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?
我的 idea 是使用轮廓查找来获得圆的中心，然后找到整个坐标数组中每个点到中心的距离，以最大距离为半径，解析地找到正方形的角点，但肯定有更好的方法(而且我也没有找到角点的公式).
就圆心居中而言，这可能还不够，但使用我的迭代处理，可以裁剪到圆区域内最大矩形的近似值.
输入:
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()
结果:
