叙利 🐋 亚装修风格在防水方面效果如何呢

作者: 杨鹿绫
来源: 投稿
2025-03-14
                         ﻿1、叙利亚装 🦆 修风格在防 🕊 水方面效果如何呢叙利亚装修风格没有明确的防水特征或优势。家庭的防水性能主要取决于所使用的具体材料和施工方法，与装修风 🍁 格。无关
2、叙 🐦 利亚装修风格效果图片 from PIL import Image
import scipy
import pywt
import numpy as np
import math
import cv2
import matplotlib.pyplot as plt
from sklearn import linear_model
def imread(filename):
    """    Read an image from file.
    Parameters:
        filename (str): The path to the image file.
    Returns:
        A numpy array of the image.
    """    return cv2.imread(filename)
def imwrite(filename, image):
    """    Write an image to file.
    Parameters:
        filename (str): The path to the image file.
        image (numpy array): The image to write.
    """    cv2.imwrite(filename, image)
def resize(image, target_size):
    """    Resize an image to a target size.
    Parameters:
        image (numpy array): The image to resize.
        target_size (tuple): The target size of the image.
    Returns:
        A numpy array of the resized image.
    """    return cv2.resize(image, target_size)
def to_grayscale(image):
    """    Convert an image to grayscale.
    Parameters:
        image (numpy array): The image to convert.
    Returns:
        A numpy array of the grayscale image.
    """    return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
def equalize_hist(image):
    """    Equalize the histogram of an image.
    Parameters:
        image (numpy array): The image to equalize.
    Returns:
        A numpy array of the equalized image.
    """    return cv2.equalizeHist(image)
def blur(image, kernel_size=5):
    """    Blur an image using a Gaussian filter.
    Parameters:
        image (numpy array): The image to blur.
        kernel_size (int): The size of the Gaussian kernel.
    Returns:
        A numpy array of the blurred image.
    """    return cv2.GaussianBlur(image, (kernel_size, kernel_size), 0)
def sharpen(image, kernel_size=5):
    """    Sharpen an image using a Laplacian filter.
    Parameters:
        image (numpy array): The image to sharpen.
        kernel_size (int): The size of the Laplacian kernel.
    Returns:
        A numpy array of the sharpened image.
    """    return cv2.Laplacian(image, cv2.CV_64F, ksize=kernel_size)
def edge_detection(image, threshold=0.5):
    """    Detect edges in an image using the Canny edge detector.
    Parameters:
        image (numpy array): The image to detect edges in.
        threshold (float): The threshold for the Canny edge detector.
    Returns:
        A numpy array of the edgedetected image.
    """    return cv2.Canny(image, threshold, threshold)
def hough_transform(image):
    """    Detect lines in an image using the Hough transform.
    Parameters:
        image (numpy array): The image to detect lines in.
    Returns:
        A list of lines detected in the image.
    """    return cv2.HoughLinesP(image, 1, np.pi / 180, 50, minLineLength=100, maxLineGap=100)
def extract_features(image):
    """    Extract features from an image.
    Parameters:
        image (numpy array): The image to extract features from.
    Returns:
        A list of features extracted from the image.
    """    features = []
    features.append(image.mean())
    features.append(image.std())
    features.append(image.min())
    features.append(image.max())
    features.append(image.shape[0])
    features.append(image.shape[1])
    return features
def train_classifier(features, labels):
    """    Train a classifier to classify images.
    Parameters:
        features (list): A list of features extracted from images.
        labels (list): A list of labels for the images.
    Returns:
        A trained classifier.
    """    classifier = linear_model.LogisticRegression()
    classifier.fit(features, labels)
    return classifier
def classify_image(classifier, image):
    """    Classify an image using a classifier.
    Parameters:
        classifier (classifier): The classifier to use.
        image (numpy array): The image to classify.
    Returns:
        A prediction for the image.
    """    features = extract_features(image)
    return classifier.predict([features])
def main():
     Load the image
    image = imread('image.jpg')
     Resize the image
    image = resize(image, (256, 256))
     Convert the image to grayscale
    image = to_grayscale(image)
     Equalize the histogram of the image
    image = equalize_hist(image)
     Blur the image
    image = blur(image)
     Sharpen the image
    image = sharpen(image)
     Detect edges in the image
    edges = edge_detection(image)
     Hough transform  Detect lines in the image
    lines = hough_transform(edges)
     Extract features from the image
    features = extract_features(image)
     Train a classifier to classify images
    classifier = train_classifier(features, labels)
     Classify the image
    prediction = classify_image(classifier, image)
     Print the prediction
    print(prediction)
if __name__ == "__main__":
    main()