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Segment an image using python and PIL to calculate centroid and rotations of multiple rectangular objects

I am using python and PIL to find the centroid and rotation of various rectangles (and squares) in a 640x480 image, similar to this one enter image description here

So far my code works for a single rectangle in an image.

import Image, math

def find_centroid(im):
    width, height = im.size
    XX, YY, count = 0, 0, 0
    for x in xrange(0, width, 1):
        for y in xrange(0, height, 1):
            if im.getpixel((x, y)) == 0:
                XX += x
                YY += y
                count += 1
    return XX/count, YY/count

#Top Left Vertex
def find_vertex1(im):
    width, height = im.size
    for y in xrange(0, height, 1):
        for x in xrange (0, width, 1):
            if im.getpixel((x, y)) == 0:
                X1=x
                Y1=y
                return X1, Y1

#Bottom Left Vertex
def find_vertex2(im):
    width, height = im.size
    for x in xrange(0, width, 1):
        for y in xrange (height-1, 0, -1):
            if im.getpixel((x, y)) == 0:
                X2=x
                Y2=y
                return X2, Y2

#Top Right Vertex
def find_vertex3(im):
    width, height = im.size
    for x in xrange(width-1, 0, -1):
        for y in xrange (0, height, 1):
            if im.getpixel((x, y)) == 0:
                X3=x
                Y3=y
                return X3, Y3

#Bottom Right Vertex
def find_vertex4 (im):
    width, height = im.size
    for y in xrange(height-1, 0, -1):
        for x in xrange (width-1, 0, -1):
            if im.getpixel((x, y)) == 0:
                X4=x
                Y4=y
                return X4, Y4

def find_angle (V1, V2, direction):
    side1=math.sqrt(((V1[0]-V2[0])**2))
    side2=math.sqrt(((V1[1]-V2[1])**2))
    if direction == 0:
        return math.degrees(math.atan(side2/side1)), 'Clockwise'
    return 90-math.degrees(math.atan(side2/side1)), 'Counter Clockwise'

#Find direction of Rotation; 0 = CW, 1 = CCW
def find_direction (vertices, C):
    high=480
    for i in range (0,4):
        if vertices[i][1]<high:
            high = vertices[i][1]
            index = i
    if vertices[index][0]<C[0]:
        return 0
    return 1

def main():
    im = Image.open('hopperrotated2.png')
    im = im.convert('1') # convert image to black and white
    print 'Centroid ', find_centroid(im)
    print 'Top Left ', find_vertex1 (im)
    print 'Bottom Left ', find_vertex2 (im)
    print 'Top Right', find_vertex3 (im)
    print 'Bottom Right ', find_vertex4 (im)
    C = find_centroid (im)
    V1 = find_vertex1 (im)
    V2 = find_vertex3 (im)
    V3 = find_vertex2 (im)
    V4 = find_vertex4 (im)
    vertices = [V1,V2,V3,V4]
    direction = find_direction(vertices, C)
    print 'angle: ', find_angle(V1,V2,direction)

if __name__ == '__main__':
  main()

Where I am having problems is when there is more than one object in the image.

I know PIL has a find_edges method that gives an image of just the edges, but I have no idea how to use this new edge image to segment the image into the separate objects.

from PIL import Image, ImageFilter

im = Image.open('hopperrotated2.png')

im1 = im.filter(ImageFilter.FIND_EDGES)
im1 = im1.convert('1')
print im1
im1.save("EDGES.jpg")

if I can use the edges to segment the image into individual rectangles then i can just run my first bit of code on each rectangle to get centroid and rotation.

But what would be better is to be able to use the edges to calculate rotation and centroid of each rectangle without needing to split the image up.

Everyone's help is greatly appreciated!

like image 426
aroushan Avatar asked Jan 14 '23 13:01

aroushan


2 Answers

You need to identify each object before finding the corners. You only need the border of the objects, so you could also reduce your initial input to that. Then it is only a matter of following each distinct border to find your corners, the centroid is directly found after you know each distinct border.

Using the code below, here is what you get (centroid is the red point, the white text is the rotation in degrees):

enter image description here

Note that your input is not binary, so I used a really simple threshold for that. Also, the following code is the simplest way to achieve this, there are faster methods in any decent library.

import sys
import math
from PIL import Image, ImageOps, ImageDraw

orig = ImageOps.grayscale(Image.open(sys.argv[1]))
orig_bin = orig.point(lambda x: 0 if x < 128 else 255)
im = orig_bin.load()

border = Image.new('1', orig.size, 'white')
width, height = orig.size
bim = border.load()
# Keep only border points
for x in xrange(width):
    for y in xrange(height):
        if im[x, y] == 255:
            continue
        if im[x+1, y] or im[x-1, y] or im[x, y+1] or im[x, y-1]:
            bim[x, y] = 0
        else:
            bim[x, y] = 255

# Find each border (the trivial dummy way).
def follow_border(im, x, y, used):
    work = [(x, y)]
    border = []
    while work:
        x, y = work.pop()
        used.add((x, y))
        border.append((x, y))
        for dx, dy in ((1, 0), (-1, 0), (0, 1), (0, -1),
                (1, 1), (-1, -1), (1, -1), (-1, 1)):
            px, py = x + dx, y + dy
            if im[px, py] == 255 or (px, py) in used:
                continue
            work.append((px, py))

    return border

used = set()
border = []
for x in xrange(width):
    for y in xrange(height):
        if bim[x, y] == 255 or (x, y) in used:
            continue
        b = follow_border(bim, x, y, used)
        border.append(b)

# Find the corners and centroid of each rectangle.
rectangle = []
for b in border:
    xmin, xmax, ymin, ymax = width, 0, height, 0
    mean_x, mean_y = 0, 0
    b = sorted(b)
    top_left, bottom_right = b[0], b[-1]
    for x, y in b:
        mean_x += x
        mean_y += y
    centroid = (mean_x / float(len(b)), mean_y / float(len(b)))
    b = sorted(b, key=lambda x: x[1])
    curr = 0
    while b[curr][1] == b[curr + 1][1]:
        curr += 1
    top_right = b[curr]
    curr = len(b) - 1
    while b[curr][1] == b[curr - 1][1]:
        curr -= 1
    bottom_left = b[curr]

    rectangle.append([
        [top_left, top_right, bottom_right, bottom_left], centroid])


result = orig.convert('RGB')
draw = ImageDraw.Draw(result)
for corner, centroid in rectangle:
    draw.line(corner + [corner[0]], fill='red', width=2)
    cx, cy = centroid
    draw.ellipse((cx - 2, cy - 2, cx + 2, cy + 2), fill='red')
    rotation = math.atan2(corner[0][1] - corner[1][1],
            corner[1][0] - corner[0][0])
    rdeg = math.degrees(rotation)
    draw.text((cx + 10, cy), text='%.2f' % rdeg)

result.save(sys.argv[2])
like image 169
mmgp Avatar answered Jan 17 '23 11:01

mmgp


Here is an example of how you can do this by labelling the image, and then taking the centroid for the centers, this is all built in to ndimage in scipy (along with a bunch of other cool image things). For the angles, I've used the rectangle corner intercepts with the edges of the bounding slices.

import numpy as np
import scipy
from scipy import ndimage

im = scipy.misc.imread('6JYjd.png',flatten=1)
im = np.where(im > 128, 0, 1)
label_im, num = ndimage.label(im)
slices = ndimage.find_objects(label_im)
centroids = ndimage.measurements.center_of_mass(im, label_im, xrange(1,num+1))

angles = []
for s in slices:
    height, width = label_im[s].shape
    opp = height - np.where(im[s][:,-1]==1)[0][-1] - 1
    adj = width - np.where(im[s][-1,:]==1)[0][0] - 1
    angles.append(np.degrees(np.arctan2(opp,adj)))
print 'centers:', centroids
print 'angles:', angles

Output:

centers: [(157.17299748926865, 214.20652790151453), (219.91948280928594, 442.7146635321775), (363.06183745583041, 288.57169725293517)]
angles: [7.864024795499545, 26.306963825741803, 7.937188000622946]
like image 21
fraxel Avatar answered Jan 17 '23 11:01

fraxel