\u4fdd\u5b58\u7d20\u63cf\u56fe<\/strong><\/h2>\ncv2.imwrite('sketch_image.jpg', sketch_image)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528PIL\u5e93\u751f\u6210\u7d20\u63cf\u56fe<\/h3>\n<\/p>\n
PIL\uff08Python Imaging Library\uff09\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u53ef\u4ee5\u4e0eNumPy\u7ed3\u5408\u4f7f\u7528\u751f\u6210\u7d20\u63cf\u56fe\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528PIL\u5e93\u751f\u6210\u7d20\u63cf\u56fe\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
1. \u5b89\u88c5PIL\u5e93<\/h4>\n<\/p>\n
\u786e\u4fdd\u4f60\u7684Python\u73af\u5883\u4e2d\u5df2\u5b89\u88c5PIL\u5e93\u3002\u5982\u679c\u6ca1\u6709\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
2. \u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h4>\n<\/p>\n
\u5728\u4f60\u7684Python\u811a\u672c\u4e2d\u5bfc\u5165PIL\u5e93\u548c\u5176\u4ed6\u5fc5\u8981\u7684\u5e93\uff1a<\/p>\n<\/p>\n
from PIL import Image, ImageFilter<\/p>\nimport numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u8bfb\u53d6\u56fe\u50cf\u5e76\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/h4>\n<\/p>\n
\u4f7f\u7528PIL\u8bfb\u53d6\u56fe\u50cf\u5e76\u5c06\u5176\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff1a<\/p>\n<\/p>\n
# \u8bfb\u53d6\u56fe\u50cf<\/p>\nimage = Image.open('path_to_your_image.jpg')<\/p>\n
\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\ngray_image = image.convert('L')<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u53cd\u8f6c\u7070\u5ea6\u56fe\u50cf<\/h4>\n<\/p>\n
\u5c06\u7070\u5ea6\u56fe\u50cf\u8fdb\u884c\u53cd\u8f6c\u5904\u7406\uff1a<\/p>\n<\/p>\n
# \u53cd\u8f6c\u7070\u5ea6\u56fe\u50cf<\/p>\ninverted_image = Image.eval(gray_image, lambda x: 255 - x)<\/p>\n
<\/code><\/pre>\n<\/p>\n
5. \u6a21\u7cca\u5904\u7406<\/h4>\n<\/p>\n
\u4f7f\u7528\u9ad8\u65af\u6a21\u7cca\u5bf9\u53cd\u8f6c\u56fe\u50cf\u8fdb\u884c\u6a21\u7cca\u5904\u7406\uff1a<\/p>\n<\/p>\n
# \u6a21\u7cca\u5904\u7406<\/p>\nblurred_image = inverted_image.filter(ImageFilter.GaussianBlur(21))<\/p>\n
<\/code><\/pre>\n<\/p>\n
6. \u53cd\u8f6c\u6a21\u7cca\u56fe\u50cf<\/h4>\n<\/p>\n
\u5c06\u6a21\u7cca\u540e\u7684\u56fe\u50cf\u518d\u6b21\u8fdb\u884c\u53cd\u8f6c\u5904\u7406\uff1a<\/p>\n<\/p>\n
# \u53cd\u8f6c\u6a21\u7cca\u56fe\u50cf<\/p>\ninverted_blurred_image = Image.eval(blurred_image, lambda x: 255 - x)<\/p>\n
<\/code><\/pre>\n<\/p>\n
7. \u751f\u6210\u7d20\u63cf\u56fe<\/h4>\n<\/p>\n
\u5c06\u7070\u5ea6\u56fe\u50cf\u4e0e\u53cd\u8f6c\u6a21\u7cca\u56fe\u50cf\u8fdb\u884c\u6df7\u5408\uff0c\u751f\u6210\u6700\u7ec8\u7684\u7d20\u63cf\u56fe\uff1a<\/p>\n<\/p>\n
# \u751f\u6210\u7d20\u63cf\u56fe<\/p>\nsketch_image = Image.blend(gray_image, inverted_blurred_image, alpha=0.5)<\/p>\n
<\/code><\/pre>\n<\/p>\n
8. \u663e\u793a\u548c\u4fdd\u5b58\u7d20\u63cf\u56fe<\/h4>\n<\/p>\n
\u4f7f\u7528PIL\u663e\u793a\u548c\u4fdd\u5b58\u751f\u6210\u7684\u7d20\u63cf\u56fe\uff1a<\/p>\n<\/p>\n
# \u663e\u793a\u7d20\u63cf\u56fe<\/p>\nsketch_image.show()<\/p>\n
\u4fdd\u5b58\u7d20\u63cf\u56fe<\/strong><\/h2>\nsketch_image.save('sketch_image.jpg')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528skimage\u5e93\u751f\u6210\u7d20\u63cf\u56fe<\/h3>\n<\/p>\n
skimage\uff08Scikit-Image\uff09\u662f\u4e00\u4e2a\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u7684Python\u5e93\uff0c\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u7ea7\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528skimage\u5e93\u751f\u6210\u7d20\u63cf\u56fe\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
1. \u5b89\u88c5skimage\u5e93<\/h4>\n<\/p>\n
\u786e\u4fdd\u4f60\u7684Python\u73af\u5883\u4e2d\u5df2\u5b89\u88c5skimage\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install scikit-image<\/p>\n<\/code><\/pre>\n<\/p>\n
2. \u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h4>\n<\/p>\n
\u5728\u4f60\u7684Python\u811a\u672c\u4e2d\u5bfc\u5165skimage\u5e93\u548c\u5176\u4ed6\u5fc5\u8981\u7684\u5e93\uff1a<\/p>\n<\/p>\n
from skimage import io, color, filters<\/p>\nimport numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u8bfb\u53d6\u56fe\u50cf\u5e76\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/h4>\n<\/p>\n
\u4f7f\u7528skimage\u8bfb\u53d6\u56fe\u50cf\u5e76\u5c06\u5176\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff1a<\/p>\n<\/p>\n
# \u8bfb\u53d6\u56fe\u50cf<\/p>\nimage = io.imread('path_to_your_image.jpg')<\/p>\n
\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\ngray_image = color.rgb2gray(image)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u53cd\u8f6c\u7070\u5ea6\u56fe\u50cf<\/h4>\n<\/p>\n
\u5c06\u7070\u5ea6\u56fe\u50cf\u8fdb\u884c\u53cd\u8f6c\u5904\u7406\uff1a<\/p>\n<\/p>\n
# \u53cd\u8f6c\u7070\u5ea6\u56fe\u50cf<\/p>\ninverted_image = 1 - gray_image<\/p>\n
<\/code><\/pre>\n<\/p>\n
5. \u6a21\u7cca\u5904\u7406<\/h4>\n<\/p>\n
\u4f7f\u7528\u9ad8\u65af\u6a21\u7cca\u5bf9\u53cd\u8f6c\u56fe\u50cf\u8fdb\u884c\u6a21\u7cca\u5904\u7406\uff1a<\/p>\n<\/p>\n
# \u6a21\u7cca\u5904\u7406<\/p>\nblurred_image = filters.gaussian(inverted_image, sigma=21)<\/p>\n
<\/code><\/pre>\n<\/p>\n
6. \u53cd\u8f6c\u6a21\u7cca\u56fe\u50cf<\/h4>\n<\/p>\n
\u5c06\u6a21\u7cca\u540e\u7684\u56fe\u50cf\u518d\u6b21\u8fdb\u884c\u53cd\u8f6c\u5904\u7406\uff1a<\/p>\n<\/p>\n
# \u53cd\u8f6c\u6a21\u7cca\u56fe\u50cf<\/p>\ninverted_blurred_image = 1 - blurred_image<\/p>\n
<\/code><\/pre>\n<\/p>\n
7. \u751f\u6210\u7d20\u63cf\u56fe<\/h4>\n<\/p>\n
\u5c06\u7070\u5ea6\u56fe\u50cf\u4e0e\u53cd\u8f6c\u6a21\u7cca\u56fe\u50cf\u8fdb\u884c\u6df7\u5408\uff0c\u751f\u6210\u6700\u7ec8\u7684\u7d20\u63cf\u56fe\uff1a<\/p>\n<\/p>\n
# \u751f\u6210\u7d20\u63cf\u56fe<\/p>\nsketch_image = gray_image \/ inverted_blurred_image<\/p>\n
sketch_image = np.clip(sketch_image, 0, 1)<\/p>\n
<\/code><\/pre>\n<\/p>\n
8. \u663e\u793a\u548c\u4fdd\u5b58\u7d20\u63cf\u56fe<\/h4>\n<\/p>\n
\u4f7f\u7528skimage\u663e\u793a\u548c\u4fdd\u5b58\u751f\u6210\u7684\u7d20\u63cf\u56fe\uff1a<\/p>\n<\/p>\n
# \u663e\u793a\u7d20\u63cf\u56fe<\/p>\nio.imshow(sketch_image)<\/p>\n
io.show()<\/p>\n
\u4fdd\u5b58\u7d20\u63cf\u56fe<\/strong><\/h2>\nio.imsave('sketch_image.jpg', sketch_image)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u603b\u7ed3<\/h3>\n<\/p>\n
\u4ee5\u4e0a\u4ecb\u7ecd\u4e86\u4f7f\u7528OpenCV\u3001PIL\u5e93\u548cskimage\u5e93\u751f\u6210\u7d20\u63cf\u56fe\u7684\u65b9\u6cd5\u3002\u5176\u4e2d\uff0c\u4f7f\u7528OpenCV\u5e93\u7684\u65b9\u6cd5\u8f83\u4e3a\u7b80\u5355\u548c\u9ad8\u6548\u3002<\/strong>\u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u6839\u636e\u9700\u8981\u9009\u62e9\u5176\u4ed6\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u5982PIL\u6216skimage\u3002\u65e0\u8bba\u4f7f\u7528\u54ea\u79cd\u65b9\u6cd5\uff0c\u751f\u6210\u7d20\u63cf\u56fe\u7684\u57fa\u672c\u6b65\u9aa4\u90fd\u662f\u76f8\u4f3c\u7684\uff0c\u5305\u62ec\u56fe\u50cf\u7070\u5ea6\u5316\u3001\u53cd\u8f6c\u3001\u6a21\u7cca\u5904\u7406\u548c\u6df7\u5408\u7b49\u6b65\u9aa4\u3002\u5e0c\u671b\u8fd9\u4e9b\u65b9\u6cd5\u80fd\u5e2e\u52a9\u4f60\u8f7b\u677e\u751f\u6210\u7d20\u63cf\u56fe\u3002<\/p>\n<\/p>\n\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n \u5982\u4f55\u4f7f\u7528Python\u751f\u6210\u7d20\u63cf\u56fe\uff1f<\/strong>
\u5728Python\u4e2d\u751f\u6210\u7d20\u63cf\u56fe\u901a\u5e38\u6d89\u53ca\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u5982OpenCV\u548cPIL\u3002\u901a\u8fc7\u8fd9\u4e9b\u5e93\uff0c\u60a8\u53ef\u4ee5\u8bfb\u53d6\u56fe\u50cf\uff0c\u5e94\u7528\u8fb9\u7f18\u68c0\u6d4b\u7b97\u6cd5\uff0c\u4ee5\u53ca\u8f6c\u6362\u56fe\u50cf\u4e3a\u7070\u5ea6\u4ee5\u8fbe\u5230\u7d20\u63cf\u6548\u679c\u3002\u5177\u4f53\u6b65\u9aa4\u5305\u62ec\uff1a\u8bfb\u53d6\u56fe\u50cf\uff0c\u5c06\u5176\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff0c\u5e94\u7528\u8fb9\u7f18\u68c0\u6d4b\uff08\u5982Canny\u7b97\u6cd5\uff09\uff0c\u7136\u540e\u5408\u5e76\u8fd9\u4e9b\u6548\u679c\u4ee5\u751f\u6210\u6700\u7ec8\u7684\u7d20\u63cf\u56fe\u3002<\/p>\n\u662f\u5426\u53ef\u4ee5\u4f7f\u7528\u5176\u4ed6\u5e93\u6765\u751f\u6210\u7d20\u63cf\u56fe\uff1f<\/strong>
\u9664\u4e86OpenCV\uff0c\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528PIL\uff08Python Imaging Library\uff09\u6216matplotlib\u5e93\u6765\u521b\u5efa\u7d20\u63cf\u6548\u679c\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u53ef\u4ee5\u901a\u8fc7\u6ee4\u955c\u548c\u56fe\u50cf\u64cd\u4f5c\u5b9e\u73b0\u4e0d\u540c\u7684\u827a\u672f\u6548\u679c\u3002\u901a\u8fc7\u7b80\u5355\u7684\u56fe\u50cf\u5904\u7406\u64cd\u4f5c\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u7d20\u63cf\u6548\u679c\u3002<\/p>\n\u751f\u6210\u7d20\u63cf\u56fe\u65f6\u9700\u8981\u6ce8\u610f\u54ea\u4e9b\u4e8b\u9879\uff1f<\/strong>
\u5728\u751f\u6210\u7d20\u63cf\u56fe\u65f6\uff0c\u9009\u62e9\u5408\u9002\u7684\u56fe\u50cf\u8d28\u91cf\u548c\u5206\u8fa8\u7387\u81f3\u5173\u91cd\u8981\u3002\u56fe\u50cf\u7684\u6e05\u6670\u5ea6\u4f1a\u76f4\u63a5\u5f71\u54cd\u7d20\u63cf\u6548\u679c\u7684\u7ec6\u817b\u7a0b\u5ea6\u3002\u6b64\u5916\uff0c\u8c03\u6574\u8fb9\u7f18\u68c0\u6d4b\u53c2\u6570\u548c\u56fe\u50cf\u5bf9\u6bd4\u5ea6\u4e5f\u975e\u5e38\u91cd\u8981\uff0c\u4ee5\u786e\u4fdd\u7d20\u63cf\u56fe\u770b\u8d77\u6765\u81ea\u7136\u4e14\u5bcc\u6709\u5c42\u6b21\u611f\u3002\u786e\u4fdd\u4f7f\u7528\u7684\u56fe\u50cf\u6e05\u6670\u4e14\u7ec6\u8282\u4e30\u5bcc\uff0c\u8fd9\u6837\u751f\u6210\u7684\u7d20\u63cf\u56fe\u6548\u679c\u66f4\u4f73\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u751f\u6210\u4e00\u4e2a\u7d20\u63cf\u56fe\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528OpenCV\u5e93\u3001PIL\u5e93\u548cskimage\u5e93\u7b49\u3002\u8fd9\u4e9b\u65b9\u6cd5\u4e00\u822c […]","protected":false},"author":3,"featured_media":1110686,"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\/1110680"}],"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=1110680"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1110680\/revisions"}],"predecessor-version":[{"id":1110687,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1110680\/revisions\/1110687"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1110686"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1110680"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1110680"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1110680"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"python\u4e2d\u5982\u4f55\u751f\u6210\u4e00\u4e2a\u7d20\u63cf\u56fe\"](\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25073427\/f992bb35-9e3c-4770-97c6-09f594e55d1e.webp\")
<\/p>\n