用python语言写一个动物识别系统

编写一个简单的动物识别系统涉及到深度学习和图像处理，这里是一个基本的概念框架，使用Python和常见的库（如TensorFlow、Keras或PIL）：

# 导入必要的库
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions
import numpy as np

# 加载预训练的ResNet50模型
model = ResNet50(weights='imagenet')

# 图像路径列表和对应的标签
image_paths = ['path/to/dogs', 'path/to/cats']
labels = ['dog', 'cat']

# 数据增强，预处理图片
datagen = ImageDataGenerator(preprocessing_function=preprocess_input)

# 构建预测函数
def predict_animal(image_path):
    img = datagen.flow_from_directory(image_path, target_size=(224, 224), class_mode=None)
    predictions = model.predict(img)
    top_prediction = decode_predictions(predictions, top=1)[0][0]
    return labels[top_prediction[1]]

# 对新图片进行识别
new_image_path = 'path_to_your_image'
print(predict_animal(new_image_path))