yolo_world/easydeploy/tools/image-demo.py

# Copyright (c) OpenMMLab. All rights reserved.
from projects.easydeploy.model import ORTWrapper, TRTWrapper  # isort:skip
import os
import random
from argparse import ArgumentParser

import cv2
import mmcv
import numpy as np
import torch
from mmcv.transforms import Compose
from mmdet.utils import get_test_pipeline_cfg
from mmengine.config import Config, ConfigDict
from mmengine.utils import ProgressBar, path

from mmyolo.utils import register_all_modules
from mmyolo.utils.misc import get_file_list


def parse_args():
    parser = ArgumentParser()
    parser.add_argument(
        'img', help='Image path, include image file, dir and URL.')
    parser.add_argument('config', help='Config file')
    parser.add_argument('checkpoint', help='Checkpoint file')
    parser.add_argument(
        '--out-dir', default='./output', help='Path to output file')
    parser.add_argument(
        '--device', default='cuda:0', help='Device used for inference')
    parser.add_argument(
        '--show', action='store_true', help='Show the detection results')
    args = parser.parse_args()
    return args


def preprocess(config):
    data_preprocess = config.get('model', {}).get('data_preprocessor', {})
    mean = data_preprocess.get('mean', [0., 0., 0.])
    std = data_preprocess.get('std', [1., 1., 1.])
    mean = torch.tensor(mean, dtype=torch.float32).reshape(1, 3, 1, 1)
    std = torch.tensor(std, dtype=torch.float32).reshape(1, 3, 1, 1)

    class PreProcess(torch.nn.Module):

        def __init__(self):
            super().__init__()

        def forward(self, x):
            x = x[None].float()
            x -= mean.to(x.device)
            x /= std.to(x.device)
            return x

    return PreProcess().eval()


def main():
    args = parse_args()

    # register all modules in mmdet into the registries
    register_all_modules()

    colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(1000)]

    # build the model from a config file and a checkpoint file
    if args.checkpoint.endswith('.onnx'):
        model = ORTWrapper(args.checkpoint, args.device)
    elif args.checkpoint.endswith('.engine') or args.checkpoint.endswith(
            '.plan'):
        model = TRTWrapper(args.checkpoint, args.device)
    else:
        raise NotImplementedError

    model.to(args.device)

    cfg = Config.fromfile(args.config)
    class_names = cfg.get('class_name')

    test_pipeline = get_test_pipeline_cfg(cfg)
    test_pipeline[0] = ConfigDict({'type': 'mmdet.LoadImageFromNDArray'})
    test_pipeline = Compose(test_pipeline)

    pre_pipeline = preprocess(cfg)

    if not args.show:
        path.mkdir_or_exist(args.out_dir)

    # get file list
    files, source_type = get_file_list(args.img)

    # start detector inference
    progress_bar = ProgressBar(len(files))
    for i, file in enumerate(files):
        bgr = mmcv.imread(file)
        rgb = mmcv.imconvert(bgr, 'bgr', 'rgb')
        data, samples = test_pipeline(dict(img=rgb, img_id=i)).values()
        pad_param = samples.get('pad_param',
                                np.array([0, 0, 0, 0], dtype=np.float32))
        h, w = samples.get('ori_shape', rgb.shape[:2])
        pad_param = torch.asarray(
            [pad_param[2], pad_param[0], pad_param[2], pad_param[0]],
            device=args.device)
        scale_factor = samples.get('scale_factor', [1., 1])
        scale_factor = torch.asarray(scale_factor * 2, device=args.device)
        data = pre_pipeline(data).to(args.device)

        result = model(data)
        if source_type['is_dir']:
            filename = os.path.relpath(file, args.img).replace('/', '_')
        else:
            filename = os.path.basename(file)
        out_file = None if args.show else os.path.join(args.out_dir, filename)

        # Get candidate predict info by num_dets
        num_dets, bboxes, scores, labels = result
        scores = scores[0, :num_dets]
        bboxes = bboxes[0, :num_dets]
        labels = labels[0, :num_dets]
        bboxes -= pad_param
        bboxes /= scale_factor

        bboxes[:, 0::2].clamp_(0, w)
        bboxes[:, 1::2].clamp_(0, h)
        bboxes = bboxes.round().int()

        for (bbox, score, label) in zip(bboxes, scores, labels):
            bbox = bbox.tolist()
            color = colors[label]

            if class_names is not None:
                label_name = class_names[label]
                name = f'cls:{label_name}_score:{score:0.4f}'
            else:
                name = f'cls:{label}_score:{score:0.4f}'

            cv2.rectangle(bgr, bbox[:2], bbox[2:], color, 2)
            cv2.putText(
                bgr,
                name, (bbox[0], bbox[1] - 2),
                cv2.FONT_HERSHEY_SIMPLEX,
                2.0, [225, 255, 255],
                thickness=3)

        if args.show:
            mmcv.imshow(bgr, 'result', 0)
        else:
            mmcv.imwrite(bgr, out_file)
        progress_bar.update()


if __name__ == '__main__':
    main()