问题描述
TL;DR:如何使用 skimage.filters.laplace(image).var()
获得与 cv2.Laplacian(image,CV_64F).var()
和 skimage.filters.sobel(image)
相同的值以获得与 {{ 相同的值1}} ?
我有以下代码可以找到 Laplace Variance for blur detection
cv2.sobel(image)
所以当我尝试从 OpenCV 和 scikit-image 中找到拉普拉斯方差时,它给了我两个不同的值:
import numpy as np
import cv2
import pandas as pd
import requests
from PIL import Image
import urllib
from skimage.filters import laplace,sobel,roberts
from io import BytesIO
from skimage import color
from skimage import io as sk_io
def url_to_image(url): # Get image from url
resp = urllib.request.urlopen(url)
image = np.asarray(bytearray(resp.read()),dtype="uint8")
image = cv2.imdecode(image,cv2.IMREAD_GRAYSCALE)
return image
def simple_blur(gray:np.ndarray)->float:
'''
Use Laplacian Variance to find if an image has blur or not. It is very critical to find the threshold and is vert data specific
args:
gray: Grayscale Image
'''
return cv2.Laplacian(gray,CV_64F).var()
另外,我如何使用 laplace(color.rgb2gray(sk_io.imread(url))).var()
>> 1.1086769139613736e-05
simple_blur(url_to_image(url))
>> 0.6622495826224196
EXACTLY 中给出的 cv2.sobel(image)
来获得 OpenCV
的结果?
解决方法
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