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43 lines
1.5 KiB
43 lines
1.5 KiB
import os
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import numpy as np
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import matplotlib.pyplot as plt #绘画
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from tensorflow.keras.preprocessing import image #图片预处理
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from tensorflow.keras.models import load_model #加载模型
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#生成图像数据
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from tensorflow.keras.preprocessing.image import ImageDataGenerator
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# 设置数据目录
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test_dir = 'D:/hand/archive/leapGestRecog'
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# 加载模型
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model = load_model('hand_gesture_model.h5')
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print("模型已从 hand_gesture_model.h5 加载")
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# 使用 ImageDataGenerator 加载测试数据
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test_datagen = ImageDataGenerator(rescale=1./255)
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test_generator = test_datagen.flow_from_directory(test_dir, target_size=(150, 150), batch_size=32, class_mode='categorical')
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# 评估模型在测试数据集上的性能
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test_loss, test_accuracy = model.evaluate(test_generator)
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print(f"Test Accuracy: {test_accuracy * 100:.2f}%")
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# 加载单个图像并进行预测
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img_path = 'D:/hand/archive/leapGestRecog/00/01_palm/frame_00_01_0001.png' # 修改为实际图像路径
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img = image.load_img(img_path, target_size=(150, 150))
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img_array = image.img_to_array(img)
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img_array = np.expand_dims(img_array, axis=0) / 255.0 # 标准化图像数据
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# 进行预测
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predictions = model.predict(img_array)
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predicted_class = np.argmax(predictions, axis=1)
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# 输出预测结果
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class_labels = {v: k for k, v in test_generator.class_indices.items()}
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print(f"预测类别: {class_labels[predicted_class[0]]}")
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# 显示图像和预测结果
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plt.imshow(img)
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plt.title(f"预测类别: {class_labels[predicted_class[0]]}")
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plt.axis('off')
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plt.show()
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