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。
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!
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
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。
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:
inRange阈值图像:
输入圆圈:
圆形掩模图像:
已屏蔽的阈值图像:
计算结果:
count = 443239