from openvino.runtime import Core
import cv2 as cv
import numpy as np 

model_path = "D:/PaddleClas/inference/inference.xml"
imagefile_path = "D:/PaddleClas/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg"

# 第一步，创建Core对象
core = Core()
# 第二步，载入并编译模型
resnet50 = core.compile_model(model_path, "AUTO")
# 第三步(3,4,5)，执行推理计算
# 3.1 读取图像数据
image = cv.cvtColor(cv.imread(imagefile_path), cv.COLOR_BGR2RGB)
# 3.2 将图像数据Resize -> (224,224)
resized_image = cv.resize(image, (224,224))
# 3.3 将图像数据形状从[224,224,3] 调整为[1,3,224,224]
input_blob = np.expand_dims(np.transpose(resized_image, (2, 0, 1)), 0)
# 执行推理计算
infer_result = resnet50([input_blob])[resnet50.outputs[0]][0]
# 第6步：处理推理结果
class_ids = np.argsort(-infer_result) #对推理结果降序排列
top1_id, top5_ids = class_ids[0], class_ids[0:5]
print(f"Top1 id:{top1_id}; Top5 ids:{top5_ids}")

# Convert the inference result to a class name.
labelfile_path = "D:/PaddleClas/ppcls/utils/imagenet1k_label_list.txt"
imagenet_classes = np.array(open(labelfile_path).read().splitlines())
# print the class name
print(f"Top1 class name:{imagenet_classes[top1_id]}")
print(f"Top5 class name:{imagenet_classes[top5_ids]}")