How can I automatically determine whether an image file depicts a photo or a 'graphic'?
For example using Imagemagick?
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问题:
回答1:
I am somewhat at the limits of my knowledge here, but I read a paper and have worked out a way to calculate image entropy with ImageMagick - some clever person might like to check it!
#!/bin/bash
image=$1
# Get number of pixels in image
px=$(convert -format "%w*%h\n" "$image" info:|bc)
# Calculate entropy
# See this paper www1.idc.ac.il/toky/imageProc-10/Lectures/04_histogram_10.ppt
convert "$image" -colorspace gray -depth 8 -format "%c" histogram:info:- | \
awk -F: -v px=$px '{p=$1/px;e+=-p*log(p)} END {print e}'
So, you would save the script above as entropy, then do the following once to make it executable:
chmod +x entropy
Then you can use it like this:
entropy image.jpg
It does seem to produce bigger numbers for true photos and lower numbers for computer graphics.
Another idea would be to look at the inter-channel correlation. Normally, on digital photos, the different wavelengths of light are quite strongly correlated with each other, so if the red component increases the green and the blue components tend to also increase, but if the red component decreases, both the green and the blue tend to also decrease. If you compare that to computer graphics, people tend to do their graphics with big bold primary colours, so a big red bar-graph or pie-chart graphic will not tend to be at all correlated between the channels. I took a digital photo of a landscape and resized it to be 1 pixel wide and 64 pixels high, and I am showing it using ImageMagick below - you will see that where red goes down so do green and blue...
convert DSC01447.JPG -resize 1x64! -depth 8 txt:
0,0: (168,199,235) #A8C7EB srgb(168,199,235)
0,1: (171,201,236) #ABC9EC srgb(171,201,236)
0,2: (174,202,236) #AECAEC srgb(174,202,236)
0,3: (176,204,236) #B0CCEC srgb(176,204,236)
0,4: (179,205,237) #B3CDED srgb(179,205,237)
0,5: (181,207,236) #B5CFEC srgb(181,207,236)
0,6: (183,208,236) #B7D0EC srgb(183,208,236)
0,7: (186,210,236) #BAD2EC srgb(186,210,236)
0,8: (188,211,235) #BCD3EB srgb(188,211,235)
0,9: (190,212,236) #BED4EC srgb(190,212,236)
0,10: (192,213,234) #C0D5EA srgb(192,213,234)
0,11: (192,211,227) #C0D3E3 srgb(192,211,227)
0,12: (191,208,221) #BFD0DD srgb(191,208,221)
0,13: (190,206,216) #BECED8 srgb(190,206,216)
0,14: (193,207,217) #C1CFD9 srgb(193,207,217)
0,15: (181,194,199) #B5C2C7 srgb(181,194,199)
0,16: (158,167,167) #9EA7A7 srgb(158,167,167)
0,17: (141,149,143) #8D958F srgb(141,149,143)
0,18: (108,111,98) #6C6F62 srgb(108,111,98)
0,19: (89,89,74) #59594A srgb(89,89,74)
0,20: (77,76,61) #4D4C3D srgb(77,76,61)
0,21: (67,64,49) #434031 srgb(67,64,49)
0,22: (57,56,43) #39382B srgb(57,56,43)
0,23: (40,40,34) #282822 srgb(40,40,34)
0,24: (39,38,35) #272623 srgb(39,38,35)
0,25: (38,37,37) #262525 srgb(38,37,37)
0,26: (40,39,38) #282726 srgb(40,39,38)
0,27: (78,78,57) #4E4E39 srgb(78,78,57)
0,28: (123,117,90) #7B755A srgb(123,117,90)
0,29: (170,156,125) #AA9C7D srgb(170,156,125)
0,30: (168,154,116) #A89A74 srgb(168,154,116)
0,31: (153,146,96) #999260 srgb(153,146,96)
0,32: (156,148,101) #9C9465 srgb(156,148,101)
0,33: (152,141,98) #988D62 srgb(152,141,98)
0,34: (151,139,99) #978B63 srgb(151,139,99)
0,35: (150,139,101) #968B65 srgb(150,139,101)
0,36: (146,135,98) #928762 srgb(146,135,98)
0,37: (145,136,97) #918861 srgb(145,136,97)
0,38: (143,133,94) #8F855E srgb(143,133,94)
0,39: (140,133,92) #8C855C srgb(140,133,92)
0,40: (137,133,92) #89855C srgb(137,133,92)
0,41: (136,133,91) #88855B srgb(136,133,91)
0,42: (131,124,81) #837C51 srgb(131,124,81)
0,43: (130,121,78) #82794E srgb(130,121,78)
0,44: (134,123,78) #867B4E srgb(134,123,78)
0,45: (135,127,78) #877F4E srgb(135,127,78)
0,46: (135,129,79) #87814F srgb(135,129,79)
0,47: (129,125,77) #817D4D srgb(129,125,77)
0,48: (106,105,65) #6A6941 srgb(106,105,65)
0,49: (97,99,60) #61633C srgb(97,99,60)
0,50: (120,121,69) #787945 srgb(120,121,69)
0,51: (111,111,63) #6F6F3F srgb(111,111,63)
0,52: (95,98,55) #5F6237 srgb(95,98,55)
0,53: (110,111,63) #6E6F3F srgb(110,111,63)
0,54: (102,105,60) #66693C srgb(102,105,60)
0,55: (118,120,66) #767842 srgb(118,120,66)
0,56: (124,124,68) #7C7C44 srgb(124,124,68)
0,57: (118,120,65) #767841 srgb(118,120,65)
0,58: (114,116,64) #727440 srgb(114,116,64)
0,59: (113,114,63) #71723F srgb(113,114,63)
0,60: (116,117,64) #747540 srgb(116,117,64)
0,61: (118,118,65) #767641 srgb(118,118,65)
0,62: (118,117,65) #767541 srgb(118,117,65)
0,63: (114,114,62) #72723E srgb(114,114,62)
Statistically, this is the covariance. I would tend to want to use red and green channels of a photo to evaluate this - because in a Bayer grid there are two green sites for each single red and blue site, so the green channel is averaged across the two and therefore least susceptible to noise. The blue is most susceptible to noise. So the code for measuring the covariance can be written like this:
#!/bin/bash
# Calculate Red Green covariance of image supplied as parameter
image=$1
convert "$image" -depth 8 txt: | awk ' \
{split($2,a,",")
sub(/\(/,"",a[1]);R[NR]=a[1];
G[NR]=a[2];
# sub(/\)/,"",a[3]);B[NR]=a[3]
}
END{
# Calculate mean of R,G and B
for(i=1;i<=NR;i++){
Rmean=Rmean+R[i]
Gmean=Gmean+G[i]
#Bmean=Bmean+B[i]
}
Rmean=Rmean/NR
Gmean=Gmean/NR
#Bmean=Bmean/NR
# Calculate Green-Red and Green-Blue covariance
for(i=1;i<=NR;i++){
GRcov+=(G[i]-Gmean)*(R[i]-Rmean)
#GBcov+=(G[i]-Gmean)*(B[i]-Bmean)
}
GRcov=GRcov/NR
#GBcov=GBcov/NR
print "Green Red covariance: ",GRcov
#print "GBcovariance: ",GBcov
}'
I did some testing and that also works quite well - however graphics with big white or black backgrounds appear to be well correlated too because red=green=blue on white and black (and all grey-toned areas) so you would need to be careful of them. That however leads to another thought, photos almost never have pure white or black (unless really poorly exposed) whereas graphics do have whit backgrounds, so another test you could use would be to calculate the number of solid black and white pixels like this:
convert photo.jpg -colorspace gray -depth 8 -format %c histogram:info:-| egrep "\(0\)|\(255\)"
2: ( 0, 0, 0) #000000 gray(0)
537: (255,255,255) #FFFFFF gray(255)
This one has 2 black and 537 pure white pixels.
I should imagine you probably have enough for a decent heuristic now!
Following on from my comment, you can use these ImageMagick commands:
# Get EXIF information
identify -format "%[EXIF*]" image.jpg
# Get number of colours
convert image.jpg -format "%k" info:
Other parameters may be suggested by other responders, and you can find most of that using:
identify -verbose image.jpg
回答2:
Compute the entropy of the image. Artificial images usually have much lower entropy than photographs.
