How to center the content/object of a binary image in python?

Question

I have a code that computes the orientation of a figure. Based on this orientation the figure is then rotated until it is straightened out. This all works fine. What I am struggling with, is getting the center of the rotated figure to the center of the whole image. So the center point of the figure should match the center point of the whole image.

Input image:

code:

import cv2
import numpy as np
import matplotlib.pyplot as plt

path = "inputImage.png"


image=cv2.imread(path)
gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
thresh=cv2.threshold(gray,0,255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]

contours,hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
cnt1 = contours[0]
cnt=cv2.convexHull(contours[0])
angle = cv2.minAreaRect(cnt)[-1]
print("Actual angle is:"+str(angle))
rect = cv2.minAreaRect(cnt)

p=np.array(rect[1])

if p[0] < p[1]:
        print("Angle along the longer side:"+str(rect[-1] + 180))
        act_angle=rect[-1]+180
else:
        print("Angle along the longer side:"+str(rect[-1] + 90))
        act_angle=rect[-1]+90
#act_angle gives the angle of the minAreaRect with the vertical

if act_angle < 90:
        angle = (90 + angle)
        print("angleless than -45")

        # otherwise, just take the inverse of the angle to make
        # it positive
else:
        angle=act_angle-180
        print("grter than 90")

# rotate the image to deskew it
(h, w) = image.shape[:2]
print(h,w)
center = (w // 2, h // 2)
print(center)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(image, M, (w, h),flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)

plt.imshow(rotated)
cv2.imwrite("rotated.png", rotated)

With output:

As you can see the white figure is slightly placed to left, I want it to be perfectly centered. Does anyone know how this can be done?

EDIT : I have tried @joe's suggestion and subtracted the centroid coordinates, from the center of the image by dividing the width and height of the picture by 2. From this I got an offset, this had to be added to the array that describes the image. But I don't know how I add the offset to the array. How would this work with the x and y coordinates?

The code:

img = cv2.imread("inputImage")
gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(gray_image,127,255,0)

height, width = gray_image.shape
print(img.shape)
wi=(width/2)
he=(height/2)
print(wi,he)
M = cv2.moments(thresh)

cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])

offsetX = (wi-cX)
offsetY = (he-cY)


print(offsetX,offsetY)
print(cX,cY)

Answer 1

Here is one way in Python/OpenCV.

Get the bounding box for the white region from the contours. Compute the offset for the recentered region. Use numpy slicing to copy that to the center of a black background the size of the input.

Input:

import cv2
import numpy as np

# read image as grayscale
img = cv2.imread('white_shape.png', cv2.COLOR_BGR2GRAY)

# get shape
hh, ww = img.shape


# get contours (presumably just one around the nonzero pixels) 
contours = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
for cntr in contours:
    x,y,w,h = cv2.boundingRect(cntr)

# recenter
startx = (ww - w)//2
starty = (hh - h)//2
result = np.zeros_like(img)
result[starty:starty+h,startx:startx+w] = img[y:y+h,x:x+w]

# view result
cv2.imshow("RESULT", result)
cv2.waitKey(0)
cv2.destroyAllWindows()

# save reentered image
cv2.imwrite('white_shape_centered.png',result)

Answer 2

One approach is to obtain the bounding box coordinates of the binary object then crop the ROI using Numpy slicing. From here we calculate the new shifted coordinates then paste the ROI onto a new blank mask.

Code

import cv2
import numpy as np

# Load image as grayscale and obtain bounding box coordinates
image = cv2.imread('1.png', 0)
height, width = image.shape
x,y,w,h = cv2.boundingRect(image)

# Create new blank image and shift ROI to new coordinates
mask = np.zeros(image.shape, dtype=np.uint8)
ROI = image[y:y+h, x:x+w]
x = width//2 - ROI.shape[0]//2 
y = height//2 - ROI.shape[1]//2 
mask[y:y+h, x:x+w] = ROI

cv2.imshow('ROI', ROI)
cv2.imshow('mask', mask)
cv2.waitKey()

Answer 3

@NawinNarain, from this point onwards where you found out the relative shifts wrt centroid of the image, it is very straightforward - You want to make an Affine matrix with this translations and apply cv2.warpAffine() to your image. That's -it.

T = np.float32([[1, 0, shift_x], [0, 1, shift_y]]) 

We then use warpAffine() to transform the image using the matrix, T

centered_image = cv2.warpAffine(image, T, (orig_width, orig_height))

This will transform your image so that the centroid is at the center. Hope this helps. The complete center image function will look like this:

def center_image(image):
  height, width = image.shape
  print(img.shape)
  wi=(width/2)
  he=(height/2)
  print(wi,he)

  ret,thresh = cv2.threshold(image,95,255,0)

  M = cv2.moments(thresh)

  cX = int(M["m10"] / M["m00"])
  cY = int(M["m01"] / M["m00"])

  offsetX = (wi-cX)
  offsetY = (he-cY)
  T = np.float32([[1, 0, offsetX], [0, 1, offsetY]]) 
  centered_image = cv2.warpAffine(image, T, (width, height))

  return centered_image