{"id":1110329,"date":"2025-01-08T17:18:41","date_gmt":"2025-01-08T09:18:41","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1110329.html"},"modified":"2025-01-08T17:18:43","modified_gmt":"2025-01-08T09:18:43","slug":"%e5%a6%82%e4%bd%95%e7%bb%99%e5%9b%be%e6%b7%bb%e5%8a%a0%e4%b8%80%e6%9d%a1%e7%9b%b4%e7%ba%bfpython","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1110329.html","title":{"rendered":"\u5982\u4f55\u7ed9\u56fe\u6dfb\u52a0\u4e00\u6761\u76f4\u7ebfpython"},"content":{"rendered":"

\"\u5982\u4f55\u7ed9\u56fe\u6dfb\u52a0\u4e00\u6761\u76f4\u7ebfpython\"<\/p>\n

\u5982\u4f55\u7ed9\u56fe\u6dfb\u52a0\u4e00\u6761\u76f4\u7ebf\uff1a\u4f7f\u7528Python<\/strong><\/p>\n<\/p>\n

\u8981\u5728\u56fe\u50cf\u4e2d\u6dfb\u52a0\u4e00\u6761\u76f4\u7ebf\uff0c\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528matplotlib<\/code>\u5e93\u3001opencv<\/code>\u5e93\u3001\u4ee5\u53caPIL<\/code>\u5e93\u4e2d\u7684\u51fd\u6570\u3002\u4f7f\u7528matplotlib\u5e93\u7ed8\u5236\u76f4\u7ebf<\/code>\u3001\u4f7f\u7528opencv\u5e93\u7ed8\u5236\u76f4\u7ebf<\/code>\u3001\u4f7f\u7528PIL\u5e93\u7ed8\u5236\u76f4\u7ebf<\/code><\/strong>\u662f\u5b9e\u73b0\u8fd9\u4e00\u76ee\u7684\u7684\u51e0\u79cd\u5e38\u7528\u65b9\u5f0f\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u5728\u56fe\u50cf\u4e2d\u6dfb\u52a0\u76f4\u7ebf\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528matplotlib\u5e93\u7ed8\u5236\u76f4\u7ebf<\/h3>\n<\/p>\n

matplotlib<\/code>\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\u4e4b\u4e00\u3002\u5b83\u80fd\u591f\u975e\u5e38\u65b9\u4fbf\u5730\u5728\u56fe\u50cf\u4e0a\u7ed8\u5236\u76f4\u7ebf\u3002<\/p>\n<\/p>\n

1. \u5b89\u88c5\u548c\u5bfc\u5165matplotlib\u5e93<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86matplotlib<\/code>\u5e93\u3002\u5982\u679c\u672a\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\u5728\u4ee3\u7801\u4e2d\u5bfc\u5165\u8be5\u5e93\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u521b\u5efa\u4e00\u4e2a\u57fa\u7840\u56fe\u50cf<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u521b\u5efa\u4e00\u4e2a\u57fa\u7840\u56fe\u50cf\uff0c\u4f5c\u4e3a\u7ed8\u5236\u76f4\u7ebf\u7684\u80cc\u666f\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6570\u636e<\/strong><\/h2>\n
x = np.linspace(0, 10, 100)<\/p>\n
y = np.sin(x)<\/p>\n
\u7ed8\u5236\u57fa\u7840\u56fe\u50cf<\/strong><\/h2>\n
plt.plot(x, y)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u5728\u56fe\u50cf\u4e0a\u6dfb\u52a0\u76f4\u7ebf<\/h4>\n<\/p>\n
\u4f7f\u7528plt.axline<\/code>\u51fd\u6570\u53ef\u4ee5\u5728\u56fe\u50cf\u4e0a\u6dfb\u52a0\u4e00\u6761\u76f4\u7ebf\uff1a<\/p>\n<\/p>\n
# \u5728\u56fe\u50cf\u4e0a\u6dfb\u52a0\u76f4\u7ebf\uff0c\u8bbe\u7f6e\u76f4\u7ebf\u7684\u8d77\u70b9\u548c\u65b9\u5411<\/p>\n
plt.axline((0, 0), slope=1, color='red', linestyle='--')<\/p>\n
\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u53ef\u4ee5\u5728\u56fe\u50cf\u4e0a\u6dfb\u52a0\u4e00\u6761\u7ea2\u8272\u7684\u865a\u7ebf\u76f4\u7ebf\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528opencv\u5e93\u7ed8\u5236\u76f4\u7ebf<\/h3>\n<\/p>\n
opencv<\/code>\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u9886\u57df\u3002\u4f7f\u7528opencv<\/code>\u53ef\u4ee5\u65b9\u4fbf\u5730\u5728\u56fe\u50cf\u4e0a\u7ed8\u5236\u76f4\u7ebf\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5\u548c\u5bfc\u5165opencv\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86opencv<\/code>\u5e93\u3002\u5982\u679c\u672a\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install opencv-python<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\u5728\u4ee3\u7801\u4e2d\u5bfc\u5165\u8be5\u5e93\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u521b\u5efa\u4e00\u4e2a\u57fa\u7840\u56fe\u50cf<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u521b\u5efa\u4e00\u4e2a\u57fa\u7840\u56fe\u50cf\uff0c\u4f5c\u4e3a\u7ed8\u5236\u76f4\u7ebf\u7684\u80cc\u666f\uff1a<\/p>\n<\/p>\n
# \u521b\u5efa\u4e00\u4e2a\u7a7a\u767d\u56fe\u50cf<\/p>\n
image = np.zeros((512, 512, 3), dtype=np.uint8)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u5728\u56fe\u50cf\u4e0a\u6dfb\u52a0\u76f4\u7ebf<\/h4>\n<\/p>\n
\u4f7f\u7528cv2.line<\/code>\u51fd\u6570\u53ef\u4ee5\u5728\u56fe\u50cf\u4e0a\u6dfb\u52a0\u4e00\u6761\u76f4\u7ebf\uff1a<\/p>\n<\/p>\n
# \u5728\u56fe\u50cf\u4e0a\u6dfb\u52a0\u76f4\u7ebf\uff0c\u8bbe\u7f6e\u8d77\u70b9\u3001\u7ec8\u70b9\u3001\u989c\u8272\u548c\u7ebf\u5bbd<\/p>\n
cv2.line(image, (0, 0), (511, 511), (0, 255, 0), 5)<\/p>\n
\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
cv2.imshow('Image with Line', image)<\/p>\n
cv2.wAI<\/a>tKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u53ef\u4ee5\u5728\u56fe\u50cf\u4e0a\u6dfb\u52a0\u4e00\u6761\u7eff\u8272\u7684\u76f4\u7ebf\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528PIL\u5e93\u7ed8\u5236\u76f4\u7ebf<\/h3>\n<\/p>\n
PIL<\/code>\uff08Python Imaging Library\uff09\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u5df2\u7ecf\u88ab\u66f4\u65b0\u4e3aPillow<\/code>\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5\u548c\u5bfc\u5165Pillow\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Pillow<\/code>\u5e93\u3002\u5982\u679c\u672a\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install pillow<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\u5728\u4ee3\u7801\u4e2d\u5bfc\u5165\u8be5\u5e93\uff1a<\/p>\n<\/p>\n
from PIL import Image, ImageDraw<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u521b\u5efa\u4e00\u4e2a\u57fa\u7840\u56fe\u50cf<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u521b\u5efa\u4e00\u4e2a\u57fa\u7840\u56fe\u50cf\uff0c\u4f5c\u4e3a\u7ed8\u5236\u76f4\u7ebf\u7684\u80cc\u666f\uff1a<\/p>\n<\/p>\n
# \u521b\u5efa\u4e00\u4e2a\u7a7a\u767d\u56fe\u50cf<\/p>\n
image = Image.new('RGB', (512, 512), 'white')<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u5728\u56fe\u50cf\u4e0a\u6dfb\u52a0\u76f4\u7ebf<\/h4>\n<\/p>\n
\u4f7f\u7528ImageDraw.Draw<\/code>\u51fd\u6570\u53ef\u4ee5\u5728\u56fe\u50cf\u4e0a\u6dfb\u52a0\u4e00\u6761\u76f4\u7ebf\uff1a<\/p>\n<\/p>\n
# \u521b\u5efa\u7ed8\u56fe\u5bf9\u8c61<\/p>\n
draw = ImageDraw.Draw(image)<\/p>\n
\u5728\u56fe\u50cf\u4e0a\u6dfb\u52a0\u76f4\u7ebf\uff0c\u8bbe\u7f6e\u8d77\u70b9\u3001\u7ec8\u70b9\u548c\u989c\u8272<\/strong><\/h2>\n
draw.line((0, 0, 511, 511), fill='blue', width=5)<\/p>\n
\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
image.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u53ef\u4ee5\u5728\u56fe\u50cf\u4e0a\u6dfb\u52a0\u4e00\u6761\u84dd\u8272\u7684\u76f4\u7ebf\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5728Jupyter Notebook\u4e2d\u663e\u793a\u56fe\u50cf<\/h3>\n<\/p>\n
\u5982\u679c\u662f\u5728Jupyter Notebook\u4e2d\u8fdb\u884c\u64cd\u4f5c\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u663e\u793a\u56fe\u50cf\uff1a<\/p>\n<\/p>\n
1. \u4f7f\u7528matplotlib\u663e\u793a\u56fe\u50cf<\/h4>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
plt.imshow(image)<\/p>\n
plt.axis('off')  # \u4e0d\u663e\u793a\u5750\u6807\u8f74<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528IPython.display\u663e\u793a\u56fe\u50cf<\/h4>\n<\/p>\n
from IPython.display import display<\/p>\n
\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
display(image)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u5185\u5bb9\uff0c\u6211\u4eec\u5b66\u4e60\u4e86\u5982\u4f55\u4f7f\u7528matplotlib<\/code>\u3001opencv<\/code>\u548cPillow<\/code>\u5e93\u5728\u56fe\u50cf\u4e0a\u7ed8\u5236\u76f4\u7ebf\u3002\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf\u548c\u9002\u7528\u573a\u666f\uff0c\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53ef\u4ee5\u66f4\u9ad8\u6548\u5730\u5b8c\u6210\u4efb\u52a1\u3002<\/p>\n<\/p>\n
\u4f7f\u7528matplotlib\u5e93\u7ed8\u5236\u76f4\u7ebf<\/strong>\uff1a\u9002\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\u573a\u666f\uff0c\u652f\u6301\u591a\u79cd\u6837\u5f0f\u7684\u76f4\u7ebf\u7ed8\u5236\u3002<\/p>\n
\u4f7f\u7528opencv\u5e93\u7ed8\u5236\u76f4\u7ebf<\/strong>\uff1a\u9002\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u573a\u666f\uff0c\u652f\u6301\u66f4\u9ad8\u6548\u7684\u56fe\u50cf\u64cd\u4f5c\u3002<\/p>\n
\u4f7f\u7528Pillow\u5e93\u7ed8\u5236\u76f4\u7ebf<\/strong>\uff1a\u9002\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u548c\u56fe\u50cf\u751f\u6210\u573a\u666f\uff0c\u652f\u6301\u7075\u6d3b\u7684\u56fe\u50cf\u7ed8\u5236\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u7684\u8be6\u7ec6\u4ecb\u7ecd\uff0c\u76f8\u4fe1\u8bfb\u8005\u5df2\u7ecf\u638c\u63e1\u4e86\u5728\u56fe\u50cf\u4e0a\u6dfb\u52a0\u76f4\u7ebf\u7684\u591a\u79cd\u65b9\u6cd5\uff0c\u5e76\u80fd\u591f\u6839\u636e\u5b9e\u9645\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u8fdb\u884c\u64cd\u4f5c\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u60a8\u6709\u6240\u5e2e\u52a9\uff01<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528Matplotlib\u7ed8\u5236\u76f4\u7ebf\uff1f<\/strong>
\u5728Python\u4e2d\uff0cMatplotlib\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u7ed8\u5236\u5404\u79cd\u56fe\u5f62\u3002\u8981\u5728\u56fe\u4e2d\u6dfb\u52a0\u4e00\u6761\u76f4\u7ebf\uff0c\u53ef\u4ee5\u4f7f\u7528plot()<\/code>\u51fd\u6570\u3002\u4f60\u53ea\u9700\u6307\u5b9a\u76f4\u7ebf\u7684\u8d77\u59cb\u548c\u7ed3\u675f\u70b9\uff0c\u4f8b\u5982\uff1a  <\/p>\n
import matplotlib.pyplot as plt\n\nplt.plot([x1, x2], [y1, y2], color='r')  # x1, y1\u4e3a\u8d77\u70b9\u5750\u6807\uff0cx2, y2\u4e3a\u7ec8\u70b9\u5750\u6807\nplt.show()\n<\/code><\/pre>\n
\u901a\u8fc7\u8c03\u6574color<\/code>\u53c2\u6570\uff0c\u53ef\u4ee5\u6539\u53d8\u76f4\u7ebf\u7684\u989c\u8272\u3002<\/p>\n
\u5982\u4f55\u81ea\u5b9a\u4e49\u76f4\u7ebf\u7684\u6837\u5f0f\u548c\u5bbd\u5ea6\uff1f<\/strong>
Matplotlib\u5141\u8bb8\u7528\u6237\u81ea\u5b9a\u4e49\u76f4\u7ebf\u7684\u6837\u5f0f\u548c\u5bbd\u5ea6\u3002\u5728plot()<\/code>\u51fd\u6570\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528linestyle<\/code>\u548clinewidth<\/code>\u53c2\u6570\u6765\u5b9e\u73b0\u3002\u4f8b\u5982\uff1a  <\/p>\n
plt.plot([x1, x2], [y1, y2], linestyle='--', linewidth=2)\n<\/code><\/pre>\n
\u8fd9\u91cclinestyle<\/code>\u53ef\u4ee5\u8bbe\u7f6e\u4e3a\u4e0d\u540c\u7c7b\u578b\uff0c\u5982\u865a\u7ebf\u3001\u70b9\u7ebf\u7b49\uff0c\u800clinewidth<\/code>\u5219\u63a7\u5236\u76f4\u7ebf\u7684\u7c97\u7ec6\u3002<\/p>\n
\u5728\u56fe\u4e2d\u6dfb\u52a0\u591a\u6761\u76f4\u7ebf\u7684\u6700\u4f73\u5b9e\u8df5\u662f\u4ec0\u4e48\uff1f<\/strong>
\u5982\u679c\u9700\u8981\u5728\u540c\u4e00\u56fe\u4e2d\u6dfb\u52a0\u591a\u6761\u76f4\u7ebf\uff0c\u53ef\u4ee5\u591a\u6b21\u8c03\u7528plot()<\/code>\u51fd\u6570\uff0c\u5e76\u786e\u4fdd\u4f7f\u7528\u4e0d\u540c\u7684\u6807\u7b7e\uff0c\u4ee5\u4fbf\u4e8e\u540e\u7eed\u7684\u56fe\u4f8b\u663e\u793a\u3002\u4f8b\u5982\uff1a  <\/p>\n
plt.plot([x1, x2], [y1, y2], label='Line 1')\nplt.plot([x3, x4], [y3, y4], label='Line 2')\nplt.legend()  # \u663e\u793a\u56fe\u4f8b\nplt.show()\n<\/code><\/pre>\n
\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u4ec5\u53ef\u4ee5\u6e05\u6670\u5730\u5c55\u793a\u591a\u6761\u76f4\u7ebf\uff0c\u8fd8\u80fd\u901a\u8fc7\u56fe\u4f8b\u5e2e\u52a9\u89c2\u4f17\u7406\u89e3\u6bcf\u6761\u76f4\u7ebf\u7684\u542b\u4e49\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5982\u4f55\u7ed9\u56fe\u6dfb\u52a0\u4e00\u6761\u76f4\u7ebf\uff1a\u4f7f\u7528Python \u8981\u5728\u56fe\u50cf\u4e2d\u6dfb\u52a0\u4e00\u6761\u76f4\u7ebf\uff0c\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528matplotlib\u5e93\u3001o […]","protected":false},"author":3,"featured_media":1110340,"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\/1110329"}],"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=1110329"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1110329\/revisions"}],"predecessor-version":[{"id":1110342,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1110329\/revisions\/1110342"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1110340"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1110329"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1110329"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1110329"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}