完成作业3.3.2
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2509165008
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digit_mlp_class/test_image.py
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digit_mlp_class/test_image.py
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# -*- coding: utf-8 -*-
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"""
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测试脚本 - 用训练好的模型识别学生手写数字图片
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使用方法：
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python test_image.py path/to/image.png
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python test_image.py path/to/image.jpg
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python test_image.py path/to/folder/ # 识别文件夹内所有图片
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依赖：
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pip install numpy pillow
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图片要求：
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- 建议尺寸：28x28 或更大（程序会自动缩放）
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- 背景最好为白色，数字为黑色
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- 手写数字清晰、无遮挡
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"""
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import sys
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import os
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import numpy as np
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from PIL import Image
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# 尝试导入模型
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try:
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from model_numpy import MLP
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except ImportError:
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print("错误：请在 digit_mlp_class 目录下运行此脚本")
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print(" cd digit_mlp_class")
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print(" python test_image.py your_image.png")
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sys.exit(1)
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def find_latest_model():
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"""查找最新的模型文件"""
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model_files = [f for f in os.listdir('.') if f.startswith('mnist_mlp_') and f.endswith('.npy')]
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if not model_files:
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return None
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# 按时间戳分组
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timestamps = set()
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for f in model_files:
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# 文件格式: mnist_mlp_YYMMDD_HHMMSS_suffix.npy
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# 去掉后缀 _W1.npy, _b1.npy 等
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base = f.rsplit('_', 1)[0]
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timestamps.add(base)
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# 选择最新的
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latest = sorted(timestamps)[-1]
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# 检查完整模型
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required = ['W1', 'b1', 'W2', 'b2']
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for r in required:
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if not os.path.exists(f'{latest}_{r}.npy'):
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return None
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return latest
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def load_model():
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"""加载训练好的模型"""
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# 查找最新模型
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model_path = find_latest_model()
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if model_path is None:
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print("错误：未找到训练好的模型！")
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print(" 请先运行: python main.py")
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print(" 或确保当前目录有 mnist_mlp_*.npy 模型文件")
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return None
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print(f"加载模型: {model_path}")
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model = MLP(input_size=784, hidden_size=128, num_classes=10, learning_rate=0.1)
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model.W1 = np.load(f'{model_path}_W1.npy')
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model.b1 = np.load(f'{model_path}_b1.npy')
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model.W2 = np.load(f'{model_path}_W2.npy')
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model.b2 = np.load(f'{model_path}_b2.npy')
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print("模型加载成功！\n")
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return model
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def preprocess_image(image_path):
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"""
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图片预处理：将任意图片转为 28x28 归一化向量
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处理流程：
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1. 打开图片并转为灰度
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2. 调整大小为 28x28
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3. 转为NumPy数组并归一化到 [0, 1]
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4. 展平为 784 维向量
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"""
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try:
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img = Image.open(image_path).convert('L') # 转灰度
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except Exception as e:
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raise ValueError(f"无法打开图片: {e}")
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# 保存原始尺寸用于调试
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orig_width, orig_height = img.size
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# 缩放到 28x28
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img = img.resize((28, 28), Image.LANCZOS)
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# 转为NumPy数组并归一化
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img_array = np.array(img, dtype=np.float32) / 255.0
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# MNIST是白底黑字(0=背景, 1=数字)，如果图片是黑底白字需要反转
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# 检查是否是黑底白字：背景均值 > 0.5
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if img_array.mean() > 0.5:
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img_array = 1.0 - img_array
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# 展平为向量
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img_vector = img_array.flatten()
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print(f" 图片: {os.path.basename(image_path)}")
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print(f" 原始尺寸: {orig_width}x{orig_height}")
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print(f" 处理后尺寸: 28x28")
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return img_vector
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def predict_image(model, image_path):
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"""
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识别单张图片
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返回：
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predicted_digit: 预测的数字 (0-9)
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confidence: 置信度 (0-1)
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all_probs: 所有数字的概率
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"""
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# 预处理
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img_vector = preprocess_image(image_path)
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# 预测
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probs = model.predict_proba(img_vector.reshape(1, -1))[0]
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predicted_digit = np.argmax(probs)
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confidence = probs[predicted_digit]
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return predicted_digit, confidence, probs
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def print_results(digit, confidence, probs):
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"""打印识别结果"""
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print(f" 预测结果: {digit}")
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print(f" 置信度: {confidence:.2%}")
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# 显示各数字概率
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print(f"\n 各数字概率:")
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print(f" ", end="")
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for i in range(10):
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bar_len = int(probs[i] * 20)
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print(f" {i}:{'█' * bar_len}{'░' * (20-bar_len)} {probs[i]:.1%}")
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print()
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def main():
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"""主函数"""
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print("\n" + "=" * 60)
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print("手写数字识别 - 图片测试")
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print("=" * 60 + "\n")
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# 加载模型
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model = load_model()
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if model is None:
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sys.exit(1)
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# 检查命令行参数
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if len(sys.argv) < 2:
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print("使用方法:")
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print(" python test_image.py path/to/image.png")
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print(" python test_image.py path/to/image.jpg")
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print(" python test_image.py path/to/folder/")
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print()
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print("示例:")
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print(" python test_image.py my_digit.png")
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print(" python test_image.py ./test_images/")
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sys.exit(1)
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target = sys.argv[1]
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# 收集所有要处理的图片
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image_paths = []
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if os.path.isdir(target):
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# 文件夹：收集所有图片
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extensions = ['.png', '.jpg', '.jpeg', '.bmp', '.gif']
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for f in os.listdir(target):
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if any(f.lower().endswith(ext) for ext in extensions):
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image_paths.append(os.path.join(target, f))
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image_paths.sort()
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elif os.path.isfile(target):
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image_paths = [target]
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else:
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print(f"错误：文件或目录不存在: {target}")
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sys.exit(1)
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if not image_paths:
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print(f"错误：在 {target} 中未找到图片文件")
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sys.exit(1)
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print(f"找到 {len(image_paths)} 张图片，开始识别...\n")
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# 批量识别
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results = []
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for path in image_paths:
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try:
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digit, confidence, probs = predict_image(model, path)
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results.append((path, digit, confidence))
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print_results(digit, confidence, probs)
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except Exception as e:
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print(f" 识别失败: {e}\n")
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# 汇总结果
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print("=" * 60)
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print(f"识别完成！共 {len(results)} 张图片")
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print("=" * 60)
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print(f"\n{'文件名':<30} {'预测':<6} {'置信度':<10}")
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print("-" * 50)
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for path, digit, confidence in results:
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filename = os.path.basename(path)
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print(f"{filename:<30} {digit:<6} {confidence:.1%}")
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# 如果有真实标签（文件夹命名中包含），可以计算准确率
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print()
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if __name__ == '__main__':
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main()
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