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计算机视觉/python环境下/机器学习之k近邻方法（KNN）实现对数据集的识别

网友: 文档中心 2022-05-20 15:53:06

机器学习

    • 一、问题描述
    • 二、效果图
    • 三、代码附录
      • KNN算法
      • 模板匹配算法
      • 模板匹配算法二
    • 四、数据集附录

一、问题描述

采用KNN，模板匹配两种方法进行机器学习，切分训练集和测试集，从而实现对数据集的识别。

这里的例子采用的数据集是200张图片，包括手写的不同的0-9图片各20张，以此观察KNN算法和模板匹配算法下对于图片的识别的准确度差别。

二、效果图

计算机视觉/python环境下/机器学习之k近邻方法（KNN）实现对数据集的识别
计算机视觉/python环境下/机器学习之k近邻方法（KNN）实现对数据集的识别
计算机视觉/python环境下/机器学习之k近邻方法（KNN）实现对数据集的识别

三、代码附录

KNN算法

import numpy as npimport osimport cv2from sklearn.model_selection import train_test_splitfrom sklearn.metrics import confusion_matrix, classification_reportX = []Y = []for i in range(0, 10):    # 遍历文件夹，读取图片    for f in os.listdir("D:/pythonProject1/image/image1/numberTest/%s" %i): # 打开一张图片并灰度化 print(f) Images = cv2.imread("D:/pythonProject1/image/image1/numberTest/%s/%s " % (i, f),0) print(("D:/pythonProject1/image/image1/numberTest/%s%s" % (i, f))) image = cv2.resize(Images, (256, 256),interpolation=cv2.INTER_CUBIC) print(image) hist = cv2.calcHist(image, [0, 1], None,[256, 256], [0.0, 255.0, 0.0, 255.0]) X.append(((hist / 255).flatten())) Y.append(i)X = np.array(X)Y = np.array(Y)# 切分训练集和测试集X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=1)# 随机率为100%（保证唯一性）选取其中的20%作为测试集class KNN:    def __init__(self, train_data, train_label, test_data): self.train_data = train_data self.train_label = train_label self.test_data = test_data    def classify(self): num_train = (self.train_data).shape[0] num_test = (self.test_data).shape[0] labels = [] for i in range(num_test):     y = []     for j in range(num_train):  dis = np.sum(np.square((self.train_data)[j] - (self.test_data)[i]))  y.append(dis)     labels.append(self.train_label[y.index(min(y))]) labels = np.array(labels) return labelsknn = KNN(X_train, y_train, X_test)predictions_labels = knn.classify()print(confusion_matrix(y_test, predictions_labels))print(classification_report(y_test, predictions_labels))

模板匹配算法

import numpy as npimport osimport cv2from matplotlib import pyplot as pltfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import confusion_matrix, classification_reportX = []Y = []for i in range(0, 10):    # 遍历文件夹，读取图片    for f in os.listdir("D:/pythonProject1/image/image1/numberTest/%s" %i): # 打开一张图片并灰度化 print(f) Images = cv2.imread("D:/pythonProject1/image/image1/numberTest/%s/%s " % (i, f),0) print(("D:/pythonProject1/image/image1/numberTest/%s%s" % (i, f))) image = cv2.cvtColor(Images,cv2.COLOR_BGR2GRAY) print(image) hist = cv2.calcHist(image, [0, 1], None,[256, 256], [0.0, 255.0, 0.0, 255.0]) X.append(((hist / 255).flatten())) Y.append(i)X = np.array(X)Y = np.array(Y)# 切分训练集和测试集X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=1)class moran:    def __init__(self, train_data, train_label, test_data): self.train_data = train_data self.train_label = train_label self.test_data = test_data    def classify(self): num_train = (self.train_data).shape[0] num_test = (self.test_data).shape[0] labels = [] for i in range(num_test):     y = []     for j in range(num_train):      res = cv2.matchTemplate(image, num_test, cv2.TM_CCOEFF_NORMED)     threshold = 0.65     loc = np.where(res >= threshold) for pt in zip(*loc[::-1]):     y.append(res) labels.append(self.train_label[y.index(min(y))]) labels = np.array(labels) return labelsmoran = moran(X_train, y_train, X_test)predictions_labels = moran.classify()print(confusion_matrix(y_test, predictions_labels))print(classification_report(y_test, predictions_labels))

模板匹配算法二

import cv2import numpy as npfrom matplotlib import pyplot as pltimg_rgb = cv2.imread('')img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)template1 = cv2.imread('roi4_1.png', 0) #相当于测试集template2 = cv2.imread('roi4_2.png', 0)template3 = cv2.imread('roi4_3.png', 0)template_list = ['roi4_1.png', 'roi4_2.png', 'roi4_3.png']w1, h1 = template1.shape[::-1] #测试集的数据w2, h2 = template1.shape[::-1]w3, h3 = template1.shape[::-1]plt.subplot(131)plt.imshow(template1)plt.subplot(132)plt.imshow(template2)plt.subplot(133)plt.imshow(template3)plt.show()for i in range(len(template_list)):    template = cv2.imread(template_list[i], 0)    w, h = template.shape[::-1]    res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)    threshold = 0.65    loc = np.where(res >= threshold)    for pt in zip(*loc[::-1]): gray = int(255/(i+1)) cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (gray, gray, gray), 2)plt.imshow(img_rgb)plt.show()

四、数据集附录

手绘数据集展示如下：
计算机视觉/python环境下/机器学习之k近邻方法（KNN）实现对数据集的识别
计算机视觉/python环境下/机器学习之k近邻方法（KNN）实现对数据集的识别
计算机视觉/python环境下/机器学习之k近邻方法（KNN）实现对数据集的识别

计算机视觉/python环境下/机器学习之k近邻方法（KNN）实现对数据集的识别

开发者涨薪指南 计算机视觉/python环境下/机器学习之k近邻方法（KNN）实现对数据集的识别 48位大咖的思考法则、工作方式、逻辑体系

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