Oops. I meant:
WhiteArea=Result.histogram()[255]
of course, not
WhiteArea=Result.histogram()[0]
Ken Starks wrote:
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)
#### Mistake here:
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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