下面是在Python/OpenCV中实现这一点的一种方法。

• 读取输入
• 转换为灰色
• 阈值
• 获取白色像素的边界
• 将输入裁剪到这些边界以移除黑色边框
• 对裁剪图像设置阈值以隔离较亮区域
• 应用形态学对其进行清理
• 获取轮廓及其边界框
• 测试边界框是否接触裁剪图像的4个边中的任何一个
• 在输入图像上绘制轮廓边界框
• 保存结果

输入：

import cv2
import numpy as np

# load image as grayscale
img = cv2.imread('streak.png')

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# threshold 
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]

# get bounds of white pixels
white = np.where(thresh==255)
xmin, ymin, xmax, ymax = np.min(white[1]), np.min(white[0]), np.max(white[1]), np.max(white[0])
print(xmin,xmax,ymin,ymax)

# crop the gray image at the bounds
crop = gray[ymin:ymax, xmin:xmax]
hh, ww = crop.shape

# do adaptive thresholding
thresh2 = cv2.adaptiveThreshold(crop, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 3, 1.1)

# apply morphology
kernel = np.ones((1,7), np.uint8)
morph = cv2.morphologyEx(thresh2, cv2.MORPH_CLOSE, kernel)
kernel = np.ones((5,5), np.uint8)
morph = cv2.morphologyEx(morph, cv2.MORPH_OPEN, kernel)

# invert
morph = 255 - morph

# get contours (presumably just one) and its bounding box
contours = cv2.findContours(morph, 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)

# draw bounding box on input
bbox = img.copy()
cv2.rectangle(bbox, (x+xmin, y+ymin), (x+xmin+w, y+ymin+h), (0,0,255), 1)

# test if contour touches sides of image
if x == 0 or y == 0 or x+w == ww or y+h == hh:
    print('region touches the sides')
else:
    print('region does not touch the sides')

# save resulting masked image
cv2.imwrite('streak_thresh.png', thresh)
cv2.imwrite('streak_crop.png', crop)
cv2.imwrite('streak_bbox.png', bbox)

# display result
cv2.imshow("thresh", thresh)
cv2.imshow("crop", crop)
cv2.imshow("thresh2", thresh2)
cv2.imshow("morph", morph)
cv2.imshow("bbox", bbox)
cv2.waitKey(0)
cv2.destroyAllWindows()

用于查找边界的阈值图像：

裁剪输入：

形态清洁第二阈值：

输入上区域轮廓的边界框：

打印消息：

region touches the sides

您可以使用此代码来查找轮廓是否接触边界，相对而言，这要快得多：

import cv2
from skimage import measure
from skimage.segmentation import clear_border

img = cv2.imread('image.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_,im_bw = cv2.threshold(gray,127,255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)

cntrs,_ = cv2.findContours(im_bw, 1, 2)
cntrs = sorted(cntrs, key=cv2.contourArea)

# you can also for loop here to find border touching for each contours
# For now, I am only doing it for single contour

cnt = cntr[-1]   #finding whether the last contour is touching the border or not

im_bw = cv2.fillPoly(im_bw, pts=[cnt], color=(1)) #converting to 0/1 pixel range
cleared = clear_border(im_bw)

if len(np.unique(cleared)) == 1:
  print("Contour is touching the border")
else:
  print("Contour is not touching the border")

# If its True, contour is touching the border else it is not touching