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import numpy as np
import cv2
from PIL import Image
def create_normalmap(depthmap,
pre_blur = None, sobel_gradient = 3, post_blur = None,
invert=False):
"""Generates normalmaps.
:param depthmap: depthmap that will be used to generate normalmap
:param pre_blur: apply gaussian blur before taking gradient, -1 for disable, otherwise kernel size
:param sobel_gradient: use Sobel gradient, None for regular gradient, otherwise kernel size
:param post_blur: apply gaussian blur after taking gradient, -1 for disable, otherwise kernel size
:param invert: depthmap will be inverted before calculating normalmap
"""
# https://stackoverflow.com/questions/53350391/surface-normal-calculation-from-depth-map-in-python
# TODO: Tiling can be improved (gradients could be matched).
# TODO: Implement bilateral filtering (16 bit deflickering)
# We invert by default, maybe there is a negative sign hiding somewhere
normalmap = depthmap if invert else depthmap * (-1.0)
normalmap = normalmap / 256.0
# pre blur (only blurs z-axis)
if pre_blur is not None and pre_blur > 0:
normalmap = cv2.GaussianBlur(normalmap, (pre_blur, pre_blur), pre_blur)
# take gradients
if sobel_gradient is not None and sobel_gradient > 0:
zx = cv2.Sobel(np.float64(normalmap), cv2.CV_64F, 1, 0, ksize=sobel_gradient)
zy = cv2.Sobel(np.float64(normalmap), cv2.CV_64F, 0, 1, ksize=sobel_gradient)
else:
zy, zx = np.gradient(normalmap)
# combine and normalize gradients
normal = np.dstack((zx, -zy, np.ones_like(normalmap)))
# every pixel of a normal map is a normal vector, it should be a unit vector
n = np.linalg.norm(normal, axis=2)
normal[:, :, 0] /= n
normal[:, :, 1] /= n
normal[:, :, 2] /= n
# TODO: this probably is not a good way to do it
if post_blur is not None and post_blur > 0:
normal = cv2.GaussianBlur(normal, (post_blur, post_blur), post_blur)
# Normalize every vector again
n = np.linalg.norm(normal, axis=2)
normal[:, :, 0] /= n
normal[:, :, 1] /= n
normal[:, :, 2] /= n
# offset and rescale values to be in 0-255, so we can export them
normal += 1
normal /= 2
normal = np.clip(normal * 256, 0, 256 - 0.1) # Clipping form above is needed to avoid overflowing
normal = normal.astype(np.uint8)
return Image.fromarray(normal)
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