3.2.1

conda env listconda activate ai
jupyter notebook

import onnxruntime as ort模型
import numpy as np
import scipy.special科学计算
from PIL import Image图片

# 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 = _________________._________________('resnet.onnx')

---

session = ort.InferenSession('resnet.onnx')

代码解释：

resnet.onnx模型，

InferenSession

# 加载图片 2分

image = _________________._________________('img_test.jpg').convert('RGB')

---

image = Image.open('img_test.jpg').convert('RGB')

代码解释：

# 预处理图片 2分

# 加载图片 2分
image = Image.open('img_test.jpg').convert('RGB')

processed_image = _________________(_________________)

---

processed_image = preprocess_image(image)

代码解释：

# 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

# 进行图片识别 2分

output = _________________._________________([output_name], {input_name: processed_image})[0]

---

output = session.run([output_name], {input_name: processed_image})[0]

代码解释：

run运行，运行就是识别

# 应用 softmax 函数获取概率 2分

probabilities = _________________._________________.softmax(output, axis=-1)

---

probabilities = scipy.special.softmax(output, axis=-1)

代码解释：

# 获取最高的5个概率和对应的类别索引 2分

top5_idx = np._________________(probabilities[0])[-5:][::-1]
top5_prob = _________________[0][top5_idx]

---

top5_idx = np.argsort(probabilities[0])[-5:][::-1]
top5_prob = probabilities[0][top5_idx]

代码解释：