利用python实现货币识别功能

import cv2

# 读取图像
image = cv2.imread('currency_image.jpg', 0)  # 以灰度图像的形式读取

# 图像增强
enhanced_image = cv2.equalizeHist(image)  # 直方图均衡化增强图像对比度

# 去噪
denoised_image = cv2.GaussianBlur(enhanced_image, (5, 5), 0)  # 高斯模糊去除噪声

# 二值化
_, binary_image = cv2.threshold(denoised_image, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)  # 自适应阈值二值化

# 显示结果
cv2.imshow('Original Image', image)
cv2.imshow('Enhanced Image', enhanced_image)
cv2.imshow('Denoised Image', denoised_image)
cv2.imshow('Binary Image', binary_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 读取图像
image = cv2.imread('currency_image.jpg')

# 创建SIFT对象
sift = cv2.SIFT_create()

# 检测关键点和计算描述符
keypoints, descriptors = sift.detectAndCompute(image, None)

# 绘制关键点
image_with_keypoints = cv2.drawKeypoints(image, keypoints, None)

# 显示结果
cv2.imshow('Image with Keypoints', image_with_keypoints)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np

# 读取图像
image = cv2.imread('currency_image.jpg')

# 创建HOG对象
hog = cv2.HOGDescriptor()

# 计算HOG特征
hog_features = hog.compute(image)

# 将一维特征向量转换为二维数组
hog_features = np.reshape(hog_features, (-1, hog_features.shape[0]))

# 显示结果
cv2.imshow('HOG Features', hog_features)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np

# 准备数据
# ...

# 提取特征
# ...

# 准备训练数据
# ...

# 创建SVM对象
svm = cv2.ml.SVM_create()
# 设置SVM参数
svm.setType(cv2.ml.SVM_C_SVC)
svm.setKernel(cv2.ml.SVM_LINEAR)
# 训练分类器
svm.train(samples, cv2.ml.ROW_SAMPLE, labels)
# 预测新样本
predicted_label = svm.predict(new_sample)
# 评估性能
accuracy = svm.evaluate(test_samples, test_labels)

import cv2
import os

# 建立保存图像的文件夹
data_dir = 'data/'
if not os.path.exists(data_dir):
    os.makedirs(data_dir)

# 打开摄像头
cap = cv2.VideoCapture(0)

# 设置每个类别的图像数量
num_samples = 50

for label in ['class1', 'class2']:
    # 建立保存当前类别图像的文件夹
    label_dir = data_dir + label + '/'
    if not os.path.exists(label_dir):
        os.makedirs(label_dir)
    
    # 按空格键收集图像
    print('Collecting images for {}...'.format(label))
    for i in range(num_samples):
        ret, frame = cap.read()
        cv2.imshow('frame', frame)

        # 按'space'键保存图像
        if cv2.waitKey(1) & 0xFF == ord(' '):
            cv2.imwrite(label_dir + '{}.jpg'.format(i), frame)
            print('{} image saved'.format(i))

    print('Finish collecting images for {}'.format(label))

# 释放摄像头，关闭窗口
cap.release()
cv2.destroyAllWindows()

import cv2
import os
import numpy as np

# 读取数据集和标签
data_dir = 'data/'
labels = ['class1', 'class2']
data = []
target = []
for label in labels:
    label_dir = data_dir + label + '/'
    for img_file in os.listdir(label_dir):
        img = cv2.imread(label_dir + img_file)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        img = cv2.resize(img, (64, 64))
        data.append(img)
        target.append(labels.index(label))

# 将数据集和标签转换为数组类型
data = np.array(data)
target = np.array(target)

# 划分训练集和测试集
train_data = data[:80]
train_target = target[:80]
test_data = data[80:]
test_target = target[80:]

import cv2
import os
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer

# 读取数据集和标签
data_dir = 'data/'
labels = ['class1', 'class2']
data = []
target = []
for label in labels:
    label_dir = data_dir + label + '/'
    for img_file in os.listdir(label_dir):
        img = cv2.imread(label_dir + img_file)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        img = cv2.resize(img, (64, 64))
        data.append(img.flatten())
        target.append(labels.index(label))

# 特征提取
vectorizer = CountVectorizer()
X = vectorizer.fit_transform([str(d) for d in data])

# 划分训练集和测试集
train_data = X[:80]
train_target = target[:80]
test_data = X[80:]
test_target = target[80:]

from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 初始化KNN分类器
knn = KNeighborsClassifier(n_neighbors=3)

# 训练分类器
knn.fit(train_data, train_target)

# 在测试集上评估分类器性能
y_pred = knn.predict(test_data)
acc = accuracy_score(test_target, y_pred)
print('Accuracy:', acc)