{"id":43430,"date":"2025-06-12T15:28:13","date_gmt":"2025-06-12T07:28:13","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/43430.html"},"modified":"2025-06-12T15:28:13","modified_gmt":"2025-06-12T07:28:13","slug":"python-opencv%ef%bc%88%e5%9b%be%e5%83%8f%e5%a4%84%e7%90%86-%e8%be%b9%e7%95%8c%e5%a1%ab%e5%85%85%e3%80%81%e5%9b%be%e5%83%8f%e8%9e%8d%e5%90%88%e3%80%81%e5%9b%be%e5%83%8f%e9%98%88","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/43430.html","title":{"rendered":"Python----OpenCV\uff08\u56fe\u50cf\u5904\u7406\u2014\u2014\u8fb9\u754c\u586b\u5145\u3001\u56fe\u50cf\u878d\u5408\u3001\u56fe\u50cf\u9608\u503c\u3001\u6df1\u62f7\u8d1d\u4e0e\u6d45\u62f7\u8d1d\uff09"},"content":{"rendered":"

Python—-\u8ba1\u7b97\u673a\u89c6\u89c9\u5904\u7406（Opencv：\u56fe\u50cf\u65cb\u8f6c：\u63d2\u503c\u65b9\u6cd5，\u8fb9\u7f18\u586b\u5145\u65b9\u6cd5）<\/p>\n

Python—-\u8ba1\u7b97\u673a\u89c6\u89c9\u5904\u7406（Opencv：\u4e8c\u503c\u5316，\u9608\u503c\u6cd5，\u53cd\u9608\u503c\u6cd5，\u622a\u65ad\u9608\u503c\u6cd5，OTSU\u9608\u503c\u6cd5）<\/p>\n

\u4e00\u3001\u8fb9\u754c\u586b\u5145<\/h2>\n

1.1\u3001\u4ec0\u4e48\u662f\u8fb9\u754c\u586b\u5145<\/h3>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u8fb9\u754c\u586b\u5145\u662f\u4e00\u79cd\u5728\u56fe\u50cf\u8fb9\u7f18\u6dfb\u52a0\u989d\u5916\u50cf\u7d20\u7684\u64cd\u4f5c\u3002\u8fd9\u4e9b\u989d\u5916\u7684\u50cf\u7d20\u7528\u4e8e\u5904\u7406\u56fe\u50cf\u5377\u79ef\u3001\u5e73\u6ed1 \u6216\u5176\u4ed6\u6d89\u53ca\u8fb9\u754c\u7684\u64cd\u4f5c，\u4ee5\u907f\u514d\u8fb9\u7f18\u6548\u5e94\u3002\u4f8b\u5982，\u5728\u8fdb\u884c\u5377\u79ef\u64cd\u4f5c\u65f6，\u5982\u679c\u4e0d\u8fdb\u884c\u8fb9\u754c\u586b \u5145，\u56fe\u50cf\u8fb9\u7f18\u7684\u50cf\u7d20\u5c06\u4f1a\u7f3a\u5c11\u5468\u56f4\u7684\u50cf\u7d20\u4fe1\u606f，\u4ece\u800c\u5bfc\u81f4\u7ed3\u679c\u56fe\u50cf\u8fb9\u7f18\u6a21\u7cca\u6216\u5931\u771f\u3002<\/p>\n

1.2\u3001\u8fb9\u754c\u586b\u5145\u65b9\u6cd5<\/h3>\n

\u539f\u56fe<\/p>\n

\"\"<\/p>\n<\/p>\n

BORDER_CONSTANT \u5e38\u6570\u586b\u5145<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u4f7f\u7528\u5e38\u6570\u586b\u5145，\u901a\u5e38\u662f\u9ed1\u8272（\u503c\u4e3a0）\u3002<\/p>\n

\"\"<\/p>\n

BORDER_REPLICATE \u590d\u5236\u8fb9\u754c\u50cf\u7d20<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u8fd9\u79cd\u65b9\u5f0f\u4f1a\u5c06\u56fe\u50cf\u7684\u8fb9\u754c\u50cf\u7d20\u590d\u5236\u5230\u56fe\u50cf\u7684\u8fb9\u754c\u5916\u90e8，\u4ea7\u751f\u7c7b\u4f3c\u8fb9\u7f18\u5ef6\u4f38\u7684\u6548\u679c\u3002<\/p>\n<\/p>\n

\"\"<\/p>\n

BORDER_REFLECT \u53cd\u5c04\u8fb9\u754c\u50cf\u7d20<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u8fd9\u79cd\u65b9\u5f0f\u5c06\u56fe\u50cf\u7684\u8fb9\u754c\u50cf\u7d20\u53cd\u5c04\u5230\u5916\u90e8，\u53cd\u5c04\u65f6\u4e0d\u5305\u62ec\u539f\u8fb9\u754c\u50cf\u7d20\u3002\u4e5f\u5c31\u662f\u8bf4，\u8fb9\u754c\u7684\u50cf\u7d20 \u4f1a\u53cd\u8f6c（\u955c\u50cf\u53cd\u5c04）\u5230\u5916\u90e8，\u4f46\u539f\u672c\u7684\u8fb9\u754c\u50cf\u7d20\u4e0d\u518d\u91cd\u590d\u3002<\/p>\n

\"\"<\/p>\n<\/p>\n

BORDER_REFLECT_101\u53cd\u5c04\u8fb9\u754c\u50cf\u7d20<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u8fd9\u79cd\u65b9\u5f0f\u7c7b\u4f3c\u4e8e BORDER_REFLECT ，\u4f46\u662f\u5b83\u53cd\u5c04\u7684\u8303\u56f4\u4f1a\u6392\u9664\u539f\u56fe\u50cf\u7684\u7b2c\u4e00\u4e2a\u548c\u6700\u540e\u4e00\u4e2a\u50cf\u7d20\u3002<\/p>\n

\"\"<\/p>\n

BORDER_WRAP<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u73af\u7ed5\u586b\u5145\u65b9\u5f0f\u4f1a\u5c06\u56fe\u50cf\u7684\u8fb9\u754c\u50cf\u7d20\u201c\u73af\u7ed5\u201d\u5230\u53e6\u4e00\u4fa7\u3002\u5373，\u56fe\u50cf\u7684\u5de6\u8fb9\u4f1a\u88ab\u53f3\u8fb9\u7684\u50cf\u7d20\u586b\u5145， \u56fe\u50cf\u7684\u4e0a\u8fb9\u4f1a\u88ab\u4e0b\u8fb9\u7684\u50cf\u7d20\u586b\u5145，\u50cf\u662f\u5728\u56fe\u50cf\u7684\u56db\u4e2a\u8fb9\u7f18\u4e4b\u95f4\u201c\u8fde\u63a5\u201d\u8d77\u6765\u3002<\/p>\n

\"\"<\/p>\n

import cv2<\/p>\n

# \u8bfb\u53d6\u56fe\u50cf
\n# cv2.imread() \u51fd\u6570\u7528\u4e8e\u4ece\u6307\u5b9a\u8def\u5f84\u52a0\u8f7d\u4e00\u5f20\u56fe\u7247\u5230\u5185\u5b58\u4e2d\u3002
\n# '.\/images\/nezha.png' \u662f\u56fe\u7247\u7684\u6587\u4ef6\u8def\u5f84\u3002
\nimg1 = cv2.imread('.\/images\/nezha.png')<\/p>\n

# \u5b9a\u4e49\u8fb9\u754c\u7684\u539a\u5ea6
\n# t, b, l, r \u5206\u522b\u4ee3\u8868\u56fe\u50cf\u7684 \u4e0a (top)\u3001\u4e0b (bottom)\u3001\u5de6 (left)\u3001\u53f3 (right) \u56db\u4e2a\u65b9\u5411\u8981\u6dfb\u52a0\u7684\u8fb9\u754c\u50cf\u7d20\u6570\u91cf\u3002
\n# \u8fd9\u91cc\u56db\u4e2a\u65b9\u5411\u90fd\u8bbe\u7f6e\u4e3a 50 \u50cf\u7d20\u3002
\nt, b, l, r = (50, 50, 50, 50)<\/p>\n

# \u4f7f\u7528 cv2.copyMakeBorder \u51fd\u6570\u6dfb\u52a0\u8fb9\u754c
\n# img1: \u539f\u59cb\u56fe\u50cf\u3002
\n# t, b, l, r: \u5b9a\u4e49\u7684\u8fb9\u754c\u539a\u5ea6\u3002
\n# cv2.BORDER_WRAP: \u8fb9\u754c\u586b\u5145\u6a21\u5f0f\u3002
\n# BORDER_WRAP \u6a21\u5f0f\u901a\u8fc7\u91cd\u590d\u56fe\u50cf\u672c\u8eab\u6765\u586b\u5145\u8fb9\u754c\u3002\u5b83\u4f1a\u5c06\u56fe\u50cf\u7684\u53e6\u4e00\u4fa7\u50cf\u7d20\u201c\u5305\u88f9\u201d\u5230\u8fb9\u754c\u4e0a，
\n# \u5c31\u50cf\u56fe\u50cf\u5185\u5bb9\u662f\u5468\u671f\u6027\u91cd\u590d\u7684\u4e00\u6837\u3002
\nimg2 = cv2.copyMakeBorder(img1, t, b, l, r, cv2.BORDER_WRAP)<\/p>\n

# \u663e\u793a\u6dfb\u52a0\u8fb9\u754c\u540e\u7684\u56fe\u50cf
\n# cv2.imshow() \u51fd\u6570\u7528\u4e8e\u5728\u7a97\u53e3\u4e2d\u663e\u793a\u56fe\u50cf\u3002
\n# 'images2' \u662f\u663e\u793a\u7a97\u53e3\u7684\u540d\u79f0\u3002
\ncv2.imshow('images2', img2)<\/p>\n

# \u7b49\u5f85\u6309\u952e
\n# cv2.waitKey(0) \u8868\u793a\u7a0b\u5e8f\u5c06\u65e0\u9650\u671f\u7b49\u5f85\u7528\u6237\u7684\u6309\u952e\u8f93\u5165\u3002
\n# \u53ea\u6709\u5f53\u7528\u6237\u6309\u4e0b\u4efb\u610f\u952e\u65f6，\u7a0b\u5e8f\u624d\u4f1a\u7ee7\u7eed\u6267\u884c\u540e\u7eed\u4ee3\u7801（\u4f8b\u5982\u5173\u95ed\u7a97\u53e3）\u3002
\ncv2.waitKey(0)<\/p>\n

# \u9500\u6bc1\u6240\u6709OpenCV\u521b\u5efa\u7684\u7a97\u53e3
\n# cv2.destroyAllWindows() \u51fd\u6570\u7528\u4e8e\u5173\u95ed\u6240\u6709\u7531OpenCV\u521b\u5efa\u7684\u7a97\u53e3\u3002
\ncv2.destroyAllWindows() <\/p>\n

1.3\u3001copyMakeBorder\u51fd\u6570<\/h3>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0cv2.copymakeBorder0 \u662f OpenCV \u4e2d\u7528\u6765\u7ed9\u56fe\u50cf\u6dfb\u52a0\u8fb9\u754c\u7684\u51fd\u6570，\u5e38\u7528\u4e8e\u5bf9\u56fe\u50cf\u8fdb\u884c\u6269\u5c55\u5904\u7406\u652f\u6301\u4e0d\u540c\u7684\u8fb9\u754c\u586b\u5145\u65b9\u5f0f\u3002\u4f60\u53ef\u4ee5\u7528\u5b83\u6765\u5728\u56fe\u50cf\u5468\u56f4\u6dfb\u52a0\u7a7a\u767d(\u5e38\u7528\u4e8e\u5377\u79ef\u64cd\u4f5c\u65f6)\u6216\u7279\u5b9a\u7684\u50cf\u7d20\u586b\u5145\u3002<\/p>\n

cv2.copyMakeBorder(src, top, bottom, left, right, borderType, value=None) <\/p>\n\n\u53c2\u6570\u540d\u8bf4\u660e<\/tr>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
src<\/td>\n\u8f93\u5165\u56fe\u50cf，\u53ef\u4ee5\u662f\u7070\u5ea6\u56fe\u50cf\u6216\u5f69\u8272\u56fe\u50cf（numpy \u6570\u7ec4\u683c\u5f0f）\u3002<\/td>\n<\/tr>\n
top<\/td>\n\u4e0a\u8fb9\u754c\u7684\u5bbd\u5ea6（\u50cf\u7d20\u6570）\u3002<\/td>\n<\/tr>\n
bottom<\/td>\n\u4e0b\u8fb9\u754c\u7684\u5bbd\u5ea6（\u50cf\u7d20\u6570）\u3002<\/td>\n<\/tr>\n
left<\/td>\n\u5de6\u8fb9\u7684\u5bbd\u5ea6（\u50cf\u7d20\u6570）\u3002<\/td>\n<\/tr>\n
right<\/td>\n\u53f3\u8fb9\u7684\u5bbd\u5ea6（\u50cf\u7d20\u6570）\u3002<\/td>\n<\/tr>\n
borderType<\/td>\n\u586b\u5145\u7c7b\u578b，\u51b3\u5b9a\u4e86\u8fb9\u754c\u5982\u4f55\u586b\u5145\u3002\u5e38\u7528\u9009\u9879\u5305\u62ec：<\/td>\n<\/tr>\n
<\/td>\n–\u00a0cv2.BORDER_CONSTANT：\u5e38\u6570\u586b\u5145\u3002<\/td>\n<\/tr>\n
<\/td>\n–\u00a0cv2.BORDER_REPLICATE：\u590d\u5236\u8fb9\u754c\u50cf\u7d20\u586b\u5145\u3002<\/td>\n<\/tr>\n
<\/td>\n–\u00a0cv2.BORDER_REFLECT：\u53cd\u5c04\u8fb9\u754c\u50cf\u7d20（\u53cd\u5c04\u6a21\u5f0f，\u4e0d\u5305\u62ec\u539f\u8fb9\u754c\u50cf\u7d20）\u3002<\/td>\n<\/tr>\n
<\/td>\n–\u00a0cv2.BORDER_REFLECT_101：\u7c7b\u4f3c\u00a0BORDER_REFLECT，\u4f46\u5bf9\u79f0\u53cd\u5c04\u4f1a\u5ffd\u7565\u7b2c\u4e00\u4e2a\u548c\u6700\u540e\u4e00\u4e2a\u50cf\u7d20\u3002<\/td>\n<\/tr>\n
<\/td>\n–\u00a0cv2.BORDER_WRAP：\u73af\u7ed5\u586b\u5145\u3002<\/td>\n<\/tr>\n
value<\/td>\n\u5f53\u00a0borderType=cv2.BORDER_CONSTANT\u00a0\u65f6，\u6307\u5b9a\u586b\u5145\u7684\u989c\u8272\u3002\u9ed8\u8ba4\u4e3a 0（\u9ed1\u8272）\u3002\u5982\u679c\u662f\u5f69\u8272\u56fe\u50cf，\u53ef\u4ee5\u8bbe\u7f6e\u4e3a\u4e09\u5143\u7ec4（\u5982\u00a0[0, 0, 255]\u00a0\u8868\u793a\u7ea2\u8272\u586b\u5145）\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4e8c\u3001\u56fe\u50cf\u878d\u5408<\/h2>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u56fe\u50cf\u878d\u5408\u662f\u5c06\u591a\u5f20\u56fe\u50cf\u5408\u6210\u4e00\u5f20\u56fe\u50cf\u7684\u8fc7\u7a0b，\u4ee5\u4fbf\u83b7\u53d6\u66f4\u4e30\u5bcc\u7684\u4fe1\u606f\u6216\u66f4\u9ad8\u7684\u56fe\u50cf\u8d28\u91cf\u3002\u5728 \u4e0d\u540c\u7684\u5e94\u7528\u4e2d，\u56fe\u50cf\u878d\u5408\u7684\u76ee\u7684\u53ef\u80fd\u4e0d\u540c，\u5982\u589e\u5f3a\u56fe\u50cf\u7ec6\u8282\u3001\u51cf\u5c0f\u566a\u58f0\u3001\u63d0\u9ad8\u56fe\u50cf\u5206\u8fa8\u7387 \u7b49\u3002<\/p>\n

\u5e38\u89c1\u7684\u56fe\u50cf\u878d\u5408\u65b9\u6cd5：<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u50cf\u7d20\u7ea7\u878d\u5408：\u76f4\u63a5\u5bf9\u4e24\u4e2a\u6216\u591a\u4e2a\u56fe\u50cf\u7684\u50cf\u7d20\u503c\u8fdb\u884c\u52a0\u6743\u5e73\u5747\u3002<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u591a\u5206\u8fa8\u7387\u878d\u5408：\u4f8b\u5982，\u4f7f\u7528\u5c0f\u6ce2\u53d8\u6362（Wavelet Transform）\u5c06\u56fe\u50cf\u5728\u4e0d\u540c\u5206\u8fa8\u7387\u4e0b\u8fdb\u884c\u878d\u5408，\u80fd\u4fdd\u7559\u66f4\u591a\u7684\u56fe\u50cf\u7ec6\u8282\u3002<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u7279\u5f81\u7ea7\u878d\u5408：\u63d0\u53d6\u56fe\u50cf\u7684\u7279\u5f81（\u5982\u8fb9\u7f18\u3001\u7eb9\u7406\u7b49）\u540e\u8fdb\u884c\u878d\u5408，\u800c\u975e\u76f4\u63a5\u5904\u7406\u50cf\u7d20\u3002<\/p>\n

\"\"<\/p>\n

import cv2<\/p>\n

# \u8bfb\u53d6\u7b2c\u4e00\u5f20\u56fe\u50cf
\n# cv2.imread() \u51fd\u6570\u7528\u4e8e\u4ece\u6307\u5b9a\u8def\u5f84\u52a0\u8f7d\u4e00\u5f20\u56fe\u7247\u5230\u5185\u5b58\u4e2d\u3002
\n# 'apple.png' \u662f\u7b2c\u4e00\u5f20\u56fe\u7247\u7684\u8def\u5f84\u3002
\nimg1 = cv2.imread('.\/images\/apple.png')<\/p>\n

# \u8bfb\u53d6\u7b2c\u4e8c\u5f20\u56fe\u50cf
\n# 'parrot.png' \u662f\u7b2c\u4e8c\u5f20\u56fe\u7247\u7684\u8def\u5f84\u3002
\nimg2 = cv2.imread('.\/images\/parrot.png')<\/p>\n

# \u6253\u5370\u7b2c\u4e00\u5f20\u56fe\u50cf\u7684\u5c3a\u5bf8（\u9ad8\u5ea6\u3001\u5bbd\u5ea6\u3001\u901a\u9053\u6570）
\n# img.shape \u8fd4\u56de\u4e00\u4e2a\u5143\u7ec4，\u4f8b\u5982 (height, width, channels)\u3002
\nprint(img1.shape)<\/p>\n

# \u6253\u5370\u7b2c\u4e8c\u5f20\u56fe\u50cf\u7684\u5c3a\u5bf8
\nprint(img2.shape)<\/p>\n

# \u8c03\u6574\u7b2c\u4e00\u5f20\u56fe\u50cf\u7684\u5927\u5c0f
\n# cv2.resize() \u51fd\u6570\u7528\u4e8e\u6539\u53d8\u56fe\u50cf\u7684\u5c3a\u5bf8\u3002
\n# (261, 272) \u662f\u76ee\u6807\u5bbd\u5ea6\u548c\u9ad8\u5ea6\u7684\u5143\u7ec4 (width, height)\u3002
\n# \u56fe\u50cf\u878d\u5408\u8981\u6c42\u53c2\u4e0e\u878d\u5408\u7684\u56fe\u50cf\u5c3a\u5bf8\u5fc5\u987b\u76f8\u540c，\u56e0\u6b64\u8fd9\u91cc\u5c06\u4e24\u5f20\u56fe\u90fd\u8c03\u6574\u4e3a\u76f8\u540c\u5927\u5c0f\u3002
\nimg1 = cv2.resize(img1, (261, 272))<\/p>\n

# \u8c03\u6574\u7b2c\u4e8c\u5f20\u56fe\u50cf\u7684\u5927\u5c0f
\nimg2 = cv2.resize(img2, (261, 272))<\/p>\n

# \u6267\u884c\u56fe\u50cf\u52a0\u6743\u878d\u5408
\n# cv2.addWeighted() \u51fd\u6570\u7528\u4e8e\u5b9e\u73b0\u4e24\u5f20\u56fe\u50cf\u7684\u7ebf\u6027\u6df7\u5408（\u4e5f\u79f0\u4e3a\u56fe\u50cf\u878d\u5408\u6216\u900f\u660e\u5ea6\u6df7\u5408）\u3002
\n# \u516c\u5f0f\u4e3a：dst = \u03b1 * src1 + \u03b2 * src2 + \u03b3
\n# img1: \u7b2c\u4e00\u5f20\u8f93\u5165\u56fe\u50cf\u3002
\n# 0.5: \u7b2c\u4e00\u5f20\u56fe\u50cf\u7684\u6743\u91cd（\u03b1）\u3002
\n# img2: \u7b2c\u4e8c\u5f20\u8f93\u5165\u56fe\u50cf\u3002
\n# 0.5: \u7b2c\u4e8c\u5f20\u56fe\u50cf\u7684\u6743\u91cd（\u03b2）\u3002
\n# 0: \u4f3d\u9a6c\u503c（\u03b3），\u4e00\u4e2a\u52a0\u5230\u7ed3\u679c\u4e0a\u7684\u6807\u91cf\u3002\u8fd9\u91cc\u8bbe\u7f6e\u4e3a0，\u8868\u793a\u4e0d\u989d\u5916\u589e\u52a0\u4eae\u5ea6\u3002
\n# \u7531\u4e8e\u4e24\u5f20\u56fe\u50cf\u7684\u6743\u91cd\u90fd\u662f0.5，\u8fd9\u610f\u5473\u7740\u5b83\u4eec\u5c06\u4ee550%\u7684\u900f\u660e\u5ea6\u76f8\u4e92\u53e0\u52a0，\u5f62\u6210\u4e00\u4e2a\u534a\u900f\u660e\u7684\u6548\u679c\u3002
\nimg3 = cv2.addWeighted(img1, 0.5, img2, 0.5, 0)<\/p>\n

# \u663e\u793a\u7b2c\u4e00\u5f20\u539f\u59cb\u56fe\u50cf（\u8c03\u6574\u5927\u5c0f\u540e）
\n# 'images1' \u662f\u7a97\u53e3\u7684\u540d\u79f0\u3002
\ncv2.imshow('images1', img1)<\/p>\n

# \u663e\u793a\u7b2c\u4e8c\u5f20\u539f\u59cb\u56fe\u50cf（\u8c03\u6574\u5927\u5c0f\u540e）
\n# 'images2' \u662f\u7a97\u53e3\u7684\u540d\u79f0\u3002
\ncv2.imshow('images2', img2)<\/p>\n

# \u663e\u793a\u878d\u5408\u540e\u7684\u56fe\u50cf
\n# 'images3' \u662f\u7a97\u53e3\u7684\u540d\u79f0\u3002
\ncv2.imshow('images3', img3)<\/p>\n

# \u7b49\u5f85\u6309\u952e
\n# cv2.waitKey(0) \u8868\u793a\u7a0b\u5e8f\u5c06\u65e0\u9650\u671f\u7b49\u5f85\u7528\u6237\u7684\u6309\u952e\u8f93\u5165\u3002
\n# \u53ea\u6709\u5f53\u7528\u6237\u6309\u4e0b\u4efb\u610f\u952e\u65f6，\u7a0b\u5e8f\u624d\u4f1a\u7ee7\u7eed\u6267\u884c\u540e\u7eed\u4ee3\u7801（\u4f8b\u5982\u5173\u95ed\u7a97\u53e3）\u3002
\ncv2.waitKey(0)<\/p>\n

# \u9500\u6bc1\u6240\u6709OpenCV\u521b\u5efa\u7684\u7a97\u53e3
\n# cv2.destroyAllWindows() \u51fd\u6570\u7528\u4e8e\u5173\u95ed\u6240\u6709\u7531OpenCV\u521b\u5efa\u7684\u7a97\u53e3\u3002
\ncv2.destroyAllWindows() <\/p>\n

addWeighted\u51fd\u6570<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0addweighted \u662f OpenCV \u4e2d\u7528\u4e8e\u56fe\u50cf\u52a0\u6743\u878d\u5408\u7684\u4e00\u4e2a\u51fd\u6570，\u5e7f\u6cdb\u7528\u4e8e\u50cf\u7d20\u7ea7\u56fe\u50cf\u878d\u5408\u3002\u5b83\u901a\u8fc7\u5bf9\u4e24\u5f20\u56fe\u50cf\u8fdb\u884c\u52a0\u6743\u6c42\u548c，\u4ea7\u751f\u4e00\u5f20\u65b0\u7684\u56fe\u50cf\u3002\u8fd9\u5bf9\u4e8e\u5b9e\u73b0\u56fe\u50cf\u7684\u878d\u5408\u3001\u8fc7\u6e21\u6548\u679c\u3001\u6216\u8005\u56fe\u50cf\u589e\u5f3a\u7b49\u4efb\u52a1\u975e\u5e38\u6709\u7528\u3002<\/p>\n

cv2.addWeighted(src1, alpha, src2, beta, gamma) <\/p>\n\n\u53c2\u6570\u540d\u8bf4\u660e<\/tr>\n\n\n\n\n\n\n
src1<\/td>\n\u7b2c\u4e00\u5f20\u8f93\u5165\u56fe\u50cf\u3002<\/td>\n<\/tr>\n
alpha<\/td>\n\u7b2c\u4e00\u5f20\u56fe\u50cf\u7684\u6743\u91cd，\u63a7\u5236\u7b2c\u4e00\u5f20\u56fe\u50cf\u5bf9\u6700\u7ec8\u878d\u5408\u56fe\u50cf\u7684\u8d21\u732e\u5ea6\u3002<\/td>\n<\/tr>\n
src2<\/td>\n\u7b2c\u4e8c\u5f20\u8f93\u5165\u56fe\u50cf\u3002<\/td>\n<\/tr>\n
beta<\/td>\n\u7b2c\u4e8c\u5f20\u56fe\u50cf\u7684\u6743\u91cd，\u63a7\u5236\u7b2c\u4e8c\u5f20\u56fe\u50cf\u5bf9\u6700\u7ec8\u878d\u5408\u56fe\u50cf\u7684\u8d21\u732e\u5ea6\u3002<\/td>\n<\/tr>\n
gamma<\/td>\n\u53ef\u9009\u7684\u504f\u79fb\u91cf，\u901a\u5e38\u7528\u4e8e\u56fe\u50cf\u4eae\u5ea6\u7684\u8c03\u6574（\u4f8b\u5982\u5bf9\u878d\u5408\u56fe\u50cf\u8fdb\u884c\u4eae\u5ea6\u589e\u52a0\u6216\u51cf\u5c11）\u3002\u9ed8\u8ba4\u503c\u4e3a 0\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4e09\u3001\u56fe\u50cf\u9608\u503c<\/h2>\n

\"\"<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u5728OpenCV\u4e2d，\u56fe\u50cf\u9608\u503c\u64cd\u4f5c\u662f\u4e00\u4e2a\u5e38\u89c1\u7684\u56fe\u50cf\u5904\u7406\u6280\u672f，\u53ef\u4ee5\u5c06\u56fe\u50cf\u7684\u50cf\u7d20\u503c\u6839\u636e\u8bbe\u5b9a\u7684 \u9608\u503c\u8fdb\u884c\u5206\u7c7b，\u901a\u5e38\u662f\u5c06\u56fe\u50cf\u5206\u4e3a\u524d\u666f\u548c\u80cc\u666f\u3002<\/p>\n

ret, dst = cv2.threshold(src, thresh, maxval, type) <\/p>\n\n\u53c2\u6570\u540d\u8bf4\u660e<\/tr>\n\n\n\n\n\n\n\n
ret<\/td>\nTrue\u00a0\u6216\u00a0False，\u8868\u793a\u662f\u5426\u6210\u529f\u8bfb\u53d6\u56fe\u7247\u3002<\/td>\n<\/tr>\n
dst<\/td>\n\u8f93\u51fa\u7684\u4e8c\u503c\u5316\u56fe\u50cf\u3002<\/td>\n<\/tr>\n
src<\/td>\n\u8f93\u5165\u56fe\u50cf（\u4ec5\u652f\u6301\u5355\u901a\u9053，\u901a\u5e38\u4e3a\u7070\u5ea6\u56fe）\u3002<\/td>\n<\/tr>\n
thresh<\/td>\n\u9608\u503c（0-255 \u4e4b\u95f4\u7684\u503c，\u5982 127）\u3002<\/td>\n<\/tr>\n
maxval<\/td>\n\u5f53\u50cf\u7d20\u503c\u8d85\u8fc7（\u6216\u4f4e\u4e8e）\u9608\u503c\u65f6\u8d4b\u4e88\u7684\u503c（\u5177\u4f53\u884c\u4e3a\u7531\u00a0type\u00a0\u51b3\u5b9a）\u3002<\/td>\n<\/tr>\n
type<\/td>\n\u4e8c\u503c\u5316\u64cd\u4f5c\u7c7b\u578b，\u5171 5 \u79cd（\u89c1\u4e0b\u65b9\u8be6\u7ec6\u5bf9\u6bd4）\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\u9608\u503c\u7c7b\u578b（type）\u5927\u4e8e\u9608\u503c\u50cf\u7d20\u7684\u5904\u7406\u5c0f\u4e8e\u6216\u7b49\u4e8e\u9608\u503c\u50cf\u7d20\u7684\u5904\u7406\u7ed3\u679c\u56fe\u50cf\u63cf\u8ff0<\/tr>\n\n\n\n\n\n\n
cv2.THRESH_BINARY<\/td>\n\u8bbe\u4e3a\u00a0maxval（\u5982 255）<\/td>\n\u8bbe\u4e3a 0<\/td>\n\u4eae\u533a\u767d\u8272，\u6697\u533a\u9ed1\u8272<\/td>\n<\/tr>\n
cv2.THRESH_BINARY_INV<\/td>\n\u8bbe\u4e3a 0<\/td>\n\u8bbe\u4e3a\u00a0maxval（\u5982 255）<\/td>\n\u4eae\u533a\u9ed1\u8272，\u6697\u533a\u767d\u8272<\/td>\n<\/tr>\n
cv2.THRESH_TRUNC<\/td>\n\u622a\u65ad\u4e3a\u00a0thresh\u00a0\u503c<\/td>\n\u4fdd\u6301\u539f\u503c<\/td>\n\u4eae\u533a\u88ab\u622a\u65ad\u81f3\u9608\u503c，\u6697\u533a\u4e0d\u53d8<\/td>\n<\/tr>\n
cv2.THRESH_TOZERO<\/td>\n\u4fdd\u6301\u539f\u503c<\/td>\n\u8bbe\u4e3a 0<\/td>\n\u4eae\u533a\u4e0d\u53d8，\u6697\u533a\u9ed1\u8272<\/td>\n<\/tr>\n
cv2.THRESH_TOZERO_INV<\/td>\n\u8bbe\u4e3a 0<\/td>\n\u4fdd\u6301\u539f\u503c<\/td>\n\u4eae\u533a\u9ed1\u8272，\u6697\u533a\u4e0d\u53d8<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

import cv2
\nimport numpy as np
\nimport matplotlib.pyplot as plt
\n# \u8bfb\u53d6\u56fe\u7247
\nimg = cv2.imread("..\/images\/ROI.png")<\/p>\n

# \u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe
\ngray_img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)<\/p>\n

ret,thresh1 = cv2.threshold(gray_img,127,255,cv2.THRESH_BINARY )
\nret,thresh2 = cv2.threshold(gray_img,127,255,cv2.THRESH_BINARY_INV )
\nret,thresh3 = cv2.threshold(gray_img,127,255,cv2.THRESH_TRUNC )
\nret,thresh4 = cv2.threshold(gray_img,127,255,cv2.THRESH_TOZERO )
\nret,thresh5 = cv2.threshold(gray_img,127,255,cv2.THRESH_TOZERO_INV )<\/p>\n

titles = ["Original Image","BINARY","BINARY_INV","TRUNC","TOZERO","TOZERO_INV"]
\nimages = [img, thresh1, thresh2, thresh3, thresh4, thresh5]
\n# \u904d\u5386\u57286\u5f20\u56fe\u7247\u4e0a matplotilb
\nfor i in range(6):
\n plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')
\n plt.title(titles[i])
\n plt.xticks([]),plt.yticks([])<\/p>\n

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

cv2.waitKey(5000)
\ncv2.destroyAllWindows() <\/p>\n

\"\"<\/p>\n

\u56db\u3001\u7ed3\u6784\u4f53Mat<\/h2>\n

\"\"<\/p>\n

4.1\u3001Mat \u7684\u57fa\u672c\u6982\u5ff5<\/h3>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Mat \u662f OpenCV \u4e2d\u7528\u4e8e\u5b58\u50a8\u56fe\u50cf\u6570\u636e\u548c\u77e9\u9635\u6570\u636e\u7684\u4e3b\u8981\u6570\u636e\u7ed3\u6784\u3002\u5b83\u4ee3\u8868\u7740\u4e00\u4e2a n \u7ef4\u7684\u5bc6 \u96c6\u6570\u503c\u578b\u6570\u7ec4，\u88ab\u8bbe\u8ba1\u7528\u6765\u5b58\u50a8\u5b9e\u6570\u6216\u590d\u6570\u5143\u7d20，\u5e76\u4e14\u53ef\u4ee5\u5b58\u50a8\u5355\u901a\u9053\u6216\u591a\u901a\u9053\u7684\u6570\u636e\u3002 \u4e0e\u4f20\u7edf\u7684 IplImage \u7ed3\u6784\u4f53\u76f8\u6bd4，Mat \u5177\u6709\u81ea\u52a8\u5185\u5b58\u7ba1\u7406\u7684\u4f18\u52bf，\u80fd\u6709\u6548\u907f\u514d\u5185\u5b58\u6cc4\u6f0f，\u4f7f \u7528\u8d77\u6765\u66f4\u52a0\u65b9\u4fbf\u548c\u5b89\u5168\u3002<\/p>\n

4.2\u3001Mat \u7684\u7ec4\u6210\u90e8\u5206<\/h3>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0OpenCV \u7684 Mat \u6570\u636e\u7ed3\u6784\u53ef\u4ee5\u5206\u4e3a\u4ee5\u4e0b\u51e0\u4e2a\u4e3b\u8981\u90e8\u5206：<\/p>\n\n\u5b57\u6bb5\u540d\u8bf4\u660e<\/tr>\n\n\n\n\n\n\n\n\n\n
dims<\/td>\n\u56fe\u50cf\u7684\u7ef4\u5ea6（\u5982 2D \u56fe\u50cf\u7684\u00a0dims=2）\u3002<\/td>\n<\/tr>\n
rows<\/td>\n\u56fe\u50cf\u7684\u884c\u6570（\u9ad8\u5ea6）\u3002<\/td>\n<\/tr>\n
cols<\/td>\n\u56fe\u50cf\u7684\u5217\u6570（\u5bbd\u5ea6）\u3002<\/td>\n<\/tr>\n
depth<\/td>\n\u50cf\u7d20\u7684\u4f4d\u6df1（\u5b58\u50a8\u683c\u5f0f），\u4f8b\u5982\u00a0CV_8U（8\u4f4d\u65e0\u7b26\u53f7\u6574\u6570）\u3001CV_32F（32\u4f4d\u6d6e\u70b9\u6570）\u7b49\u3002<\/td>\n<\/tr>\n
channels<\/td>\n\u56fe\u50cf\u7684\u901a\u9053\u6570（\u5982 RGB\/BGR \u56fe\u50cf\u4e3a 3，\u7070\u5ea6\u56fe\u4e3a 1）\u3002<\/td>\n<\/tr>\n
size<\/td>\n\u77e9\u9635\u7684\u5927\u5c0f（\u683c\u5f0f\u4e3a\u00a0(rows, cols)）\u3002<\/td>\n<\/tr>\n
type<\/td>\n\u6570\u636e\u7c7b\u578b\u4e0e\u901a\u9053\u7684\u7ec4\u5408（\u5982\u00a0CV_8UC3\u00a0\u8868\u793a 8\u4f4d\u65e0\u7b26\u53f7\u6574\u6570 + 3\u901a\u9053）\u3002<\/td>\n<\/tr>\n
data<\/td>\n\u5b9e\u9645\u5b58\u50a8\u7684\u50cf\u7d20\u6570\u636e（\u901a\u5e38\u662f\u5185\u5b58\u6307\u9488\u6216\u6570\u7ec4）\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

import cv2
\nimg = cv2.imread("..\/images\/ROI.png")<\/p>\n

print("rows",img.shape[0]) #\u9ad8\u5ea6rows 578
\nprint("cols",img.shape[1]) #\u5bbd\u5ea6cols 398
\nprint("channels",img.shape[2]) #\u901a\u9053\u6570channels 3
\nprint("cols",img.dtype) #\u6570\u636e\u7c7b\u578bcols uint8 <\/p>\n

\u4e94\u3001\u6df1\u62f7\u8d1d\u4e0e\u6d45\u62f7\u8d1d<\/h2>\n

\"\"<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u5728 Python \u4e2d，\u6df1\u62f7\u8d1d（Deep Copy）\u548c\u6d45\u62f7\u8d1d（Shallow Copy）\u662f\u4e24\u79cd\u4e0d\u540c\u7684\u5bf9\u8c61\u590d\u5236 \u65b9\u5f0f，\u4e3b\u8981\u533a\u522b\u5728\u4e8e\u662f\u5426\u590d\u5236\u4e86\u5bf9\u8c61\u4e2d\u7684\u5d4c\u5957\u5bf9\u8c61\u3002<\/p>\n\n\n\n\n
\u6df1\u62f7\u8d1d<\/td>\n\u6d45\u62f7\u8d1d<\/td>\n<\/tr>\n
img.copy()<\/td>\n\u6b63\u5e38\u590d\u5236<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

import cv2
\n# \u52a0\u8f7d\u56fe\u7247
\nimg = cv2.imread("..\/images\/ROI.png")<\/p>\n

# \u6d45\u62f7\u8d1d
\nimg2 = img
\n# \u6df1\u62f7\u8d1d
\nimg3 = img.copy()<\/p>\n

# \u4fee\u6539img2
\nimg[10:100,10:100] = [0, 0, 255]<\/p>\n

cv2.imshow('img', img)
\ncv2.imshow('img2', img2)
\ncv2.imshow('img3', img3)<\/p>\n

cv2.waitKey(0)
\ncv2.destroyAllWindows() <\/p>\n

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

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