import cv2

# 讀取圖像文件
image = cv2.imread('animal.jpg')

# 顯示圖像
cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 讀取圖像文件
image = cv2.imread('animal.jpg')

# 進(jìn)行均值濾波平滑操作
blurred_image = cv2.blur(image, (5, 5))

# 顯示處理后的圖像
cv2.imshow('Blurred Image', blurred_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 讀取灰度圖像
image = cv2.imread('animal.jpg', 0)

# 進(jìn)行簡(jiǎn)單閾值分割
ret, binary_image = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)

# 顯示二值化圖像
cv2.imshow('Binary Image', binary_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
from skimage.feature import greycomatrix, greycoprops

# 讀取灰度圖像
image = cv2.imread('animal.jpg', 0)

# 計(jì)算灰度共生矩陣
glcm = greycomatrix(image, [1], [0], symmetric=True, normed=True)

# 計(jì)算共生矩陣的對(duì)比度、相關(guān)度、能量和均勻性
contrast = greycoprops(glcm, 'contrast')
correlation = greycoprops(glcm, 'correlation')
energy = greycoprops(glcm, 'energy')
homogeneity = greycoprops(glcm, 'homogeneity')

import cv2
from sklearn import svm
from sklearn.model_selection import train_test_split

# 讀取圖像和標(biāo)簽數(shù)據(jù)
X = []
Y = []
for i in range(1, 101):
    image = cv2.imread('animal%d.jpg'%i, 0)
    X.append(image.reshape(-1))
    Y.append(i // 10)

# 劃分訓(xùn)練集和測(cè)試集
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=0)

# 訓(xùn)練SVM分類器
clf = svm.SVC(kernel='linear')
clf.fit(X_train, Y_train)

# 在測(cè)試集上進(jìn)行預(yù)測(cè)并計(jì)算準(zhǔn)確率
accuracy = clf.score(X_test, Y_test)

import cv2
import numpy as np
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from keras.utils import to_categorical

# 讀取圖像和標(biāo)簽數(shù)據(jù)
X = []
Y = []
for i in range(1, 101):
    image = cv2.imread('animal%d.jpg'%i)
    X.append(cv2.resize(image, (50, 50)))
    Y.append(i // 10 - 1)
X = np.array(X)
Y = to_categorical(Y)

# 構(gòu)建CNN模型
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(50, 50, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

# 訓(xùn)練CNN模型
model.fit(X, Y, epochs=50, batch_size=16, validation_split=0.2)

import requests
from bs4 import BeautifulSoup
import urllib

# 指定目標(biāo)網(wǎng)頁(yè)URL
url = 'http://example.com'
# 發(fā)送HTTP請(qǐng)求獲取網(wǎng)頁(yè)內(nèi)容
response = requests.get(url)
# 解析HTML
soup = BeautifulSoup(response.content, 'html.parser')
# 查找所有的圖片標(biāo)簽
img_tags = soup.find_all('img')
# 遍歷圖片標(biāo)簽并下載圖片
for img in img_tags:
    img_url = urllib.parse.urljoin(url, img['src'])
    # 發(fā)送HTTP請(qǐng)求下載圖片
    img_response = requests.get(img_url)
    # 保存圖片到本地
    with open('image.jpg', 'wb') as f:
        f.write(img_response.content)

import cv2

# 讀取圖像文件
image = cv2.imread('image.jpg')

# 圖像灰度化
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 調(diào)整圖像大小
resized_image = cv2.resize(image, (new_width, new_height))

import cv2
import numpy as np

# 平滑處理（高斯模糊）
blurred_image = cv2.GaussianBlur(image, (kernel_size, kernel_size), 0)

# 圖像增強(qiáng)（直方圖均衡化）
equalized_image = cv2.equalizeHist(gray_image)

# 銳化處理（拉普拉斯算子）
sharp_kernel = np.array([[-1, -1, -1],
                        [-1,  9, -1],
                        [-1, -1, -1]])
sharpened_image = cv2.filter2D(image, -1, sharp_kernel)

from sklearn.preprocessing import StandardScaler

# 標(biāo)準(zhǔn)化數(shù)據(jù)
scaler = StandardScaler()
normalized_data = scaler.fit_transform(data)

from torchvision import transforms

# 定義數(shù)據(jù)增強(qiáng)的轉(zhuǎn)換
data_transform = transforms.Compose([
    transforms.RandomResizedCrop(size=(224, 224)),
    transforms.RandomHorizontalFlip(),
    transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 對(duì)圖像進(jìn)行數(shù)據(jù)增強(qiáng)
augmented_image = data_transform(image)

import numpy as np

# 計(jì)算均值
mean_value = np.mean(data)

# 計(jì)算方差
variance = np.var(data)

# 計(jì)算最大值
max_value = np.max(data)

# 計(jì)算最小值
min_value = np.min(data)

# 計(jì)算中位數(shù)
median_value = np.median(data)

import numpy as np
from scipy.fft import fft

# 進(jìn)行傅里葉變換
spectrum = fft(data)

# 提取頻域特征，如幅值、相位等
amplitude = np.abs(spectrum)
phase = np.angle(spectrum)

import pywt

# 進(jìn)行小波變換
coefficients = pywt.wavedec(data, wavelet='db4', level=5)

# 提取小波系數(shù)特征
approximation_coefficient = coefficients[0]  # 近似系數(shù)
detail_coefficients = coefficients[1:]       # 細(xì)節(jié)系數(shù)

import cv2

# 計(jì)算圖像直方圖特征
histogram = cv2.calcHist([image], channels=[0], mask=None, histSize=[256], ranges=[0, 256])

# 計(jì)算圖像顏色特征（顏色直方圖）
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
hsv_histogram = cv2.calcHist([hsv_image], channels=[0, 1, 2], mask=None, histSize=[180, 256, 256], ranges=[0, 180, 0, 256, 0, 256])

# 計(jì)算圖像紋理特征（灰度共生矩陣）
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cooccurrence_matrix = cv2.calcGLCM(gray_image, distances=[1], angles=[0], symmetric=True)

from sklearn.feature_selection import VarianceThreshold

# 創(chuàng)建VarianceThreshold對(duì)象，并設(shè)置方差閾值
selector = VarianceThreshold(threshold=0.1)

# 使用VarianceThreshold進(jìn)行特征選擇
selected_features = selector.fit_transform(data)

import pandas as pd

# 計(jì)算特征之間的相關(guān)系數(shù)矩陣
corr_matrix = pd.DataFrame(data).corr()

# 設(shè)置相關(guān)系數(shù)閾值
corr_threshold = 0.5

# 根據(jù)相關(guān)系數(shù)閾值進(jìn)行特征選擇
selected_features = []
for i in range(len(corr_matrix.columns)):
    for j in range(i+1, len(corr_matrix.columns)):
        if corr_matrix.iloc[i, j] < corr_threshold:
            selected_features.append(corr_matrix.columns[i])

from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2

# 創(chuàng)建SelectKBest對(duì)象，并設(shè)置評(píng)估函數(shù)chi2及k值
selector = SelectKBest(score_func=chi2, k=10)

# 使用SelectKBest進(jìn)行特征選擇
selected_features = selector.fit_transform(data, labels)