As others have said, PIL has the 'histogram' method to do most of the
work. However, as histogram works on each band separately, you have
a bit of preliminary programming first to combine them.
The ImageChops darker method is one easy-to-understand way (done twice),
but there are lots of alternatives, I am sure.
# ------------------------------------
import Image
import ImageChops
Im = Image.open("\\\\server\\vol\\temp\\image.jpg")
R,G,B = Im.split()
Result=ImageChops.darker(R,G)
Result=ImageChops.darker(Result,B)
WhiteArea=Result.histogram()[0]
TotalArea=Im.size[0] * Im.size[1]
PercentageWhite = (WhiteArea * 100.0)/TotalArea
Poppy wrote:
I've put together some code to demonstrate what my goal is though looping
pixel by pixel it's rather slow.
import Image
def check_whitespace():
im = Image.open("\\\\server\\vol\\temp\\image.jpg")
size = im.size
i = 0
whitePixCount = 0
while i in range(size[1]):
j = 0
while j in range(size[0]):
p1 = im.getpixel((j,i))
if p1 == (255, 255, 255):
whitePixCount = whitePixCount + 1
if whitePixCount >= 492804: ## ((image dimensions 1404 x
1404) / 4) 25%
return "image no good"
j = j + 1
i = i + 1
print whitePixCount
return "image is good"
print check_whitespace()
"Poppy" <[EMAIL PROTECTED]> wrote in message news:...
I need to write a program to examine images (JPG) and determine how much
area is whitespace. We need to throw a returned image out if too much of it
is whitespace from the dataset we're working with. I've been examining the
Python Image Library and can not determine if it offers the needed
functionality. Does anyone have suggestions of other image libraries I
should be looking at it, or if PIL can do what I need?
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