import onnxruntime as ort
import numpy as np
import scipy.special
from PIL import Image
# 预处理图像
def preprocess_image(image, resize_size=256, crop_size=224, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
image = image.resize((resize_size, resize_size), Image.BILINEAR)
w, h = image.size
left = (w - crop_size) / 2
top = (h - crop_size) / 2
image = image.crop((left, top, left + crop_size, top + crop_size))
image = np.array(image).astype(np.float32)
image = image / 255.0
image = (image - mean) / std
image = np.transpose(image, (2, 0, 1))
image = image.reshape((1,) + image.shape)
return image
# 模型加载 2分
session =
# 加载类别标签
labels_path = 'labels.txt'
with open(labels_path) as f:
labels = [line.strip() for line in f.readlines()]
# 获取模型输入和输出的名称
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name
# 加载图片 2分
image = ('RGB')
# 预处理图片 2分
processed_image =
# 确保输入数据是 float32 类型
processed_image = processed_image.astype(np.float32)
# 进行图片识别 2分
output = ([output_name], {input_name: processed_image})[0]
# 应用 softmax 函数获取概率 2分
probabilities = (output, axis=-1)
# 获取最高的5个概率和对应的类别索引 3分
top5_idx = [-5:][::-1]
top5_prob =
# 打印结果
print("Top 5 predicted classes:")
for i in range(5):
print(f"{i+1}: {labels[top5_idx[i]]} - Probability: {top5_prob[i]}")