OpenCV使用cv2.canny和cv2.findContours查找异构图像上的数据区域

Question

这是我在这里的第一个问题，所以我要求大家理解。我要处理几百张卫星图像。
我试着找出图像上有用数据所在区域的轮廓-只有最大的一个。 然后我想保存这个轮廓对应的几个点（x，y）的坐标。在最简单的情况下，面积是一个正方形，可以用4个点来表示，但是对于更复杂的形状，轮廓将由更多的点近似（最好不超过15个点）。然而，我仍然无法找到我的图像区域。有时该区域会触及图像的边缘。因此，在这个脚本中，我放大了图片并添加了由背景色填充的附加边界。您将在这里找到图片示例satellite1，satellite2，satellite3 如您所见，这些图像可以有不同的背景颜色，此外，它们还包含国家边界和图例。我尝试过使用Aidenhjj技巧OpenCV - using cv2.approxPolyDP() correctly并准备了我的脚本。我尝试了许多方法，过滤和调整参数，但仍然无法成功地处理我的数据。我在请求你的帮助。在

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

image = cv2.imread('image1.jpg')
image = cv2.resize(image, None,fx=0.25, fy=0.25, interpolation = cv2.INTER_CUBIC)
ysize, xsize, channels = image.shape
print("Image size: {} x {}".format(xsize, ysize))

#calculate the histograms in r,g,b channels, measure background color
r, g, b = cv2.split(image)
image_data = image

histr = cv2.calcHist([r],[0],None,[256],[0,256])
for y in range(0,len(histr)):
    elem = histr[y]
    if elem == histr.max():
     break
else:
    y = none
R=y

histr = cv2.calcHist([g],[0],None,[256],[0,256])
for y in range(0,len(histr)):
    elem = histr[y]
    if elem == histr.max():
     break
else:
    y = none
G=y

histr = cv2.calcHist([b],[0],None,[256],[0,256])
for y in range(0,len(histr)):
    elem = histr[y]
    if elem == histr.max():
     break
else:
    y = none
B=y
color = (R, G, B)

#add borders around the image colorized as background. This will allow me to find closed contour around area with data.
bordersize=100
new_xsize = xsize + bordersize*2
new_ysize = ysize + bordersize*2
#image_border.show()
image_border=cv2.copyMakeBorder(image, top=bordersize, bottom=bordersize, left=bordersize, right=bordersize, borderType= cv2.BORDER_CONSTANT, value=[R,G,B] )

#ysizeb, xsizeb, channelsb = image_border.shape

# get a blank canvas for drawing contour on and convert image to grayscale
canvas = np.zeros(image_border.shape, np.uint8)
#imgc = cv2.medianBlur(img,21)
img2gray = cv2.cvtColor(image_border,cv2.COLOR_BGR2GRAY)

# filter out country borders
kernel = np.ones((5,5),np.float32)/25
img2gray = cv2.filter2D(img2gray,-1,kernel)

# threshold the image and extract contours
thresh = cv2.adaptiveThreshold(img2gray,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,11)
contours,hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
plt.subplot(111),plt.imshow(thresh,'gray')
plt.show()
# find the biggest area
cnt = contours[0]

max_area = cv2.contourArea(cnt)

for cont in contours:
    if cv2.contourArea(cont) > max_area:
        cnt = cont
        max_area = cv2.contourArea(cont)
perimeter = cv2.arcLength(cnt,True)
epsilon = 0.01*cv2.arcLength(cnt,True)
approx = cv2.approxPolyDP(cnt,epsilon,True)

hull = cv2.convexHull(cnt)

# cv2.isContourConvex(cnt)
cv2.drawContours(canvas, cnt, -1, (0, 255, 0), 3)
cv2.drawContours(canvas, approx, -1, (0, 0, 255), 3)
#cv2.drawContours(canvas, [hull], -1, (0, 0, 255), 3) 

cv2.imshow("Contour", canvas)
k = cv2.waitKey(0)

if k == 27:         # wait for ESC key to exit
    cv2.destroyAllWindows()