[Feature] Add script for GIF demo (#4573)

* Create gif for demo

* Add install tips

demo/create_result_gif.py

+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
+
+try:
+    import imageio
+except ImportError:
+    imageio = None
+
+
+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(mmcv.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()