在Python中,我试图将图像分成多个圆圈并计算每个圆圈中黑色像素的数量。
例如,我有一个用鱼眼镜头捕获的图像(半球形图像)(如下所示),我想将图像分成小圆圈,捕获图像的一部分,从中间的小圆圈到整个的图像。
一旦我有了圆形图像,我就可以计算每个图像中的像素数。
我尝试过:
Image=Image.new("RGB", (2000,2000))
draw = ImageDraw.Draw(image)
draw.ellipse((20,20,1800,1800),fill(255,255,255)
然后由此创建了一个蒙版,但是无论我如何更改draw.ellipse中的数字,圆都只会捕获整个图像,但会使图像本身变小。
任何有关如何解决此问题的想法或建议将不胜感激!
最佳答案
您应该使用 OpenCV 来完成此类任务。您可以将圆变换为整个轮廓并计算圆的半径。然后,您可以绘制圆形并将它们绘制在掩模上,并执行cv2.bitwise_and
以使圆形在图像上产生ROI。您可以迭代并乘以您选择的整数(在我的例子中为 10)ROI 圆的半径。希望能帮助到你。干杯!
示例代码:
import cv2
import numpy as np
img = cv2.imread('circle.png')
h, w = img.shape[:2]
mask = np.zeros((h, w), np.uint8)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
kernel = np.ones((10,10),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_CLOSE,kernel, iterations = 2)
_, contours, hierarchy = cv2.findContours(opening,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cnt = max(contours, key=cv2.contourArea)
extLeft = tuple(cnt[cnt[:, :, 0].argmin()][0])
extRight = tuple(cnt[cnt[:, :, 0].argmax()][0])
radius = (extRight[0] - extLeft[0])/2
print(extRight[0], extLeft[0])
print(radius)
M = cv2.moments(cnt)
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
print(cx, cy)
for i in range(1,30):
if i*10<radius:
print(i*10)
cv2.circle(mask,(cx,cy), i*10, 255, -1)
res = cv2.bitwise_and(img, img, mask=mask)
pixels = np.sum(res == 255)
cv2.putText(res,'Pixel count: '+str(pixels),(30,30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA)
cv2.imshow('img', res)
cv2.waitKey(0)
cv2.destroyAllWindows()
else:
res = cv2.bitwise_and(img, img, mask=opening)
pixels = np.sum(res == 255)
cv2.putText(img,'Pixel count: '+str(pixels),(30,30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA)
cv2.imshow('img', res)
cv2.waitKey(0)
cv2.destroyAllWindows()
break
结果:
编辑:
尝试用不同的方法来计算中间
import cv2
import numpy as np
img = cv2.imread('circle.png')
h, w = img.shape[:2]
mask = np.zeros((h, w), np.uint8)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
kernel = np.ones((10,10),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_CLOSE,kernel, iterations = 2)
_, contours, hierarchy = cv2.findContours(opening,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cnt = max(contours, key=cv2.contourArea)
cv2.imshow('img22', opening)
extLeft = tuple(cnt[cnt[:, :, 0].argmin()][0])
extRight = tuple(cnt[cnt[:, :, 0].argmax()][0])
radius = (extRight[0] - extLeft[0])/2
x,y,w,h = cv2.boundingRect(cnt)
cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
cx = int(x+(w/2))
cy = int(y+h/2)
for i in range(1,30):
if i*10<radius:
print(i*10)
cv2.circle(mask,(cx,cy), i*10, 255, -1)
res = cv2.bitwise_and(img, img, mask=mask)
pixels = np.sum(res == 255)
cv2.putText(res,'Pixel count: '+str(pixels),(30,30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA)
cv2.imshow('img', res)
cv2.waitKey(0)
cv2.destroyAllWindows()
else:
res = cv2.bitwise_and(img, img, mask=opening)
pixels = np.sum(res == 255)
cv2.putText(img,'Pixel count: '+str(pixels),(30,30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA)
cv2.imshow('img', res)
cv2.waitKey(0)
cv2.destroyAllWindows()
break
编辑2:
好吧,我对你的第一个示例图像的假设是,你的图像从一开始就几乎是一个圆圈。因为它不是你必须以不同的方式计算中心(就像我的第一次编辑 - 从边界框)并制作一个更大的内核(40,40) - 由于图像非常大。另外,你必须使 i 在范围阈值内(例如 10000)。这将起作用:
import cv2
import numpy as np
img = cv2.imread('circleroi.jpg')
h, w = img.shape[:2]
mask = np.zeros((h, w), np.uint8)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
kernel = np.ones((40,40),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_CLOSE,kernel, iterations = 2)
_, contours, hierarchy = cv2.findContours(opening,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cnt = max(contours, key=cv2.contourArea)
extLeft = tuple(cnt[cnt[:, :, 0].argmin()][0])
extRight = tuple(cnt[cnt[:, :, 0].argmax()][0])
radius = (extRight[0] - extLeft[0])/2
x,y,w,h = cv2.boundingRect(cnt)
cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
cx = int(x+(w/2))
cy = int(y+h/2)
for i in range(1,10000):
if i*10<radius:
cv2.circle(mask,(cx,cy), i*10, 255, -1)
res = cv2.bitwise_and(img, img, mask=mask)
pixels = np.sum(res == 255)
cv2.putText(res,'Pixel count: '+str(pixels),(30,30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA)
cv2.imshow('img', res)
cv2.waitKey(0)
cv2.destroyAllWindows()
else:
res = cv2.bitwise_and(img, img, mask=opening)
pixels = np.sum(res == 255)
cv2.putText(img,'Pixel count: '+str(pixels),(30,30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA)
cv2.imshow('img', res)
cv2.waitKey(0)
cv2.destroyAllWindows()
break
关于python - 在python中创建圆圈来掩盖图像并计算每个圆圈内的像素,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/52762929/