# -*- coding: utf-8 -*-
"""
分类器示例
"""

import cv2
import numpy as np

import onnxruntime as ort

def Preprocessing(pathOfImage):
    # 读取图像
    image = cv2.imread(pathOfImage, cv2.IMREAD_COLOR)             
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    
    # 调整大小，使短边为256，保持长宽比
    ratio = float(256) / min(image.shape[0], image.shape[1])
    if image.shape[0] > image.shape[1]:
        new_size = [int(round(ratio * image.shape[0])), 256]
    else:
        new_size = [256, int(round(ratio * image.shape[1]))]
    image = np.array(cv2.resize(image, (new_size[1],new_size[0])))
    
    # 裁剪中心窗口为224*224
    h, w, c = image.shape
    start_x = w//2 - 224//2
    start_y = h//2 - 224//2
    image = image[start_y:start_y+224, start_x:start_x+224, :]
    
    # transpose
    image = image.transpose(2, 0, 1)
    
    # 将输入数据转换为float32
    img_data = image.astype('float32')
    
    # normalize
    mean_vec = np.array([123.675, 116.28, 103.53])
    stddev_vec = np.array([58.395, 57.12, 57.375])
    norm_img_data = np.zeros(img_data.shape).astype('float32')
    for i in range(img_data.shape[0]):
        norm_img_data[i,:,:] = (img_data[i,:,:] - mean_vec[i]) / stddev_vec[i]
    
    # 调整尺寸
    norm_img_data = norm_img_data.reshape(1, 3, 224, 224).astype('float32')
    return norm_img_data


def postprocess(scores):
    '''
    Postprocessing with mxnet gluon
    The function takes scores generated by the network and returns the class IDs in decreasing order
    of probability
    '''
    with open('../Resource/synset.txt', 'r') as f:
        labels = [l.rstrip() for l in f]
    preds = np.squeeze(scores)
    a = np.argsort(preds)[::-1]
    print('class=%s ; probability=%f' %(labels[a[0]],preds[a[0]]))


def ort_seg_dcu(model_path,image):
    
    #创建sess_options
    sess_options = ort.SessionOptions()

    #设置图优化
    sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_BASIC

    #是否开启profiling
    sess_options.enable_profiling = False
    dcu_session = ort.InferenceSession(model_path,sess_options,providers=['ROCMExecutionProvider'],)
    input_name=dcu_session.get_inputs()[0].name

    results = dcu_session.run(None, input_feed={input_name:image })
    scores=np.array(results[0])
    print("ort result.shape:",scores.shape)

    return scores

if __name__ == '__main__':
    
    pathOfImage ="../Resource/Images/ImageNet_01.jpg"
    image = Preprocessing(pathOfImage)
    model_path = "../Resource/Models/resnet50-v2-7.onnx"
    
    # 推理
    result = ort_seg_dcu(model_path,image)
    
    # 解析分类结果
    postprocess(result)
