{"id":680,"date":"2014-11-05T11:20:13","date_gmt":"2014-11-05T18:20:13","guid":{"rendered":"http:\/\/homepages.uc.edu\/~yaozo\/wordpress\/?p=680"},"modified":"2014-11-05T11:20:13","modified_gmt":"2014-11-05T18:20:13","slug":"r-image-analysis-using-ebimage","status":"publish","type":"post","link":"https:\/\/zhuoyao.net\/index.php\/2014\/11\/05\/r-image-analysis-using-ebimage\/","title":{"rendered":"R: Image Analysis using EBImage"},"content":{"rendered":"
\n
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Currently, I am taking Statistics for Image Analysis<\/i> on my masteral, and have been exploring this topic in R. One package that has the capability in this field is the EBImage<\/a> from Bioconductor<\/a>, which will be showcased in this post.<\/p>\n

Installation<\/h3>\n
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1<\/span>2<\/span>3<\/span><\/td>\n\n
source<\/span>(<\/span>“http:\/\/bioconductor.org\/biocLite.R”<\/span>)<\/span><\/div>\n
biocLite()<\/span><\/div>\n
biocLite(<\/span>“EBImage”<\/span>)<\/span><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
view raw<\/a>EBIm3.R<\/a> hosted with \u2764 by GitHub<\/a><\/div>\n<\/div>\n<\/div>\n

For those using Ubuntu, you may likely to encounter this error:<\/p>\n

\n
\n
\n
\n\n\n\n
1<\/span>2<\/span>3<\/span><\/td>\n\n
In file included from common.c:<\/span>1<\/span>:<\/span>0<\/span>:<\/span><\/div>\n
common.h:<\/span>5<\/span>:<\/span>18<\/span>:<\/span> fatal error:<\/span> tiff.h:<\/span> No such file or directory<\/div>\n
#include <tiff.h><\/span><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
view raw<\/a>imageErr.R<\/a> hosted with \u2764 by GitHub<\/a><\/div>\n<\/div>\n<\/div>\n

It has something to do with the tiff.h<\/code> C header file<\/a>, but it’s not that serious since mytechscribblings<\/a> has an effective solution<\/a> for this, do check that out.<\/p>\n

Importing Data<\/h3>\n

To import a raw image, consider the following codes:
\n<\/a><\/p>\n

\n
\n
\n
\n\n\n\n
1<\/span>2<\/span>3<\/span><\/td>\n\n
# Reading Image<\/span><\/div>\n
Image <-<\/span> readImage(<\/span>‘~\/Documents\/R Files\/Image Analysis\/tinago.JPG’<\/span>)<\/span><\/div>\n
display(<\/span>Image)<\/span><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
view raw<\/a>EBIm1.R<\/a> hosted with \u2764 by GitHub<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n\n
\"\"<\/a><\/td>\n<\/tr>\n
Output of display(Image)<\/code>.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Yes, this is the photo that we are going to use for our analysis. Needless to say, that’s me and my friends. In the proceeding section we will do image manipulation and other processing.<\/p>\n

Image Properties<\/h3>\n

So what do we get from our raw image? To answer that, simply runprint(Image)<\/code>. This will return the properties of the image, including the array of pixel values. With these information, we apply mathematical and statistical operations to do enhancement on the image.<\/p>\n

\n
\n
\n
\n\n\n\n
1<\/span>2<\/span>3<\/span>4<\/span>5<\/span>6<\/span>7<\/span>8<\/span>9<\/span>10<\/span>11<\/span>12<\/span>13<\/span>14<\/span>15<\/span>16<\/span>17<\/span><\/td>\n\n
print<\/span>(<\/span>Image)<\/span><\/div>\n
<\/div>\n
#OUTPUT<\/span><\/div>\n
Image<\/div>\n
colormode:<\/span> Color<\/div>\n
storage.mode<\/span>:<\/span> double<\/span><\/div>\n
dim<\/span>:<\/span> 1984<\/span> 1488<\/span> 3<\/span><\/div>\n
nb.total.frames:<\/span> 3<\/span><\/div>\n
nb.render.frames:<\/span> 1<\/span><\/div>\n
<\/div>\n
imageData(<\/span>object)[<\/span>1<\/span>:<\/span>5<\/span>,<\/span>1<\/span>:<\/span>6<\/span>,<\/span>1<\/span>]<\/span>:<\/span><\/div>\n
[,<\/span>1<\/span>]<\/span> [,<\/span>2<\/span>]<\/span> [,<\/span>3<\/span>]<\/span> [,<\/span>4<\/span>]<\/span> [,<\/span>5<\/span>]<\/span> [,<\/span>6<\/span>]<\/span><\/div>\n
[<\/span>1<\/span>,]<\/span> 0.3647059<\/span> 0.3843137<\/span> 0.3803922<\/span> 0.3490196<\/span> 0.3215686<\/span> 0.3058824<\/span><\/div>\n
[<\/span>2<\/span>,]<\/span> 0.4196078<\/span> 0.4000000<\/span> 0.3921569<\/span> 0.4039216<\/span> 0.3843137<\/span> 0.3254902<\/span><\/div>\n
[<\/span>3<\/span>,]<\/span> 0.4784314<\/span> 0.4705882<\/span> 0.4705882<\/span> 0.4666667<\/span> 0.4117647<\/span> 0.3411765<\/span><\/div>\n
[<\/span>4<\/span>,]<\/span> 0.4901961<\/span> 0.5647059<\/span> 0.5960784<\/span> 0.5411765<\/span> 0.4392157<\/span> 0.3843137<\/span><\/div>\n
[<\/span>5<\/span>,]<\/span> 0.4509804<\/span> 0.5686275<\/span> 0.6549020<\/span> 0.6392157<\/span> 0.5607843<\/span> 0.4901961<\/span><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
view raw<\/a>EBIm4.R<\/a> hosted with \u2764 by GitHub<\/a><\/div>\n<\/div>\n<\/div>\n

There are two sections (Summary and array of the pixels) in the above output, with the following entries for the first section:<\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n
CODE<\/th>\nVALUE<\/th>\nDESCRIPTION<\/th>\n<\/tr>\n<\/thead>\n
\n
Table 1: Information from 1st section of print(Image)<\/code>.<\/i><\/div>\n<\/td>\n<\/tr>\n<\/tfoot>\n
colormode<\/code><\/td>\nColor<\/td>\nThe type (Color\/Grayscale) of the color of the image.<\/td>\n<\/tr>\n
storage.mode<\/code><\/td>\ndouble<\/td>\nType of values in the array.<\/td>\n<\/tr>\n
dim<\/code><\/td>\n1984 1488 3<\/td>\nDimension of the array, (x, y, z).<\/td>\n<\/tr>\n
nb.total.frames:<\/code><\/td>\n3<\/td>\nNumber of channels in each pixel, z entry in dim<\/code>.<\/td>\n<\/tr>\n
nb.render.frames<\/code><\/td>\n1<\/td>\nNumber of channels rendered.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

The second section is the obtained values from mapping pixels in the image to the real line between 0 and 1 (inclusive). Both extremes of this interval [0, 1], are black and white colors, respectively. Hence, pixels with values closer to any of these end points are expected to be darker or lighter, respectively. And because pixels are contained in a large array, then we can do all matrix manipulations available in R for processing.<\/p>\n

Adjusting Brightness<\/h3>\n

It is better to start with the basic first, one of which is the brightness. As discussed above, brightness can be manipulated using +<\/code> or -<\/code>:<\/p>\n

\n
\n
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1<\/span>2<\/span>3<\/span>4<\/span><\/td>\n\n
# Brightness<\/span><\/div>\n
Image1 <-<\/span> Image +<\/span> 0.2<\/span><\/div>\n
Image2 <-<\/span> Image –<\/span> 0.2<\/span><\/div>\n
display(<\/span>Image1);<\/span> display(<\/span>Image2)<\/span><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
view raw<\/a>EBIm2.R<\/a> hosted with \u2764 by GitHub<\/a><\/div>\n<\/div>\n<\/div>\n
\n\n\n\n\n\n\n\n
LIGHTER<\/th>\nDARKER<\/th>\n<\/tr>\n<\/thead>\n
\n
Table 2: Adjusting Brightness.<\/i><\/div>\n<\/td>\n<\/tr>\n<\/tfoot>\n
\n\n\n\n\n
\"\"<\/td>\n<\/tr>\n
Output of display(Image1)<\/code>.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
<\/div>\n<\/td>\n
\n\n\n\n\n
\"\"<\/td>\n<\/tr>\n
Output of display(Image2)<\/code>.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Adjusting Contrast<\/h3>\n

Contrast can be manipulated using multiplication operator(*<\/code>):<\/p>\n

\n
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1<\/span>2<\/span>3<\/span><\/td>\n\n
Image3 <-<\/span> Image *<\/span> 0.5<\/span><\/div>\n
Image4 <-<\/span> Image *<\/span> 2<\/span><\/div>\n
display(<\/span>Image3);<\/span> display(<\/span>Image4)<\/span><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
view raw<\/a>EBIm5.R<\/a> hosted with \u2764 by GitHub<\/a><\/div>\n<\/div>\n<\/div>\n
\n\n\n\n\n\n\n\n
LOW<\/th>\nHIGH<\/th>\n<\/tr>\n<\/thead>\n
\n
Table 3: Adjusting Contrast.<\/i><\/div>\n<\/td>\n<\/tr>\n<\/tfoot>\n
\n\n\n\n\n
\"\"<\/td>\n<\/tr>\n
Output of display(Image3)<\/code>.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
<\/div>\n<\/td>\n
\n\n\n\n\n
\"\"<\/td>\n<\/tr>\n
Output of display(Image4)<\/code>.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Gamma Correction<\/h3>\n

Gamma correction is the name of a nonlinear operation used to code and decode luminance or tristimulus values in video or still image systems, defined by the following power-law expression: begin{equation}nonumber V_{mathrm{out}} = AV_{mathrm{in}}^{gamma} end{equation} where $A$ is a constant and the input and output values are non-negative real values; in the common case of $A = 1$, inputs and outputs are typically in the range 0-1. A gamma value $gamma< 1$ is sometimes called an encoding gamma<\/b> (Wikipedia, Ref. 1).<\/p>\n

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1<\/span>2<\/span>3<\/span><\/td>\n\n
Image5 <-<\/span> Image ^<\/span> 2<\/span><\/div>\n
Image6 <-<\/span> Image ^<\/span> 0.7<\/span><\/div>\n
display(<\/span>Image5);<\/span> display(<\/span>Image6)<\/span><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
view raw<\/a>EBIm6.R<\/a> hosted with \u2764 by GitHub<\/a><\/div>\n<\/div>\n<\/div>\n
\n\n\n\n\n\n\n\n
$GAMMA = 2$<\/th>\n$GAMMA = 0.7$<\/th>\n<\/tr>\n<\/thead>\n
\n
Table 4: Adjusting Gamma Correction.<\/i><\/div>\n<\/td>\n<\/tr>\n<\/tfoot>\n
\n\n\n\n\n
\"\"<\/td>\n<\/tr>\n
Output of display(Image5)<\/code>.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
<\/div>\n<\/td>\n
\n\n\n\n\n
\"\"<\/td>\n<\/tr>\n
Output of display(Image6)<\/code>.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

Cropping<\/h3>\n

Slicing array of pixels, simply mean cropping the image.<\/p>\n

\n
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1<\/span><\/td>\n\n
display(<\/span>Image[<\/span>289<\/span>:<\/span>1547<\/span>,<\/span> 669<\/span>:<\/span>1406<\/span>,])<\/span><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
view raw<\/a>EBIm7.R<\/a> hosted with \u2764 by GitHub<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n\n
\"\"<\/td>\n<\/tr>\n
Output of the above code.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
<\/div>\n

Spatial Transformation<\/h3>\n

Spatial manipulation like rotate (rotate<\/code>), flip (flip<\/code>), and translate (translate<\/code>) are also available in the package. Check this out,<\/p>\n

\n
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1<\/span>2<\/span><\/td>\n\n
Imagetr <-<\/span> translate(<\/span>rotate(<\/span>Image,<\/span> 45<\/span>),<\/span> c<\/span>(<\/span>50<\/span>,<\/span> 0<\/span>))<\/span><\/div>\n
display(<\/span>Imagetr)<\/span><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
view raw<\/a>EBIm8.R<\/a> hosted with \u2764 by GitHub<\/a><\/div>\n<\/div>\n<\/div>\n
\"\"<\/div>\n

Color Management<\/h3>\n

Since the array of pixels has three axes in its dimension, for example in our case is 1984 x 1488 x 3. The third axis is the slot for the three channels: Red, Green and Blue, or RGB. Hence, transforming the color.mode<\/code> from Color<\/code> toGrayscale<\/code>, implies disjoining the three channels from single rendered frame (three channels for each pixel) to three separate array of pixels for red, green, and blue frames.<\/p>\n

\n
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\n\n\n\n
1<\/span>2<\/span>3<\/span>4<\/span>5<\/span>6<\/span>7<\/span>8<\/span>9<\/span>10<\/span>11<\/span>12<\/span>13<\/span>14<\/span>15<\/span>16<\/span>17<\/span>18<\/span><\/td>\n\n
colorMode(<\/span>Image)<\/span> <-<\/span> Grayscale<\/div>\n
print<\/span>(<\/span>Image)<\/span><\/div>\n
<\/div>\n
#OUTPUT<\/span><\/div>\n
Image<\/div>\n
colormode:<\/span> Grayscale<\/div>\n
storage.mode<\/span>:<\/span> double<\/span><\/div>\n
dim<\/span>:<\/span> 1984<\/span> 1488<\/span> 3<\/span><\/div>\n
nb.total.frames:<\/span> 3<\/span><\/div>\n
nb.render.frames:<\/span> 3<\/span><\/div>\n
<\/div>\n
imageData(<\/span>object)[<\/span>1<\/span>:<\/span>5<\/span>,<\/span>1<\/span>:<\/span>6<\/span>,<\/span>1<\/span>]<\/span>:<\/span><\/div>\n
[,<\/span>1<\/span>]<\/span> [,<\/span>2<\/span>]<\/span> [,<\/span>3<\/span>]<\/span> [,<\/span>4<\/span>]<\/span> [,<\/span>5<\/span>]<\/span> [,<\/span>6<\/span>]<\/span><\/div>\n
[<\/span>1<\/span>,]<\/span> 0.3647059<\/span> 0.3843137<\/span> 0.3803922<\/span> 0.3490196<\/span> 0.3215686<\/span> 0.3058824<\/span><\/div>\n
[<\/span>2<\/span>,]<\/span> 0.4196078<\/span> 0.4000000<\/span> 0.3921569<\/span> 0.4039216<\/span> 0.3843137<\/span> 0.3254902<\/span><\/div>\n
[<\/span>3<\/span>,]<\/span> 0.4784314<\/span> 0.4705882<\/span> 0.4705882<\/span> 0.4666667<\/span> 0.4117647<\/span> 0.3411765<\/span><\/div>\n
[<\/span>4<\/span>,]<\/span> 0.4901961<\/span> 0.5647059<\/span> 0.5960784<\/span> 0.5411765<\/span> 0.4392157<\/span> 0.3843137<\/span><\/div>\n
[<\/span>5<\/span>,]<\/span> 0.4509804<\/span> 0.5686275<\/span> 0.6549020<\/span> 0.6392157<\/span> 0.5607843<\/span> 0.4901961<\/span><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
view raw<\/a>EBIm9.R<\/a> hosted with \u2764 by GitHub<\/a><\/div>\n<\/div>\n<\/div>\n
\n\n\n\n\n\n\n\n\n\n\n\n
ORIGINAL<\/th>\nRED CHANNEL<\/th>\n<\/tr>\n<\/thead>\n
\n
Table 5: Color Mode Transformation.<\/i><\/div>\n<\/td>\n<\/tr>\n<\/tfoot>\n
\n
\"\"<\/div>\n<\/td>\n
\n
\"\"<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n
GREEN CHANNEL<\/th>\nBLUE CHANNEL<\/th>\n<\/tr>\n<\/thead>\n
\n
\"\"<\/div>\n<\/td>\n
\n
\"\"<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

To revert the color mode, simply run<\/p>\n

\n
\n
\n
\n\n\n\n
1<\/span><\/td>\n\n
colorMode(<\/span>Image)<\/span> <-<\/span> Color<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
view raw<\/a>EBIm12.R<\/a> hosted with \u2764 by GitHub<\/a><\/div>\n<\/div>\n<\/div>\n

Filtering<\/h3>\n

In this section, we will do smoothing\/blurring using low-pass filter, and edge-detection using high-pass filter. In addition, we will also investigate median filter to remove noise.<\/p>\n

\n\n\n\n\n\n\n\n
LOW-PASS (BLUR)<\/th>\n<\/tr>\n<\/thead>\n
\n
Table 6: Image Filtering.<\/i><\/div>\n<\/td>\n<\/tr>\n<\/tfoot>\n
\n
\"\"<\/div>\n
\n
\n
\n
\n
\n\n\n\n\n\n\n\n
1<\/span>2<\/span>3<\/span>4<\/span><\/td>\n\n
fLow <-<\/span> makeBrush(<\/span>21<\/span>,<\/span> shape=<\/span> ‘disc’<\/span>,<\/span> step=<\/span>FALSE<\/span>)<\/span>^<\/span>2<\/span><\/div>\n
fLow <-<\/span> fLow\/<\/span>sum<\/span>(<\/span>fLow)<\/span><\/div>\n
Image.fLow <-<\/span> filter2(<\/span>Image,<\/span> fLow)<\/span><\/div>\n
display(<\/span>Image.fLow)<\/span><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
view raw<\/a>EBIm10.R<\/a> hosted with \u2764 by GitHub<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n
HIGH PASS<\/th>\n<\/tr>\n<\/thead>\n
\n
\"\"<\/div>\n
\n
\n
\n
\n
\n\n\n\n
1<\/span>2<\/span>3<\/span>4<\/span><\/td>\n\n
fHigh <-<\/span> matrix<\/span>(<\/span>1<\/span>,<\/span> nc =<\/span> 3<\/span>,<\/span> nr =<\/span> 3<\/span>)<\/span><\/div>\n
fHigh[<\/span>2<\/span>,<\/span> 2<\/span>]<\/span> <-<\/span> -8<\/span><\/div>\n
Image.fHigh <-<\/span> filter2(<\/span>Image,<\/span> fHigh)<\/span><\/div>\n
display(<\/span>Image.fHigh)<\/span><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
view raw<\/a>EBIm11.R<\/a> hosted with \u2764 by GitHub<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n

 <\/p>\n

\n\n\n\n\n\n\n\n
ORIGINAL<\/th>\nFILTERED<\/th>\n<\/tr>\n<\/thead>\n
\n
Table 7: Median Filter.<\/i><\/div>\n<\/td>\n<\/tr>\n<\/tfoot>\n
\n\n\n\n\n
\"\"<\/td>\n<\/tr>\n
From Google, Link Here<\/a>.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
<\/div>\n<\/td>\n
\n\n\n\n\n
\"\"<\/td>\n<\/tr>\n
Output of display(medFltr)<\/code><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

 <\/p>\n

\n
\n
\n
\n\n\n\n
1<\/span>2<\/span>3<\/span><\/td>\n\n
Image <-<\/span> readImage(<\/span>“~\/Documents\/R Files\/Image Analysis\/peppersalt.jpg”<\/span>)<\/span><\/div>\n
medFltr <-<\/span> medianFilter(<\/span>Image,<\/span> 1.1<\/span>)<\/span><\/div>\n
display(<\/span>medFltr)<\/span><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
view raw<\/a>EBIm13.R<\/a> hosted with \u2764 by GitHub<\/a><\/div>\n<\/div>\n<\/div>\n

For comparison, I run median filter on first-neighborhood in Mathematica, and I got this<\/p>\n

\"\"<\/div>\n

Clearly, Mathematica has better enhancement than R for this particular filter. But R has a good foundation already, as we witness with EBImage. There are still lots of interesting functions in the said package, that is worth exploring, I suggest you check that out.<\/p>\n

For the meantime, we will stop here, but hoping we can play more on this topic in the succeeding post.<\/p>\n

References<\/h3>\n
    \n
  1. Gamma Correction<\/a>. Wikipedia<\/a><\/i>. Retrieved August 31, 2014.<\/li>\n
  2. Gregoire Pau, Oleg Sklyar, Wolfgang Huber (2014). Introduction to EBImage, an image processing and analysis toolkit for R<\/a><\/i>.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    Currently, I am taking Statistics for Image Analysis on my masteral, and have been exploring this topic in R. One package that has the capability… <\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-680","post","type-post","status-publish","format-standard","hentry","category-r"],"_links":{"self":[{"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/posts\/680","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/comments?post=680"}],"version-history":[{"count":0,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/posts\/680\/revisions"}],"wp:attachment":[{"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/media?parent=680"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/categories?post=680"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/tags?post=680"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}