[英]creating circles in python to mask an image and count pixels within each circle
在python中，我试图将图像分成圆圈并计算每个圆圈中的黑色像素数。
例如，我有一个用鱼眼镜头拍摄的图像（一个半球形图像）（下面的绘制示例），我想将图像分成小圆圈，从中间的一个小圆圈到整个图像捕捉图像的一部分。
我想将图像分成x个圆圈，每次捕捉图像的一部分（见下图）
一旦我有圆形图像，我就可以计算每个图像中的像素数。
我试过了： Image=Image.new("RGB", (2000,2000)) draw = ImageDraw.Draw(image) draw.ellipse((20,20,1800,1800),fill(255,255,255)
然后从中创建了一个蒙版，但无论我如何更改draw.ellipse中的数字，圆圈只会捕获整个图像，但会使图像本身变小。
如何解决这个问题的任何想法或建议将非常感谢！
你应该看看OpenCV来完成这些任务。 您可以将圆转换为整个轮廓并计算圆的半径。 然后，您可以绘制圆圈并在蒙版上绘制它们并执行cv2.bitwise_and并在cv2.bitwise_and上创建圆形ROI。 您可以使用您选择的整数（在我的情况下为10）迭代并乘以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：
尝试用不同的方法计算中间值
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，所以我从你的第一个示例图像中得到的假设是，你的图像几乎就是一个圆圈。 因为不是你必须以不同方式计算中心（比如从我的第一次编辑 - 来自边界框）并制作更大的内核（40,40） - 因为图像非常大。 另外，你必须在范围阈值（如10000）中制作i。 这将有效：
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
如果你对图书馆足够熟悉，这在纯粹的numpy中几乎是直截了当的：
# Create some fake data
np.random.seed(100)
fake_im_arr = np.random.randint(low=0, high=2, size=(2000,2000))
# Function definition for creating masks
def create_circle_mask(X_arr, Y_arr, center, radius):
c_x, c_y = center
dists_sqrd = (X_arr - c_x)**2 + (Y_arr - c_y)**2
return dists_sqrd <= radius**2
# Using the two together:
center, radius = (1000, 1000), 5
size_x, size_y = fake_im_arr.shape
mask = create_circle_mask(*np.ogrid[0:size_x, 0:size_y], center=center, radius=radius)
n_black_in_circle = ((fake_im_arr == 1) & mask).sum() # This is your answer (39 in this case)
要查看各种数组的外观：
fake_im_arr[center[0] - radius:center[0] + (radius + 1),
center[1] - radius:center[1] + (radius + 1)]
array([[0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1],
[0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1],
[1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1],
[1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0],
[0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1],
[1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0],
[0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0],
[1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0]])
mask[center[0] - radius:center[0] + (radius + 1),
center[1] - radius:center[1] + (radius + 1)].astype('int')
array([[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]])
np.where(mask, fake_im_arr, 0)[center[0] - radius:center[0] + (radius + 1),
center[1] - radius:center[1] + (radius + 1)]
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0],
[1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1],
[0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0],
[0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0],
[0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0],
[0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]])
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