计算cv2.circle内部的白色像素

Question

我正在尝试使用python和openCV实施视神经胶质瘤的鉴定。

为了完成对视神经胶质瘤的成功分类，我需要执行以下步骤。

找到图像的最亮部分，然后使用cv2.circle在其上画一个圆圈-Done
计算cv2.circle内部图像上的白色部分-需要帮助

这是我的代码，用于识别图像的最亮部分

gray = cv2.GaussianBlur(gray, (371, 371), 0)
(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(gray)
image = orig.copy()
cv2.circle(image, maxLoc, 371, (255, 0, 0), 2)

sought = [254,254,254]
amount = 0

for x in range(image.shape[0]):
    for y in range(image.shape[1]):
        b, g, r = image[x, y]
        if (b, g, r) == sought:
            amount += 1

print(amount)

image = imutils.resize(image, width=400)

# display the results of our newly improved method
cv2.imshow("Optic Image", image)
cv2.waitKey(0)

上面的代码返回以下输出

[我现在要做的是确定cv2.circle内图像白色区域的大小。

非常感谢！

Answer 1

我不确定您认为什么是“白色”，但这是在Python / OpenCV中进行计数的一种方法。只需阅读图像即可。转换为灰度。阈值在某个级别。然后只需计算阈值图像中白色像素的数量即可。

如果我将您的输出图像用作输入（删除白色边框之后：]

import cv2
import numpy as np

# read image
img = cv2.imread('optic.png')

# convert to HSV and extract saturation channel
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

# threshold
thresh = cv2.threshold(gray, 175, 255, cv2.THRESH_BINARY)[1]

# count number of white pixels
count = np.sum(np.where(thresh == 255))
print("count =",count)

# write result to disk
cv2.imwrite("optic_thresh.png", thresh)

# display it
cv2.imshow("IMAGE", img)
cv2.imshow("THRESH", thresh)
cv2.waitKey(0)

阈值图像：

阈值中的白色像素计数：

count = 1025729

Answer 2

我仍然不确定您认为白色和黄色圆圈是什么。但是这是使用Python / OpenCV的另一种尝试。

读取输入
将输入转换为0至1的范围作为一维数据
使用kmeans聚类减少色彩数量并将其转换回2图像的范围是0到255
使用inRange颜色阈值分离“黄色”区域
用形态清理并获得轮廓
获得最小的封闭圆心和半径并稍微偏心
在输入上绘制未填充的白色圆圈
在黑色背景上绘制白色填充圆作为黄色区域的圆形蒙版
将输入转换为灰度
阈值灰度图像
将蒙版应用于阈值灰度图像
计算白色像素数

输入：

import cv2
import numpy as np
from sklearn import cluster

# read image
img = cv2.imread('optic.png')
h, w, c = img.shape

# convert to range 0 to 1
image = img.copy()/255

# reshape to 1D array
image_1d = image.reshape(h*w, c)

# do kmeans processing
kmeans_cluster = cluster.KMeans(n_clusters=int(5))
kmeans_cluster.fit(image_1d)
cluster_centers = kmeans_cluster.cluster_centers_
cluster_labels = kmeans_cluster.labels_

# need to scale result back to range 0-255
newimage = cluster_centers[cluster_labels].reshape(h, w, c)*255.0
newimage = newimage.astype('uint8')

# threshold brightest region
lowcolor = (150,180,230)
highcolor = (170,200,250)
thresh1 = cv2.inRange(newimage, lowcolor, highcolor)

# apply morphology open and close
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7,7))
thresh1 = cv2.morphologyEx(thresh1, cv2.MORPH_OPEN, kernel, iterations=1)
thresh1 = cv2.morphologyEx(thresh1, cv2.MORPH_CLOSE, kernel, iterations=1)

# get contour
cntrs = cv2.findContours(thresh1, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cntrs = cntrs[0] if len(cntrs) == 2 else cntrs[1]
c = cntrs[0]

# get enclosing circle and bias center, if desired, since it is slightly offset (or alternately, increase the radius)
bias = 5
center, radius = cv2.minEnclosingCircle(c)
cx = int(round(center[0]))-bias
cy = int(round(center[1]))+bias
rr = int(round(radius))

# draw filled circle over black and also outline circle over input
mask = np.zeros_like(img)
cv2.circle(mask, (cx,cy), rr, (255, 255, 255), -1)
circle = img.copy()
cv2.circle(circle, (cx,cy), rr, (255, 255, 255), 1)

# convert to gray
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

# threshold gray image
thresh2 = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY)[1]

# apply mask to thresh2
thresh2 = cv2.bitwise_and(thresh2, mask[:,:,0])

# count number of white pixels
count = np.sum(np.where(thresh2 == 255))
print("count =",count)

# write result to disk
#cv2.imwrite("optic_thresh.png", thresh)
cv2.imwrite("optic_kmeans.png", newimage)
cv2.imwrite("optic_thresh1.png", thresh1)
cv2.imwrite("optic_mask.png", mask)
cv2.imwrite("optic_circle.png", circle)
cv2.imwrite("optic_thresh2.png", thresh2)

# display it
cv2.imshow("IMAGE", img)
cv2.imshow("KMEANS", newimage)
cv2.imshow("THRESH1", thresh1)
cv2.imshow("MASK", mask)
cv2.imshow("CIRCLE", circle)
cv2.imshow("GRAY", gray)
cv2.imshow("THRESH2", thresh2)
cv2.waitKey(0)

kmeans图片：