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EDIT: This is a deeper explanation of a question I asked earlier, which is still not solved for me.

I'm currently trying to write some code that can extract data from some uncommon graphs in a book. I scanned the pages of the book, and by using opencv I would like to detect some features ofthe graphs in order to convert them into useable data. In the left graph I'm looking for the height of the "triangles" and in the right graph the distance from the center to the points where the dotted lines intersect with the gray area. In both cases I would like to convert these values into numeric data for further usage.

enter image description here

For the left graph, I thought of detecting all the individual colors and computing the area of each sector by counting the amount of pixels in that color. When I have the area of these sectors, I can easily calculate their heights, using basic math. The following code snippet shows how far I've gotten already with identifying different colors. However I can't manage to make this work accurately. It always seems to detect some colors of other sectors as well, or not detect all pixels of one sector. I think it has something to do with the boundaries I'm using. I can't quite figure out how to make them work. Does someone know how I can determine these values?

import numpy as np
import cv2

img = cv2.imread('images/test2.jpg')

lower = np.array([0,0,100])
upper = np.array([50,56,150])

mask = cv2.inRange(img, lower, upper)
output = cv2.bitwise_and(img, img, mask = mask)

cv2.imshow('img', img)
cv2.imshow('mask', mask)
cv2.imshow('output', output)
cv2.waitKey(0)
cv2.destroyAllWindows()

For the right graph, I still have no idea how to extract data from it. I thought of identifying the center by detecting all the dotted lines, and then by detecting the intersections of these dotted lines with the gray area, I could measure the distance between the center and these intersections. However I couldn't yet figure out how to do this properly, since it sounds quite complex. The following code snippet shows how far I've gotten with the line detection. Also in this case the detection is far from accurate. Does someone have an idea how to tackle this problem?

import numpy as np
import cv2

# Reading the image
img = cv2.imread('test2.jpg')
# Convert the image to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Apply edge detection
edges = cv2.Canny(gray,50,150,apertureSize = 3)

# Line detection
lines = cv2.HoughLinesP(edges,1,np.pi/180,100,minLineLength=50,maxLineGap=20)

for line in lines:
    x1,y1,x2,y2 = line[0]
    cv2.line(img,(x1,y1),(x2,y2),(0,0,255),2)

cv2.imwrite('linesDetected.jpg',img)
Frederik Vanclooster
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1 Answers1

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For the left image, using your approach, try to look at the RGB histogram, the colors should be significant peaks, if you would like to use the relative area of the segments.

Another alternative could be to use Hough Circle Transform, which should work on circle segments. See also here.

For the right image ... let me think ...

You could create a "empty" diagram with no data inside. You know the locations of the circle segment ("cake pieces"). Then you could identify the area where the data is (the dark ones), either by using a grey threshold, an RGB threshold, or Find Contours or look for Watershed / Distance Transform.

In the end the idea is to make a boolean overlay between the cleared image and the segments (your data) that was found. Then you can identify which share of your circle segments is covered, or knowing the center, find the farthest point from the center.

Joe
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