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
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!
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):
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])
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]
来源：https://stackoverflow.com/questions/14263050/segment-an-image-using-python-and-pil-to-calculate-centroid-and-rotations-of-mul