问题
I must preface this, with the fact that I have a working method using bitshift and putpixel, but it is incredibly slow, and I am looking to leverage numpy to speed up the process. I believe I am close, but not quite there. Having timed what I think should work, I'm seeing a 0.3 second improvement in time, hence my motivation.
The current working code:
buff # a binary set of data
im = Image.new("RGBA",(xdim,ydim))
for y in xrange(ydim):
for x in xrange(xdim):
px = buff[x*y]
# the 255 is for the alpha channel which I plan to use later
im.putpixel((x,y),(px&0xF800) >> 8, (px&0x07E0) >> 3, (px&0x001F) <<3, 255))
return im
The code I'm trying to get work looks like this:
im16 = numpy.fromstring(buff,dtype=numpy.uint16) #read data as shorts
im16 = numpy.array(im16,dtype=numpy.uint32) #now that it's in the correct order, convert to 32 bit so there is room to do shifting
r = numpy.right_shift(8, im16.copy() & 0xF800)
g = numpy.right_shift(3, im16.copy() & 0x07E0)
b = numpy.left_shift( 3, im16 & 0x001F)
pA = numpy.append(r,g)
pB = numpy.append(b,numpy.ones((xdim,ydim),dtype=numpy.uint32) * 0xFF) #this is a black alpha channel
img = numpy.left_shift(img,8) #gives me green channel
im24 = Image.fromstring("RGBA",(xdim,ydim),img)
return im24
so the final problem, is that the channels are not combining and I don't believe I should have to do that final bit shift (note that I get the red channel if I don't bit-shift by 8). Assistance on how to combine everything correctly would be much appreciated.
SOLUTION
import numpy as np
arr = np.fromstring(buff,dtype=np.uint16).astype(np.uint32)
arr = 0xFF000000 + ((arr & 0xF800) >> 8) + ((arr & 0x07E0) << 5) + ((arr & 0x001F) << 19)
return Image.frombuffer('RGBA', (xdim,ydim), arr, 'raw', 'RGBA', 0, 1)
the difference is that you need to pack it as MSB(ALPHA,B,G,R)LSB counter intuitive from putpixel, but it works, and works well
回答1:
Warning: the following code hasn't been checked, but I think that this will do what you want (if I'm understanding everything correctly):
import numpy as np
arr = np.fromstring(buff,dtype=np.uint16).astype(np.uint32)
arr = ((arr & 0xF800) << 16) + ((arr & 0x07E0) << 13) + ((arr & 0x001F) << 11) + 0xFF
return Image.frombuffer('RGBA', (xdim,ydim), arr, 'raw', 'RGBA', 0, 1)
I'm combining all of the channels together into 32-bits on the line that does all of the bit shifting. The leftmost 8-bits are the red, the next 8 are the green, the next 8 blue, and the last 8 alpha. The shifting numbers may seem a little strange because I incorporated the shifts from the 16-bit format. Also, I'm using frombuffer because then we want to take advantage of the buffer being used by Numpy rather than converting to a string first.
It might help to look at this page. It's not super great in my opinion, but that's how things go with PIL in my experience. The documentation is really not very user-friendly, in fact I often find it confusing, but I'm not about to volunteer to rewrite it because I don't use PIL much.
回答2:
If you want to do the scaling appropriately, here is a more PIL-ish to solve your problem.
FROM_5 = ((np.arange(32, dtype=numpy.uint16) * 255 + 15) // 31).astype(numpy.ubyte)
FROM_6 = ((np.arange(64, dtype=numpy.uint16) * 255 + 31) // 63).astype(numpy.ubyte)
data = numpy.fromstring(buff, dtype=numpy.uint16)
r = Image.frombuffer('L', shape, FROM_5[data >> 11], 'raw', 'L', 0, 1)
g = Image.frombuffer('L', shape, FROM_6[(data >> 5) & 0x3F], 'raw', 'L', 0, 1)
b = Image.frombuffer('L', shape, FROM_5[data & 0x1F], 'raw', 'L', 0, 1)
return Image.merge('RGB', (r, g, b))
来源：https://stackoverflow.com/questions/5414638/using-numpy-and-pil-to-convert-56516bit-color-to-88824bit-color