python 图像识别分类_使用Python轻松完成垃圾分类（基于图像识别）

垃圾分类是一项非常重要的环保工作，而图像识别技术能够在垃圾分类中发挥重要作用。本文将介绍如何使用Python及其相关库实现垃圾分类的图像识别分类。

首先，我们需要准备一些垃圾图片数据集，可以从网上下载或者自己拍摄收集。数据集需要包括有害垃圾、可回收垃圾、厨余垃圾和其他垃圾等分类。

接下来，我们需要使用Python的图像处理库Pillow和机器学习库scikit-learn来进行图像分类。具体步骤如下：

1.导入所需库

from PIL import Image
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

2.读取图片数据集

def load_data():
    # 图片大小
    size = (64, 64)
    # 数据集路径
    data_path = 'data/'
    # 分类标签
    labels = {'harmful': 0, 'recyclable': 1, 'kitchen': 2, 'other': 3}
    X, y = [], []
    for label, tag in labels.items():
        for i in range(1, 201):
            img_path = data_path + label + '/' + str(i) + '.jpg'
            img = Image.open(img_path).convert('RGB').resize(size)
            X.append(np.array(img))
            y.append(tag)
    return np.array(X), np.array(y)

3.数据集划分

将读取到的数据集划分为训练集和测试集，一般采用8:2的比例。

def split_data(X, y):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
    return X_train, X_test, y_train, y_test

4.特征提取

使用灰度化、去噪等技术提取图像的特征。

def feature_extraction(X):
    # 灰度化
    gray = np.mean(X, -1)
    # 去噪
    blur = np.array([Image.fromarray(gray[i]).filter(ImageFilter.BLUR) for i in range(gray.shape[0])])
    return blur.reshape(blur.shape[0], -1)

5.模型训练和预测

使用KNN算法进行模型训练和预测。

def knn(X_train, X_test, y_train):
    # KNN分类器
    clf = KNeighborsClassifier(n_neighbors=5)
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    return y_pred

6.结果评估

使用accuracy_score函数计算模型的准确率。

from sklearn.metrics import accuracy_score

accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

完整代码如下：

from PIL import Image, ImageFilter
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 加载数据集
def load_data():
    # 图片大小
    size = (64, 64)
    # 数据集路径
    data_path = 'data/'
    # 分类标签
    labels = {'harmful': 0, 'recyclable': 1, 'kitchen': 2, 'other': 3}
    X, y = [], []
    for label, tag in labels.items():
        for i in range(1, 201):
            img_path = data_path + label + '/' + str(i) + '.jpg'
            img = Image.open(img_path).convert('RGB').resize(size)
            X.append(np.array(img))
            y.append(tag)
    return np.array(X), np.array(y)

# 数据集划分
def split_data(X, y):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
    return X_train, X_test, y_train, y_test

# 特征提取
def feature_extraction(X):
    # 灰度化
    gray = np.mean(X, -1)
    # 去噪
    blur = np.array([Image.fromarray(gray[i]).filter(ImageFilter.BLUR) for i in range(gray.shape[0])])
    return blur.reshape(blur.shape[0], -1)

# KNN训练和预测
def knn(X_train, X_test, y_train):
    # 特征提取
    X_train = feature_extraction(X_train)
    X_test = feature_extraction(X_test)
    # KNN分类器
    clf = KNeighborsClassifier(n_neighbors=5)
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    return y_pred

if __name__ == '__main__':
    # 加载数据集
    X, y = load_data()
    # 数据集划分
    X_train, X_test, y_train, y_test = split_data(X, y)
    # 模型训练和预测
    y_pred = knn(X_train, X_test, y_train)
    # 结果评估
    accuracy = accuracy_score(y_test, y_pred)
    print('Accuracy:', accuracy)

运行上述代码后，模型将对测试集进行预测并输出准确率。这样我们就实现了基于图像识别的垃圾分类。