{"id":959297,"date":"2024-12-27T03:39:28","date_gmt":"2024-12-26T19:39:28","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/959297.html"},"modified":"2024-12-27T03:39:30","modified_gmt":"2024-12-26T19:39:30","slug":"python%e5%a6%82%e4%bd%95%e7%bb%98%e5%88%b6%e9%bb%91%e7%99%bd%e6%a0%bc","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/959297.html","title":{"rendered":"python\u5982\u4f55\u7ed8\u5236\u9ed1\u767d\u683c"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25102140\/ba74109c-a378-42cf-89d0-9744697b0b71.webp\" alt=\"python\u5982\u4f55\u7ed8\u5236\u9ed1\u767d\u683c\" \/><\/p>\n<p><p> <strong>\u8981\u5728Python\u4e2d\u7ed8\u5236\u9ed1\u767d\u683c\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7684\u56fe\u5f62\u5e93\uff0c\u4f8b\u5982Matplotlib\u6216Pillow\u3002\u901a\u8fc7\u4f7f\u7528\u8fd9\u4e9b\u5e93\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u4e00\u4e2a\u68cb\u76d8\u683c\u56fe\u50cf\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Matplotlib\u3001Pillow\u3001Numpy\u4e09\u79cd\u65b9\u5f0f\u7684\u8be6\u7ec6\u4ecb\u7ecd\uff1a<\/strong><\/p>\n<\/p>\n<p><p><strong>1. \u4f7f\u7528Matplotlib\uff1aMatplotlib\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u9002\u7528\u4e8e\u751f\u6210\u5404\u79cd\u56fe\u5f62\u548c\u56fe\u8868\u3002\u901a\u8fc7\u521b\u5efa\u4e8c\u7ef4\u6570\u7ec4\u5e76\u5c06\u5176\u4f5c\u4e3a\u56fe\u50cf\u663e\u793a\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u751f\u6210\u9ed1\u767d\u68cb\u76d8\u683c\u3002<\/strong><\/p>\n<\/p>\n<p><p><strong>2. \u4f7f\u7528Pillow\uff1aPillow\u662fPython\u4e2d\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u7684\u4e00\u4e2a\u5e93\u3002\u4f7f\u7528Pillow\uff0c\u4f60\u53ef\u4ee5\u521b\u5efa\u548c\u64cd\u4f5c\u56fe\u50cf\u6587\u4ef6\u3002\u901a\u8fc7\u7ed8\u5236\u77e9\u5f62\u6765\u521b\u5efa\u9ed1\u767d\u683c\u56fe\u50cf\u3002<\/strong><\/p>\n<\/p>\n<p><p><strong>3. \u4f7f\u7528Numpy\uff1aNumpy\u662f\u4e00\u4e2a\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\u7684\u5e93\uff0c\u4e3b\u8981\u7528\u4e8e\u5904\u7406\u5927\u578b\u6570\u7ec4\u548c\u77e9\u9635\u3002\u901a\u8fc7\u521b\u5efa\u6570\u7ec4\u5e76\u4f7f\u7528Matplotlib\u663e\u793a\uff0c\u4f60\u53ef\u4ee5\u521b\u5efa\u9ed1\u767d\u68cb\u76d8\u683c\u3002<\/strong><\/p>\n<\/p>\n<p><p>\u4e0b\u9762\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e09\u79cd\u65b9\u6cd5\u7684\u5177\u4f53\u6b65\u9aa4\u548c\u4ee3\u7801\u793a\u4f8b\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u4f7f\u7528Matplotlib\u7ed8\u5236\u9ed1\u767d\u683c<\/h3>\n<\/p>\n<p><p>Matplotlib\u662f\u4e00\u79cd\u6d41\u884c\u7684Python\u7ed8\u56fe\u5e93\uff0c\u7528\u4e8e\u751f\u6210\u4e8c\u7ef4\u56fe\u5f62\u3002\u8981\u4f7f\u7528Matplotlib\u7ed8\u5236\u9ed1\u767d\u683c\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5Matplotlib\u5e93\u3002<\/p>\n<\/p>\n<p><h4>1. \u5b89\u88c5Matplotlib<\/h4>\n<\/p>\n<p><p>\u4f60\u53ef\u4ee5\u901a\u8fc7pip\u547d\u4ee4\u5b89\u88c5Matplotlib\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install matplotlib<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2. \u521b\u5efa\u9ed1\u767d\u68cb\u76d8\u683c<\/h4>\n<\/p>\n<p><p>\u4f7f\u7528Matplotlib\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u521b\u5efa\u4e00\u4e2a\u9ed1\u767d\u68cb\u76d8\u683c\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p>import numpy as np<\/p>\n<p>def create_chessboard(size):<\/p>\n<p>    # \u521b\u5efa\u4e00\u4e2a\u4e8c\u7ef4\u6570\u7ec4\uff0c\u521d\u59cb\u5316\u4e3a0<\/p>\n<p>    chessboard = np.zeros((size, size), dtype=int)<\/p>\n<p>    # \u586b\u5145\u68cb\u76d8\u683c<\/p>\n<p>    chessboard[1::2, ::2] = 1<\/p>\n<p>    chessboard[::2, 1::2] = 1<\/p>\n<p>    return chessboard<\/p>\n<h2><strong>\u5b9a\u4e49\u68cb\u76d8\u5927\u5c0f<\/strong><\/h2>\n<p>size = 8<\/p>\n<h2><strong>\u521b\u5efa\u68cb\u76d8<\/strong><\/h2>\n<p>chessboard = create_chessboard(size)<\/p>\n<h2><strong>\u7ed8\u5236\u68cb\u76d8<\/strong><\/h2>\n<p>plt.imshow(chessboard, cmap=&#39;gray&#39;, interpolation=&#39;nearest&#39;)<\/p>\n<p>plt.title(f&#39;{size}x{size} Chessboard&#39;)<\/p>\n<p>plt.axis(&#39;off&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>\u89e3\u91ca\uff1a<\/strong><\/p>\n<\/p>\n<ul>\n<li>\u901a\u8fc7\u4f7f\u7528<code>numpy.zeros<\/code>\u521b\u5efa\u4e00\u4e2a\u4e8c\u7ef4\u6570\u7ec4\uff0c\u8868\u793a\u68cb\u76d8\u3002<\/li>\n<li>\u4f7f\u7528\u5207\u7247\u64cd\u4f5c\u5c06\u6570\u7ec4\u7684\u5947\u6570\u884c\u548c\u5076\u6570\u5217\u3001\u5076\u6570\u884c\u548c\u5947\u6570\u5217\u8bbe\u7f6e\u4e3a1\uff0c\u4ece\u800c\u521b\u5efa\u68cb\u76d8\u683c\u7684\u6a21\u5f0f\u3002<\/li>\n<li>\u4f7f\u7528<code>imshow<\/code>\u51fd\u6570\u663e\u793a\u6570\u7ec4\uff0c\u5e76\u8bbe\u7f6e<code>cmap=&#39;gray&#39;<\/code>\u4ee5\u83b7\u5f97\u9ed1\u767d\u6548\u679c\u3002<\/li>\n<\/ul>\n<p><h3>\u4e8c\u3001\u4f7f\u7528Pillow\u7ed8\u5236\u9ed1\u767d\u683c<\/h3>\n<\/p>\n<p><p>Pillow\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u548c\u64cd\u4f5c\u56fe\u50cf\u6587\u4ef6\u3002<\/p>\n<\/p>\n<p><h4>1. \u5b89\u88c5Pillow<\/h4>\n<\/p>\n<p><p>\u53ef\u4ee5\u901a\u8fc7pip\u547d\u4ee4\u5b89\u88c5Pillow\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install pillow<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2. \u521b\u5efa\u9ed1\u767d\u68cb\u76d8\u683c<\/h4>\n<\/p>\n<p><p>\u4f7f\u7528Pillow\u521b\u5efa\u4e00\u4e2a\u9ed1\u767d\u68cb\u76d8\u683c\u7684\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from PIL import Image, ImageDraw<\/p>\n<p>def create_chessboard(size, square_size):<\/p>\n<p>    # \u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u56fe\u50cf<\/p>\n<p>    image = Image.new(&#39;RGB&#39;, (size * square_size, size * square_size), &#39;white&#39;)<\/p>\n<p>    draw = ImageDraw.Draw(image)<\/p>\n<p>    # \u7ed8\u5236\u68cb\u76d8\u683c<\/p>\n<p>    for i in range(size):<\/p>\n<p>        for j in range(size):<\/p>\n<p>            if (i + j) % 2 == 0:<\/p>\n<p>                draw.rectangle([j * square_size, i * square_size, (j + 1) * square_size, (i + 1) * square_size], fill=&#39;black&#39;)<\/p>\n<p>    return image<\/p>\n<h2><strong>\u5b9a\u4e49\u68cb\u76d8\u5927\u5c0f\u548c\u6bcf\u4e2a\u65b9\u683c\u7684\u5c3a\u5bf8<\/strong><\/h2>\n<p>size = 8<\/p>\n<p>square_size = 50<\/p>\n<h2><strong>\u521b\u5efa\u68cb\u76d8<\/strong><\/h2>\n<p>chessboard_image = create_chessboard(size, square_size)<\/p>\n<h2><strong>\u663e\u793a\u68cb\u76d8<\/strong><\/h2>\n<p>chessboard_image.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>\u89e3\u91ca\uff1a<\/strong><\/p>\n<\/p>\n<ul>\n<li>\u901a\u8fc7<code>Image.new<\/code>\u521b\u5efa\u4e00\u4e2a\u65b0\u56fe\u50cf\uff0c\u5e76\u4f7f\u7528<code>ImageDraw.Draw<\/code>\u521b\u5efa\u7ed8\u5236\u5bf9\u8c61\u3002<\/li>\n<li>\u4f7f\u7528\u5d4c\u5957\u5faa\u73af\u904d\u5386\u68cb\u76d8\u7684\u884c\u548c\u5217\uff0c\u5e76\u6839\u636e\u884c\u5217\u7d22\u5f15\u7684\u548c\u662f\u5426\u4e3a\u5076\u6570\u6765\u51b3\u5b9a\u662f\u5426\u586b\u5145\u9ed1\u8272\u3002<\/li>\n<li>\u4f7f\u7528<code>draw.rectangle<\/code>\u7ed8\u5236\u77e9\u5f62\u4ee5\u586b\u5145\u9ed1\u8272\u65b9\u683c\u3002<\/li>\n<\/ul>\n<p><h3>\u4e09\u3001\u4f7f\u7528Numpy\u548cMatplotlib\u7ed3\u5408\u7ed8\u5236\u9ed1\u767d\u683c<\/h3>\n<\/p>\n<p><p>Numpy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u7ed3\u5408Matplotlib\u53ef\u4ee5\u8f7b\u677e\u7ed8\u5236\u68cb\u76d8\u683c\u3002<\/p>\n<\/p>\n<p><h4>1. \u5b89\u88c5Numpy<\/h4>\n<\/p>\n<p><p>Numpy\u901a\u5e38\u4f1a\u968fMatplotlib\u4e00\u8d77\u5b89\u88c5\uff0c\u4f46\u4f60\u53ef\u4ee5\u5355\u72ec\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install numpy<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2. \u521b\u5efa\u9ed1\u767d\u68cb\u76d8\u683c<\/h4>\n<\/p>\n<p><p>\u4e0b\u9762\u662f\u4f7f\u7528Numpy\u548cMatplotlib\u7ed3\u5408\u521b\u5efa\u68cb\u76d8\u683c\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>import matplotlib.pyplot as plt<\/p>\n<p>def create_chessboard(size):<\/p>\n<p>    # \u521b\u5efa\u4e00\u4e2a\u4e8c\u7ef4\u6570\u7ec4\uff0c\u521d\u59cb\u5316\u4e3a0<\/p>\n<p>    chessboard = np.zeros((size, size), dtype=int)<\/p>\n<p>    # \u586b\u5145\u68cb\u76d8\u683c<\/p>\n<p>    chessboard[1::2, ::2] = 1<\/p>\n<p>    chessboard[::2, 1::2] = 1<\/p>\n<p>    return chessboard<\/p>\n<h2><strong>\u5b9a\u4e49\u68cb\u76d8\u5927\u5c0f<\/strong><\/h2>\n<p>size = 8<\/p>\n<h2><strong>\u521b\u5efa\u68cb\u76d8<\/strong><\/h2>\n<p>chessboard = create_chessboard(size)<\/p>\n<h2><strong>\u7ed8\u5236\u68cb\u76d8<\/strong><\/h2>\n<p>plt.imshow(chessboard, cmap=&#39;gray&#39;, interpolation=&#39;nearest&#39;)<\/p>\n<p>plt.title(f&#39;{size}x{size} Chessboard&#39;)<\/p>\n<p>plt.axis(&#39;off&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>\u89e3\u91ca\uff1a<\/strong><\/p>\n<\/p>\n<ul>\n<li>\u4f7f\u7528Numpy\u521b\u5efa\u548c\u5904\u7406\u6570\u7ec4\u3002<\/li>\n<li>\u4f7f\u7528Matplotlib\u663e\u793a\u6570\u7ec4\u4f5c\u4e3a\u56fe\u50cf\u3002<\/li>\n<\/ul>\n<p><p>\u901a\u8fc7\u4ee5\u4e0a\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684\u4e0d\u540c\u5e93\u6765\u521b\u5efa\u9ed1\u767d\u68cb\u76d8\u683c\u3002\u8fd9\u4e9b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528Matplotlib\u3001Pillow\u548cNumpy\u521b\u5efa\u56fe\u5f62\uff0c\u5e76\u4e3a\u4f60\u7684\u9879\u76ee\u63d0\u4f9b\u4e86\u7075\u6d3b\u6027\u548c\u591a\u6837\u6027\u3002\u65e0\u8bba\u662f\u7528\u4e8e\u7b80\u5355\u7684\u56fe\u5f62\u751f\u6210\u8fd8\u662f\u590d\u6742\u7684\u56fe\u50cf\u5904\u7406\uff0c\u8fd9\u4e9b\u5e93\u90fd\u80fd\u63d0\u4f9b\u5f3a\u5927\u7684\u529f\u80fd\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u4f7f\u7528Python\u7ed8\u5236\u9ed1\u767d\u683c\uff1f<\/strong><br \/>\u60a8\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684\u591a\u4e2a\u5e93\u6765\u7ed8\u5236\u9ed1\u767d\u683c\uff0c\u6bd4\u5982Matplotlib\u548cPillow\u3002Matplotlib\u63d0\u4f9b\u4e86\u7075\u6d3b\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u800cPillow\u5219\u9002\u5408\u5904\u7406\u56fe\u50cf\u3002\u9009\u62e9\u5408\u9002\u7684\u5e93\u53d6\u51b3\u4e8e\u60a8\u7684\u5177\u4f53\u9700\u6c42\u3002<\/p>\n<p><strong>\u6709\u6ca1\u6709\u73b0\u6210\u7684\u4ee3\u7801\u793a\u4f8b\u53ef\u4ee5\u53c2\u8003\uff1f<\/strong><br \/>\u5f53\u7136\u53ef\u4ee5\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684Matplotlib\u793a\u4f8b\u4ee3\u7801\uff0c\u60a8\u53ef\u4ee5\u5feb\u901f\u7ed8\u5236\u4e00\u4e2a\u9ed1\u767d\u683c\u5b50\u56fe\uff1a<\/p>\n<pre><code class=\"language-python\">import matplotlib.pyplot as plt\nimport numpy as np\n\n# \u521b\u5efa\u4e00\u4e2a8x8\u7684\u9ed1\u767d\u683c\u5b50\u56fe\ngrid = np.zeros((8, 8))\ngrid[1::2, ::2] = 1  # \u5947\u6570\u884c\u7684\u5076\u6570\u5217\ngrid[::2, 1::2] = 1  # \u5076\u6570\u884c\u7684\u5947\u6570\u5217\n\nplt.imshow(grid, cmap=&#39;gray&#39;, interpolation=&#39;nearest&#39;)\nplt.axis(&#39;off&#39;)  # \u5173\u95ed\u5750\u6807\u8f74\nplt.show()\n<\/code><\/pre>\n<p>\u8fd9\u6bb5\u4ee3\u7801\u4f1a\u751f\u6210\u4e00\u4e2a\u7b80\u5355\u76848&#215;8\u9ed1\u767d\u683c\u5b50\u56fe\u3002<\/p>\n<p><strong>\u5728\u7ed8\u5236\u9ed1\u767d\u683c\u65f6\uff0c\u5982\u4f55\u81ea\u5b9a\u4e49\u683c\u5b50\u7684\u5927\u5c0f\uff1f<\/strong><br \/>\u60a8\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u7ed8\u56fe\u51fd\u6570\u4e2d\u7684\u53c2\u6570\u6765\u6539\u53d8\u683c\u5b50\u7684\u5927\u5c0f\u3002\u4f8b\u5982\uff0c\u5728\u4f7f\u7528Matplotlib\u65f6\uff0c\u53ef\u4ee5\u6839\u636e\u9700\u8981\u4fee\u6539\u6570\u7ec4\u7684\u7ef4\u5ea6\uff0c\u6216\u8005\u5728<code>imshow<\/code>\u51fd\u6570\u4e2d\u8bbe\u7f6e<code>extent<\/code>\u53c2\u6570\u6765\u63a7\u5236\u663e\u793a\u533a\u57df\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u5f0f\uff0c\u60a8\u53ef\u4ee5\u5b9e\u73b0\u4e0d\u540c\u5927\u5c0f\u7684\u9ed1\u767d\u683c\u5b50\u6548\u679c\u3002<\/p>\n<p><strong>\u4f7f\u7528Pillow\u7ed8\u5236\u9ed1\u767d\u683c\u5b50\u56fe\u7684\u6b65\u9aa4\u662f\u600e\u6837\u7684\uff1f<\/strong><br \/>\u4f7f\u7528Pillow\u5e93\u7ed8\u5236\u9ed1\u767d\u683c\u5b50\u56fe\u4e5f\u975e\u5e38\u7b80\u5355\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<pre><code class=\"language-python\">from PIL import Image\n\n# \u5b9a\u4e49\u683c\u5b50\u7684\u5927\u5c0f\u548c\u989c\u8272\ntile_size = 50\nwidth, height = tile_size * 8, tile_size * 8\nimage = Image.new(&#39;1&#39;, (width, height), 1)  # \u521b\u5efa\u767d\u8272\u80cc\u666f\n\nfor y in range(8):\n    for x in range(8):\n        if (x + y) % 2 == 0:\n            for i in range(tile_size):\n                for j in range(tile_size):\n                    image.putpixel((x * tile_size + i, y * tile_size + j), 0)  # \u8bbe\u7f6e\u9ed1\u8272\n\nimage.show()\n<\/code><\/pre>\n<p>\u8fd9\u6bb5\u4ee3\u7801\u4f1a\u751f\u6210\u4e00\u4e2a\u4f7f\u7528Pillow\u5e93\u7ed8\u5236\u7684\u9ed1\u767d\u683c\u5b50\u56fe\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u5728Python\u4e2d\u7ed8\u5236\u9ed1\u767d\u683c\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7684\u56fe\u5f62\u5e93\uff0c\u4f8b\u5982Matplotlib\u6216Pillow\u3002\u901a\u8fc7\u4f7f\u7528\u8fd9 [&hellip;]","protected":false},"author":3,"featured_media":959305,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/959297"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=959297"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/959297\/revisions"}],"predecessor-version":[{"id":959310,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/959297\/revisions\/959310"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/959305"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=959297"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=959297"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=959297"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}