制作混淆矩阵

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
import itertools
import cv2

def load_labels(label_file):
    idx_to_labels = np.load(label_file, allow_pickle=True).item()
    classes = list(idx_to_labels.values())
    return idx_to_labels, classes

def load_predictions(predictions_file):
    df = pd.read_csv(predictions_file)
    return df

def generate_confusion_matrix(true_labels, predicted_labels, classes):
    confusion_matrix_model = confusion_matrix(true_labels, predicted_labels, labels=classes)
    return confusion_matrix_model

def plot_confusion_matrix(cm, classes, cmap=plt.cm.Blues):
    plt.figure(figsize=(6, 6))
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    tick_marks = np.arange(len(classes))
    plt.title('Confusion Matrix', fontsize=12)
    plt.xlabel('Prediction', fontsize=12, c='r')
    plt.ylabel('True', fontsize=12, c='r')
    plt.tick_params(labelsize=12)
    plt.xticks(tick_marks, classes, rotation=90)
    plt.yticks(tick_marks, classes)

    threshold = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > threshold else "black",
                 fontsize=12)
    plt.tight_layout()
    plt.savefig('混淆矩阵.pdf', dpi=300)
    plt.show()

def find_misclassified_images(df, true_class, predicted_class):
    wrong_df = df[(df['标注类别名称'] == true_class) & (df['top-1-预测名称'] == predicted_class)]
    return wrong_df

def visualize_misclassified_images(wrong_df):
    for idx, row in wrong_df.iterrows():
        img_path = row['图像路径']
        img_bgr = cv2.imread(img_path)
        img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
        plt.imshow(img_rgb)
        title_str = row['标注类别名称'] + ' Pred:' + row['top-1-预测名称']
        plt.title(title_str)
        plt.show()

if __name__ == '__main__':
    label_file = 'idx_to_labels.npy'
    predictions_file = '测试集预测结果.csv'

    idx_to_labels, classes = load_labels(label_file)
    df = load_predictions(predictions_file)

    confusion_matrix_model = generate_confusion_matrix(df['标注类别名称'], df['top-1-预测名称'], classes)
    plot_confusion_matrix(confusion_matrix_model, classes, cmap='Blues')

    true_class = 'daisy'
    predicted_class = 'dandelion'
    wrong_df = find_misclassified_images(df, true_class, predicted_class)
    print('误判：', wrong_df)
    visualize_misclassified_images(wrong_df)