openvino_inference.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) Megvii, Inc. and its affiliates.

import argparse
import logging as log
import os
import sys

import cv2
import numpy as np

from openvino.inference_engine import IECore

from yolox.data.data_augment import preproc as preprocess
from yolox.data.datasets import COCO_CLASSES
from yolox.utils import mkdir, multiclass_nms, demo_postprocess, vis


def parse_args() -> argparse.Namespace:
    """Parse and return command line arguments"""
    parser = argparse.ArgumentParser(add_help=False)
    args = parser.add_argument_group('Options')
    args.add_argument(
        '-h',
        '--help',
        action='help',
        help='Show this help message and exit.')
    args.add_argument(
        '-m',
        '--model',
        required=True,
        type=str,
        help='Required. Path to an .xml or .onnx file with a trained model.')
    args.add_argument(
        '-i',
        '--input',
        required=True,
        type=str,
        help='Required. Path to an image file.')
    args.add_argument(
        '-o',
        '--output_dir',
        type=str,
        default='demo_output',
        help='Path to your output dir.')
    args.add_argument(
        '-s',
        '--score_thr',
        type=float,
        default=0.3,
        help="Score threshould to visualize the result.")
    args.add_argument(
        '-d',
        '--device',
        default='CPU',
        type=str,
        help='Optional. Specify the target device to infer on; CPU, GPU, \
              MYRIAD, HDDL or HETERO: is acceptable. The sample will look \
              for a suitable plugin for device specified. Default value \
              is CPU.')
    args.add_argument(
        '--labels',
        default=None,
        type=str,
        help='Option:al. Path to a labels mapping file.')
    args.add_argument(
        '-nt',
        '--number_top',
        default=10,
        type=int,
        help='Optional. Number of top results.')
    return parser.parse_args()


def main():
    log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
    args = parse_args()

    # ---------------------------Step 1. Initialize inference engine core--------------------------------------------------
    log.info('Creating Inference Engine')
    ie = IECore()

    # ---------------------------Step 2. Read a model in OpenVINO Intermediate Representation or ONNX format---------------
    log.info(f'Reading the network: {args.model}')
    # (.xml and .bin files) or (.onnx file)
    net = ie.read_network(model=args.model)

    if len(net.input_info) != 1:
        log.error('Sample supports only single input topologies')
        return -1
    if len(net.outputs) != 1:
        log.error('Sample supports only single output topologies')
        return -1

    # ---------------------------Step 3. Configure input & output----------------------------------------------------------
    log.info('Configuring input and output blobs')
    # Get names of input and output blobs
    input_blob = next(iter(net.input_info))
    out_blob = next(iter(net.outputs))

    # Set input and output precision manually
    net.input_info[input_blob].precision = 'FP32'
    net.outputs[out_blob].precision = 'FP16'

    # Get a number of classes recognized by a model
    num_of_classes = max(net.outputs[out_blob].shape)

    # ---------------------------Step 4. Loading model to the device-------------------------------------------------------
    log.info('Loading the model to the plugin')
    exec_net = ie.load_network(network=net, device_name=args.device)

    # ---------------------------Step 5. Create infer request--------------------------------------------------------------
    # load_network() method of the IECore class with a specified number of requests (default 1) returns an ExecutableNetwork
    # instance which stores infer requests. So you already created Infer requests in the previous step.

    # ---------------------------Step 6. Prepare input---------------------------------------------------------------------
    origin_img = cv2.imread(args.input)
    _, _, h, w = net.input_info[input_blob].input_data.shape
    mean = (0.485, 0.456, 0.406)
    std = (0.229, 0.224, 0.225)
    image, ratio = preprocess(origin_img, (h, w), mean, std)

    # ---------------------------Step 7. Do inference----------------------------------------------------------------------
    log.info('Starting inference in synchronous mode')
    res = exec_net.infer(inputs={input_blob: image})

    # ---------------------------Step 8. Process output--------------------------------------------------------------------
    res = res[out_blob]

    predictions = demo_postprocess(res, (h, w), p6=False)[0]

    boxes = predictions[:, :4]
    scores = predictions[:, 4, None] * predictions[:, 5:]

    boxes_xyxy = np.ones_like(boxes)
    boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2]/2.
    boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3]/2.
    boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2]/2.
    boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3]/2.
    boxes_xyxy /= ratio
    dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1)

    if dets is not None:
        final_boxes = dets[:, :4]
        final_scores, final_cls_inds = dets[:, 4], dets[:, 5]
        origin_img = vis(origin_img, final_boxes, final_scores, final_cls_inds,
                         conf=args.score_thr, class_names=COCO_CLASSES)

    mkdir(args.output_dir)
    output_path = os.path.join(args.output_dir, args.input.split("/")[-1])
    cv2.imwrite(output_path, origin_img)


if __name__ == '__main__':
    sys.exit(main())