web3-202226701039/Main.py

import os
import time
import torch.multiprocessing as mp
import matplotlib.pyplot as plt
import numpy as np
from ChexnetTrainer import ChexnetTrainer
from DatasetGenerator import DatasetGenerator
from visual import HeatmapGenerator

# 确保 images 目录存在
if not os.path.exists('images'):
    os.makedirs('images')


# 主函数，负责启动不同的功能（训练、测试或运行演示）
def main():
    # runDemo()  # 运行演示模式
    # runTest()  # 测试模式（注释掉，可以通过解除注释来运行）
    runTrain()  # 训练模式（注释掉，可以通过解除注释来运行）


# --------------------------------------------------------------------------------

# 训练函数，定义训练所需的参数并启动模型训练
def runTrain():
    DENSENET121 = 'DENSE-NET-121'  # 定义DenseNet121模型名称
    DENSENET169 = 'DENSE-NET-169'  # 定义DenseNet169模型名称
    DENSENET201 = 'DENSE-NET-201'  # 定义DenseNet201模型名称
    Resnet50 = 'RESNET-50'  # 定义Resnet50模型名称

    # 获取当前的时间戳，作为训练过程的标记
    timestampTime = time.strftime("%H%M%S")
    timestampDate = time.strftime("%d%m%Y")
    timestampLaunch = timestampDate + '-' + timestampTime
    print("Launching " + timestampLaunch)

    # 图像数据所在的路径
    pathDirData = './chest xray14'

    # 训练、验证和测试数据集文件路径
    # 每个文件中包含图像路径及其对应的标签
    pathFileTrain = './dataset/train_2.txt'
    pathFileVal = './dataset/valid_2.txt'
    pathFileTest = './dataset/test_2.txt'

    # 神经网络参数：模型架构、是否加载预训练模型、分类的类别数量
    nnArchitecture = DENSENET121
    nnIsTrained = True  # 使用预训练的权重
    nnClassCount = 14  # 数据集包含14个分类

    # 训练参数：批量大小和最大迭代次数（epochs）
    trBatchSize = 2
    trMaxEpoch = 2

    # 图像预处理相关参数：图像缩放的大小和裁剪后的大小
    imgtransResize = 256
    imgtransCrop = 224

    # 保存模型的路径，包含时间戳
    pathModel = 'm-' + timestampLaunch + '.pth.tar'

    print('Training NN architecture = ', nnArchitecture)
    ChexnetTrainer.train(pathDirData, pathFileTrain, pathFileVal,
                         nnArchitecture, nnIsTrained, nnClassCount, trBatchSize,
                         trMaxEpoch, imgtransResize, imgtransCrop,
                         timestampLaunch, None)
    print('Testing the trained model')

    pathRfModel = os.path.join('images', 'random_forest_model.pkl')  # 更新路径
    labels, rf_preds = ChexnetTrainer.test(pathDirData, pathFileTest, pathModel, pathRfModel, nnArchitecture,
                                           nnClassCount, nnIsTrained, trBatchSize, imgtransResize,
                                           imgtransCrop, timestampLaunch)

    # 生成并保存热力图
    # 选择一些测试集中的样本来生成热力图
    transformSequence = ChexnetTrainer._get_transform(imgtransCrop)
    datasetTest = DatasetGenerator(pathImageDirectory=pathDirData, pathDatasetFile=pathFileTest,
                                   transform=transformSequence, model=None)

    # 确保测试集中有足够的样本
    num_samples = min(8, len(datasetTest))
    sample_indices = np.random.choice(len(datasetTest), size=num_samples, replace=False)  # 随机选择8个样本

    # 定义分类名称
    CLASS_NAMES = ['Atelectasis', 'Cardiomegaly', 'Effusion',
                   'Infiltration', 'Mass', 'Nodule', 'Pneumonia',
                   'Pneumothorax', 'Consolidation', 'Edema', 'Emphysema',
                   'Fibrosis', 'Pleural_Thickening',
                   'Hernia']

    # 创建一个图形窗口
    plt.figure(figsize=(20, 10))
    n_cols = 4
    n_rows = 2

    for idx, sample_idx in enumerate(sample_indices):
        image_path = datasetTest.listImagePaths[sample_idx]
        true_labels = labels[sample_idx]
        pred_labels = rf_preds[sample_idx]

        # 加载图像
        img = plt.imread(image_path)

        # 创建子图
        ax = plt.subplot(n_rows, n_cols, idx + 1)
        ax.imshow(img)
        ax.axis('off')

        # 获取真实标签和预测标签的名称
        true_label_names = [CLASS_NAMES[i] for i in range(len(true_labels)) if true_labels[i] == 1]
        pred_label_names = [CLASS_NAMES[i] for i in range(len(pred_labels)) if pred_labels[i] == 1]

        # 设置标题
        title = f"Predicted: {', '.join(pred_label_names)}\nTrue: {', '.join(true_label_names)}"
        ax.set_title(title, fontsize=10)

    plt.tight_layout()
    output_plot_path = os.path.join('images', 'test_predictions.png')
    plt.savefig(output_plot_path)
    plt.show()
    print(f"预测结果图已保存到 {output_plot_path}")

    # 生成热力图（可选）
    for idx in sample_indices:
        image_path = datasetTest.listImagePaths[idx]
        output_heatmap_path = os.path.join('images', f'heatmap_test_{idx}.png')
        h = HeatmapGenerator(pathModel, nnArchitecture, nnClassCount, imgtransCrop, transformSequence)
        h.generate(image_path, output_heatmap_path, imgtransCrop)
        print(f"热力图已保存到 {output_heatmap_path}")


# --------------------------------------------------------------------------------

# 测试函数，加载预训练模型并在测试数据集上进行测试
def runTest():
    pathDirData = '/chest xray14'
    pathFileTest = './dataset/test.txt'
    nnArchitecture = 'DENSE-NET-121'
    nnIsTrained = True
    nnClassCount = 14
    trBatchSize = 4
    imgtransResize = 256
    imgtransCrop = 224

    pathModel = 'm-06102024-235412BCELoss()delete.pth.tar'

    timestampLaunch = ''

    # 获取统一的 transformSequence
    transformSequence = ChexnetTrainer._get_transform(imgtransCrop)
    pathRfModel = 'images/random_forest_model.pkl'  # 确保路径正确
    labels, rf_preds = ChexnetTrainer.test(pathDirData, pathFileTest, pathModel, pathRfModel, nnArchitecture,
                                           nnClassCount, nnIsTrained, trBatchSize, imgtransResize,
                                           imgtransCrop, timestampLaunch)

    # 生成并保存热力图
    datasetTest = DatasetGenerator(pathImageDirectory=pathDirData, pathDatasetFile=pathFileTest,
                                   transform=transformSequence, model=None)

    # 确保测试集中有足够的样本
    num_samples = min(8, len(datasetTest))
    sample_indices = np.random.choice(len(datasetTest), size=num_samples, replace=False)  # 随机选择8个样本

    # 定义分类名称
    CLASS_NAMES = ['Atelectasis', 'Cardiomegaly', 'Effusion',
                   'Infiltration', 'Mass', 'Nodule', 'Pneumonia',
                   'Pneumothorax', 'Consolidation', 'Edema', 'Emphysema',
                   'Fibrosis', 'Pleural_Thickening',
                   'Hernia']

    # 创建一个图形窗口
    plt.figure(figsize=(20, 10))
    n_cols = 4
    n_rows = 2

    for idx, sample_idx in enumerate(sample_indices):
        image_path = datasetTest.listImagePaths[sample_idx]
        true_labels = labels[sample_idx]
        pred_labels = rf_preds[sample_idx]

        # 加载图像
        img = plt.imread(image_path)

        # 创建子图
        ax = plt.subplot(n_rows, n_cols, idx + 1)
        ax.imshow(img)
        ax.axis('off')

        # 获取真实标签和预测标签的名称
        true_label_names = [CLASS_NAMES[i] for i in range(len(true_labels)) if true_labels[i] == 1]
        pred_label_names = [CLASS_NAMES[i] for i in range(len(pred_labels)) if pred_labels[i] == 1]

        # 设置标题
        title = f"Predicted: {', '.join(pred_label_names)}\nTrue: {', '.join(true_label_names)}"
        ax.set_title(title, fontsize=10)

    plt.tight_layout()
    output_plot_path = os.path.join('images', 'test_predictions.png')
    plt.savefig(output_plot_path)
    plt.show()
    print(f"预测结果图已保存到 {output_plot_path}")

    # 生成热力图（可选）
    for idx in sample_indices:
        image_path = datasetTest.listImagePaths[idx]
        output_heatmap_path = os.path.join('images', f'heatmap_test_{idx}.png')
        h = HeatmapGenerator(pathModel, nnArchitecture, nnClassCount, imgtransCrop, transformSequence)
        h.generate(image_path, output_heatmap_path, imgtransCrop)
        print(f"热力图已保存到 {output_heatmap_path}")


# --------------------------------------------------------------------------------

# 演示函数，展示模型在测试集上的推理过程
def runDemo():
    # 原有代码保持不变
    pass


# 确保代码在主进程中运行
if __name__ == '__main__':
    mp.set_start_method('spawn', force=True)
    main()  # 启动主函数