I have frames of a video taken from a microscope. I need to crop them to a square inscribed to the circle but the issue is that the circle isn't whole (like in the following image). How can I do it?

My idea was to use contour finding to get the center of the circle and then find the distance from each point over the whole array of coordinates to the center, take the maximum distance as the radius and find the corners of the square analytically but there must be a better way to do it (also I don't really have a formula to find the corners).

CodePudding user response：

This may not be adequate in terms of centered at center of circle, but using my iterative processing, one can crop to an approximation of the largest rectangle inside your circle area.

Input:

import cv2
import numpy as np

# read image
img = cv2.imread('img.jpg')
h, w = img.shape[:2]

# threshold so border is black and rest is white (invert as needed). 
# Here I needed to specify the upper threshold at 20 as your black is not pure black.

lower = (0,0,0)
upper = (20,20,20)
mask = cv2.inRange(img, lower, upper)
mask = 255 - mask

# define top and left starting coordinates and starting width and height
top = 0
left = 0
bottom = h
right = w

# compute the mean of each side of the image and its stop test
mean_top = np.mean( mask[top:top 1, left:right] )
mean_left = np.mean( mask[top:bottom, left:left 1] )
mean_bottom = np.mean( mask[bottom-1:bottom, left:right] )
mean_right = np.mean( mask[top:bottom, right-1:right] )

mean_minimum = min(mean_top, mean_left, mean_bottom, mean_right)

top_test = "stop" if (mean_top == 255) else "go"
left_test = "stop" if (mean_left == 255) else "go"
bottom_test = "stop" if (mean_bottom == 255) else "go"
right_test = "stop" if (mean_right == 255) else "go"

# iterate to compute new side coordinates if mean of given side is not 255 (all white) and it is the current darkest side
while top_test == "go" or left_test == "go" or right_test == "go" or bottom_test == "go":

    # top processing
    if top_test == "go":
        if mean_top != 255:
            if mean_top == mean_minimum:
                top  = 1
                mean_top = np.mean( mask[top:top 1, left:right] )
                mean_left = np.mean( mask[top:bottom, left:left 1] )
                mean_bottom = np.mean( mask[bottom-1:bottom, left:right] )
                mean_right = np.mean( mask[top:bottom, right-1:right] )
                mean_minimum = min(mean_top, mean_left, mean_right, mean_bottom)
                #print("top",mean_top)
                continue
        else:
            top_test = "stop"   

    # left processing
    if left_test == "go":
        if mean_left != 255:
            if mean_left == mean_minimum:
                left  = 1
                mean_top = np.mean( mask[top:top 1, left:right] )
                mean_left = np.mean( mask[top:bottom, left:left 1] )
                mean_bottom = np.mean( mask[bottom-1:bottom, left:right] )
                mean_right = np.mean( mask[top:bottom, right-1:right] )
                mean_minimum = min(mean_top, mean_left, mean_right, mean_bottom)
                #print("left",mean_left)
                continue
        else:
            left_test = "stop"  

    # bottom processing
    if bottom_test == "go":
        if mean_bottom != 255:
            if mean_bottom == mean_minimum:
                bottom -= 1
                mean_top = np.mean( mask[top:top 1, left:right] )
                mean_left = np.mean( mask[top:bottom, left:left 1] )
                mean_bottom = np.mean( mask[bottom-1:bottom, left:right] )
                mean_right = np.mean( mask[top:bottom, right-1:right] )
                mean_minimum = min(mean_top, mean_left, mean_right, mean_bottom)
                #print("bottom",mean_bottom)
                continue
        else:
            bottom_test = "stop"    

    # right processing
    if right_test == "go":
        if mean_right != 255:
            if mean_right == mean_minimum:
                right -= 1
                mean_top = np.mean( mask[top:top 1, left:right] )
                mean_left = np.mean( mask[top:bottom, left:left 1] )
                mean_bottom = np.mean( mask[bottom-1:bottom, left:right] )
                mean_right = np.mean( mask[top:bottom, right-1:right] )
                mean_minimum = min(mean_top, mean_left, mean_right, mean_bottom)
                #print("right",mean_right)
                continue
        else:
            right_test = "stop" 


# crop input
result = img[top:bottom, left:right]

# print crop values 
print("top: ",top)
print("bottom: ",bottom)
print("left: ",left)
print("right: ",right)
print("height:",result.shape[0])
print("width:",result.shape[1])

# save cropped image
#cv2.imwrite('border_image1_cropped.png',result)
cv2.imwrite('img_cropped.png',result)
cv2.imwrite('img_mask.png',mask)

# show the images
cv2.imshow("mask", mask)
cv2.imshow("cropped", result)
cv2.waitKey(0)
cv2.destroyAllWindows()

Result: