segmentation of balloons

[cso]

Hi wizards,

How can I get the bbox info of the region of interest of a conversation balloon in messenger apps?
Different for user1 and user2.

Example image 1: https://cornerplay.files.wordpress.com/ ... -46-30.png
Example image 2: http://2.media.collegehumor.cvcdn.com/8 ... 351497.jpg
Example image 3: https://www.groovypost.com/wp-content/u ... 1x480.jpeg
Example image 4: http://www.iphon.fr/public/jcsatanas2/2 ... to-1_m.jpg

[fmw42]

Using your second image, since it was the cleanest to use. You can convert your image into black everywhere there is no blue and white where there is blue. Then run connected-components processing with small area removal to remove the text from inside the balloons so you have solid white regions on a black background. I ran it first without the area threshold to see what would be a good area threshold. See http://magick.imagemagick.org/script/co ... onents.php

Input:


# convert to black and white

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convert text_balloons.jpg -fuzz 20% -fill black +opaque "#509DFB" -fill white +opaque black text_balloons_bw.png

# do connected components processing

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convert text_balloons_bw.png \
-define connected-components:verbose=true \
-define connected-components:area-threshold=200 \
-define connected-components:mean-color=true \
-connected-components 8 \
text_balloons_bw_ccl.png

# text result:
Objects (id: bounding-box centroid area mean-color):
0: 600x900+0+0 267.4,472.5 391802 gray(0)
1: 418x207+172+130 376.5,233.2 83865 gray(255)
133: 421x135+170+492 375.7,559.2 54790 gray(255)
209: 169x64+421+745 502.0,777.0 9543 gray(255)

The bounding boxes are the second item each row. You want the ones that have color gray(255).

# Image result:

[cso]

Thanks Fred!
What about the gray regions (user2)?
Also what if I don't know the color of the balloons beforehand, e.g. #509DFB ?

Edit: it's ok if the tip of the balloon is not included or even if they are cropped into rectangular regions (instead of a rounded rectangle).

[fmw42]

You can run the connected components on your original image after reducing the number of colors to some reasonable amount. It looks like you have about 6 colors. So I will allow 2 more or 8 colors. So I add +dither -colors 8 to the command. Pick a large threshold that is smaller than the smallest item (gray or blue balloon) in area.

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convert text_balloons.jpg +dither -colors 8 \
-define connected-components:verbose=true \
-define connected-components:area-threshold=5000 \
-define connected-components:mean-color=true \
-connected-components 8 \
text_balloons_ccl.png

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Objects (id: bounding-box centroid area mean-color):
  1673: 600x706+0+120 257.1,508.7 227856 srgb(249,249,250)
  1685: 411x205+173+131 376.3,233.2 81373 srgb(42,140,248)
  0: 600x120+0+0 298.1,59.9 70181 srgb(249,249,250)
  8446: 417x134+170+493 375.2,559.8 53362 srgb(42,140,248)
  11856: 300x99+79+637 231.5,686.2 28061 srgb(212,224,228)
  13959: 473x53+15+837 251.0,863.0 25023 srgb(249,249,250)
  7226: 354x63+79+346 258.9,377.2 20652 srgb(212,224,228)
  13957: 600x74+0+826 362.2,861.9 19363 srgb(249,249,250)
  13273: 163x63+422+746 502.1,777.4 9215 srgb(42,140,248)

They are sorted by largest area first. So you will have to analyze the regions by size and color and select appropriate ones.

[fmw42]

You can also run it twice to further remove smaller areas. Here I do it twice. The first time I skip -define connected-components:verbose=true

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convert text_balloons.jpg +dither -colors 8 \
-define connected-components:area-threshold=5000 \
-define connected-components:mean-color=true \
-connected-components 8 \
-define connected-components:verbose=true \
-define connected-components:area-threshold=5000 \
-define connected-components:mean-color=true \
-connected-components 8 \
text_balloons_ccl_2.png

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Objects (id: bounding-box centroid area mean-color):
  0: 600x900+0+0 271.5,460.4 344256 srgb(98%,98%,98%)
  14: 411x205+173+131 376.8,233.2 82887 srgb(17%,55%,97%)
  59: 417x134+170+493 375.8,559.7 54358 srgb(17%,55%,97%)
  90: 300x99+79+637 231.5,686.2 28151 srgb(83%,88%,89%)
  56: 354x63+79+346 257.9,377.2 21019 srgb(83%,88%,89%)
  96: 163x63+422+746 502.3,777.3 9313 srgb(17%,55%,97%)

[cso]

Thanks!
Doesn't seem to work very well on the others, if you think of an alternative way let me know!

[fmw42]

The issue is that you need to have separated regions and the area threshold is going to be dependent upon the size of your balloons. Also it will depend upon the number of unique colors. So you may want to increase the value of -colors 8. And you need to know the colors you have for the balloons to separate them out from the list.

Sorry, I do not know of any other better way.

[cso]

Hm, I thought of a histogram approach because the balloons of user1 start at the same "x" coordinates. Same with user2.
Maybe crop 20% of the image on the left and the right and do some kind of histogram analysis (e.g. most pixels start at x=20)?
Alternatively perform hough transform (to find the starting line)?
I don't know how to implement this, can you help?

[fmw42]

The histogram does not tell locations. You can get locations and colors by converting to txt: output format, if you reduce the number of colors to 256 or less.

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convert image txt:

To do the hough transform, you need to do edge detection first. See
viewtopic.php?f=4&t=25405
viewtopic.php?f=4&t=25476

I do not think those will help much, but you can try. I suspect you will get too many lines.

[cso]

Ah then I meant something whose output looks like a histogram, I think it's called vertical projections (where it sums all the pixels of a specific y). What do you think?
Is there anything that can do that?

[fmw42]

I am not sure what you are asking? You want the average value of each column of data? If so, just average the image down to one row. Each pixel value will be the sum of all pixels in that column.

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convert image -scale x1! result

You can save result as txt: and have it list all the x values and their respective color.

If you mean something else, then please clarify or show us an example.

[cso]

I searched for it and it's called vertical (or horizontal) projection histograms.

Example:
https://www.researchgate.net/profile/Da ... e-blob.png

https://stackoverflow.com/questions/376 ... am-profile

For example, canny edge detection and then count the number of pixels for each column (for each y). Immediately before the balloon tip, it will be zero (zero white pixels), at the tip it will be a small number and after the tip it will be a big number (the left edge of the balloon).
I don't think what you posted is the same thing, please correct me if I'm wrong.

[fmw42]

It is the same thing as what I did except they graph the intensity of the resulting values. It is easy to do in black and white. For a color image, you would have to graph the red, green and blue channels. See my script profile or plot.



Input:


1) Convert to black and white, scale to 1 column, do profile

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convert text_balloons.jpg -fuzz 20% -fill black +opaque white -negate -type grayscale text_balloons_binary.png

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convert text_balloons_binary.png -scale 1x! text_balloons_binary_column.png
profile -c 1 -f 1 text_balloons_binary_column.png text_balloons_binary_column_profile.png

or convert column image to txt and see the coordinates vs colors

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convert text_balloons_binary_column.png txt:

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# ImageMagick pixel enumeration: 1,900,65535,gray
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0,821: (0,0,0)  #000000  gray(0)
0,822: (0,0,0)  #000000  gray(0)
0,823: (0,0,0)  #000000  gray(0)
0,824: (0,0,0)  #000000  gray(0)
0,825: (65535,65535,65535)  #FFFFFF  gray(255)
0,826: (2570,2570,2570)  #0A0A0A  gray(10)
0,827: (1542,1542,1542)  #060606  gray(6)
0,828: (2313,2313,2313)  #090909  gray(9)
0,829: (1799,1799,1799)  #070707  gray(7)
0,830: (2313,2313,2313)  #090909  gray(9)
0,831: (2056,2056,2056)  #080808  gray(8)
0,832: (2056,2056,2056)  #080808  gray(8)
0,833: (2056,2056,2056)  #080808  gray(8)
0,834: (2056,2056,2056)  #080808  gray(8)
0,835: (2056,2056,2056)  #080808  gray(8)
0,836: (1799,1799,1799)  #070707  gray(7)
0,837: (9509,9509,9509)  #252525  gray(37)
0,838: (2056,2056,2056)  #080808  gray(8)
0,839: (1028,1028,1028)  #040404  gray(4)
0,840: (771,771,771)  #030303  gray(3)
0,841: (771,771,771)  #030303  gray(3)
0,842: (771,771,771)  #030303  gray(3)
0,843: (771,771,771)  #030303  gray(3)
0,844: (771,771,771)  #030303  gray(3)
0,845: (771,771,771)  #030303  gray(3)
0,846: (771,771,771)  #030303  gray(3)
0,847: (771,771,771)  #030303  gray(3)
0,848: (771,771,771)  #030303  gray(3)
0,849: (771,771,771)  #030303  gray(3)
0,850: (771,771,771)  #030303  gray(3)
0,851: (1799,1799,1799)  #070707  gray(7)
0,852: (2313,2313,2313)  #090909  gray(9)
0,853: (2570,2570,2570)  #0A0A0A  gray(10)
0,854: (3855,3855,3855)  #0F0F0F  gray(15)
0,855: (5140,5140,5140)  #141414  gray(20)
0,856: (5397,5397,5397)  #151515  gray(21)
0,857: (7710,7710,7710)  #1E1E1E  gray(30)
0,858: (8481,8481,8481)  #212121  gray(33)
0,859: (9252,9252,9252)  #242424  gray(36)
0,860: (11565,11565,11565)  #2D2D2D  gray(45)
0,861: (13878,13878,13878)  #363636  gray(54)
0,862: (13364,13364,13364)  #343434  gray(52)
0,863: (15934,15934,15934)  #3E3E3E  gray(62)
0,864: (14649,14649,14649)  #393939  gray(57)
0,865: (15163,15163,15163)  #3B3B3B  gray(59)
0,866: (16448,16448,16448)  #404040  gray(64)
0,867: (20046,20046,20046)  #4E4E4E  gray(78)
0,868: (17219,17219,17219)  #434343  gray(67)
0,869: (15934,15934,15934)  #3E3E3E  gray(62)
0,870: (17733,17733,17733)  #454545  gray(69)
0,871: (18761,18761,18761)  #494949  gray(73)
0,872: (15677,15677,15677)  #3D3D3D  gray(61)
0,873: (12079,12079,12079)  #2F2F2F  gray(47)
0,874: (9252,9252,9252)  #242424  gray(36)
0,875: (3598,3598,3598)  #0E0E0E  gray(14)
0,876: (1542,1542,1542)  #060606  gray(6)
0,877: (1542,1542,1542)  #060606  gray(6)
0,878: (1799,1799,1799)  #070707  gray(7)
0,879: (2056,2056,2056)  #080808  gray(8)
0,880: (2056,2056,2056)  #080808  gray(8)
0,881: (1285,1285,1285)  #050505  gray(5)
0,882: (771,771,771)  #030303  gray(3)
0,883: (771,771,771)  #030303  gray(3)
0,884: (771,771,771)  #030303  gray(3)
0,885: (771,771,771)  #030303  gray(3)
0,886: (771,771,771)  #030303  gray(3)
0,887: (771,771,771)  #030303  gray(3)
0,888: (3341,3341,3341)  #0D0D0D  gray(13)
0,889: (7710,7710,7710)  #1E1E1E  gray(30)
0,890: (2056,2056,2056)  #080808  gray(8)
0,891: (1799,1799,1799)  #070707  gray(7)
0,892: (1799,1799,1799)  #070707  gray(7)
0,893: (2313,2313,2313)  #090909  gray(9)
0,894: (1799,1799,1799)  #070707  gray(7)
0,895: (1799,1799,1799)  #070707  gray(7)
0,896: (1799,1799,1799)  #070707  gray(7)
0,897: (1799,1799,1799)  #070707  gray(7)
0,898: (1799,1799,1799)  #070707  gray(7)
0,899: (1799,1799,1799)  #070707  gray(7)

2) Use color image, scale to 1 column, do profile

Code: Select all

convert text_balloons.jpg -scale 1x! text_balloons_column.png
profile -c 1 text_balloons_column.png text_balloons_column_profile.png

P.S. In this last example, perhaps it would be better to convert white to black before scaling to 1 column.

[cso]

Nice!
I meant scale to one row but it's simple to change that(1x! vs !x1).
How can I get the max value out of text_balloons_binary_column.png?

[fmw42]

Code: Select all

convert text_balloons_binary_column.png -format "%[fx:maxima]" info:

This will be a value in the range of 0 to 1, but you can scale for example to percent as

Code: Select all

convert text_balloons_binary_column.png -format "%[fx:100*maxima]" info:

This will be the maximum value of all the pixels in the column. Is that what you want?