{"id":1106035,"date":"2025-01-08T16:36:23","date_gmt":"2025-01-08T08:36:23","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1106035.html"},"modified":"2025-01-08T16:36:25","modified_gmt":"2025-01-08T08:36:25","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%bb%98%e5%88%b6%e7%81%b0%e5%ba%a6%e7%9b%b4%e6%96%b9%e5%87%bd%e6%95%b0%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1106035.html","title":{"rendered":"\u5982\u4f55\u7528python\u7ed8\u5236\u7070\u5ea6\u76f4\u65b9\u51fd\u6570\u56fe"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u7ed8\u5236\u7070\u5ea6\u76f4\u65b9\u51fd\u6570\u56fe\"<\/p>\n

\u5982\u4f55\u7528Python\u7ed8\u5236\u7070\u5ea6\u76f4\u65b9\u51fd\u6570\u56fe<\/strong><\/p>\n<\/p>\n

Python\u7ed8\u5236\u7070\u5ea6\u76f4\u65b9\u51fd\u6570\u56fe\u7684\u6b65\u9aa4\u4e3b\u8981\u5305\u62ec\uff1a\u5bfc\u5165\u56fe\u50cf\u3001\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u3001\u8ba1\u7b97\u76f4\u65b9\u56fe\u3001\u7ed8\u5236\u76f4\u65b9\u56fe<\/strong>\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u4e2d\u7684\u51e0\u4e2a\u5e38\u7528\u5e93\uff08\u5982OpenCV\u3001Matplotlib\uff09\u6765\u5b8c\u6210\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u5e76\u63d0\u4f9b\u4e00\u4e9b\u4e13\u4e1a\u7684\u89c1\u89e3\u548c\u5efa\u8bae\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/p>\n<\/p>\n

\u5728\u5f00\u59cb\u7ed8\u5236\u7070\u5ea6\u76f4\u65b9\u51fd\u6570\u56fe\u4e4b\u524d\uff0c\u9700\u8981\u5bfc\u5165\u4e00\u4e9b\u5fc5\u8981\u7684Python\u5e93\u3002\u5e38\u7528\u7684\u5e93\u6709OpenCV\uff08\u7528\u4e8e\u56fe\u50cf\u5904\u7406\uff09\u3001Matplotlib\uff08\u7528\u4e8e\u7ed8\u56fe\uff09\u548cNumpy\uff08\u7528\u4e8e\u6570\u7ec4\u64cd\u4f5c\uff09\u3002\u9996\u5148\uff0c\u786e\u4fdd\u8fd9\u4e9b\u5e93\u5df2\u7ecf\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528pip\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install opencv-python matplotlib numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\uff0c\u5728\u4ee3\u7801\u4e2d\u5bfc\u5165\u8fd9\u4e9b\u5e93\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u8bfb\u53d6\u548c\u663e\u793a\u56fe\u50cf<\/p>\n<\/p>\n
\u9996\u5148\uff0c\u8bfb\u53d6\u56fe\u50cf\u5e76\u5c06\u5176\u663e\u793a\u51fa\u6765\u3002\u4f7f\u7528OpenCV\u7684cv2.imread()<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6\u56fe\u50cf\uff1a<\/p>\n<\/p>\n
# \u8bfb\u53d6\u56fe\u50cf<\/p>\n
image = cv2.imread('path_to_your_image.jpg')<\/p>\n
\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
cv2.imshow('Original Image', image)<\/p>\n
cv2.wAI<\/a>tKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8bfb\u53d6\u56fe\u50cf\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528OpenCV\u7684cv2.imshow()<\/code>\u51fd\u6570\u663e\u793a\u56fe\u50cf\u3002cv2.waitKey(0)<\/code>\u7528\u4e8e\u7b49\u5f85\u7528\u6237\u6309\u4efb\u610f\u952e\u540e\u5173\u95ed\u7a97\u53e3\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/p>\n<\/p>\n
\u4e3a\u4e86\u7ed8\u5236\u7070\u5ea6\u76f4\u65b9\u56fe\uff0c\u9996\u5148\u9700\u8981\u5c06\u5f69\u8272\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u3002\u53ef\u4ee5\u4f7f\u7528OpenCV\u7684cv2.cvtColor()<\/code>\u51fd\u6570\u5b8c\u6210\u8fd9\u4e00\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
# \u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/p>\n
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n
\u663e\u793a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n
cv2.imshow('Gray Image', gray_image)<\/p>\n
cv2.waitKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u8ba1\u7b97\u7070\u5ea6\u76f4\u65b9\u56fe<\/p>\n<\/p>\n
\u7070\u5ea6\u76f4\u65b9\u56fe\u663e\u793a\u4e86\u56fe\u50cf\u4e2d\u6bcf\u4e2a\u7070\u5ea6\u7ea7\u7684\u50cf\u7d20\u6570\u91cf\u3002\u53ef\u4ee5\u4f7f\u7528OpenCV\u7684cv2.calcHist()<\/code>\u51fd\u6570\u8ba1\u7b97\u76f4\u65b9\u56fe\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b97\u7070\u5ea6\u76f4\u65b9\u56fe<\/p>\n
hist = cv2.calcHist([gray_image], [0], None, [256], [0, 256])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c[gray_image]\u8868\u793a\u8981\u8ba1\u7b97\u76f4\u65b9\u56fe\u7684\u56fe\u50cf\uff0c[0]\u8868\u793a\u901a\u9053\u6570\u91cf\uff08\u7070\u5ea6\u56fe\u50cf\u53ea\u6709\u4e00\u4e2a\u901a\u9053\uff09\uff0cNone\u8868\u793a\u6ca1\u6709\u63a9\u7801\uff0c[256]\u8868\u793a\u76f4\u65b9\u56fe\u7684\u5927\u5c0f\uff0c\u8303\u56f4\u662f\u4ece0\u5230256\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u7ed8\u5236\u7070\u5ea6\u76f4\u65b9\u56fe<\/p>\n<\/p>\n
\u4f7f\u7528Matplotlib\u5e93\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u7ed8\u5236\u76f4\u65b9\u56fe\uff1a<\/p>\n<\/p>\n
# \u7ed8\u5236\u7070\u5ea6\u76f4\u65b9\u56fe<\/p>\n
plt.figure()<\/p>\n
plt.title("Grayscale Histogram")<\/p>\n
plt.xlabel("Bins")<\/p>\n
plt.ylabel("# of Pixels")<\/p>\n
plt.plot(hist)<\/p>\n
plt.xlim([0, 256])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f7f\u7528plt.plot()<\/code>\u51fd\u6570\u7ed8\u5236\u76f4\u65b9\u56fe\uff0c\u5e76\u4f7f\u7528plt.xlim([0, 256])<\/code>\u8bbe\u7f6ex\u8f74\u8303\u56f4\u3002<\/p>\n<\/p>\n
\u516d\u3001\u4fdd\u5b58\u76f4\u65b9\u56fe<\/p>\n<\/p>\n
\u5982\u679c\u9700\u8981\u4fdd\u5b58\u76f4\u65b9\u56fe\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u7684plt.savefig()<\/code>\u51fd\u6570\uff1a<\/p>\n<\/p>\n
# \u4fdd\u5b58\u7070\u5ea6\u76f4\u65b9\u56fe<\/p>\n
plt.figure()<\/p>\n
plt.title("Grayscale Histogram")<\/p>\n
plt.xlabel("Bins")<\/p>\n
plt.ylabel("# of Pixels")<\/p>\n
plt.plot(hist)<\/p>\n
plt.xlim([0, 256])<\/p>\n
plt.savefig('grayscale_histogram.png')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u4f18\u5316\u548c\u6269\u5c55<\/p>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6709\u65f6\u9700\u8981\u5bf9\u56fe\u50cf\u8fdb\u884c\u9884\u5904\u7406\uff0c\u5982\u53bb\u566a\u3001\u589e\u5f3a\u5bf9\u6bd4\u5ea6\u7b49\u3002\u53ef\u4ee5\u4f7f\u7528OpenCV\u7684\u5404\u79cd\u56fe\u50cf\u5904\u7406\u51fd\u6570\u8fdb\u884c\u8fd9\u4e9b\u64cd\u4f5c\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u9ad8\u65af\u6a21\u7cca\u53bb\u566a\uff1a<\/p>\n<\/p>\n
# \u9ad8\u65af\u6a21\u7cca\u53bb\u566a<\/p>\n
blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)<\/p>\n
\u8ba1\u7b97\u53bb\u566a\u540e\u7684\u7070\u5ea6\u76f4\u65b9\u56fe<\/strong><\/h2>\n
blurred_hist = cv2.calcHist([blurred_image], [0], None, [256], [0, 256])<\/p>\n
\u7ed8\u5236\u53bb\u566a\u540e\u7684\u7070\u5ea6\u76f4\u65b9\u56fe<\/strong><\/h2>\n
plt.figure()<\/p>\n
plt.title("Blurred Grayscale Histogram")<\/p>\n
plt.xlabel("Bins")<\/p>\n
plt.ylabel("# of Pixels")<\/p>\n
plt.plot(blurred_hist)<\/p>\n
plt.xlim([0, 256])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6b64\u5916\uff0c\u53ef\u4ee5\u4f7f\u7528CLAHE\uff08\u5bf9\u6bd4\u5ea6\u53d7\u9650\u81ea\u9002\u5e94\u76f4\u65b9\u56fe\u5747\u8861\u5316\uff09\u6765\u589e\u5f3a\u5bf9\u6bd4\u5ea6\uff1a<\/p>\n<\/p>\n
# \u4f7f\u7528CLAHE\u589e\u5f3a\u5bf9\u6bd4\u5ea6<\/p>\n
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))<\/p>\n
clahe_image = clahe.apply(gray_image)<\/p>\n
\u8ba1\u7b97\u589e\u5f3a\u5bf9\u6bd4\u5ea6\u540e\u7684\u7070\u5ea6\u76f4\u65b9\u56fe<\/strong><\/h2>\n
clahe_hist = cv2.calcHist([clahe_image], [0], None, [256], [0, 256])<\/p>\n
\u7ed8\u5236\u589e\u5f3a\u5bf9\u6bd4\u5ea6\u540e\u7684\u7070\u5ea6\u76f4\u65b9\u56fe<\/strong><\/h2>\n
plt.figure()<\/p>\n
plt.title("CLAHE Grayscale Histogram")<\/p>\n
plt.xlabel("Bins")<\/p>\n
plt.ylabel("# of Pixels")<\/p>\n
plt.plot(clahe_hist)<\/p>\n
plt.xlim([0, 256])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516b\u3001\u603b\u7ed3<\/p>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u8f7b\u677e\u7ed8\u5236\u7070\u5ea6\u76f4\u65b9\u51fd\u6570\u56fe\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u5bf9\u56fe\u50cf\u8fdb\u884c\u9884\u5904\u7406\uff0c\u4ee5\u83b7\u5f97\u66f4\u597d\u7684\u6548\u679c\u3002\u638c\u63e1\u8fd9\u4e9b\u57fa\u672c\u64cd\u4f5c\u540e\uff0c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u63a2\u7d22\u56fe\u50cf\u5904\u7406\u548c\u5206\u6790\u7684\u66f4\u591a\u9ad8\u7ea7\u6280\u672f\uff0c\u5982\u8fb9\u7f18\u68c0\u6d4b\u3001\u56fe\u50cf\u5206\u5272\u7b49\uff0c\u4ece\u800c\u66f4\u6df1\u5165\u5730\u7406\u89e3\u548c\u5e94\u7528\u56fe\u50cf\u5904\u7406\u6280\u672f\u3002<\/p>\n<\/p>\n
\u5728\u5904\u7406\u5927\u89c4\u6a21\u56fe\u50cf\u6570\u636e\u65f6\uff0c\u53ef\u4ee5\u7ed3\u5408\u5176\u4ed6\u5e93\u548c\u5de5\u5177\uff08\u5982Pandas\u3001Scikit-learn\uff09\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60<\/a>\uff0c\u4ee5\u5b9e\u73b0\u66f4\u590d\u6742\u7684\u56fe\u50cf\u5904\u7406\u4efb\u52a1\u3002\u6b64\u5916\uff0c\u4f18\u5316\u4ee3\u7801\u6548\u7387\u548c\u5904\u7406\u901f\u5ea6\u4e5f\u662f\u5b9e\u9645\u5e94\u7528\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u65b9\u9762\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u7ebf\u7a0b\u5904\u7406\u3001GPU\u52a0\u901f\u7b49\u6280\u672f\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u4e0d\u65ad\u5b66\u4e60\u548c\u5b9e\u8df5\uff0c\u53ef\u4ee5\u9010\u6b65\u63d0\u5347\u5728\u56fe\u50cf\u5904\u7406\u9886\u57df\u7684\u4e13\u4e1a\u6c34\u5e73\uff0c\u4e3a\u66f4\u5e7f\u6cdb\u7684\u5e94\u7528\u6253\u4e0b\u575a\u5b9e\u7684\u57fa\u7840\u3002\u65e0\u8bba\u662f\u5728\u79d1\u7814\u3001\u5de5\u4e1a\u8fd8\u662f\u65e5\u5e38\u751f\u6d3b\u4e2d\uff0c\u56fe\u50cf\u5904\u7406\u6280\u672f\u90fd\u5177\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u524d\u666f\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u7ed8\u5236\u56fe\u50cf\u7684\u7070\u5ea6\u76f4\u65b9\u56fe\uff1f<\/strong>
\u8981\u7ed8\u5236\u56fe\u50cf\u7684\u7070\u5ea6\u76f4\u65b9\u56fe\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684matplotlib<\/code>\u548cPIL<\/code>\uff08\u6216OpenCV<\/code>\uff09\u5e93\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u52a0\u8f7d\u56fe\u50cf\u5e76\u5c06\u5176\u8f6c\u6362\u4e3a\u7070\u5ea6\u6a21\u5f0f\u3002\u63a5\u7740\uff0c\u4f7f\u7528numpy<\/code>\u5e93\u8ba1\u7b97\u50cf\u7d20\u503c\u7684\u9891\u7387\uff0c\u5e76\u901a\u8fc7matplotlib<\/code>\u7ed8\u5236\u76f4\u65b9\u56fe\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff1a  <\/p>\n
from PIL import Image\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# \u52a0\u8f7d\u56fe\u50cf\u5e76\u8f6c\u6362\u4e3a\u7070\u5ea6\nimage = Image.open('your_image_path.jpg').convert('L')\nimage_array = np.array(image)\n\n# \u8ba1\u7b97\u76f4\u65b9\u56fe\nhist, bins = np.histogram(image_array.flatten(), bins=256, range=[0,256])\n\n# \u7ed8\u5236\u76f4\u65b9\u56fe\nplt.figure(figsize=(10, 6))\nplt.title('Gray Level Histogram')\nplt.xlabel('Pixel Values')\nplt.ylabel('Frequency')\nplt.xlim([0, 256])\nplt.plot(hist)\nplt.show()\n<\/code><\/pre>\n
\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5730\u83b7\u5f97\u56fe\u50cf\u7684\u7070\u5ea6\u76f4\u65b9\u56fe\u3002<\/p>\n
Python\u7ed8\u5236\u7070\u5ea6\u76f4\u65b9\u56fe\u9700\u8981\u54ea\u4e9b\u5e93\uff1f<\/strong>
\u4e3a\u4e86\u7ed8\u5236\u7070\u5ea6\u76f4\u65b9\u56fe\uff0c\u60a8\u9700\u8981\u5b89\u88c5\u51e0\u4e2a\u4e3b\u8981\u7684Python\u5e93\u3002PIL<\/code>\uff08\u6216Pillow<\/code>\uff09\u7528\u4e8e\u5904\u7406\u56fe\u50cf\uff0cnumpy<\/code>\u7528\u4e8e\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\uff0cmatplotlib<\/code>\u5219\u7528\u4e8e\u7ed8\u56fe\u3002\u60a8\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5\u8fd9\u4e9b\u5e93\uff1a  <\/p>\n
pip install Pillow numpy matplotlib\n<\/code><\/pre>\n
\u786e\u4fdd\u5728\u5f00\u59cb\u7f16\u5199\u4ee3\u7801\u4e4b\u524d\uff0c\u60a8\u5df2\u6210\u529f\u5b89\u88c5\u8fd9\u4e9b\u5e93\u3002<\/p>\n
\u7070\u5ea6\u76f4\u65b9\u56fe\u7684\u5b9e\u9645\u5e94\u7528\u662f\u4ec0\u4e48\uff1f<\/strong>
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