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bumptonormalmap.py
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bumptonormalmap.py
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##################################
# BUMP to NORMAL MAP
##################################
# python bumptonormalmap.py <path to bump map> <strength>
#
# <path to bump map> : string -> path to the input image (the bump map, i.e. the height map)
#
# <strength> : float > 0 -> "strength" of the normal map
# results in smoother (strength -> 0) or sharper (strength -> \infty) features
# strength = 1 (recommended to start with)
# strength = 2 (more defined features)
# strength = 10 (really strong normal mapping effect...)
# just experiment a little bit :)
##################################
# Uses horizontal and vertical sobel filters (https://docs.opencv.org/3.4/d2/d2c/tutorial_sobel_derivatives.html)
# to detect edges to determine the gradients in horizontal and vertical direction respectively.
#
# GX = [ -1 0 1] = [1]
# [ -2 0 2] [2] * [-1 0 1]
# [ -1 0 1] [1]
#
# GY = [ -1 -2 -1] = [-1]
# [ 0 0 0] [ 0] * [1 2 1]
# [ 1 2 1] [ 1]
#
# The sobel filter is separable which allows to compute two one-dimensional convolutions instead of one two-dimensional.
#
# The normals are computed from the gradients in horizontal (dx) and vertical direction (dy) as follows:
# normal = normalize(vec3(dx, dy, 1.0 / strength))
#
# Note that the normals are transformed from [-1,1] space to [0,1] space (normal * vec3(0.5) + vec3(0.5)).
# Remember to undo the transformation (vec3(2.0) * normal - vec3(1.0)) when reading the normals from the normal map.
import cv2
import numpy as np
import sys
import time
#################
# UTILITY
#################
def normalize(vec: np.array) -> np.array:
length = np.sqrt(np.dot(vec, vec))
return vec / length
def normalToColor(vec: np.array) -> np.array:
return np.array(
[np.uint8((vec[2] * 0.5 + 0.5) * 255), np.uint8((vec[1] * 0.5 + 0.5) * 255),
np.uint8((vec[0] * 0.5 + 0.5) * 255)]) # BGR
def pixel_value(img, dim_x: int, dim_y: int, x: int, y: int) -> float:
x_clamped = min(max(x, 0), dim_x - 1)
y_clamped = min(max(y, 0), dim_y - 1)
return img[y_clamped][x_clamped][0] / 255.0
def storage_value(storage: np.array, dim_x: int, dim_y: int, x: int, y: int) -> float:
x_clamped = min(max(x, 0), dim_x - 1)
y_clamped = min(max(y, 0), dim_y - 1)
return storage[y_clamped][x_clamped]
# https://stackoverflow.com/questions/736043/checking-if-a-string-can-be-converted-to-float-in-python/20929881#20929881
def is_float(element: any) -> bool:
if element is None:
return False
try:
float(element)
return True
except ValueError:
return False
##################################
# SOBEL (slow)
##################################
# if __name__ == '__main__':
# if len(sys.argv) != 3:
# print("arguments: <path to image> <strength>")
# sys.exit()
#
# if not is_float(sys.argv[2]):
# print(sys.argv[2] + " is not a float")
# sys.exit()
#
# img = cv2.imread(sys.argv[1])
# dim = img.shape
# height = dim[0]
# width = dim[1]
#
# strength = float(sys.argv[2])
# if strength <= 0.0:
# print("strength has to be >0")
# sys.exit()
#
# targetImg = np.zeros((height, width, 3), np.uint8)
#
# start = time.time()
#
# for y in range(height):
# for x in range(width):
# if (y * width + x) % 10000 == 0:
# print("Converting... " + "{:.{}f}".format((y * width + x) / (width * height) * 100.0, 2) + "%")
#
# mXmY = pixel_value(img, width, height, x - 1, y - 1)
# mXoY = pixel_value(img, width, height, x - 1, y)
# mXpY = pixel_value(img, width, height, x - 1, y + 1)
# oXpY = pixel_value(img, width, height, x, y + 1)
# pXpY = pixel_value(img, width, height, x + 1, y + 1)
# pXoY = pixel_value(img, width, height, x + 1, y)
# pXmY = pixel_value(img, width, height, x + 1, y - 1)
# oXmY = pixel_value(img, width, height, x, y - 1)
#
# dx = -mXmY - 2 * mXoY - mXpY + pXmY + 2 * pXoY + pXpY
# dy = mXpY + 2 * oXpY + pXpY - mXmY - 2 * oXmY - pXmY
#
# normal = normalize(np.array([dx, dy, 1.0 / strength]))
#
# targetImg[y][x] = normalToColor(normal)
#
# end = time.time()
#
# cv2.imwrite("normal_map.png", targetImg)
#
# print("Finished. Conversion took " + str(end - start) + "s.")
##################################
# SEPARABLE SOBEL (faster)
##################################
# if __name__ == '__main__':
# if len(sys.argv) != 3:
# print("arguments: <path to image> <strength>")
# sys.exit()
#
# if not is_float(sys.argv[2]):
# print(sys.argv[2] + " is not a float")
# sys.exit()
#
# img = cv2.imread(sys.argv[1])
# dim = img.shape
# height = dim[0]
# width = dim[1]
#
# strength = float(sys.argv[2])
# if strength <= 0.0:
# print("strength has to be >0")
# sys.exit()
#
# targetImg = np.zeros((height, width, 3), np.uint8)
#
# start = time.time()
#
# tempHorizontalGX = np.zeros((height, width), float)
# tempHorizontalGY = np.zeros((height, width), float)
# for y in range(height):
# for x in range(width):
# if (y * width + x) % 10000 == 0:
# print(
# "Converting horizontal... " + "{:.{}f}".format((y * width + x) / (width * height) * 100.0, 2) + "%")
#
# mXoY = pixel_value(img, width, height, x - 1, y)
# oXoY = pixel_value(img, width, height, x, y)
# pXoY = pixel_value(img, width, height, x + 1, y)
#
# dx = -mXoY + pXoY
# dy = mXoY + 2.0 * oXoY + pXoY
#
# tempHorizontalGX[y][x] = dx
# tempHorizontalGY[y][x] = dy
#
# for y in range(height):
# for x in range(width):
# if (y * width + x) % 10000 == 0:
# print("Converting vertical... " + "{:.{}f}".format((y * width + x) / (width * height) * 100.0, 2) + "%")
#
# oXpYGX = storage_value(tempHorizontalGX, width, height, x, y + 1)
# oXoYGX = storage_value(tempHorizontalGX, width, height, x, y)
# oXmYGX = storage_value(tempHorizontalGX, width, height, x, y - 1)
#
# oXpYGY = storage_value(tempHorizontalGY, width, height, x, y + 1)
# oXmYGY = storage_value(tempHorizontalGY, width, height, x, y - 1)
#
# dx = oXmYGX + 2.0 * oXoYGX + oXpYGX
# dy = -oXmYGY + oXpYGY
#
# normal = normalize(np.array([dx, dy, 1.0 / strength]))
#
# targetImg[y][x] = normalToColor(normal)
#
# end = time.time()
#
# cv2.imwrite("normal_map.png", targetImg)
#
# print("Finished. Conversion took " + str(end - start) + "s.")
##################################
# OPENCV's SOBEL (fastest)
##################################
if __name__ == '__main__':
if len(sys.argv) != 3:
print("arguments: <path to image> <strength>")
sys.exit()
if not is_float(sys.argv[2]):
print(sys.argv[2] + " is not a float")
sys.exit()
img = cv2.imread(sys.argv[1])
dim = img.shape
height = dim[0]
width = dim[1]
strength = float(sys.argv[2])
if strength <= 0.0:
print("strength has to be >0")
sys.exit()
targetImg = np.zeros((height, width, 3), np.uint8)
start = time.time()
scale = 1
delta = 0
ddepth = cv2.CV_16S
grad_x = cv2.Sobel(img, ddepth, 1, 0, ksize=3, scale=scale, delta=delta, borderType=cv2.BORDER_REPLICATE)
grad_y = cv2.Sobel(img, ddepth, 0, 1, ksize=3, scale=scale, delta=delta, borderType=cv2.BORDER_REPLICATE)
print("Sobel filters applied...")
for y in range(height):
for x in range(width):
if (y * width + x) % 10000 == 0:
print("Writing normal map... " + "{:.{}f}".format((y * width + x) / (width * height) * 100.0, 2) + "%")
dx = grad_x[y][x][0] / 255.0
dy = grad_y[y][x][0] / 255.0
normal = normalize(np.array([dx, dy, 1.0 / strength]))
targetImg[y][x] = normalToColor(normal)
end = time.time()
cv2.imwrite("normal_map.png", targetImg)
print("Finished. Conversion took " + str(end - start) + "s.")