Created by: mozpp

我根据 https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_usage/deploy/inference/python_infer_cn.html 进行YOLOv3的服务部署。 model是用https://github.com/PaddlePaddle/models/blob/release/1.5/PaddleCV/yolov3/infer.py 保存的 结果不同！！！

# -*- coding: utf-8 -*-
import argparse
import numpy as np

from paddle.fluid.core import PaddleBuf
from paddle.fluid.core import PaddleDType
from paddle.fluid.core import PaddleTensor
from paddle.fluid.core import AnalysisConfig
from paddle.fluid.core import create_paddle_predictor
import cv2

def main():
    args = parse_args()

    # Set config
    config = AnalysisConfig(args.model_dir)
    # config.disable_gpu()

    # Create PaddlePredictor
    predictor = create_paddle_predictor(config)

    # Set inputs
    inputs = fake_input(args.batch_size)
    # img = cv2.imread('test.jpg')
    # img = img_reader1(img, 608)
    # inputs={'image':img}
    # Infer
    outputs = predictor.run(inputs)

    # parse outputs
    output = outputs[0]
    print(output.name)
    output_data = output.data.float_data()
    print(len(output_data))
    b=np.array(output_data)
    b=b.reshape([-1,6])
    print(b)
    # bboxes = np.array(outputs[0])
    # print(bboxes)
    # assert len(output_data) == 512 * args.batch_size
    # for i in range(args.batch_size):
    #     print(np.argmax(output_data[i * 512:(i + 1) * 512]))

def fake_input(batch_size):
    image = PaddleTensor()
    image.name = "image"


    image.dtype = PaddleDType.FLOAT32
    # image.data = PaddleBuf(
    #     np.random.randn(*image.shape).flatten().astype("float32").tolist())
    img = cv2.imread('test.jpg')
    h, w, _ = img.shape
    input_size=608
    image.shape = [batch_size, 3, input_size, input_size]
    # pixel mean values
    pixel_means = [0.485, 0.456, 0.406]
    # pixel std values
    pixel_stds = [0.229, 0.224, 0.225]
    img=img_reader1(img,input_size,pixel_means,pixel_stds)
    img=img.reshape(image.shape)
    image.data=PaddleBuf(img.flatten().astype("float32").tolist())
    im_shape= PaddleTensor()
    im_shape.name="im_shape"
    im_shape.dtype=PaddleDType.INT32
    im_shape.shape = [batch_size,2]
    im_shape.data=PaddleBuf(
        np.array([h,w]).flatten().astype("int32").tolist())
    return [image,im_shape]

def img_reader1(im, size, mean, std):
    h, w, _ = im.shape
    im_scale_x = size / float(w)
    im_scale_y = size / float(h)
    out_img = cv2.resize(im, None, None,
                         fx=im_scale_x, fy=im_scale_y,
                         interpolation=cv2.INTER_CUBIC)
    mean = np.array(mean).reshape((1, 1, -1))
    std = np.array(std).reshape((1, 1, -1))
    out_img = (out_img / 255.0 - mean) / std
    out_img = out_img.transpose((2, 0, 1))
    return out_img

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_dir", type=str, help="model dir")
    parser.add_argument("--prog_file", type=str, help="program filename")
    parser.add_argument("--params_file", type=str, help="parameter filename")
    parser.add_argument("--batch_size", type=int, default=1, help="batch size")

    return parser.parse_args()

if __name__ == "__main__":
    main()