thanks, sharing an enhancement as i'm obliged to for open source #1
Description
This is a great little tool.
Sorry I haven't done this as a propper pull request (i'm doing this quite adhoc) but here's a little modification I have made to this, which saves a kind of AO in the alpha channel of the output files. I have a couple of other extra enhancements in mind for my workflow. this would want to be a commandline option I guess, and would need a couple of control parameters (the size of the shadow gaussian, the bias, and whether to combine it or not)
from PIL import Image
import math
def normalized(a):
factor = 1.0/math.sqrt( np.sum(a*a)) # normalize
return a*factor
def my_gauss(im):
im_smooth = im.astype(float)
kernel = np.array([0.5,1.0,0.5]).astype(float);
kernel=normalized(kernel)
im_smooth = scipy.ndimage.convolve(im_smooth, kernel[np.newaxis])
im_smooth = scipy.ndimage.convolve(im_smooth, kernel[np.newaxis].T)
return im_smooth
#calculate a local internal shadowing/AO aproximation using 'difference of gaussians'
def shadow(im):
im1 = im.astype(float)
im0 = im1.copy()
im00 = im1.copy()
im000 = im1.copy()
for i in range(0,2):
im00 = my_gauss(im00)
for i in range(0,64):
im0 = my_gauss(im0)
for i in range(0,128):
im1 = my_gauss(im1)
im000=normalized(im000)
im00=normalized(im00)
im0=normalized(im0)
im1=normalized(im1)
im00=normalized(im00)
shadow=im00*2.0+im000-im1*2.0-im0 # np.clip(im1, 0.0, 1000000.0)
shadow=normalized(shadow)
mean = np.mean(shadow)
rmse = rms_error(shadow)
shadow = np.clip(shadow, mean-rmse*2.0,mean+rmse*0.5)
return shadow
# ....
# added to the end of main()
# save the pure normal map and AO/shadow images as seperatejpgs
scipy.misc.imsave("normal.jpg", normal_map)
im_shadow = shadow(im)
scipy.misc.imsave("shadow.jpg", im_shadow)
# read these images back in, and combine them to make an image with RGB=normal, Alpha=AO
# really I did this because i couldn't get it working from numpy arrays directly.
imgsh=Image.open("shadow.jpg")
#imgsh.show()
imgnorm=Image.open("normal.jpg")
#imgnorm.show()
imgnormao = np.zeros( (img.size[0],img.size[1],4), np.float)
imgnormao[ :, :, 0:3] = np.asarray(imgnorm)[:,:,0:3] #RGB = normal
imgnormao[ :, :, 3] = np.asarray(imgsh)[:,:] #Alpha = AO
scipy.misc.imsave(output_file, imgnormao)
imgnormao=Image.open(output_file) # save the RGBA image containing Normal,AO