如何解决灰度图像中OpenCV轮廓图的平均强度
有了这个人造图像,我能够找到它们并创建一个蒙版,然后使用两个丑陋的for循环计算它们的平均强度(灰度),这会花费大量时间,并且图像中包含相当数量的数字。
#!/usr/bin/env python3
import imutils
import cv2
import numpy as np
from scipy.stats import norm
image = cv2.imread("./test_images/test_artificial2.png")
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
# Noise reduction
blurred = cv2.GaussianBlur(gray,(3,3),0)
# thresholding
mean,std=norm.fit(blurred)
thresh_min_value = int(mean + 3.6*std)
thresh = cv2.threshold(blurred,thresh_min_value,255,cv2.THRESH_BINARY)[1]
# find contours in the thresholded image
cnts = cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
for c in cnts:
# Moments
M = cv2.moments(c)
# Area of contour
area = M["m00"]
# Centroid of contour
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
# Perimeter
perimeter = cv2.arcLength(c,True)
print(f'{area=}')
print(f'{perimeter=}')
epsilon = 0.02 * perimeter
approx = cv2.approxPolyDP(c,epsilon,True)
print(f'{approx=}')
# Create blank mask
mask_contour = np.zeros(gray.shape,np.uint8)
# Draw contour in the mask
cv2.drawContours(mask_contour,[approx],-1,(255,255),-1)
# This calculates the intensity of the polygon correctly
intensity = []
for i in range(0,gray.shape[0]):
for j in range(0,gray.shape[1]):
if mask_contour[i][j] == 255:
intensity.append(gray[i][j])
print(sum(intensity)/len(intensity))
# But I would like to speed up the process somehow
#masked_image = np.where(mask_contour == 255,gray,0)
#average_intensity = np.mean(masked_image)
# print(f'{average_intensity=}')
cv2.imshow("Image",mask_contour)
cv2.waitKey(0)
正如在一条评论中已经建议的那样,我可以使用NumPy来计算其平均强度,但是我不能仅用图形的像素来计算它,而是将其余像素相加。
是否可以使用其他任何更快的方法来实现?
谢谢。
解决方法
最后,这是一个可行的解决方案,再次感谢@ fmw42向我指出正确的方向,并感谢@imochoa提供了最短的工作代码。
#!/usr/bin/env python3
import imutils
import cv2
import numpy as np
from scipy.stats import norm
image = cv2.imread("./test_images/test_artificial2.png")
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
# Noise reduction
blurred = cv2.GaussianBlur(gray,(3,3),0)
# thresholding
mean,std = norm.fit(blurred)
thresh_min_value = int(mean + 3.6*std)
thresh = cv2.threshold(blurred,thresh_min_value,255,cv2.THRESH_BINARY)[1]
# find contours in the thresholded image
cnts = cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
for c in cnts:
# Moments
M = cv2.moments(c)
# Area of contour
area = M["m00"]
# Centroid of contour
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
# Perimeter
perimeter = cv2.arcLength(c,True)
print(f'{area=}')
print(f'{perimeter=}')
epsilon = 0.02 * perimeter
approx = cv2.approxPolyDP(c,epsilon,True)
print(f'{approx=}')
# Create blank image
blank_image = np.zeros(gray.shape,np.uint8)
# Draw contour in the mask
cv2.drawContours(blank_image,[approx],-1,(255,255),-1)
# Create a mask to select pixels inside the figure
mask_contour = blank_image == 255
# Calculate the intensity from the grayscale image
# filtering out the pixels where in the blank_image their value is not 255
intensity = np.mean(gray[mask_contour])
print(f'{intensity=}')
cv2.imshow("Image",blank_image)
cv2.waitKey(0)
版权声明:本文内容由互联网用户自发贡献,该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容, 请发送邮件至 dio@foxmail.com 举报,一经查实,本站将立刻删除。