香澄派分类

import argparse
import cv2
import numpy as np
from ais_bench.infer.interface import InferSession

CLASSES = {0: 'class_0', 1: 'class_1', 2: 'class_2', ...}  # 更换为你模型对应的类别列表

def preprocess_image(image_path, target_size=(224, 224)):  # 假设预处理尺寸为224x224
    """
    预处理图像至模型所需的尺寸。
    """
    image = cv2.imread(image_path)
    image = cv2.resize(image, target_size, interpolation=cv2.INTER_LINEAR)
    image = image / 255.0  # 归一化到0-1之间
    image = image.astype(np.float32)
    image = np.expand_dims(image, axis=0)  # 添加批量维度
    return image

def classify_image(session, image_path):
    """
    执行图像分类推理并打印分类结果。
    """
    image_data = preprocess_image(image_path)
    begin_time = time.time()
    outputs = session.infer(feeds=image_data, mode="static")
    end_time = time.time()
    print("OM infer time:", end_time - begin_time)
    
    # 假设模型输出是一个长度等于类别的向量，直接取最大值的索引作为预测类别
    prediction = outputs[0]
    predicted_class_id = np.argmax(prediction)
    predicted_class = CLASSES[predicted_class_id]
    confidence = prediction[predicted_class_id]
    
    print(f"Predicted Class: {predicted_class} with confidence {confidence:.2f}")

def main(om_model, input_image):
    session = InferSession(device_id=0, model_path=om_model)
    classify_image(session, input_image)

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model", default="classification_model.om", help="Input your OM model for classification.")
    parser.add_argument("--img", default="path_to_your_image.jpg", help="Path to input image.")
    args = parser.parse_args()
    main(args.model, args.img)
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