146 in b39bc34

return sunutils.sunPosFromEnvmap(self._envmap)

Updated function names can be found here

UV pixels missmatched with normalized uv space

Missing transformation from normalized uv coordinates space to pixel UV coordinates is causing frequent error, even within skylibs.

problem:
example 1D pixel space as array of size n=8

x_ = np.linspace(0,7,8, dtype=int)

gives discrete coordinates = array([0, 1, 2, 3, 4, 5, 6, 7])

example 1D normalized space as array of size n=8

x = np.linspace(0,1,(2*8)+1); 
x=x[1::2]

gives normalized coordinates = array([0.0625, 0.1875, 0.3125, 0.4375, 0.5625, 0.6875, 0.8125, 0.9375])

as such, defining x = x_ / n as done in the erroneous sample from skylibs below
x_ = np.linspace(0,7,8, dtype=int); x = x_/8
gives normalized coordinates array([0. , 0.125, 0.25 , 0.375, 0.5 , 0.625, 0.75 , 0.875])
which in an invalid distribution of the normalized space

Example error within skylibs:

c = findBrightestSpot(envmapInput.data)
u, v = c[1] / envmapInput.data.shape[1], c[0] / envmapInput.data.shape[0]

pixels are not centered properly in normalized coordinate space

Add antialiasing option when downsampling using `envmap.resize()`.

Converting to skylatlong upsamples x2 the environment map

targetSize ignored when downsampling

Hello @soravux,

It seems there's a little incoherence when resizing an environment map using the resize() method.

I have 2048x1024 images that I'm trying to resize to 256x256 but even when I pass a tuple to the resize() method I get a 512x256 result instead. This is due to the fact that the code checks if the original height is divisible by the new one (here), but then applies the same downscaling factor for both the height and the width (here). Is this the intended behaviour (given that when the divisibility condition isn't met, the target height and width are both respected here)?

Thanks in advance for your response!

[hdrio] Add support for raw files

Can be done using dcraw:
dcraw -v -4 -T -t 0 -j filename.CR2

Check also the ImageMagick Python wrappers.

remove print

Hi! This keeps printing '3' when using this funcion.

skylibs/ezexr/__init__.py

Line 52 in b39bc34

print(nc)

envmap function downscaleEnvmap(...) produces artifact

downscaleEnvmap(...) produces an needle-like artifact in downsizing operation.

Artifact, as observed on a skyangular image (converted from latlong skydome):

Artifact, as observed on a synthetic (ones) skyangular image (converted from synthetic (ones) latlong ):

Code to reproduce:

# OS tools
import os
os.environ["OPENCV_IO_ENABLE_OPENEXR"]="1" # Enable EXR

# Machine vision tools
import cv2
import numpy as np
from envmap import EnvironmentMap

def downscaleEnvmap(nenvmap, sao, sat, times):
    """Energy-preserving environment map downscaling by factors of 2.
    Usage:
    sao = EnvironmentMap(512, 'LatLong').solidAngles() # Source envmap solid angles, could be replaced by `sao = envmap.soldAngles()`
    sat = EnvironmentMap(128, 'LatLong').solidAngles() # Target envmap solid angles
    downscaleEnvmap(envmap, sao, sat, 3)
    Note : `times` is the number of downscales, so the total downscaling factor is 2**times"""

    nenvmap.data *= sao[:, :, np.newaxis]
    nenvmap.data = np.pad(nenvmap.data, [(0, 1), (0, 1), (0, 0)], 'constant')
    sx = np.cumsum(nenvmap.data, axis=1)
    tmp = sx[:, 2**times::2**times, ...] - sx[:, :-2**times:2**times, ...]
    sy = np.cumsum(tmp, axis=0)
    nenvmap.data = sy[2**times::2**times, :, ...] - sy[:-2**times:2**times, :, ...]
    if nenvmap.data.shape[1] > 2*nenvmap.data.shape[0]:
        nenvmap.data[:, -2, :] += nenvmap[:, -1, :]
        nenvmap.data = nenvmap.data[:, :-1, :]
    nenvmap.data /= sat[:, :, np.newaxis]
    nenvmap.data = np.ascontiguousarray(nenvmap.data)
    return nenvmap

## ---- envmap Sample ---- ##
print("-> Loading image sample")
img_path = "unzipped_envmap/20141003_130155_envmap.exr"
img_latlong = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
cv2.imwrite("img_latlong_original.exr", img_latlong)

# Create Envmap
e_ll = EnvironmentMap(np.copy(img_latlong), 'latlong')
cv2.imwrite("e_ll.exr", e_ll.data.astype(np.float32))

# Convert Envmap to Skyangular
e_sa = EnvironmentMap(np.copy(img_latlong), 'latlong').convertTo('skyangular')
cv2.imwrite("e_sa.exr", e_sa.data.astype(np.float32))

# Convert Envmap to Skyangular
e_sa = EnvironmentMap(np.copy(img_latlong), 'latlong').convertTo('skyangular', 512)
cv2.imwrite("e_sa512.exr", e_sa.data.astype(np.float32))

# Downscale
sao = e_ll.solidAngles()
sat = EnvironmentMap(512, 'Latlong').solidAngles()
e_llD = downscaleEnvmap(e_ll, sao, sat, 1)
cv2.imwrite("e_ll512D.exr", e_llD.data.astype(np.float32))

# Convert Envmap to Skyangular
e_saD = e_llD.convertTo('skyangular')
cv2.imwrite("e_sa512D.exr", e_saD.data.astype(np.float32))


## ---- Synthetic Sample ---- ##
print("-> Creating grey-image sample")
img_latlong = np.zeros((1024, 2048,3), np.float64) +1
# img_latlong[int(img_latlong.shape[0]/2):, :]=0
# img_latlong[::2, 1::2]=0

# Create Envmap
e_ll = EnvironmentMap(np.copy(img_latlong), 'latlong')

# Downscale
sao = e_ll.solidAngles()
sat = EnvironmentMap(512, 'Latlong').solidAngles()
e_llD = downscaleEnvmap(e_ll, sao, sat, 1)
cv2.imwrite("e_ll512D_synthetic.exr", e_llD.data.astype(np.float32))

# Convert Envmap to Skyangular
e_saD = e_llD.convertTo('skyangular')
cv2.imwrite("e_sa512D_synthetic.exr", e_saD.data.astype(np.float32))
'''

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