\u624b\u52a8\u53bb\u9664\u6c34\u5370<\/strong><\/p>\n<\/p>\n\u6c34\u5370\u7684\u53bb\u9664\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5b9e\u73b0\uff0c\u4f8b\u5982\u4f7f\u7528\u56fe\u50cf\u7684\u4eff\u5236\u56fe\u7ae0\u5de5\u5177\uff0c\u6216\u8005\u901a\u8fc7\u56fe\u50cf\u7684\u6a21\u7cca\u5904\u7406\u6765\u51cf\u5f31\u6c34\u5370\u7684\u5b58\u5728\u611f\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528OpenCV\u4e2d\u7684\u6a21\u7cca\u5904\u7406\u6765\u53bb\u9664\u6c34\u5370\u3002<\/p>\n<\/p>\n
# \u5e94\u7528\u9ad8\u65af\u6a21\u7cca<\/p>\nblurred_image = cv2.GaussianBlur(image, (15, 15), 0)<\/p>\n
\u663e\u793a\u5904\u7406\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\ncv2.imshow('Blurred Image', blurred_image)<\/p>\n
cv2.waitKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6a21\u7cca\u5904\u7406\u5e76\u4e0d\u80fd\u5b8c\u5168\u53bb\u9664\u6c34\u5370\uff0c\u4f46\u53ef\u4ee5\u51cf\u5f31\u5176\u53ef\u89c1\u6027\u3002\u624b\u52a8\u53bb\u9664\u6c34\u5370\u901a\u5e38\u8fd8\u9700\u8981\u7ed3\u5408\u5176\u4ed6\u6280\u672f\uff0c\u5982\u533a\u57df\u586b\u5145\u548c\u4fee\u590d\u5de5\u5177\u3002<\/p>\n<\/p>\n<\/li>\n
\n\u4f7f\u7528inpaint\u529f\u80fd<\/strong><\/p>\n<\/p>\nOpenCV\u63d0\u4f9b\u4e86inpaint()<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u7528\u4e8e\u4fee\u590d\u56fe\u50cf\u4e2d\u6307\u5b9a\u533a\u57df\u3002\u9996\u5148\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u63a9\u7801\uff0c\u6807\u8bb0\u51fa\u9700\u8981\u53bb\u9664\u7684\u6c34\u5370\u533a\u57df\uff0c\u7136\u540e\u901a\u8fc7inpaint()<\/code>\u51fd\u6570\u5bf9\u8be5\u533a\u57df\u8fdb\u884c\u4fee\u590d\u3002<\/p>\n<\/p>\n# \u521b\u5efa\u63a9\u7801<\/p>\nmask = cv2.threshold(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY), 1, 255, cv2.THRESH_BINARY)[1]<\/p>\n
\u4f7f\u7528inpaint\u8fdb\u884c\u4fee\u590d<\/strong><\/h2>\ninpainted_image = cv2.inpaint(image, mask, 3, cv2.INPAINT_TELEA)<\/p>\n
\u663e\u793a\u4fee\u590d\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\ncv2.imshow('Inpainted Image', inpainted_image)<\/p>\n
cv2.waitKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u80cc\u666f\u8f83\u4e3a\u7b80\u5355\uff0c\u4e14\u6c34\u5370\u4e0e\u80cc\u666f\u989c\u8272\u5dee\u5f02\u660e\u663e\u7684\u60c5\u51b5\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
\u4e8c\u3001\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u8fdb\u884c\u53bb\u6c34\u5370<\/p>\n<\/p>\n
\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\u5728\u56fe\u50cf\u5904\u7406\u9886\u57df\u8868\u73b0\u51fa\u8272\uff0c\u7279\u522b\u662f\u5728\u53bb\u566a\u3001\u4fee\u590d\u548c\u751f\u6210\u7b49\u4efb\u52a1\u4e2d\u3002\u5229\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u53bb\u9664\u6c34\u5370\u662f\u8fd1\u5e74\u6765\u7684\u4e00\u79cd\u6d41\u884c\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
\n- \n
\u6df1\u5ea6\u56fe\u50cf\u4fee\u590d\u6a21\u578b<\/strong><\/p>\n<\/p>\n\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u53bb\u9664\u6c34\u5370\uff0c\u901a\u5e38\u9700\u8981\u9884\u5148\u8bad\u7ec3\u7684\u6a21\u578b\u3002Deep Image Prior\u662f\u4e00\u79cd\u65e0\u9700\u5927\u89c4\u6a21\u6570\u636e\u8bad\u7ec3\u7684\u6a21\u578b\uff0c\u5b83\u5229\u7528\u7f51\u7edc\u7ed3\u6784\u672c\u8eab\u7684\u5148\u9a8c\u4fe1\u606f\u8fdb\u884c\u56fe\u50cf\u4fee\u590d\u3002<\/p>\n<\/p>\n
from deep_image_prior import DIP<\/p>\n\u521d\u59cb\u5316\u6a21\u578b<\/strong><\/h2>\nmodel = DIP()<\/p>\n
\u52a0\u8f7d\u56fe\u50cf<\/strong><\/h2>\nimage = cv2.imread('image_with_watermark.jpg')<\/p>\n
\u4f7f\u7528\u6a21\u578b\u8fdb\u884c\u53bb\u6c34\u5370<\/strong><\/h2>\nrestored_image = model.remove_watermark(image)<\/p>\n
\u663e\u793a\u4fee\u590d\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\ncv2.imshow('Restored Image', restored_image)<\/p>\n
cv2.waitKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n
- \n
\u4f7f\u7528TensorFlow\u6216PyTorch\u8bad\u7ec3\u6a21\u578b<\/strong><\/p>\n<\/p>\n\u5982\u679c\u6709\u8db3\u591f\u7684\u8ba1\u7b97\u8d44\u6e90\u548c\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528TensorFlow\u6216PyTorch\u7b49\u6846\u67b6\u8bad\u7ec3\u4e00\u4e2a\u4e13\u95e8\u7528\u4e8e\u53bb\u6c34\u5370\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u3002\u8fd9\u901a\u5e38\u9700\u8981\u5927\u91cf\u6807\u6ce8\u7684\u56fe\u50cf\u6570\u636e\uff0c\u5305\u542b\u6c34\u5370\u548c\u65e0\u6c34\u5370\u7248\u672c\u3002<\/p>\n<\/p>\n
import tensorflow as tf<\/p>\nfrom tensorflow.keras import layers, models<\/p>\n
\u5b9a\u4e49\u7b80\u5355\u7684\u53bb\u6c34\u5370\u6a21\u578b<\/strong><\/h2>\nmodel = models.Sequential([<\/p>\n
layers.Input(shape=(256, 256, 3)),<\/p>\n
layers.Conv2D(64, (3, 3), activation='relu'),<\/p>\n
layers.MaxPooling2D((2, 2)),<\/p>\n
layers.Conv2D(128, (3, 3), activation='relu'),<\/p>\n
layers.UpSampling2D((2, 2)),<\/p>\n
layers.Conv2D(3, (3, 3), activation='sigmoid')<\/p>\n
])<\/p>\n
\u7f16\u8bd1\u6a21\u578b<\/strong><\/h2>\nmodel.compile(optimizer='adam', loss='mean_squared_error')<\/p>\n
\u8bad\u7ec3\u6a21\u578b\uff08\u9700\u8981\u51c6\u5907\u597d\u6570\u636e\u96c6\uff09<\/strong><\/h2>\nmodel.fit(train_images, train_labels, epochs=10)<\/strong><\/h2>\n\u4f7f\u7528\u6a21\u578b\u8fdb\u884c\u9884\u6d4b<\/strong><\/h2>\npredicted_image = model.predict(image_with_watermark)<\/strong><\/h2>\n<\/code><\/pre>\n<\/p>\n
\u8fd9\u79cd\u65b9\u6cd5\u9700\u8981\u5927\u91cf\u7684\u65f6\u95f4\u548c\u8d44\u6e90\uff0c\u9002\u5408\u6709\u4e30\u5bcc\u6df1\u5ea6\u5b66\u4e60\u7ecf\u9a8c\u7684\u5f00\u53d1\u8005\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
\u4e09\u3001\u4f7f\u7528\u4e13\u95e8\u7684\u5de5\u5177\u5e93<\/p>\n<\/p>\n
\u5bf9\u4e8e\u4e0d\u60f3\u6df1\u5165\u7814\u7a76\u56fe\u50cf\u5904\u7406\u548c\u6df1\u5ea6\u5b66\u4e60\u7684\u7528\u6237\uff0c\u4f7f\u7528\u4e13\u95e8\u7684\u53bb\u6c34\u5370\u5de5\u5177\u5e93\u662f\u4e00\u4e2a\u7b80\u5355\u5feb\u6377\u7684\u9009\u62e9\u3002<\/p>\n<\/p>\n
\n- \n
rembg\u5e93<\/strong><\/p>\n<\/p>\nrembg\u662f\u4e00\u4e2a\u7528\u4e8e\u53bb\u9664\u56fe\u50cf\u80cc\u666f\u7684Python\u5e93\uff0c\u4f46\u5b83\u540c\u6837\u9002\u7528\u4e8e\u53bb\u9664\u6c34\u5370\u3002\u5176\u4f7f\u7528\u7b80\u5355\uff0c\u53ea\u9700\u51e0\u884c\u4ee3\u7801\u5373\u53ef\u5b8c\u6210\u6c34\u5370\u53bb\u9664\u3002<\/p>\n<\/p>\n
from rembg import remove<\/p>\nfrom PIL import Image<\/p>\n
\u6253\u5f00\u56fe\u50cf<\/strong><\/h2>\ninput_image = Image.open('image_with_watermark.jpg')<\/p>\n
\u53bb\u9664\u6c34\u5370<\/strong><\/h2>\noutput_image = remove(input_image)<\/p>\n
\u4fdd\u5b58\u7ed3\u679c<\/strong><\/h2>\noutput_image.save('image_without_watermark.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
rembg\u5e93\u4f7f\u7528\u4e86\u9884\u8bad\u7ec3\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u53ef\u4ee5\u5feb\u901f\u53bb\u9664\u5e38\u89c1\u7684\u6c34\u5370\u548c\u80cc\u666f\u3002<\/p>\n<\/p>\n<\/li>\n
- \n
\u5176\u4ed6\u5de5\u5177\u5e93<\/strong><\/p>\n<\/p>\n\u9664\u4e86rembg\uff0c\u8fd8\u6709\u5176\u4ed6\u4e13\u95e8\u7528\u4e8e\u56fe\u50cf\u4fee\u590d\u548c\u53bb\u9664\u6c34\u5370\u7684\u5e93\uff0c\u4f8b\u5982ImageMagick\u3001GIMP\u7b49\u3002\u8fd9\u4e9b\u5de5\u5177\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u53ef\u4ee5\u901a\u8fc7\u547d\u4ee4\u884c\u6216\u811a\u672c\u8fdb\u884c\u81ea\u52a8\u5316\u5904\u7406\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
\u603b\u7ed3\uff0cPython\u63d0\u4f9b\u4e86\u591a\u79cd\u53bb\u9664\u6c34\u5370\u7684\u65b9\u6cd5\uff0c\u5305\u62ec\u624b\u52a8\u50cf\u7d20\u5904\u7406\u3001\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u548c\u4e13\u95e8\u7684\u5de5\u5177\u5e93\u3002\u4e0d\u540c\u65b9\u6cd5\u9002\u7528\u4e8e\u4e0d\u540c\u7684\u5e94\u7528\u573a\u666f\uff0c\u7528\u6237\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u89e3\u51b3\u65b9\u6848\u3002\u5728\u9009\u62e9\u65b9\u6cd5\u65f6\uff0c\u5e94\u8003\u8651\u56fe\u50cf\u7684\u590d\u6742\u6027\u3001\u8ba1\u7b97\u8d44\u6e90\u548c\u5f00\u53d1\u7ecf\u9a8c\u7b49\u56e0\u7d20\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n \u5982\u4f55\u4f7f\u7528Python\u53bb\u9664\u56fe\u7247\u4e0a\u7684\u6c34\u5370\uff1f<\/strong>
\u4f7f\u7528Python\u53bb\u6c34\u5370\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u901a\u5e38\u53ef\u4ee5\u4f9d\u8d56\u4e8e\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u5982OpenCV\u548cPillow\u3002\u9996\u5148\uff0c\u60a8\u53ef\u4ee5\u5c1d\u8bd5\u901a\u8fc7\u56fe\u50cf\u88c1\u526a\u6765\u53bb\u9664\u6c34\u5370\uff0c\u9009\u62e9\u6c34\u5370\u6240\u5728\u533a\u57df\u5e76\u8fdb\u884c\u88c1\u526a\u3002\u53e6\u4e00\u79cd\u65b9\u6cd5\u662f\u4f7f\u7528\u56fe\u50cf\u4fee\u590d\u529f\u80fd\uff0c\u5229\u7528\u5468\u56f4\u50cf\u7d20\u586b\u5145\u6c34\u5370\u533a\u57df\u3002\u5177\u4f53\u5b9e\u73b0\u9700\u8981\u6839\u636e\u6c34\u5370\u7684\u7c7b\u578b\u548c\u4f4d\u7f6e\u8fdb\u884c\u8c03\u6574\u3002<\/p>\n\u53bb\u6c34\u5370\u7684Python\u5e93\u6709\u54ea\u4e9b\u63a8\u8350\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u5e38\u7528\u7684\u53bb\u6c34\u5370\u5e93\u5305\u62ecOpenCV\u3001Pillow\u548cNumPy\u3002OpenCV\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u53ef\u4ee5\u8fdb\u884c\u590d\u6742\u7684\u56fe\u50cf\u5904\u7406\u548c\u4fee\u590d\u3002Pillow\u5219\u9002\u7528\u4e8e\u7b80\u5355\u7684\u56fe\u50cf\u64cd\u4f5c\uff0c\u9002\u5408\u521d\u5b66\u8005\u4f7f\u7528\u3002NumPy\u53ef\u4ee5\u5e2e\u52a9\u5904\u7406\u56fe\u50cf\u6570\u636e\uff0c\u8fdb\u884c\u77e9\u9635\u64cd\u4f5c\uff0c\u4ece\u800c\u5b9e\u73b0\u53bb\u6c34\u5370\u7684\u6548\u679c\u3002<\/p>\n\u53bb\u6c34\u5370\u5bf9\u56fe\u7247\u8d28\u91cf\u6709\u5f71\u54cd\u5417\uff1f<\/strong>
\u53bb\u6c34\u5370\u53ef\u80fd\u4f1a\u5f71\u54cd\u56fe\u7247\u7684\u8d28\u91cf\uff0c\u7279\u522b\u662f\u5f53\u6c34\u5370\u8986\u76d6\u5728\u91cd\u8981\u7ec6\u8282\u4e0a\u65f6\u3002\u5728\u53bb\u6c34\u5370\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5982\u679c\u4f7f\u7528\u7b80\u5355\u7684\u88c1\u526a\u65b9\u6cd5\uff0c\u53ef\u80fd\u4f1a\u5bfc\u81f4\u56fe\u50cf\u5931\u771f\u6216\u7f3a\u5931\u91cd\u8981\u4fe1\u606f\u3002\u4f7f\u7528\u56fe\u50cf\u4fee\u590d\u6280\u672f\u53ef\u4ee5\u5c3d\u91cf\u4fdd\u7559\u7ec6\u8282\uff0c\u4f46\u7ed3\u679c\u4ecd\u7136\u53d6\u51b3\u4e8e\u6c34\u5370\u7684\u590d\u6742\u7a0b\u5ea6\u548c\u53bb\u9664\u65b9\u6cd5\u7684\u6709\u6548\u6027\u3002\u5efa\u8bae\u5728\u5904\u7406\u524d\u5907\u4efd\u539f\u56fe\uff0c\u4ee5\u4fbf\u8fdb\u884c\u6bd4\u8f83\u548c\u9009\u62e9\u6700\u4f73\u6548\u679c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u53bb\u6c34\u5370\u7684\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528\u56fe\u50cf\u5904\u7406\u5e93\u5982OpenCV\u5bf9\u56fe\u50cf\u8fdb\u884c\u50cf\u7d20\u5904\u7406\u3001\u501f\u52a9\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u5982Deep Ima […]","protected":false},"author":3,"featured_media":943742,"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\/943741"}],"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=943741"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/943741\/revisions"}],"predecessor-version":[{"id":943743,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/943741\/revisions\/943743"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/943742"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=943741"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=943741"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=943741"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"python\u5982\u4f55\u53bb\u6c34\u5370\"](\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25081026\/442deb4b-046d-428a-a164-bd44ec19a16c.webp\")
<\/p>\n