为什么在使用Python的OpenCV中SIFT不能用于8位图像（JPEG）？ [英] Why doesn't doesn't SIFT work for 8 bit images (JPEG) in OpenCV with Python?

问题描述

我将SIFT用于所有其他24位JPEG图像，没有任何问题，但是8位总是给我以下错误。

I used SIFT for all my other 24 bit JPEG images without any problems, however, the 8-bit one always give me the following error.

图像为空或功能cv :: SIFT :: operator（）中的深度不正确（！= CV_8U）

image is empty or has incorrect depth (!=CV_8U) in function cv::SIFT::operator ()

有人知道如何处理吗？

这是我的代码：

import cv2 
import numpy as np 
import os 
import glob
import scipy.cluster
os.chdir('\mydirectory')
images = []

for infile in glob.glob('./*.jpg'):
  pic = cv2.imread(infile,0)
  images.append(pic)

my_set = images
descriptors = np.array([])
feaL=np.array([])

for pic in my_set:
  kp, des = cv2.SIFT().detectAndCompute(pic, None)
  feaL=np.append(feaL,des.shape[0])
  descriptors = np.append(descriptors, des)

然后错误图像为空或深度不正确（弹出功能cv :: SIFT :: operator（）中的！= CV_8U）。

Then the error "image is empty or has incorrect depth (!=CV_8U) in function cv::SIFT::operator ()" pops up.

推荐答案

编辑：键入此命令后，我只看到 imread 。尝试在读入图像时打印图像，听起来像 imread 可能会自动失败，并留下空白的Mats。

After typing this I just saw the grayscale flag on imread. Try printing the images as they are read in, it sounds like imread may be silently failing and leaving you with empty Mats.

cv2.SIFT.detectAndCompute 除了8位灰度外，从不接受其他任何内容，因此我不确定您是否确实在24位上使用了SIFT位图像，没有问题。

cv2.SIFT.detectAndCompute never takes anything other than 8-bit grayscale, so I'm not sure that you actually did use SIFT on a 24-bit image without problems.

cv2.SIFT.detectAndCompute

Python: cv2.SIFT.detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) → keypoints, descriptors

因此更改为8位灰度紧接在检测和提取之前：

So to change to 8 bit grayscale immediately prior to detection and extraction:

for pic in my_set:
    pic = cv2.cvtColor(pic, cv2.COLOR_BGR2GRAY)
    kp, des = cv2.SIFT().detectAndCompute(pic, None)

当然这是一个愚蠢的地方，但是由您自己确定是否需要保留BGR原件，等等。

Of course that is a dumb place to put it, but it's up to you to figure out if you need to keep the BGR originals or not, etc.

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