mmdetection/demo/create_result_gif.py

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp

import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import mmcv
import numpy as np
from mmengine.utils import scandir

try:
    import imageio
except ImportError:
    imageio = None


# TODO verify after refactoring analyze_results.py
def parse_args():
    parser = argparse.ArgumentParser(description='Create GIF for demo')
    parser.add_argument(
        'image_dir',
        help='directory where result '
        'images save path generated by ‘analyze_results.py’')
    parser.add_argument(
        '--out',
        type=str,
        default='result.gif',
        help='gif path where will be saved')
    args = parser.parse_args()
    return args


def _generate_batch_data(sampler, batch_size):
    batch = []
    for idx in sampler:
        batch.append(idx)
        if len(batch) == batch_size:
            yield batch
            batch = []
    if len(batch) > 0:
        yield batch


def create_gif(frames, gif_name, duration=2):
    """Create gif through imageio.

    Args:
        frames (list[ndarray]): Image frames
        gif_name (str): Saved gif name
        duration (int): Display interval (s),
            Default: 2
    """
    if imageio is None:
        raise RuntimeError('imageio is not installed,'
                           'Please use “pip install imageio” to install')
    imageio.mimsave(gif_name, frames, 'GIF', duration=duration)


def create_frame_by_matplotlib(image_dir,
                               nrows=1,
                               fig_size=(300, 300),
                               font_size=15):
    """Create gif frame image through matplotlib.

    Args:
        image_dir (str): Root directory of result images
        nrows (int): Number of rows displayed, Default: 1
        fig_size (tuple): Figure size of the pyplot figure.
           Default: (300, 300)
        font_size (int): Font size of texts. Default: 15

    Returns:
        list[ndarray]: image frames
    """

    result_dir_names = os.listdir(image_dir)
    assert len(result_dir_names) == 2
    # Longer length has higher priority
    result_dir_names.reverse()

    images_list = []
    for dir_names in result_dir_names:
        images_list.append(scandir(osp.join(image_dir, dir_names)))

    frames = []
    for paths in _generate_batch_data(zip(*images_list), nrows):

        fig, axes = plt.subplots(nrows=nrows, ncols=2)
        fig.suptitle('Good/bad case selected according '
                     'to the COCO mAP of the single image')

        det_patch = mpatches.Patch(color='salmon', label='prediction')
        gt_patch = mpatches.Patch(color='royalblue', label='ground truth')
        # bbox_to_anchor may need to be finetuned
        plt.legend(
            handles=[det_patch, gt_patch],
            bbox_to_anchor=(1, -0.18),
            loc='lower right',
            borderaxespad=0.)

        if nrows == 1:
            axes = [axes]

        dpi = fig.get_dpi()
        # set fig size and margin
        fig.set_size_inches(
            (fig_size[0] * 2 + fig_size[0] // 20) / dpi,
            (fig_size[1] * nrows + fig_size[1] // 3) / dpi,
        )

        fig.tight_layout()
        # set subplot margin
        plt.subplots_adjust(
            hspace=.05,
            wspace=0.05,
            left=0.02,
            right=0.98,
            bottom=0.02,
            top=0.98)

        for i, (path_tuple, ax_tuple) in enumerate(zip(paths, axes)):
            image_path_left = osp.join(
                osp.join(image_dir, result_dir_names[0], path_tuple[0]))
            image_path_right = osp.join(
                osp.join(image_dir, result_dir_names[1], path_tuple[1]))
            image_left = mmcv.imread(image_path_left)
            image_left = mmcv.rgb2bgr(image_left)
            image_right = mmcv.imread(image_path_right)
            image_right = mmcv.rgb2bgr(image_right)

            if i == 0:
                ax_tuple[0].set_title(
                    result_dir_names[0], fontdict={'size': font_size})
                ax_tuple[1].set_title(
                    result_dir_names[1], fontdict={'size': font_size})
            ax_tuple[0].imshow(
                image_left, extent=(0, *fig_size, 0), interpolation='bilinear')
            ax_tuple[0].axis('off')
            ax_tuple[1].imshow(
                image_right,
                extent=(0, *fig_size, 0),
                interpolation='bilinear')
            ax_tuple[1].axis('off')

        canvas = fig.canvas
        s, (width, height) = canvas.print_to_buffer()
        buffer = np.frombuffer(s, dtype='uint8')
        img_rgba = buffer.reshape(height, width, 4)
        rgb, alpha = np.split(img_rgba, [3], axis=2)
        img = rgb.astype('uint8')

        frames.append(img)

    return frames


def main():
    args = parse_args()
    frames = create_frame_by_matplotlib(args.image_dir)
    create_gif(frames, args.out)


if __name__ == '__main__':
    main()