I wrote a python script with combines images in unique ways for an OpenGL shader. The problem is that I have a large number of very large maps and it takes a long time to process. Is there a way to write this in a quicker fashion?
import numpy as np
map_data = {}
image_data = {}
for map_postfix in names:
file_name = inputRoot + '-' + map_postfix + resolution + '.png'
print 'Loading ' + file_name
image_data[map_postfix] = Image.open(file_name, 'r')
map_data[map_postfix] = image_data[map_postfix].load()
color = mapData['ColorOnly']
ambient = mapData['AmbientLight']
shine = mapData['Shininess']
width = imageData['ColorOnly'].size[0]
height = imageData['ColorOnly'].size[1]
arr = np.zeros((height, width, 4), dtype=int)
for i in range(width):
for j in range(height):
ambient_mod = ambient[i,j][0] / 255.0
arr[j, i, :] = [color[i,j][0] * ambient_mod , color[i,j][1] * ambient_mod , color[i,j][2] * ambient_mod , shine[i,j][0]]
print 'Converting Color Map to image'
return Image.fromarray(arr.astype(np.uint8))
This is just a sample of a large number of batch processes so I am more interested in if there is a faster way to iterate and modify an image file. Almost all the time is being spent on the nested loop vs loading and saving.
Vectorised-code example -- test effect on yours in timeit or zmq.Stopwatch()
Reported to have 22.14 seconds >> 0.1624 seconds speedup!
While your code seems to loop just over RGBA[x,y], let me show a "vectorised"-syntax of a code, that benefits from numpy matrix-manipulation utilities ( forget the RGB/YUV manipulation ( originally based on OpenCV rather than PIL ), but re-use the vectorised-syntax approach to avoid for-loops and adapt it to work efficiently for your calculus. Wrong order of operations may more than double yours processing time.
And use a test / optimise / re-test loop for speeding up.
For testing, use standard python timeit if [msec] resolution is enough.
Go rather for zmq.StopWatch() if you need going into [usec] resolution.
# Vectorised-code example, to see the syntax & principles
# do not mind another order of RGB->BRG layers
# it has been OpenCV traditional convention
# it has no other meaning in this demo of VECTORISED code
def get_YUV_U_Cb_Rec709_BRG_frame( brgFRAME ): # For the Rec. 709 primaries used in gamma-corrected sRGB, fast, VECTORISED MUL/ADD CODE
out = numpy.zeros( brgFRAME.shape[0:2] )
out -= 0.09991 / 255 * brgFRAME[:,:,1] # // Red
out -= 0.33601 / 255 * brgFRAME[:,:,2] # // Green
out += 0.436 / 255 * brgFRAME[:,:,0] # // Blue
return out
# normalise to <0.0 - 1.0> before vectorised MUL/ADD, saves [usec] ...
# on 480x640 [px] faster goes about 2.2 [msec] instead of 5.4 [msec]
In your case, using dtype = numpy.int, guess it shall be faster to MUL first by ambient[:,:,0] and finally DIV to normalisearr[:,:,:3] /= 255
# test if this goes even faster once saving the vectorised overhead on matrix DIV
arr[:,:,0] = color[:,:,0] * ambient[:,:,0] / 255 # MUL remains INT, shall precede DIV
arr[:,:,1] = color[:,:,1] * ambient[:,:,0] / 255 #
arr[:,:,2] = color[:,:,2] * ambient[:,:,0] / 255 #
arr[:,:,3] = shine[:,:,0] # STO alpha
So how it may look in your algo?
One need not have Peter Jackson's impressive budget and time once planned, spanned and executed immense number-crunching over 3 years in a New Zealand hangar, overcrowded by a herd of SGI workstations, as he was producing "The Lord of The Rings" fully-digital mastering assembly-line, right by the frame-by-frame pixel manipulation, to realise that miliseconds and microseconds and even nanoseconds in the mass-production pipe-line simply do matter.
So, take a deep breath and test and re-test so as to optimise your real-world imagery processing performance to levels that your project needs.
Hope this may help you on this:
# OPTIONAL for performance testing -------------# ||||||||||||||||||||||||||||||||
from zmq import Stopwatch # _MICROSECOND_ timer
# # timer-resolution step ~ 21 nsec
# # Yes, NANOSECOND-s
# OPTIONAL for performance testing -------------# ||||||||||||||||||||||||||||||||
arr = np.zeros( ( height, width, 4 ), dtype = int )
aStopWatch = zmq.Stopwatch() # ||||||||||||||||||||||||||||||||
# /\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\# <<< your original code segment
# aStopWatch.start() # |||||||||||||__.start
# for i in range( width ):
# for j in range( height ):
# ambient_mod = ambient[i,j][0] / 255.0
# arr[j, i, :] = [ color[i,j][0] * ambient_mod, \
# color[i,j][1] * ambient_mod, \
# color[i,j][2] * ambient_mod, \
# shine[i,j][0] \
# ]
# usec_for = aStopWatch.stop() # |||||||||||||__.stop
# print 'Converting Color Map to image'
# print ' FOR processing took ', usec_for, ' [usec]'
# /\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\# <<< proposed alternative
aStopWatch.start() # |||||||||||||__.start
# reduced numpy broadcasting one dimension less # ref. comments below
arr[:,:, 0] = color[:,:,0] * ambient[:,:,0] # MUL ambient[0] * [{R}]
arr[:,:, 1] = color[:,:,1] * ambient[:,:,0] # MUL ambient[0] * [{G}]
arr[:,:, 2] = color[:,:,2] * ambient[:,:,0] # MUL ambient[0] * [{B}]
arr[:,:,:3] /= 255 # DIV 255 to normalise
arr[:,:, 3] = shine[:,:,0] # STO shine[ 0] in [3]
usec_Vector = aStopWatch.stop() # |||||||||||||__.stop
print 'Converting Color Map to image'
print ' Vectorised processing took ', usec_Vector, ' [usec]'
return Image.fromarray( arr.astype( np.uint8 ) )