{"id":65226,"date":"2026-01-24T17:24:55","date_gmt":"2026-01-24T09:24:55","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/65226.html"},"modified":"2026-01-24T17:24:55","modified_gmt":"2026-01-24T09:24:55","slug":"%e5%b0%8f%e7%99%bd%e4%bb%8e%e9%9b%b6%e5%bc%80%e5%a7%8b%e5%8b%87%e9%97%af%e4%ba%ba%e5%b7%a5%e6%99%ba%e8%83%bd%ef%bc%9a%e8%ae%a1%e7%ae%97%e6%9c%ba%e8%a7%86%e8%a7%89%e5%88%9d%e7%ba%a7%e7%af%87%ef%bc%88op","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/65226.html","title":{"rendered":"\u5c0f\u767d\u4ece\u96f6\u5f00\u59cb\u52c7\u95ef\u4eba\u5de5\u667a\u80fd\uff1a\u8ba1\u7b97\u673a\u89c6\u89c9\u521d\u7ea7\u7bc7\uff08OpenCV\u8fdb\u9636\u64cd\u4f5c(\u4e0a)\uff09"},"content":{"rendered":"

\u5f15\u8a00<\/h2>\n

\u00a0\u00a0\u6b22\u8fce\u56de\u6765，\u6211\u4eec\u5df2\u7ecf\u638c\u63e1\u4e86OpenCV\u7684\u57fa\u7840\u64cd\u4f5c\u5982\u8bfb\u53d6\u3001\u663e\u793a\u548c\u88c1\u526a\u56fe\u50cf，\u73b0\u5728\u6211\u4eec\u5c06\u5171\u540c\u63a2\u7d22\u66f4\u9ad8\u7ea7\u7684\u89c6\u89c9\u5904\u7406\u6280\u672f\u3002\u8ba1\u7b97\u673a\u89c6\u89c9\u4e0d\u4ec5\u4ec5\u662f\u201c\u770b\u201d\u56fe\u50cf，\u66f4\u662f\u6df1\u5165\u7406\u89e3\u5176\u5185\u5bb9\u3001\u63d0\u53d6\u5173\u952e\u7279\u5f81\u5e76\u8fdb\u884c\u5206\u6790\u7684\u8fc7\u7a0b，\u8fd9\u7c7b\u4f3c\u4e8e\u4eba\u7c7b\u901a\u8fc7\u773c\u775b\u89c2\u5bdf\u4e16\u754c\u5e76\u7531\u5927\u8111\u89e3\u6790\u4fe1\u606f，\u800cOpenCV\u6b63\u5145\u5f53\u4e86\u8ba1\u7b97\u673a\u7684\u201c\u773c\u775b\u548c\u5927\u8111\u201d\u3002\u672c\u6587\u6211\u4eec\u5c06\u5b66\u4e60\u56fe\u50cf\u91d1\u5b57\u5854\u3001\u56fe\u50cf\u76f4\u65b9\u56fe\u3001\u900f\u89c6\u53d8\u6362，\u8fd9\u4e9b\u6280\u672f\u662f\u8ba1\u7b97\u673a\u89c6\u89c9\u7684\u57fa\u77f3，\u5e7f\u6cdb\u5e94\u7528\u4e8e\u56fe\u50cf\u62fc\u63a5\u3001\u7269\u4f53\u8bc6\u522b\u7b49\u5173\u952e\u9886\u57df\u3002<\/p>\n

\u4e00\u3001\u56fe\u50cf\u91d1\u5b57\u5854\u2014\u2014\u591a\u5c3a\u5ea6\u89c6\u89c9\u5206\u6790<\/h3>\n

1\u3001\u4ec0\u4e48\u662f\u56fe\u50cf\u91d1\u5b57\u5854？<\/h4>\n

\u00a0\u00a0\u56fe\u50cf\u91d1\u5b57\u5854\u662f\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d\u4e00\u79cd\u6a21\u62df\u591a\u5c3a\u5ea6\u89c2\u5bdf\u7684\u56fe\u50cf\u8868\u793a\u65b9\u6cd5\u3002\u5b83\u662f\u901a\u8fc7\u5bf9\u539f\u59cb\u56fe\u50cf（\u91d1\u5b57\u5854\u5e95\u5c42）\u8fdb\u884c\u8fde\u7eed\u964d\u91c7\u6837（\u901a\u5e38\u7ed3\u5408\u5e73\u6ed1\u6ee4\u6ce2），\u751f\u6210\u4e00\u7cfb\u5217\u5206\u8fa8\u7387\u9010\u5c42\u964d\u4f4e\u7684\u56fe\u50cf，\u5e76\u6309\u987a\u5e8f\u5806\u53e0，\u5f62\u6210\u4ece\u5e95\u90e8\u5230\u9876\u90e8\u7684\u201c\u91d1\u5b57\u5854\u201d\u7ed3\u6784\u3002\u8fd9\u79cd\u591a\u5c3a\u5ea6\u8868\u8fbe\u4f7f\u7b97\u6cd5\u80fd\u591f\u50cf\u4eba\u7c7b\u4e00\u6837，\u65e2\u80fd\u201c\u9000\u540e\u201d\u89c2\u5bdf\u6574\u4f53\u7ed3\u6784\u4e0e\u4e0a\u4e0b\u6587，\u4e5f\u80fd\u201c\u8d70\u8fd1\u201d\u5206\u6790\u7cbe\u7ec6\u7684\u5c40\u90e8\u7279\u5f81\u3002\u5b83\u5728\u56fe\u50cf\u878d\u5408（\u5982\u65e0\u7f1d\u62fc\u63a5）\u3001\u76ee\u6807\u68c0\u6d4b（\u8bc6\u522b\u4e0d\u540c\u5927\u5c0f\u7684\u7269\u4f53）\u7b49\u65b9\u9762\u81f3\u5173\u91cd\u8981\u3002\u6700\u5e38\u89c1\u7684\u7c7b\u578b\u5305\u62ec\u7528\u4e8e\u4e0b\u91c7\u6837\u7684\u9ad8\u65af\u91d1\u5b57\u5854\u548c\u7528\u4e8e\u91cd\u5efa\u56fe\u50cf\u7ec6\u8282\u7684\u62c9\u666e\u62c9\u65af\u91d1\u5b57\u5854\u3002<\/p>\n

2\u3001\u5411\u4e0a\u91c7\u6837\u4e0e\u5411\u4e0b\u91c7\u6837<\/h4>\n

\u5411\u4e0b\u91c7\u6837（\u6784\u5efa\u91d1\u5b57\u5854）：<\/h5>\n

\u00a0\u00a0\u5728\u6784\u5efa\u56fe\u50cf\u91d1\u5b57\u5854\u7684\u5411\u4e0b\u91c7\u6837\u8fc7\u7a0b\u4e2d，\u968f\u7740\u5c42\u7ea7\u5411\u91d1\u5b57\u5854\u9876\u7aef\u79fb\u52a8，\u56fe\u50cf\u7684\u5c3a\u5bf8\u548c\u5206\u8fa8\u7387\u9010\u7ea7\u964d\u4f4e\u3002\u901a\u5e38，\u6bcf\u4e0a\u5347\u4e00\u5c42，\u56fe\u50cf\u7684\u5bbd\u5ea6\u548c\u9ad8\u5ea6\u5747\u7f29\u51cf\u4e3a\u539f\u5148\u7684\u4e00\u534a\u3002\u5176\u6807\u51c6\u5b9e\u73b0\u65b9\u6cd5\u5305\u542b\u4e24\u4e2a\u5173\u952e\u6b65\u9aa4：\u9996\u5148，\u5bf9\u5f53\u524d\u5c42\u7ea7\u56fe\u50cf\u8fdb\u884c\u9ad8\u65af\u6ee4\u6ce2，\u4ee5\u5e73\u6ed1\u56fe\u50cf\u5e76\u9632\u6b62\u540e\u7eed\u964d\u91c7\u6837\u4ea7\u751f\u6df7\u53e0\u6548\u5e94，\u63a5\u7740，\u76f4\u63a5\u5220\u9664\u6ee4\u6ce2\u540e\u56fe\u50cf\u7684\u5076\u6570\u884c\u4e0e\u5076\u6570\u5217，\u4ece\u800c\u5b9e\u73b0\u5c3a\u5bf8\u7684\u51cf\u534a，\u751f\u6210\u4e0a\u4e00\u5c42\u7684\u4f4e\u5206\u8fa8\u7387\u56fe\u50cf\u3002<\/p>\n

\u5411\u4e0a\u91c7\u6837（\u91cd\u5efa\u56fe\u50cf）：<\/h5>\n

\u00a0\u00a0\u5411\u4e0a\u91c7\u6837\u662f\u56fe\u50cf\u91d1\u5b57\u5854\u4e2d\u7528\u4e8e\u91cd\u5efa\u9ad8\u5206\u8fa8\u7387\u56fe\u50cf\u7684\u8fc7\u7a0b\u3002\u9996\u5148，\u5c06\u56fe\u50cf\u7684\u5bbd\u5ea6\u548c\u9ad8\u5ea6\u5747\u6269\u5927\u4e3a\u539f\u6765\u76842\u500d，\u4ece\u800c\u4f7f\u603b\u50cf\u7d20\u6570\u91cf\u53d8\u4e3a\u539f\u59cb\u76844\u500d\u3002\u6b64\u65f6\u65b0\u589e\u7684\u50cf\u7d20\u70b9\u6682\u65e0\u6709\u6548\u503c，\u9700\u8981\u901a\u8fc7\u63d2\u503c\u7b97\u6cd5（\u5982\u53cc\u7ebf\u6027\u63d2\u503c）\u6765\u4f30\u8ba1\u5e76\u586b\u5145\u8fd9\u4e9b\u70b9\u7684\u50cf\u7d20\u503c\u3002\u968f\u540e，\u901a\u5e38\u4f1a\u65bd\u52a0\u4e00\u6b21\u9ad8\u65af\u6ee4\u6ce2\u5bf9\u6574\u4f53\u56fe\u50cf\u8fdb\u884c\u5e73\u6ed1\u5904\u7406，\u4ee5\u6d88\u9664\u56e0\u63d2\u503c\u53ef\u80fd\u5f15\u5165\u7684\u5757\u72b6\u6216\u4e0d\u81ea\u7136\u75d5\u8ff9，\u4f7f\u91cd\u5efa\u540e\u7684\u56fe\u50cf\u66f4\u52a0\u5e73\u6ed1\u81ea\u7136\u3002<\/p>\n

\u00a0\u00a0\u6ce8\u610f：\u5411\u4e0a\u91c7\u6837\u548c\u5411\u4e0b\u91c7\u6837\u4e0d\u662f\u53ef\u9006\u64cd\u4f5c！\u7531\u4e8e\u5411\u4e0b\u91c7\u6837\u4f1a\u4e22\u5931\u50cf\u7d20\u4fe1\u606f，\u5373\u4f7f\u518d\u5411\u4e0a\u91c7\u6837\u4e5f\u65e0\u6cd5\u5b8c\u5168\u6062\u590d\u539f\u59cb\u56fe\u50cf\u3002<\/p>\n

3\u3001\u62c9\u666e\u62c9\u65af\u91d1\u5b57\u5854<\/h4>\n

\u00a0\u00a0\u62c9\u666e\u62c9\u65af\u91d1\u5b57\u5854\u7684\u6784\u5efa\u65e8\u5728\u89e3\u51b3\u9ad8\u65af\u91d1\u5b57\u5854\u4e0b\u91c7\u6837\u8fc7\u7a0b\u4e2d\u4fe1\u606f\u4e22\u5931\u7684\u95ee\u9898\u3002\u5177\u4f53\u800c\u8a00，\u5176\u6bcf\u4e00\u5c42\u7531\u5bf9\u5e94\u7684\u9ad8\u65af\u91d1\u5b57\u5854\u5c42\u7ea7\"G_{i}\"，\u51cf\u53bb\u8be5\u5c42\u5148\u7ecf\u4e0b\u91c7\u6837\u518d\u4e0a\u91c7\u6837\u91cd\u5efa\u540e\u7684\u56fe\u50cf\u5f97\u51fa，\u5373\"L_{i}=G_{i}-pyrUp(pyrDown(G_{i}))\"\u3002\u8fd9\u4e2a\u5dee\u503c\u56fe\u50cf\u6355\u6349\u4e86\u5728\u964d\u91c7\u6837\u4e0e\u91cd\u5efa\u8fc7\u7a0b\u4e2d\u4e22\u5931\u7684\u9ad8\u9891\u7ec6\u8282\u4fe1\u606f，\u5982\u8fb9\u7f18\u548c\u7eb9\u7406\u3002\u56e0\u6b64，\u62c9\u666e\u62c9\u65af\u91d1\u5b57\u5854\u672c\u8d28\u4e0a\u662f\u8bb0\u5f55\u9ad8\u65af\u91d1\u5b57\u5854\u5404\u5c42\u95f4\u201c\u7f3a\u5931\u90e8\u5206\u201d\u7684\u5dee\u5206\u91d1\u5b57\u5854\u3002<\/p>\n

4\u3001\u4ee3\u7801\u793a\u4f8b<\/h4>\n

\u00a0 \u00a0\u9996\u5148\u6211\u4eec\u8981\u8bfb\u53d6\u7070\u5ea6\u56fe\u50cf，\u901a\u8fc7\u4e24\u6b21pyrDown\u64cd\u4f5c\u751f\u6210\u4e24\u5c42\u9ad8\u65af\u91d1\u5b57\u5854\u3002\u968f\u540e，\u901a\u8fc7\u5bf9\u539f\u56fe\u548c\u4e2d\u95f4\u5c42\u8fdb\u884cpyrUp\u4e0a\u91c7\u6837，\u5e76\u8ba1\u7b97\u5176\u4e0e\u4e0a\u4e00\u5c42\u56fe\u50cf\u7684\u5dee\u503c，\u6784\u5efa\u51fa\u62c9\u666e\u62c9\u65af\u91d1\u5b57\u5854\u7684\u4e24\u5c42，\u8fd9\u4e9b\u5dee\u503c\u56fe\u50cf\u6355\u6349\u4e86\u5728\u4e0b\u91c7\u6837\u8fc7\u7a0b\u4e2d\u4e22\u5931\u7684\u9ad8\u9891\u7ec6\u8282\u3002\u6700\u540e，\u7528\u62c9\u666e\u62c9\u65af\u5c42\u4e0e\u4e0a\u91c7\u6837\u56fe\u50cf\u76f8\u52a0，\u6765\u8fd1\u4f3c\u6062\u590d\u51fa\u539f\u59cb\u56fe\u50cf\u3002 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n

import cv2
\nimport numpy as np<\/p>\n

# \u8bfb\u53d6\u56fe\u50cf（\u7070\u5ea6\u56fe）
\nface = cv2.imread('R-C.jpg', cv2.IMREAD_GRAYSCALE)<\/p>\n

# \u5411\u4e0b\u91c7\u6837\u6784\u5efa\u9ad8\u65af\u91d1\u5b57\u5854
\nface_down_1 = cv2.pyrDown(face) # \u7b2c\u4e00\u5c42\u4e0b\u91c7\u6837
\nface_down_2 = cv2.pyrDown(face_down_1) # \u7b2c\u4e8c\u5c42\u4e0b\u91c7\u6837<\/p>\n

# \u5411\u4e0a\u91c7\u6837
\nface_up_1 = cv2.pyrUp(face) # \u7b2c\u4e00\u5c42\u4e0a\u91c7\u6837
\nface_up_2 = cv2.pyrUp(face_up_1) # \u7b2c\u4e8c\u5c42\u4e0a\u91c7\u6837<\/p>\n

# \u5c1d\u8bd5\u6062\u590d：\u4e0b\u91c7\u6837\u540e\u518d\u4e0a\u91c7\u6837（\u4f1a\u635f\u5931\u4fe1\u606f）
\nface_down_1_up = cv2.pyrUp(face_down_1)
\nface_down_2_up = cv2.pyrUp(face_down_2)<\/p>\n

# \u6784\u5efa\u62c9\u666e\u62c9\u65af\u91d1\u5b57\u5854
\nif face_down_1_up.shape != face.shape:
\n # \u8c03\u6574\u4e0a\u91c7\u6837\u56fe\u50cf\u5c3a\u5bf8\u4ee5\u5339\u914d\u539f\u56fe
\n face_down_1_up = cv2.resize(face_down_1_up, (face.shape[1], face.shape[0]))<\/p>\n

if face_down_2_up.shape != face_down_1.shape:
\n # \u8c03\u6574\u4e0a\u91c7\u6837\u56fe\u50cf\u5c3a\u5bf8\u4ee5\u5339\u914d\u539f\u56fe
\n face_down_2_up = cv2.resize(face_down_2_up, (face_down_1.shape[1], face_down_1.shape[0]))<\/p>\n

L0 = face – face_down_1_up # \u7b2c0\u5c42\u62c9\u666e\u62c9\u65af\u91d1\u5b57\u5854
\nL1 = face_down_1 – face_down_2_up # \u7b2c1\u5c42\u62c9\u666e\u62c9\u65af\u91d1\u5b57\u5854<\/p>\n

# \u5c1d\u8bd5\u7528\u62c9\u666e\u62c9\u65af\u91d1\u5b57\u5854\u6062\u590d\u56fe\u50cf
\nfuyuan = face_down_1_up + L0<\/p>\n

# \u663e\u793a\u7ed3\u679c
\ncv2.imshow('Original', face)
\ncv2.imshow('Down 1', face_down_1)
\ncv2.imshow('Down 2', face_down_2)<\/p>\n

# \u5bf9\u62c9\u666e\u62c9\u65af\u91d1\u5b57\u5854\u8fdb\u884c\u5f52\u4e00\u5316\u4ee5\u4fbf\u663e\u793a
\nL0_display = cv2.normalize(L0, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
\ncv2.imshow('Laplacian L0', L0_display)
\ncv2.waitKey(0)
\ncv2.destroyAllWindows()<\/p>\n

\"\"<\/p>\n

\u4e8c\u3001\u56fe\u50cf\u76f4\u65b9\u56fe\u2014\u2014\u56fe\u50cf\u7684"DNA\u5206\u6790"<\/h3>\n

1\u3001\u4ec0\u4e48\u662f\u76f4\u65b9\u56fe？<\/h4>\n

\u00a0 \u76f4\u65b9\u56fe\u662f\u56fe\u50cf\u5904\u7406\u4e2d\u4e00\u79cd\u57fa\u7840\u7684\u7edf\u8ba1\u5de5\u5177，\u5b83\u4ee5\u56fe\u5f62\u5316\u7684\u65b9\u5f0f\u76f4\u89c2\u5c55\u793a\u4e86\u56fe\u50cf\u4e2d\u6240\u6709\u50cf\u7d20\u7684\u5f3a\u5ea6\u503c\u5206\u5e03\u60c5\u51b5，\u5982\u540c\u56fe\u50cf\u7684\u201c\u6307\u7eb9\u201d\u6216\u201cDNA\u201d\u3002\u5b83\u901a\u8fc7\u8ba1\u7b97\u5e76\u7ed8\u5236\u6bcf\u4e2a\u7279\u5b9a\u50cf\u7d20\u5f3a\u5ea6\u503c\u5728\u6574\u5e45\u56fe\u50cf\u4e2d\u51fa\u73b0\u7684\u9891\u7387\u6216\u6b21\u6570，\u6765\u7cbe\u786e\u63cf\u8ff0\u56fe\u50cf\u7684\u660e\u6697\u6784\u6210\u3002\u901a\u8fc7\u89c2\u5bdf\u76f4\u65b9\u56fe\u7684\u5f62\u72b6\u4e0e\u5206\u5e03\u8303\u56f4，\u6211\u4eec\u53ef\u4ee5\u76f4\u63a5\u89e3\u8bfb\u51fa\u56fe\u50cf\u7684\u5173\u952e\u7279\u6027，\u4f8b\u5982\u6574\u4f53\u4eae\u5ea6\u503e\u5411\u3001\u5bf9\u6bd4\u5ea6\u9ad8\u4f4e\u4ee5\u53ca\u7ec6\u8282\u5c42\u6b21\u7684\u4e30\u5bcc\u7a0b\u5ea6，\u8fd9\u4e9b\u4fe1\u606f\u662f\u8fdb\u884c\u56fe\u50cf\u5206\u6790\u3001\u589e\u5f3a\u548c\u8c03\u6574\u7684\u91cd\u8981\u4f9d\u636e\u3002<\/p>\n

2\u3001\u76f4\u65b9\u56fe\u7684\u6838\u5fc3\u6982\u5ff5：Bins（\u533a\u95f4）<\/h4>\n

\u00a0\u00a0\u76f4\u65b9\u56fe\u901a\u8fc7\u7edf\u8ba1\u56fe\u50cf\u4e2d\u50cf\u7d20\u5f3a\u5ea6\u7684\u5206\u5e03\u6765\u63ed\u793a\u5176\u5185\u5728\u7279\u5f81，\u5176*Bins（\u533a\u95f4）\u662f\u5176\u6838\u5fc3\u7ed3\u6784\u3002\u4e3a\u4e86\u4fbf\u4e8e\u5206\u6790，\u8fde\u7eed\u7684\u50cf\u7d20\u503c\u8303\u56f4（\u59820-255）\u88ab\u5212\u5206\u4e3a\u82e5\u5e72\u4e2a\u8fde\u7eed\u7684\u5b50\u533a\u95f4，\u6bcf\u4e2a\u533a\u95f4\u79f0\u4e3a\u4e00\u4e2aBin\u3002\u7edf\u8ba1\u843d\u5165\u6bcf\u4e2aBin\u7684\u50cf\u7d20\u6570\u91cf，\u4fbf\u5f62\u6210\u4e86\u63cf\u8ff0\u56fe\u50cf\u4eae\u5ea6\u3001\u5bf9\u6bd4\u5ea6\u7b49\u7279\u6027\u7684\u5206\u5e03\u56fe\u8868\u3002\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u4ec5\u51cf\u5c11\u4e86\u8ba1\u7b97\u91cf，\u8fd8\u80fd\u7a81\u51fa\u4e3b\u8981\u5206\u5e03\u6a21\u5f0f\u3002<\/p>\n

\u5bf9\u4e8e\u7070\u5ea6\u56fe\u50cf，\u50cf\u7d20\u503c\u8303\u56f4\u662f0-255\u5171256\u4e2a\u53ef\u80fd\u503c\u3002\u6211\u4eec\u53ef\u4ee5\u5c06\u8fd9\u4e2a\u8303\u56f4\u5212\u5206\u4e3a\u591a\u4e2a\u5b50\u533a\u95f4：<\/p>\n

[0,255]=[0,15]\u222a[16,31]\u222a\u2026\u222a[240,255]<\/p>\n

3\u3001\u4ee3\u7801\u793a\u4f8b<\/h4>\n

\u00a0\u00a0\u6211\u4eec\u9996\u5148\u8981\u8bfb\u53d6\u4e00\u5f20\u7070\u5ea6\u56fe\u50cf，\u4e4b\u540e\u6709\u4e09\u79cd\u65b9\u6cd5：\u7b2c\u4e00\u79cd\u65b9\u6cd5\u76f4\u63a5\u4f7f\u7528Matplotlib\u7684hist\u51fd\u6570，\u5c06\u56fe\u50cf\u5c55\u5e73\u4e3a\u4e00\u7ef4\u6570\u7ec4\u5e76\u7ed8\u5236256\u4e2a\u533a\u95f4\u7684\u5b8c\u6574\u76f4\u65b9\u56fe\u3002\u7b2c\u4e8c\u79cd\u65b9\u6cd5\u8c03\u7528OpenCV\u7684calcHist\u51fd\u6570，\u5c06\u50cf\u7d20\u5f3a\u5ea6\u8303\u56f4\u5212\u5206\u4e3a16\u4e2a\u533a\u95f4（Bins）\u8fdb\u884c\u7edf\u8ba1，\u751f\u6210\u66f4\u4e3a\u6982\u62ec\u7684\u5206\u5e03\u56fe\u8868，\u4ece\u800c\u7a81\u51fa\u4e3b\u8981\u4eae\u5ea6\u7279\u5f81\u3002\u7b2c\u4e09\u79cd\u65b9\u6cd5\u5219\u9488\u5bf9\u5f69\u8272\u56fe\u50cf，\u5206\u522b\u8ba1\u7b97\u84dd\u3001\u7eff\u3001\u7ea2\u4e09\u4e2a\u989c\u8272\u901a\u9053\u7684256\u533a\u95f4\u76f4\u65b9\u56fe，\u5e76\u5728\u540c\u4e00\u56fe\u8868\u4e2d\u53e0\u52a0\u663e\u793a，\u4ee5\u5206\u6790\u5404\u901a\u9053\u7684\u8272\u5f69\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n

import cv2
\nimport matplotlib.pyplot as plt
\nimport numpy as np<\/p>\n

# \u8bfb\u53d6\u56fe\u50cf
\nphone = cv2.imread('phone.png', cv2.IMREAD_GRAYSCALE)<\/p>\n

# \u65b9\u6cd51：\u4f7f\u7528matplotlib\u76f4\u63a5\u7ed8\u5236\u76f4\u65b9\u56fe
\nplt.figure(figsize=(12, 5))<\/p>\n

plt.subplot(1, 3, 1)
\nplt.hist(phone.ravel(), bins=256) # ravel\u5c06\u591a\u7ef4\u6570\u7ec4\u5c55\u5e73\u4e3a\u4e00\u7ef4
\nplt.title('Matplotlib Histogram')<\/p>\n

# \u65b9\u6cd52：\u4f7f\u7528OpenCV\u8ba1\u7b97\u76f4\u65b9\u56fe
\nphone_hist = cv2.calcHist([phone], [0], None, [16], [0, 256])<\/p>\n

plt.subplot(1, 3, 2)
\nplt.plot(phone_hist)
\nplt.title('OpenCV Histogram (16 bins)')<\/p>\n

# \u65b9\u6cd53：\u5f69\u8272\u56fe\u50cf\u76f4\u65b9\u56fe
\nimg = cv2.imread('phone.png')
\ncolors = ('b', 'g', 'r')
\nplt.subplot(1, 3, 3)
\nfor i, color in enumerate(colors):
\n histr = cv2.calcHist([img], [i], None, [256], [0, 256])
\n plt.plot(histr, color=color)
\nplt.title('Color Histogram')<\/p>\n

plt.tight_layout()
\nplt.show()<\/p>\n

\"\"<\/p>\n

\u63a9\u819c（Mask）\u64cd\u4f5c：\u5c40\u90e8\u76f4\u65b9\u56fe\u5206\u6790：<\/h5>\n

\u00a0 \u6709\u65f6\u6211\u4eec\u53ea\u5173\u5fc3\u56fe\u50cf\u7684\u67d0\u4e00\u90e8\u5206，\u8fd9\u65f6\u53ef\u4ee5\u4f7f\u7528\u63a9\u819c：<\/p>\n

# \u521b\u5efa\u63a9\u819c
\nmask = np.zeros(phone.shape[:2], np.uint8)
\nmask[50:350, 100:470] = 255 # \u53ea\u5173\u6ce8\u4e2d\u5fc3\u533a\u57df<\/p>\n

# \u5e94\u7528\u63a9\u819c
\nphone_masked = cv2.bitwise_and(phone, phone, mask=mask)<\/p>\n

# \u8ba1\u7b97\u63a9\u819c\u533a\u57df\u7684\u76f4\u65b9\u56fe
\nphone_hist_mask = cv2.calcHist([phone], [0], mask, [256], [0, 256])<\/p>\n

# \u663e\u793a\u7ed3\u679c
\ncv2.imshow('Original', phone)
\ncv2.imshow('Mask', mask)
\ncv2.imshow('Masked Image', phone_masked)
\ncv2.waitKey(0)<\/p>\n

plt.figure()
\nplt.plot(phone_hist_mask)
\nplt.title('Masked Region Histogram')
\nplt.show()<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\u76f4\u65b9\u56fe\u5747\u8861\u5316：\u589e\u5f3a\u56fe\u50cf\u5bf9\u6bd4\u5ea6：<\/h5>\n

\u00a0\u00a0\u76f4\u65b9\u56fe\u5747\u8861\u5316\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u56fe\u50cf\u589e\u5f3a\u6280\u672f，\u901a\u8fc7\u91cd\u65b0\u5206\u5e03\u50cf\u7d20\u503c\u6765\u589e\u52a0\u56fe\u50cf\u5bf9\u6bd4\u5ea6：<\/p>\n

import cv2
\nimport matplotlib.pyplot as plt
\nimport numpy as np<\/p>\n

# \u8bfb\u53d6\u4f4e\u5bf9\u6bd4\u5ea6\u56fe\u50cf
\nimg = cv2.imread('R-C.jpg', cv2.IMREAD_GRAYSCALE)<\/p>\n

# \u5168\u5c40\u76f4\u65b9\u56fe\u5747\u8861\u5316
\nimg_equalized = cv2.equalizeHist(img)<\/p>\n

# \u81ea\u9002\u5e94\u76f4\u65b9\u56fe\u5747\u8861\u5316（\u5c40\u90e8\u5904\u7406，\u4fdd\u7559\u66f4\u591a\u7ec6\u8282）
\nclahe = cv2.createCLAHE(clipLimit=1.0, tileGridSize=(16, 16))
\nimg_clahe = clahe.apply(img)<\/p>\n

# \u663e\u793a\u5bf9\u6bd4
\nplt.figure(figsize=(15, 5))<\/p>\n

plt.subplot(1, 4, 1)
\nplt.hist(img.ravel(), bins=256)
\nplt.title('Original Histogram')<\/p>\n

plt.subplot(1, 4, 2)
\nplt.hist(img_equalized.ravel(), bins=256)
\nplt.title('Global Equalization')<\/p>\n

plt.subplot(1, 4, 3)
\nplt.hist(img_clahe.ravel(), bins=256)
\nplt.title('CLAHE Histogram')<\/p>\n

# \u56fe\u50cf\u5bf9\u6bd4
\nresult = np.hstack((img, img_equalized, img_clahe))
\nplt.subplot(1, 4, 4)
\nplt.imshow(result, cmap='gray')
\nplt.title('Image Comparison')<\/p>\n

plt.tight_layout()
\nplt.show()<\/p>\n

\"\"<\/p>\n

\u4e09\u3001\u900f\u89c6\u53d8\u6362\u2014\u2014\u77eb\u6b63\u56fe\u50cf\u89c6\u89d2<\/h3>\n

1\u3001\u4ec0\u4e48\u662f\u900f\u89c6\u53d8\u6362？<\/h4>\n

\u00a0\u00a0\u900f\u89c6\u53d8\u6362\u662f\u4e00\u79cd\u6838\u5fc3\u7684\u56fe\u50cf\u51e0\u4f55\u53d8\u6362\u6280\u672f，\u5b83\u901a\u8fc7\u4e00\u4e2a3×3\u7684\u53d8\u6362\u77e9\u9635，\u5c06\u56fe\u50cf\u4ece\u4e00\u4e2a\u4e09\u7ef4\u7a7a\u95f4\u7684\u89c2\u5bdf\u5e73\u9762\u6295\u5f71\u5230\u53e6\u4e00\u4e2a\u5168\u65b0\u7684\u89c6\u5e73\u9762\u4e0a\u3002\u8fd9\u4e2a\u8fc7\u7a0b\u6a21\u62df\u4e86\u4eba\u7c7b\u89c6\u89c9\u6216\u76f8\u673a\u955c\u5934\u7684\u900f\u89c6\u6548\u679c，\u80fd\u591f\u6539\u53d8\u56fe\u50cf\u7684\u89c6\u89d2\u3002\u5176\u6700\u5178\u578b\u7684\u5e94\u7528\u662f\u201c\u77eb\u6b63\u201d\u56e0\u62cd\u6444\u89d2\u5ea6\u5bfc\u81f4\u7684\u51e0\u4f55\u5f62\u53d8，\u4f8b\u5982\u5c06\u4e00\u5f20\u503e\u659c\u62cd\u6444\u7684\u540d\u7247\u56fe\u50cf，\u53d8\u6362\u4e3a\u89c4\u6574\u7684\u6b63\u9762\u5411\u89c6\u56fe\u3002\u4e0e\u4ec5\u80fd\u5904\u7406\u65cb\u8f6c\u3001\u7f29\u653e\u548c\u5e73\u79fb\u7684\u4eff\u5c04\u53d8\u6362\u4e0d\u540c，\u900f\u89c6\u53d8\u6362\u53ef\u4ee5\u5904\u7406\u201c\u8fd1\u5927\u8fdc\u5c0f\u201d\u7684\u6df1\u5ea6\u611f\u53d8\u5316，\u4f7f\u5f97\u539f\u672c\u5e73\u884c\u7684\u76f4\u7ebf\u5728\u53d8\u6362\u540e\u53ef\u80fd\u4e0d\u518d\u5e73\u884c，\u4ece\u800c\u5b9e\u73b0\u4e86\u66f4\u590d\u6742\u548c\u771f\u5b9e\u7684\u89c6\u89d2\u8f6c\u6362\u3002<\/p>\n

2\u3001OpenCV\u4e2d\u7684\u900f\u89c6\u53d8\u6362<\/h4>\n

\u00a0\u00a0\u9996\u5148\u6211\u4eec\u8981\u8bfb\u53d6\u56fe\u50cf\u5e76\u5b9a\u4e49\u4e24\u7ec4\u5bf9\u5e94\u7684\u5173\u952e\u70b9：\u4e00\u7ec4\u662f\u539f\u59cb\u56fe\u50cf\u4e2d\u5f85\u77eb\u6b63\u533a\u57df\u7684\u56db\u4e2a\u89d2\u70b9，\u53e6\u4e00\u7ec4\u662f\u8fd9\u4e9b\u70b9\u77eb\u6b63\u540e\u5e94\u4f4d\u4e8e\u7684\u76ee\u6807\u6807\u51c6\u77e9\u5f62\u4f4d\u7f6e\u3002\u63a5\u7740，\u901a\u8fc7cv2.getPerspectiveTransform\u51fd\u6570\u6839\u636e\u8fd9\u4e24\u7ec4\u70b9\u8ba1\u7b97\u51fa\u4e00\u4e2a3×3\u7684\u900f\u89c6\u53d8\u6362\u77e9\u9635M\u3002\u6700\u540e，\u4f7f\u7528cv2.warpPerspective\u51fd\u6570\u5c06\u539f\u59cb\u56fe\u50cf\u5e94\u7528\u8be5\u77e9\u9635\u8fdb\u884c\u53d8\u6362，\u751f\u6210\u4e00\u5e45\u89c6\u89d2\u88ab\u201c\u62c9\u6b63\u201d\u7684\u65b0\u56fe\u50cf\u3002<\/p>\n

import cv2
\nimport numpy as np<\/p>\n

def perspective_transform_demo():
\n # \u8bfb\u53d6\u56fe\u50cf
\n img = cv2.imread('R-C.jpg')
\n h, w = img.shape[:2]
\n print(f"\u56fe\u50cf\u5c3a\u5bf8：\u5bbd\u5ea6={w}, \u9ad8\u5ea6={h}")<\/p>\n

# \u5b9a\u4e49\u539f\u59cb\u70b9（\u901a\u5e38\u5728\u56fe\u50cf\u4e2d\u6807\u8bb0\u6587\u6863\u7684\u56db\u4e2a\u89d2）
\n src_points = np.float32([
\n [50, 50], # \u5de6\u4e0a\u89d2
\n [w – 50, 100], # \u53f3\u4e0a\u89d2
\n [w – 100, h – 50], # \u53f3\u4e0b\u89d2
\n [100, h – 100] # \u5de6\u4e0b\u89d2
\n ])<\/p>\n

# \u5b9a\u4e49\u76ee\u6807\u70b9（\u77eb\u6b63\u540e\u7684\u4f4d\u7f6e）
\n dst_points = np.float32([
\n [0, 0], # \u5de6\u4e0a\u89d2
\n [w, 0], # \u53f3\u4e0a\u89d2
\n [w, h], # \u53f3\u4e0b\u89d2
\n [0, h] # \u5de6\u4e0b\u89d2
\n ])<\/p>\n

# \u8ba1\u7b97\u900f\u89c6\u53d8\u6362\u77e9\u9635
\n M = cv2.getPerspectiveTransform(src_points, dst_points)<\/p>\n

# \u5e94\u7528\u900f\u89c6\u53d8\u6362
\n warped = cv2.warpPerspective(img, M, (w, h))<\/p>\n

# \u663e\u793a\u7ed3\u679c
\n cv2.imshow('Original', img)
\n cv2.imshow('Perspective Transformed', warped)
\n cv2.waitKey(0)
\n cv2.destroyAllWindows()<\/p>\n

return M<\/p>\n

# \u8c03\u7528\u51fd\u6570
\nM = perspective_transform_demo()<\/p>\n

\"\"<\/p>\n","protected":false},"excerpt":{"rendered":"

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