我有图片
我正在寻找python解决方案,以根据图像中的轮廓将图像中的形状分成较小的部分。
我已经研究了OpenCV中Canny和findContours的解决方案,但是它们都不适合我。
使用的代码:
import cv2 import numpy as np
img = cv2.imread('area_of_blob_maxcontrast_white.jpg') edges = cv2.Canny(img, 100, 200)
cv2.imwrite('area_of_blob_maxcontrast_white_edges.jpg',edges)
import numpy as np
import argparse
import cv2
image = cv2.imread('area_of_blob_maxcontrast_white.png')
lower = np.array([0, 0, 0]) upper = np.array([15, 15, 15]) shapeMask = cv2.inRange(image, lower, upper)
(_,cnts, _) = cv2.findContours(shapeMask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE) print "I found %d black shapes" % (len(cnts)) cv2.imshow("Mask", shapeMask)
for c in cnts:
# draw the contour and show it
cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
cv2.imshow("Image", image)
cv2.waitKey(0)
答案 0 :(得分:3)
诀窍是使模糊的单个像素边界稍微大胆。我通过将具有两个相邻黑色像素(在上方,下方,左侧或右侧)的任何白色像素更改为黑色来实现。 (不过,我的速度非常慢。我敢肯定,必须有一种更聪明的方法来使用OpenCV或Numpy来做到这一点。)
这是我的代码:
#!/usr/bin/env python
import numpy as np
import cv2
THRESH = 240
orig = cv2.imread("map.png")
img = cv2.cvtColor(orig, cv2.COLOR_BGR2GRAY)
# Make the faint 1-pixel boundary bolder
rows, cols = img.shape
new_img = np.full_like(img, 255) # pure white image
for y in range(rows):
if not (y % 10):
print ('Row = %d (%.2f%%)' % (y, 100.*y/rows))
for x in range(cols):
score = 1 if y > 0 and img.item(y-1, x) < THRESH else 0
score += 1 if x > 0 and img.item(y, x-1) < THRESH else 0
score += 1 if y < rows-1 and img.item(y+1, x) < THRESH else 0
score += 1 if x < cols-1 and img.item(y, x+1) < THRESH else 0
if img.item(y, x) < THRESH or score >= 2:
new_img[y, x] = 0 # black pixels show boundary
cv2.imwrite('thresh.png', new_img)
# Find all contours on the map
_th, contours, hierarchy = cv2.findContours(new_img,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
print "Number of contours detected = %d" % len(contours)
# Fill second level regions on the map
coln = 0
colors = [
[127, 0, 255],
[255, 0, 127],
[255, 127, 0],
[127, 255, 0],
[0, 127, 255],
[0, 255, 127],
]
hierarchy = hierarchy[0]
for i in range(len(contours)):
area = cv2.contourArea(contours[i])
if hierarchy[i][3] == 1:
print (i, area)
coln = (coln + 1) % len(colors)
cv2.drawContours(orig, contours, i, colors[coln], -1)
cv2.imwrite("colored_map.png", orig)
输入图像:
输出图像:
在这里,我仅对最外轮廓(hierarchy[i][3] == 1
)的直接后代进行着色。但是您可以更改它以排除湖泊。