python动物识别系统代码

Python动物识别系统代码可以通过机器学习和图像处理技术来实现。以下是一个简单的示例代码，用于识别动物的图像：

import cv2
import numpy as np
from tensorflow.keras.models import load_model

# 加载训练好的模型
model = load_model('animal_classification_model.h5')

# 定义动物类别
classes = ['cat', 'dog', 'elephant', 'lion', 'tiger']

# 加载图像并进行预处理
def preprocess_image(image_path):
    image = cv2.imread(image_path)
    image = cv2.resize(image, (224, 224))
    image = image / 255.0
    image = np.expand_dims(image, axis=0)
    return image

# 进行动物识别
def predict_animal(image_path):
    image = preprocess_image(image_path)
    predictions = model.predict(image)
    predicted_class = np.argmax(predictions)
    animal = classes[predicted_class]
    confidence = predictions[0][predicted_class] * 100
    return animal, confidence

# 测试识别结果
image_path = 'test_image.jpg'
animal, confidence = predict_animal(image_path)
print(f"The image is classified as {animal} with {confidence:.2f}% confidence.")

请注意，上述代码仅为示例，实际应用中可能需要更复杂的模型和数据集来提高准确性。此外，还需要安装相应的库，如OpenCV和TensorFlow。