{"id":1138834,"date":"2025-01-08T22:04:06","date_gmt":"2025-01-08T14:04:06","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1138834.html"},"modified":"2025-01-08T22:04:08","modified_gmt":"2025-01-08T14:04:08","slug":"python%e4%b8%ad%e7%9a%84%e5%9b%be%e5%83%8f%e6%98%be%e7%a4%ba%e5%a6%82%e4%bd%95%e6%94%be%e5%a4%a7%e4%b8%80%e7%82%b9","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1138834.html","title":{"rendered":"python\u4e2d\u7684\u56fe\u50cf\u663e\u793a\u5982\u4f55\u653e\u5927\u4e00\u70b9"},"content":{"rendered":"

\"python\u4e2d\u7684\u56fe\u50cf\u663e\u793a\u5982\u4f55\u653e\u5927\u4e00\u70b9\"<\/p>\n

PYTHON\u4e2d\u7684\u56fe\u50cf\u663e\u793a\u5982\u4f55\u653e\u5927\u4e00\u70b9<\/h3>\n<\/p>\n

\u5728Python\u4e2d\u4f7f\u7528\u56fe\u50cf\u663e\u793a\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u56fe\u50cf\u7684\u5c3a\u5bf8\u3001\u5206\u8fa8\u7387\u3001\u548c\u663e\u793a\u7a97\u53e3\u7684\u5927\u5c0f\u7b49\u65b9\u5f0f\u8fdb\u884c\u653e\u5927\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u8c03\u6574Matplotlib\u7ed8\u56fe\u5c3a\u5bf8\u3001\u4f7f\u7528OpenCV\u8c03\u6574\u7a97\u53e3\u5927\u5c0f\u3001\u4ee5\u53caPillow\u5e93\u7684\u56fe\u50cf\u7f29\u653e\u3002<\/strong> \u5176\u4e2d\uff0c\u8c03\u6574Matplotlib\u7ed8\u56fe\u5c3a\u5bf8<\/strong> \u662f\u4e00\u79cd\u7b80\u5355\u4e14\u6709\u6548\u7684\u65b9\u5f0f\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u56fe\u50cf\u7684\u5c3a\u5bf8\u53c2\u6570\u6765\u5b9e\u73b0\u56fe\u50cf\u653e\u5927\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Matplotlib\u8c03\u6574\u56fe\u50cf\u5c3a\u5bf8<\/h3>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u7528\u6765\u751f\u6210\u9ad8\u8d28\u91cf\u7684\u56fe\u50cf\u548c\u56fe\u8868\u3002\u901a\u8fc7\u8bbe\u7f6e\u56fe\u50cf\u7684\u5c3a\u5bf8\u53c2\u6570\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u653e\u5927\u663e\u793a\u56fe\u50cf\u3002<\/p>\n<\/p>\n

1\u3001\u8bbe\u7f6e\u56fe\u50cf\u5c3a\u5bf8<\/h4>\n<\/p>\n

\u8981\u653e\u5927\u56fe\u50cf\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6eMatplotlib\u7684 figsize<\/code> \u53c2\u6570\u6765\u8c03\u6574\u56fe\u50cf\u7684\u5927\u5c0f\u3002figsize<\/code> \u53c2\u6570\u63a5\u53d7\u4e00\u4e2a\u5305\u542b\u5bbd\u5ea6\u548c\u9ad8\u5ea6\u7684\u5143\u7ec4\uff0c\u4f8b\u5982\uff1a<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
import matplotlib.image as mpimg<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
img = mpimg.imread('example_image.png')<\/p>\n
\u8bbe\u7f6e\u56fe\u50cf\u5c3a\u5bf8<\/strong><\/h2>\n
plt.figure(figsize=(10, 10))  # \u5bbd\u5ea6\u548c\u9ad8\u5ea6\u5747\u8bbe\u7f6e\u4e3a10\u82f1\u5bf8<\/p>\n
\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
plt.imshow(img)<\/p>\n
plt.axis('off')  # \u5173\u95ed\u5750\u6807\u8f74<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cplt.figure(figsize=(10, 10))<\/code> \u8bbe\u7f6e\u4e86\u56fe\u50cf\u7684\u5c3a\u5bf8\uff0c\u4f7f\u5f97\u56fe\u50cf\u5728\u663e\u793a\u65f6\u53d8\u5f97\u66f4\u5927\u3002<\/p>\n<\/p>\n
2\u3001\u8c03\u6574\u5206\u8fa8\u7387<\/h4>\n<\/p>\n
\u9664\u4e86\u8c03\u6574\u56fe\u50cf\u7684\u5c3a\u5bf8\u5916\uff0c\u8fd8\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u56fe\u50cf\u7684\u5206\u8fa8\u7387\u6765\u589e\u5f3a\u56fe\u50cf\u7684\u6e05\u6670\u5ea6\u3002\u53ef\u4ee5\u4f7f\u7528 dpi<\/code> \u53c2\u6570\u6765\u8bbe\u7f6e\u56fe\u50cf\u7684\u5206\u8fa8\u7387\uff0c\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
plt.figure(figsize=(10, 10), dpi=100)  # \u8bbe\u7f6e\u56fe\u50cf\u5c3a\u5bf8\u4e3a10x10\u82f1\u5bf8\uff0c\u5206\u8fa8\u7387\u4e3a100 DPI<\/p>\n
plt.imshow(img)<\/p>\n
plt.axis('off')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528OpenCV\u8c03\u6574\u7a97\u53e3\u5927\u5c0f<\/h3>\n<\/p>\n
OpenCV\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u53ef\u4ee5\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u3002\u4f7f\u7528OpenCV\u53ef\u4ee5\u65b9\u4fbf\u5730\u8c03\u6574\u56fe\u50cf\u663e\u793a\u7a97\u53e3\u7684\u5927\u5c0f\u3002<\/p>\n<\/p>\n
1\u3001\u8bfb\u53d6\u548c\u663e\u793a\u56fe\u50cf<\/h4>\n<\/p>\n
\u4f7f\u7528OpenCV\u8bfb\u53d6\u548c\u663e\u793a\u56fe\u50cf\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
img = cv2.imread('example_image.png')<\/p>\n
\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
cv2.imshow('Image', img)<\/p>\n
cv2.wAI<\/a>tKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8c03\u6574\u7a97\u53e3\u5927\u5c0f<\/h4>\n<\/p>\n
\u8981\u653e\u5927\u663e\u793a\u56fe\u50cf\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u663e\u793a\u7a97\u53e3\u7684\u5927\u5c0f\u6765\u5b9e\u73b0\uff0c\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
# \u8c03\u6574\u7a97\u53e3\u5927\u5c0f<\/p>\n
cv2.namedWindow('Image', cv2.WINDOW_NORMAL)<\/p>\n
cv2.resizeWindow('Image', 800, 800)  # \u8bbe\u7f6e\u7a97\u53e3\u5927\u5c0f\u4e3a800x800\u50cf\u7d20<\/p>\n
cv2.imshow('Image', img)<\/p>\n
cv2.waitKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0ccv2.resizeWindow('Image', 800, 800)<\/code> \u8bbe\u7f6e\u4e86\u7a97\u53e3\u7684\u5927\u5c0f\uff0c\u4f7f\u5f97\u56fe\u50cf\u663e\u793a\u65f6\u53d8\u5f97\u66f4\u5927\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Pillow\u5e93\u7684\u56fe\u50cf\u7f29\u653e<\/h3>\n<\/p>\n
Pillow\u662fPython\u7684\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u56fe\u50cf\u7684\u8bfb\u53d6\u3001\u5904\u7406\u548c\u663e\u793a\u3002\u901a\u8fc7Pillow\u53ef\u4ee5\u8fdb\u884c\u56fe\u50cf\u7684\u7f29\u653e\u64cd\u4f5c\uff0c\u4ee5\u5b9e\u73b0\u56fe\u50cf\u7684\u653e\u5927\u663e\u793a\u3002<\/p>\n<\/p>\n
1\u3001\u8bfb\u53d6\u548c\u663e\u793a\u56fe\u50cf<\/h4>\n<\/p>\n
\u4f7f\u7528Pillow\u8bfb\u53d6\u548c\u663e\u793a\u56fe\u50cf\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n
from PIL import Image<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
img = Image.open('example_image.png')<\/p>\n
\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
img.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u56fe\u50cf\u7f29\u653e<\/h4>\n<\/p>\n
\u8981\u653e\u5927\u663e\u793a\u56fe\u50cf\uff0c\u53ef\u4ee5\u901a\u8fc7Pillow\u7684 resize<\/code> \u65b9\u6cd5\u5bf9\u56fe\u50cf\u8fdb\u884c\u7f29\u653e\uff0c\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
# \u83b7\u53d6\u56fe\u50cf\u7684\u5c3a\u5bf8<\/p>\n
width, height = img.size<\/p>\n
\u8bbe\u7f6e\u653e\u5927\u6bd4\u4f8b<\/strong><\/h2>\n
scale_factor = 2<\/p>\n
\u7f29\u653e\u56fe\u50cf<\/strong><\/h2>\n
img_resized = img.resize((width * scale_factor, height * scale_factor))<\/p>\n
\u663e\u793a\u7f29\u653e\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n
img_resized.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cimg.resize((width * scale_factor, height * scale_factor))<\/code> \u901a\u8fc7\u8bbe\u7f6e\u65b0\u7684\u5bbd\u5ea6\u548c\u9ad8\u5ea6\u5bf9\u56fe\u50cf\u8fdb\u884c\u4e86\u653e\u5927\u3002<\/p>\n<\/p>\n
\u56db\u3001\u7efc\u5408\u5e94\u7528<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u8fdb\u884c\u56fe\u50cf\u7684\u653e\u5927\u663e\u793a\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7efc\u5408\u5e94\u7528\u7684\u793a\u4f8b\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u7ed3\u5408\u4f7f\u7528Matplotlib\u548cOpenCV\u8fdb\u884c\u56fe\u50cf\u7684\u653e\u5927\u663e\u793a\u3002<\/p>\n<\/p>\n
1\u3001\u8bfb\u53d6\u56fe\u50cf<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u4f7f\u7528Matplotlib\u8bfb\u53d6\u56fe\u50cf\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import matplotlib.image as mpimg<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
img = mpimg.imread('example_image.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u663e\u793a\u56fe\u50cf\uff08Matplotlib\uff09<\/h4>\n<\/p>\n
\u4f7f\u7528Matplotlib\u663e\u793a\u56fe\u50cf\uff0c\u5e76\u8c03\u6574\u56fe\u50cf\u7684\u5c3a\u5bf8\uff1a<\/p>\n<\/p>\n
plt.figure(figsize=(12, 12), dpi=120)  # \u8bbe\u7f6e\u56fe\u50cf\u5c3a\u5bf8\u548c\u5206\u8fa8\u7387<\/p>\n
plt.imshow(img)<\/p>\n
plt.axis('off')  # \u5173\u95ed\u5750\u6807\u8f74<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u663e\u793a\u56fe\u50cf\uff08OpenCV\uff09<\/h4>\n<\/p>\n
\u4f7f\u7528OpenCV\u663e\u793a\u56fe\u50cf\uff0c\u5e76\u8c03\u6574\u663e\u793a\u7a97\u53e3\u7684\u5927\u5c0f\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
\u5c06Matplotlib\u8bfb\u53d6\u7684\u56fe\u50cf\u8f6c\u6362\u4e3aOpenCV\u683c\u5f0f<\/strong><\/h2>\n
img_cv = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)<\/p>\n
\u8c03\u6574\u7a97\u53e3\u5927\u5c0f<\/strong><\/h2>\n
cv2.namedWindow('Image', cv2.WINDOW_NORMAL)<\/p>\n
cv2.resizeWindow('Image', 1000, 1000)  # \u8bbe\u7f6e\u7a97\u53e3\u5927\u5c0f<\/p>\n
cv2.imshow('Image', img_cv)<\/p>\n
cv2.waitKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u8c03\u6574\u56fe\u50cf\u7684\u5c3a\u5bf8\u3001\u5206\u8fa8\u7387\u3001\u548c\u663e\u793a\u7a97\u53e3\u7684\u5927\u5c0f<\/strong> \u662f\u653e\u5927\u56fe\u50cf\u663e\u793a\u7684\u5e38\u7528\u65b9\u6cd5\u3002\u901a\u8fc7Matplotlib\u3001OpenCV\u3001Pillow<\/strong> \u7b49\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5b9e\u73b0\u56fe\u50cf\u7684\u653e\u5927\u663e\u793a\u3002\u5177\u4f53\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\u53ef\u4ee5\u6839\u636e\u5b9e\u9645\u9700\u6c42\u548c\u5e94\u7528\u573a\u666f\u6765\u51b3\u5b9a\u3002<\/p>\n<\/p>\n
    \n
  1. \u4f7f\u7528Matplotlib\u8c03\u6574\u56fe\u50cf\u5c3a\u5bf8<\/strong>\uff1a\u9002\u7528\u4e8e\u9700\u8981\u751f\u6210\u9ad8\u8d28\u91cf\u56fe\u50cf\u548c\u56fe\u8868\u7684\u573a\u666f\u3002<\/li>\n
  2. \u4f7f\u7528OpenCV\u8c03\u6574\u7a97\u53e3\u5927\u5c0f<\/strong>\uff1a\u9002\u7528\u4e8e\u5b9e\u65f6\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u3002<\/li>\n
  3. \u4f7f\u7528Pillow\u5e93\u7684\u56fe\u50cf\u7f29\u653e<\/strong>\uff1a\u9002\u7528\u4e8e\u7b80\u5355\u7684\u56fe\u50cf\u5904\u7406\u548c\u663e\u793a\u3002<\/li>\n<\/ol>\n
    \u901a\u8fc7\u5408\u7406\u9009\u62e9\u548c\u7ec4\u5408\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u5b9e\u73b0Python\u4e2d\u56fe\u50cf\u663e\u793a\u7684\u653e\u5927\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528Matplotlib\u653e\u5927\u56fe\u50cf\u663e\u793a\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u4f7f\u7528Matplotlib\u5e93\u663e\u793a\u56fe\u50cf\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u56fe\u5f62\u7684\u5927\u5c0f\u53c2\u6570\u6765\u653e\u5927\u56fe\u50cf\u3002\u53ef\u4ee5\u4f7f\u7528plt.figure(figsize=(\u5bbd\u5ea6, \u9ad8\u5ea6))<\/code>\u6765\u8bbe\u7f6e\u56fe\u5f62\u7684\u5c3a\u5bf8\u3002\u4f8b\u5982\uff0cplt.figure(figsize=(10, 8))<\/code>\u4f1a\u5c06\u56fe\u50cf\u653e\u5927\u523010×8\u82f1\u5bf8\u3002\u6b64\u5916\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6edpi<\/code>\u53c2\u6570\u6765\u63d0\u9ad8\u56fe\u50cf\u7684\u5206\u8fa8\u7387\uff0c\u4f7f\u56fe\u50cf\u66f4\u52a0\u6e05\u6670\u3002<\/p>\n
    \u5728\u56fe\u50cf\u663e\u793a\u65f6\uff0c\u5982\u4f55\u4fdd\u6301\u6bd4\u4f8b\u800c\u4e0d\u5931\u771f\uff1f<\/strong>
    \u8981\u4fdd\u6301\u56fe\u50cf\u7684\u6bd4\u4f8b\uff0c\u53ef\u4ee5\u4f7f\u7528plt.axis('equal')<\/code>\u547d\u4ee4\u3002\u8fd9\u4f1a\u786e\u4fddx\u8f74\u548cy\u8f74\u7684\u523b\u5ea6\u76f8\u540c\uff0c\u4ece\u800c\u907f\u514d\u56fe\u50cf\u5728\u653e\u5927\u65f6\u5931\u771f\u3002\u4f7f\u7528\u6b64\u547d\u4ee4\u540e\uff0c\u5373\u4f7f\u8c03\u6574\u4e86\u56fe\u50cf\u7684\u5927\u5c0f\uff0c\u56fe\u50cf\u7684\u5f62\u72b6\u4e5f\u4f1a\u88ab\u4fdd\u6301\u4e0d\u53d8\u3002<\/p>\n
    \u5982\u4f55\u5728\u653e\u5927\u56fe\u50cf\u65f6\u6dfb\u52a0\u7f29\u653e\u529f\u80fd\uff1f<\/strong>
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