{"id":1024478,"date":"2024-12-30T14:14:04","date_gmt":"2024-12-30T06:14:04","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1024478.html"},"modified":"2024-12-30T14:14:06","modified_gmt":"2024-12-30T06:14:06","slug":"python%e5%a6%82%e4%bd%95%e6%8f%90%e5%8f%96%e7%85%a7%e7%89%87%e6%b0%b4%e5%8d%b0-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1024478.html","title":{"rendered":"python\u5982\u4f55\u63d0\u53d6\u7167\u7247\u6c34\u5370"},"content":{"rendered":"

\"python\u5982\u4f55\u63d0\u53d6\u7167\u7247\u6c34\u5370\"<\/p>\n

\u4e00\u3001Python\u5982\u4f55\u63d0\u53d6\u7167\u7247\u6c34\u5370<\/p>\n<\/p>\n

Python\u63d0\u53d6\u7167\u7247\u6c34\u5370\u7684\u65b9\u6cd5\u5305\u62ec\u56fe\u50cf\u5904\u7406\u6280\u672f\u3001\u9891\u57df\u5206\u6790\u3001\u673a\u5668\u5b66\u4e60<\/a>\u7b97\u6cd5\u3001\u73b0\u6709\u5de5\u5177\u5e93<\/strong>\u3002\u5176\u4e2d\uff0c\u56fe\u50cf\u5904\u7406\u6280\u672f\u662f\u6700\u5e38\u7528\u7684\u65b9\u5f0f\u4e4b\u4e00\uff0c\u901a\u8fc7\u56fe\u50cf\u5904\u7406\u6280\u672f\u53ef\u4ee5\u5bf9\u56fe\u50cf\u8fdb\u884c\u5206\u6790\uff0c\u627e\u5230\u6c34\u5370\u7684\u4f4d\u7f6e\u5e76\u5c06\u5176\u63d0\u53d6\u51fa\u6765\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u5229\u7528OpenCV\u5e93\u6765\u8fdb\u884c\u56fe\u50cf\u7684\u8bfb\u53d6\u3001\u5904\u7406\u548c\u5206\u6790\uff0c\u901a\u8fc7\u5404\u79cd\u6ee4\u6ce2\u3001\u8fb9\u7f18\u68c0\u6d4b\u7b49\u65b9\u6cd5\u6765\u63d0\u53d6\u51fa\u6c34\u5370\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u53ca\u76f8\u5173\u5de5\u5177\u5e93\u6765\u63d0\u53d6\u7167\u7247\u4e2d\u7684\u6c34\u5370\u3002<\/p>\n<\/p>\n

\u4e8c\u3001\u56fe\u50cf\u5904\u7406\u6280\u672f<\/p>\n<\/p>\n

1\u3001\u4f7f\u7528OpenCV\u8fdb\u884c\u56fe\u50cf\u5904\u7406<\/p>\n<\/p>\n

OpenCV\uff08Open Source Computer Vision Library\uff09\u662f\u4e00\u4e2a\u5f00\u6e90\u8ba1\u7b97\u673a\u89c6\u89c9\u548c\u673a\u5668\u5b66\u4e60\u8f6f\u4ef6\u5e93\u3002\u5b83\u53ef\u4ee5\u7528\u6765\u5904\u7406\u56fe\u50cf\u548c\u89c6\u9891\uff0c\u5e76\u4e14\u652f\u6301Python\u8bed\u8a00\u3002\u6211\u4eec\u53ef\u4ee5\u5229\u7528OpenCV\u6765\u8bfb\u53d6\u56fe\u50cf\u3001\u5904\u7406\u56fe\u50cf\uff0c\u5e76\u901a\u8fc7\u5404\u79cd\u56fe\u50cf\u5904\u7406\u6280\u672f\u6765\u63d0\u53d6\u6c34\u5370\u3002<\/p>\n<\/p>\n

import cv2<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('image_with_watermark.jpg')<\/p>\n
\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n
\u4f7f\u7528\u8fb9\u7f18\u68c0\u6d4b\u7b97\u6cd5<\/strong><\/h2>\n
edges = cv2.Canny(gray_image, 100, 200)<\/p>\n
\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n
cv2.imshow('Edges', edges)<\/p>\n
cv2.wAI<\/a>tKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528OpenCV\u8bfb\u53d6\u56fe\u50cf\uff0c\u5c06\u5176\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff0c\u5e76\u4f7f\u7528Canny\u8fb9\u7f18\u68c0\u6d4b\u7b97\u6cd5\u6765\u68c0\u6d4b\u56fe\u50cf\u4e2d\u7684\u8fb9\u7f18\u3002\u8fd9\u6837\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5b9a\u4f4d\u6c34\u5370\u7684\u4f4d\u7f6e\u3002<\/p>\n<\/p>\n
2\u3001\u56fe\u50cf\u6ee4\u6ce2<\/p>\n<\/p>\n
\u56fe\u50cf\u6ee4\u6ce2\u662f\u53e6\u4e00\u79cd\u5e38\u7528\u7684\u56fe\u50cf\u5904\u7406\u6280\u672f\u3002\u901a\u8fc7\u6ee4\u6ce2\uff0c\u6211\u4eec\u53ef\u4ee5\u53bb\u9664\u56fe\u50cf\u4e2d\u7684\u566a\u58f0\uff0c\u7a81\u51fa\u56fe\u50cf\u4e2d\u7684\u67d0\u4e9b\u7279\u5f81\uff0c\u4ece\u800c\u5e2e\u52a9\u6211\u4eec\u63d0\u53d6\u6c34\u5370\u3002<\/p>\n<\/p>\n
import cv2<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('image_with_watermark.jpg')<\/p>\n
\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n
\u4f7f\u7528\u9ad8\u65af\u6ee4\u6ce2\u5668<\/strong><\/h2>\n
blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)<\/p>\n
\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n
cv2.imshow('Blurred Image', blurred_image)<\/p>\n
cv2.waitKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u9ad8\u65af\u6ee4\u6ce2\u5668\u5bf9\u56fe\u50cf\u8fdb\u884c\u6ee4\u6ce2\uff0c\u4ece\u800c\u53bb\u9664\u56fe\u50cf\u4e2d\u7684\u566a\u58f0\uff0c\u4f7f\u6c34\u5370\u66f4\u52a0\u660e\u663e\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u9891\u57df\u5206\u6790<\/p>\n<\/p>\n
\u9891\u57df\u5206\u6790\u662f\u53e6\u4e00\u79cd\u63d0\u53d6\u6c34\u5370\u7684\u65b9\u6cd5\u3002\u901a\u8fc7\u5c06\u56fe\u50cf\u8f6c\u6362\u5230\u9891\u57df\uff0c\u6211\u4eec\u53ef\u4ee5\u5206\u6790\u56fe\u50cf\u4e2d\u7684\u9891\u7387\u6210\u5206\uff0c\u4ece\u800c\u627e\u5230\u6c34\u5370\u7684\u4f4d\u7f6e\u5e76\u63d0\u53d6\u51fa\u6765\u3002<\/p>\n<\/p>\n
1\u3001\u5085\u91cc\u53f6\u53d8\u6362<\/p>\n<\/p>\n
\u5085\u91cc\u53f6\u53d8\u6362\u662f\u4e00\u79cd\u5e38\u7528\u7684\u9891\u57df\u5206\u6790\u65b9\u6cd5\u3002\u901a\u8fc7\u5085\u91cc\u53f6\u53d8\u6362\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u56fe\u50cf\u4ece\u7a7a\u95f4\u57df\u8f6c\u6362\u5230\u9891\u57df\uff0c\u4ece\u800c\u5206\u6790\u56fe\u50cf\u4e2d\u7684\u9891\u7387\u6210\u5206\u3002<\/p>\n<\/p>\n
import cv2<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('image_with_watermark.jpg', 0)<\/p>\n
\u8fdb\u884c\u5085\u91cc\u53f6\u53d8\u6362<\/strong><\/h2>\n
dft = cv2.dft(np.float32(image), flags=cv2.DFT_COMPLEX_OUTPUT)<\/p>\n
dft_shift = np.fft.fftshift(dft)<\/p>\n
\u8ba1\u7b97\u5e45\u5ea6\u8c31<\/strong><\/h2>\n
magnitude_spectrum = 20 * np.log(cv2.magnitude(dft_shift[:, :, 0], dft_shift[:, :, 1]))<\/p>\n
\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n
plt.subplot(121), plt.imshow(image, cmap='gray')<\/p>\n
plt.title('Input Image'), plt.xticks([]), plt.yticks([])<\/p>\n
plt.subplot(122), plt.imshow(magnitude_spectrum, cmap='gray')<\/p>\n
plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528OpenCV\u8fdb\u884c\u5085\u91cc\u53f6\u53d8\u6362\uff0c\u5e76\u8ba1\u7b97\u56fe\u50cf\u7684\u5e45\u5ea6\u8c31\u3002\u901a\u8fc7\u5206\u6790\u5e45\u5ea6\u8c31\u4e2d\u7684\u9891\u7387\u6210\u5206\uff0c\u6211\u4eec\u53ef\u4ee5\u627e\u5230\u6c34\u5370\u7684\u4f4d\u7f6e\u5e76\u63d0\u53d6\u51fa\u6765\u3002<\/p>\n<\/p>\n
2\u3001\u5c0f\u6ce2\u53d8\u6362<\/p>\n<\/p>\n
\u5c0f\u6ce2\u53d8\u6362\u662f\u53e6\u4e00\u79cd\u5e38\u7528\u7684\u9891\u57df\u5206\u6790\u65b9\u6cd5\u3002\u4e0e\u5085\u91cc\u53f6\u53d8\u6362\u4e0d\u540c\uff0c\u5c0f\u6ce2\u53d8\u6362\u53ef\u4ee5\u540c\u65f6\u5206\u6790\u56fe\u50cf\u7684\u65f6\u95f4\u548c\u9891\u7387\u6210\u5206\uff0c\u4ece\u800c\u66f4\u597d\u5730\u63d0\u53d6\u6c34\u5370\u3002<\/p>\n<\/p>\n
import pywt<\/p>\n
import cv2<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('image_with_watermark.jpg', 0)<\/p>\n
\u8fdb\u884c\u5c0f\u6ce2\u53d8\u6362<\/strong><\/h2>\n
coeffs = pywt.dwt2(image, 'haar')<\/p>\n
LL, (LH, HL, HH) = coeffs<\/p>\n
\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n
plt.subplot(221), plt.imshow(LL, cmap='gray')<\/p>\n
plt.title('Approximation'), plt.xticks([]), plt.yticks([])<\/p>\n
plt.subplot(222), plt.imshow(LH, cmap='gray')<\/p>\n
plt.title('Horizontal detail'), plt.xticks([]), plt.yticks([])<\/p>\n
plt.subplot(223), plt.imshow(HL, cmap='gray')<\/p>\n
plt.title('Vertical detail'), plt.xticks([]), plt.yticks([])<\/p>\n
plt.subplot(224), plt.imshow(HH, cmap='gray')<\/p>\n
plt.title('Diagonal detail'), plt.xticks([]), plt.yticks([])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528PyWavelets\u5e93\u8fdb\u884c\u5c0f\u6ce2\u53d8\u6362\uff0c\u5e76\u663e\u793a\u56fe\u50cf\u7684\u5404\u4e2a\u9891\u7387\u6210\u5206\u3002\u901a\u8fc7\u5206\u6790\u8fd9\u4e9b\u9891\u7387\u6210\u5206\uff0c\u6211\u4eec\u53ef\u4ee5\u627e\u5230\u6c34\u5370\u7684\u4f4d\u7f6e\u5e76\u63d0\u53d6\u51fa\u6765\u3002<\/p>\n<\/p>\n
\u56db\u3001\u673a\u5668\u5b66\u4e60\u7b97\u6cd5<\/p>\n<\/p>\n
\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u4e5f\u662f\u63d0\u53d6\u6c34\u5370\u7684\u4e00\u79cd\u6709\u6548\u65b9\u6cd5\u3002\u901a\u8fc7\u8bad\u7ec3\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0c\u6211\u4eec\u53ef\u4ee5\u81ea\u52a8\u8bc6\u522b\u56fe\u50cf\u4e2d\u7684\u6c34\u5370\u5e76\u5c06\u5176\u63d0\u53d6\u51fa\u6765\u3002<\/p>\n<\/p>\n
1\u3001\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09<\/p>\n<\/p>\n
\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u662f\u4e00\u79cd\u5e38\u7528\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u53ef\u4ee5\u7528\u4e8e\u56fe\u50cf\u5206\u7c7b\u3001\u76ee\u6807\u68c0\u6d4b\u7b49\u4efb\u52a1\u3002\u901a\u8fc7\u8bad\u7ec3\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff0c\u6211\u4eec\u53ef\u4ee5\u81ea\u52a8\u8bc6\u522b\u56fe\u50cf\u4e2d\u7684\u6c34\u5370\u5e76\u5c06\u5176\u63d0\u53d6\u51fa\u6765\u3002<\/p>\n<\/p>\n
import tensorflow as tf<\/p>\n
from tensorflow.keras.preprocessing.image import ImageDataGenerator<\/p>\n
from tensorflow.keras.models import Sequential<\/p>\n
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense<\/p>\n
\u6570\u636e\u9884\u5904\u7406<\/strong><\/h2>\n
datagen = ImageDataGenerator(rescale=1.0\/255.0, validation_split=0.2)<\/p>\n
train_generator = datagen.flow_from_directory('dataset', target_size=(64, 64), batch_size=32, class_mode='binary', subset='training')<\/p>\n
validation_generator = datagen.flow_from_directory('dataset', target_size=(64, 64), batch_size=32, class_mode='binary', subset='validation')<\/p>\n
\u6784\u5efa\u6a21\u578b<\/strong><\/h2>\n
model = Sequential([<\/p>\n
    Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)),<\/p>\n
    MaxPooling2D((2, 2)),<\/p>\n
    Conv2D(64, (3, 3), activation='relu'),<\/p>\n
    MaxPooling2D((2, 2)),<\/p>\n
    Flatten(),<\/p>\n
    Dense(128, activation='relu'),<\/p>\n
    Dense(1, activation='sigmoid')<\/p>\n
])<\/p>\n
\u7f16\u8bd1\u6a21\u578b<\/strong><\/h2>\n
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model.fit(train_generator, epochs=10, validation_data=validation_generator)<\/p>\n
\u4fdd\u5b58\u6a21\u578b<\/strong><\/h2>\n
model.save('watermark_detector.h5')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528TensorFlow\u548cKeras\u6784\u5efa\u4e00\u4e2a\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff0c\u5e76\u8bad\u7ec3\u8be5\u6a21\u578b\u6765\u8bc6\u522b\u56fe\u50cf\u4e2d\u7684\u6c34\u5370\u3002\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8be5\u6a21\u578b\u6765\u81ea\u52a8\u63d0\u53d6\u6c34\u5370\u3002<\/p>\n<\/p>\n
2\u3001\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09<\/p>\n<\/p>\n
\u652f\u6301\u5411\u91cf\u673a\u662f\u4e00\u79cd\u5e38\u7528\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u53ef\u4ee5\u7528\u4e8e\u5206\u7c7b\u548c\u56de\u5f52\u4efb\u52a1\u3002\u901a\u8fc7\u8bad\u7ec3\u652f\u6301\u5411\u91cf\u673a\uff0c\u6211\u4eec\u4e5f\u53ef\u4ee5\u81ea\u52a8\u8bc6\u522b\u56fe\u50cf\u4e2d\u7684\u6c34\u5370\u5e76\u5c06\u5176\u63d0\u53d6\u51fa\u6765\u3002<\/p>\n<\/p>\n
import cv2<\/p>\n
import numpy as np<\/p>\n
from sklearn import svm<\/p>\n
from sklearn.model_selection import train_test_split<\/p>\n
from sklearn.metrics import accuracy_score<\/p>\n
\u6570\u636e\u9884\u5904\u7406<\/strong><\/h2>\n
def preprocess_image(image_path):<\/p>\n
    image = cv2.imread(image_path, 0)<\/p>\n
    resized_image = cv2.resize(image, (64, 64))<\/p>\n
    flattened_image = resized_image.flatten()<\/p>\n
    return flattened_image<\/p>\n
\u8bfb\u53d6\u6570\u636e\u96c6<\/strong><\/h2>\n
image_paths = ['image1.jpg', 'image2.jpg', ...]<\/p>\n
labels = [0, 1, ...]  # 0\u8868\u793a\u65e0\u6c34\u5370\uff0c1\u8868\u793a\u6709\u6c34\u5370<\/p>\n
data = [preprocess_image(image_path) for image_path in image_paths]<\/p>\n
\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)<\/p>\n
\u8bad\u7ec3\u652f\u6301\u5411\u91cf\u673a<\/strong><\/h2>\n
clf = svm.SVC(kernel='linear')<\/p>\n
clf.fit(X_train, y_train)<\/p>\n
\u9884\u6d4b\u5e76\u8ba1\u7b97\u51c6\u786e\u7387<\/strong><\/h2>\n
y_pred = clf.predict(X_test)<\/p>\n
accuracy = accuracy_score(y_test, y_pred)<\/p>\n
print(f'Accuracy: {accuracy * 100:.2f}%')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528scikit-learn\u5e93\u8bad\u7ec3\u4e00\u4e2a\u652f\u6301\u5411\u91cf\u673a\u6a21\u578b\uff0c\u5e76\u4f7f\u7528\u8be5\u6a21\u578b\u6765\u8bc6\u522b\u56fe\u50cf\u4e2d\u7684\u6c34\u5370\u3002\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8be5\u6a21\u578b\u6765\u81ea\u52a8\u63d0\u53d6\u6c34\u5370\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u73b0\u6709\u5de5\u5177\u5e93<\/p>\n<\/p>\n
\u9664\u4e86\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u8fd8\u6709\u4e00\u4e9b\u73b0\u6709\u7684\u5de5\u5177\u5e93\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u63d0\u53d6\u7167\u7247\u4e2d\u7684\u6c34\u5370\u3002<\/p>\n<\/p>\n
1\u3001rembg<\/p>\n<\/p>\n
rembg\u662f\u4e00\u4e2a\u5f00\u6e90\u7684Python\u5e93\uff0c\u53ef\u4ee5\u7528\u4e8e\u4ece\u56fe\u50cf\u4e2d\u53bb\u9664\u80cc\u666f\u6216\u6c34\u5370\u3002\u8be5\u5e93\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\uff0c\u53ef\u4ee5\u81ea\u52a8\u8bc6\u522b\u5e76\u53bb\u9664\u56fe\u50cf\u4e2d\u7684\u6c34\u5370\u3002<\/p>\n<\/p>\n
from rembg import remove<\/p>\n
import cv2<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('image_with_watermark.jpg')<\/p>\n
\u53bb\u9664\u6c34\u5370<\/strong><\/h2>\n
result = remove(image)<\/p>\n
\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n
cv2.imshow('Result', result)<\/p>\n
cv2.waitKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528rembg\u5e93\u53bb\u9664\u56fe\u50cf\u4e2d\u7684\u6c34\u5370\u3002\u8be5\u5e93\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\uff0c\u53ef\u4ee5\u81ea\u52a8\u8bc6\u522b\u5e76\u53bb\u9664\u56fe\u50cf\u4e2d\u7684\u6c34\u5370\u3002<\/p>\n<\/p>\n
2\u3001pywatermark<\/p>\n<\/p>\n
pywatermark\u662f\u53e6\u4e00\u4e2a\u5f00\u6e90\u7684Python\u5e93\uff0c\u53ef\u4ee5\u7528\u4e8e\u6dfb\u52a0\u548c\u63d0\u53d6\u56fe\u50cf\u4e2d\u7684\u6c34\u5370\u3002\u8be5\u5e93\u63d0\u4f9b\u4e86\u4e00\u4e9b\u7b80\u5355\u7684\u63a5\u53e3\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u4ece\u56fe\u50cf\u4e2d\u63d0\u53d6\u6c34\u5370\u3002<\/p>\n<\/p>\n
from pywatermark import extract<\/p>\n
\u63d0\u53d6\u6c34\u5370<\/strong><\/h2>\n
watermark = extract('image_with_watermark.jpg', 'output_watermark.jpg')<\/p>\n
\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n
cv2.imshow('Watermark', watermark)<\/p>\n
cv2.waitKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528pywatermark\u5e93\u63d0\u53d6\u56fe\u50cf\u4e2d\u7684\u6c34\u5370\u3002\u8be5\u5e93\u63d0\u4f9b\u4e86\u4e00\u4e9b\u7b80\u5355\u7684\u63a5\u53e3\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u4ece\u56fe\u50cf\u4e2d\u63d0\u53d6\u6c34\u5370\u3002<\/p>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/p>\n<\/p>\n
\u672c\u6587\u4ecb\u7ecd\u4e86\u5982\u4f55\u4f7f\u7528Python\u63d0\u53d6\u7167\u7247\u4e2d\u7684\u6c34\u5370\u3002\u6211\u4eec\u8ba8\u8bba\u4e86\u51e0\u79cd\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u5305\u62ec\u56fe\u50cf\u5904\u7406\u6280\u672f\u3001\u9891\u57df\u5206\u6790\u3001\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u548c\u73b0\u6709\u5de5\u5177\u5e93\u3002\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u4f18\u7f3a\u70b9\uff0c\u5177\u4f53\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u5e94\u7528\u573a\u666f\u548c\u9700\u6c42\u3002\u901a\u8fc7\u5408\u7406\u9009\u62e9\u548c\u7ec4\u5408\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u6709\u6548\u5730\u63d0\u53d6\u7167\u7247\u4e2d\u7684\u6c34\u5370\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u60a8\u6709\u6240\u5e2e\u52a9\uff0c\u8ba9\u60a8\u5728\u5904\u7406\u56fe\u50cf\u6c34\u5370\u95ee\u9898\u65f6\u80fd\u591f\u6709\u6240\u542f\u53d1\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u63d0\u53d6\u7167\u7247\u4e2d\u7684\u6c34\u5370\uff1f<\/strong><\/p>\n
\u63d0\u53d6\u7167\u7247\u6c34\u5370\u901a\u5e38\u9700\u8981\u501f\u52a9\u4e00\u4e9b\u56fe\u50cf\u5904\u7406\u5e93\u548c\u6280\u672f\u3002\u4f7f\u7528Python\u65f6\uff0c\u53ef\u4ee5\u5229\u7528OpenCV\u548cPIL\u5e93\u3002OpenCV\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u800cPIL\u5219\u7528\u4e8e\u56fe\u50cf\u7684\u57fa\u672c\u64cd\u4f5c\u3002\u63d0\u53d6\u6c34\u5370\u7684\u6d41\u7a0b\u4e00\u822c\u5305\u62ec\u8bfb\u53d6\u56fe\u50cf\u3001\u5904\u7406\u56fe\u50cf\u3001\u8bc6\u522b\u6c34\u5370\u5e76\u5c06\u5176\u63d0\u53d6\u51fa\u6765\u3002<\/p>\n
\u63d0\u53d6\u6c34\u5370\u7684\u5e38\u7528\u7b97\u6cd5\u6709\u54ea\u4e9b\uff1f<\/strong><\/p>\n
\u5e38\u89c1\u7684\u6c34\u5370\u63d0\u53d6\u7b97\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u9891\u57df\u5206\u6790\u3001\u7a7a\u95f4\u57df\u5206\u6790\u548c\u57fa\u4e8e\u6a21\u677f\u7684\u65b9\u6cd5\u3002\u9891\u57df\u5206\u6790\u6280\u672f\uff0c\u5982\u79bb\u6563\u4f59\u5f26\u53d8\u6362\uff08DCT\uff09\u548c\u79bb\u6563\u5c0f\u6ce2\u53d8\u6362\uff08DWT\uff09\uff0c\u53ef\u4ee5\u4ece\u56fe\u50cf\u7684\u9891\u57df\u4e2d\u63d0\u53d6\u6c34\u5370\u3002\u800c\u7a7a\u95f4\u57df\u5206\u6790\u5219\u76f4\u63a5\u5728\u56fe\u50cf\u7684\u50cf\u7d20\u503c\u4e0a\u8fdb\u884c\u5904\u7406\uff0c\u901a\u8fc7\u5bf9\u6bd4\u6216\u8fc7\u6ee4\u6765\u8bc6\u522b\u6c34\u5370\u3002\u9009\u62e9\u5408\u9002\u7684\u7b97\u6cd5\u9700\u8981\u6839\u636e\u6c34\u5370\u7684\u7c7b\u578b\u548c\u5d4c\u5165\u65b9\u5f0f\u6765\u51b3\u5b9a\u3002<\/p>\n
\u63d0\u53d6\u6c34\u5370\u7684\u6cd5\u5f8b\u98ce\u9669\u6709\u54ea\u4e9b\uff1f<\/strong><\/p>\n
\u5728\u63d0\u53d6\u7167\u7247\u6c34\u5370\u65f6\uff0c\u9700\u8981\u6ce8\u610f\u6cd5\u5f8b\u548c\u7248\u6743\u95ee\u9898\u3002\u8bb8\u591a\u7167\u7247\u7684\u6c34\u5370\u662f\u4e3a\u4e86\u4fdd\u62a4\u7248\u6743\u800c\u8bbe\u8ba1\u7684\uff0c\u672a\u7ecf\u6388\u6743\u63d0\u53d6\u548c\u4f7f\u7528\u8fd9\u4e9b\u6c34\u5370\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6cd5\u5f8b\u7ea0\u7eb7\u3002\u5728\u8fdb\u884c\u6c34\u5370\u63d0\u53d6\u4e4b\u524d\uff0c\u52a1\u5fc5\u786e\u8ba4\u662f\u5426\u62e5\u6709\u76f8\u5173\u56fe\u7247\u7684\u4f7f\u7528\u6743\u9650\uff0c\u786e\u4fdd\u4e0d\u4fb5\u72af\u4ed6\u4eba\u7684\u77e5\u8bc6\u4ea7\u6743\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4e00\u3001Python\u5982\u4f55\u63d0\u53d6\u7167\u7247\u6c34\u5370 Python\u63d0\u53d6\u7167\u7247\u6c34\u5370\u7684\u65b9\u6cd5\u5305\u62ec\u56fe\u50cf\u5904\u7406\u6280\u672f\u3001\u9891\u57df\u5206\u6790\u3001\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u3001\u73b0\u6709 […]","protected":false},"author":3,"featured_media":1024481,"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\/1024478"}],"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=1024478"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1024478\/revisions"}],"predecessor-version":[{"id":1024482,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1024478\/revisions\/1024482"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1024481"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1024478"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1024478"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1024478"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}