问题描述
我正在使用python和PIL在640x480图像中找到各种矩形（和正方形）的质心和旋转，类似于这个
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
到目前为止，我的代码适用于图像中的单个矩形。
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()
我遇到问题的地方是图像中有多个对象的时候。
Where I am having problems is when there is more than one object in the image.
我知道PIL有一个find_edges方法，它只给出了边缘的图像，但我不知道如何使用这个新的边缘图像将图像分割成单独的对象。
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")
如果我可以使用边缘将图像分割成单独的矩形然后我可以在每个矩形上运行我的第一个代码来获得质心和旋转。
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.
但更好的是成为能够使用边缘计算每个矩形的旋转和质心，而无需向上分割图像。
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.
非常感谢大家的帮助！
Everyone's help is greatly appreciated!
推荐答案
您需要在找到角落之前识别每个对象。您只需要对象的边框，因此您也可以减少初始输入。然后只需要跟踪每个不同的边界来找到你的角落，在你知道每个不同的边界后直接找到质心。
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.
使用下面的代码，这里是你得到了什么（质心是红点，白色文字是以度为单位的旋转）：
Using the code below, here is what you get (centroid is the red point, the white text is the rotation in degrees):
请注意，您的输入不是二进制，因此我使用了一个非常简单的阈值。此外，以下代码是实现此目的的最简单方法，任何体面的库中都有更快的方法。
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])
这篇关于使用python和PIL对图像进行分割，以计算多个矩形对象的质心和旋转的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！