openvino/samples/python/hello_classification/hello_classification.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import argparse
import logging as log
import sys

import cv2
import numpy as np
from openvino.inference_engine import IECore


def parse_args() -> argparse.Namespace:
    """Parse and return command line arguments"""
    parser = argparse.ArgumentParser(add_help=False)
    args = parser.add_argument_group('Options')
    # fmt: off
    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('-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='Optional. Path to a labels mapping file.')
    args.add_argument('-nt', '--number_top', default=10, type=int, help='Optional. Number of top results.')
    # fmt: on
    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 = 'U8'
    net.outputs[out_blob].precision = 'FP32'

    # 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---------------------------------------------------------------------
    original_image = cv2.imread(args.input)
    image = original_image.copy()
    _, _, h, w = net.input_info[input_blob].input_data.shape

    if image.shape[:-1] != (h, w):
        log.warning(f'Image {args.input} is resized from {image.shape[:-1]} to {(h, w)}')
        image = cv2.resize(image, (w, h))

    # Change data layout from HWC to CHW
    image = image.transpose((2, 0, 1))
    # Add N dimension to transform to NCHW
    image = np.expand_dims(image, axis=0)

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

    # ---------------------------Step 8. Process output--------------------------------------------------------------------
    # Generate a label list
    if args.labels:
        with open(args.labels, 'r') as f:
            labels = [line.split(',')[0].strip() for line in f]

    res = res[out_blob]
    # Change a shape of a numpy.ndarray with results to get another one with one dimension
    probs = res.reshape(num_of_classes)
    # Get an array of args.number_top class IDs in descending order of probability
    top_n_idexes = np.argsort(probs)[-args.number_top :][::-1]

    header = 'classid probability'
    header = header + ' label' if args.labels else header

    log.info(f'Image path: {args.input}')
    log.info(f'Top {args.number_top} results: ')
    log.info(header)
    log.info('-' * len(header))

    for class_id in top_n_idexes:
        probability_indent = ' ' * (len('classid') - len(str(class_id)) + 1)
        label_indent = ' ' * (len('probability') - 8) if args.labels else ''
        label = labels[class_id] if args.labels else ''
        log.info(f'{class_id}{probability_indent}{probs[class_id]:.7f}{label_indent}{label}')
    log.info('')

    # ----------------------------------------------------------------------------------------------------------------------
    log.info('This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool\n')
    return 0


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