根据《任务书.doc》中的要求，编写基于Python的代码来实现铁路货运列车车号识别的任务可以按以下步骤进行： ### 步骤1: 项目环境设置 首先需要安装必要的依赖库，如`tensorflow`（或其他深度学习框架）、`numpy`、`pandas`、`matplotlib`等。可以通过pip命令安装这些库： ```bash pip install tensorflow numpy pandas matplotlib opencv-python ``` ### 步骤2: 数据加载与预处理 使用Pandas读取CSV格式的数据标注文件，并使用OpenCV或PIL加载图片。然后对图片进行裁剪，只保留车号所在区域，并对其进行标准化处理。 ```python import os import cv2 import pandas as pd from sklearn.model_selection import train_test_split def load_data(image_dir, label_path): labels = pd.read_csv(label_path) images = [] targets = [] for _, row in labels.iterrows(): img_path = os.path.join(image_dir, f"{row['NO.']}.jpg") image = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE) # 加载灰度图 # 根据坐标裁剪图片 cropped_image = image[row['Top Left'][1]:row['Lower Right'][1], row['Top Left'][0]:row['Lower Right'][0]] # 对图片进行尺寸归一化 resized_image = cv2.resize(cropped_image, (64, 64)) normalized_image = resized_image / 255.0 images.append(normalized_image) targets.append(str(row['Number'])) return np.array(images), np.array(targets) X_train, y_train = load_data('train_images', 'train_labels.csv') X_test, y_test = load_data('test_images', 'test_labels.csv') # 将标签转换为one-hot编码形式 y_train_encoded = tf.keras.utils.to_categorical(y_train) y_test_encoded = tf.keras.utils.to_categorical(y_test) ``` ### 步骤3: 构建CNN模型 这里我们构建了一个简单的卷积神经网络(CNN)，当然你可以根据具体需求调整网络结构。 ```python from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout model = Sequential([ Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(64, 64, 1)), MaxPooling2D(pool_size=(2, 2)), Conv2D(64, kernel_size=(3, 3), activation='relu'), MaxPooling2D(pool_size=(2, 2)), Flatten(), Dense(128, activation='relu'), Dropout(0.5), Dense(len(set(y_train)), activation='softmax') # 输出层节点数等于类别数量 ]) model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) ``` ### 步骤4: 训练模型并保存最佳权重 ```python history = model.fit(X_train.reshape(-1, 64, 64, 1), y_train_encoded, batch_size=32, epochs=20, validation_split=0.2) model.save_weights('best_model.h5') ``` ### 步骤5: 测试与评估 ```python loss, accuracy = model.evaluate(X_test.reshape(-1, 64, 64, 1), y_test_encoded) print(f'Test Loss: {loss}') print(f'Test Accuracy: {accuracy}') predictions = model.predict(X_test.reshape(-1, 64, 64, 1)) predicted_classes = [np.argmax(pred) for pred in predictions] true_classes = [np.argmax(true) for true in y_test_encoded] confusion_matrix = pd.crosstab(pd.Series(true_classes, name='Actual'), pd.Series(predicted_classes, name='Predicted')) print(confusion_matrix) ``` 以上代码只是一个基础示例，在实际开发过程中可能还需要进一步优化参数配置、增加数据增强手段以及改进模型架构等操作。此外，请注意代码的健壮性和可维护性，确保满足文档中提到的各项评价指标。记得按照规定格式整理好最终的代码和报告文件后按时提交。