mirror of https://github.com/open-mmlab/mmyolo.git
[Improvement] `browse_dataset.py` (#304)
* Create browse_transform.py Upgrate the brow_transform 对pipeline中的transform过程实现了可视化，以及可以将transform中key参数的变化打印出来。 * Update browse_dataset.py * Delete browse_transform.py * Update browse_dataset.py * Update browse_dataset.py * Update browse_dataset.py * Update browse_dataset.py * Update browse_dataset.py * Update browse_dataset.py * Update browse_dataset.py * Update browse_dataset.py * Update browse_dataset.py 修改了215行result_i = [result['dataset_sample'] for result in intermediate_imgs]通过lint * fix some error Co-authored-by: huanghaian <huanghaian@sensetime.com>pull/331/head
MingJian.L
GitHub
parent
98e6fcccc7
commit
1f06f4f594
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@ -1,28 +1,64 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os.path as osp
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import sys
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from typing import Tuple
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import cv2
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import mmcv
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import numpy as np
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from mmdet.models.utils import mask2ndarray
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from mmdet.structures.bbox import BaseBoxes
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from mmengine.config import Config, DictAction
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from mmengine.dataset import Compose
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from mmengine.utils import ProgressBar
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from mmengine.visualization import Visualizer
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from mmyolo.registry import DATASETS, VISUALIZERS
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from mmyolo.utils import register_all_modules
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# TODO: Support for printing the change in key of results
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def parse_args():
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parser = argparse.ArgumentParser(description='Browse a dataset')
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parser.add_argument('config', help='train config file path')
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parser.add_argument(
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'--phase',
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'-p',
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default='train',
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type=str,
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choices=['train', 'test', 'val'],
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help='phase of dataset to visualize, accept "train" "test" and "val".'
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' Defaults to "train".')
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parser.add_argument(
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'--mode',
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'-m',
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default='transformed',
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type=str,
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choices=['original', 'transformed', 'pipeline'],
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help='display mode; display original pictures or '
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'transformed pictures or comparison pictures. "original" '
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'means show images load from disk; "transformed" means '
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'to show images after transformed; "pipeline" means show all '
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'the intermediate images. Defaults to "transformed".')
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parser.add_argument(
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'--output-dir',
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default=None,
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type=str,
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help='If there is no display interface, you can save it')
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help='If there is no display interface, you can save it.')
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parser.add_argument('--not-show', default=False, action='store_true')
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parser.add_argument(
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'--show-number',
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'-n',
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type=int,
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default=sys.maxsize,
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help='number of images selected to visualize, '
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'must bigger than 0. if the number is bigger than length '
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'of dataset, show all the images in dataset; '
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'default "sys.maxsize", show all images in dataset')
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parser.add_argument(
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'--show-interval',
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'-i',
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type=float,
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default=3,
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help='the interval of show (s)')
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@ -40,49 +76,180 @@ def parse_args():
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return args
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def _get_adaptive_scale(img_shape: Tuple[int, int],
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min_scale: float = 0.3,
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max_scale: float = 3.0) -> float:
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"""Get adaptive scale according to image shape.
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The target scale depends on the the short edge length of the image. If the
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short edge length equals 224, the output is 1.0. And output linear
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scales according the short edge length. You can also specify the minimum
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scale and the maximum scale to limit the linear scale.
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Args:
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img_shape (Tuple[int, int]): The shape of the canvas image.
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min_scale (int): The minimum scale. Defaults to 0.3.
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max_scale (int): The maximum scale. Defaults to 3.0.
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Returns:
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int: The adaptive scale.
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"""
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short_edge_length = min(img_shape)
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scale = short_edge_length / 224.
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return min(max(scale, min_scale), max_scale)
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def make_grid(imgs, names):
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"""Concat list of pictures into a single big picture, align height here."""
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visualizer = Visualizer.get_current_instance()
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ori_shapes = [img.shape[:2] for img in imgs]
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max_height = int(max(img.shape[0] for img in imgs) * 1.1)
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min_width = min(img.shape[1] for img in imgs)
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horizontal_gap = min_width // 10
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img_scale = _get_adaptive_scale((max_height, min_width))
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texts = []
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text_positions = []
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start_x = 0
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for i, img in enumerate(imgs):
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pad_height = (max_height - img.shape[0]) // 2
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pad_width = horizontal_gap // 2
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# make border
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imgs[i] = cv2.copyMakeBorder(
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img,
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pad_height,
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max_height - img.shape[0] - pad_height + int(img_scale * 30 * 2),
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pad_width,
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pad_width,
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cv2.BORDER_CONSTANT,
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value=(255, 255, 255))
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texts.append(f'{"execution: "}{i}\n{names[i]}\n{ori_shapes[i]}')
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text_positions.append(
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[start_x + img.shape[1] // 2 + pad_width, max_height])
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start_x += img.shape[1] + horizontal_gap
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display_img = np.concatenate(imgs, axis=1)
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visualizer.set_image(display_img)
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img_scale = _get_adaptive_scale(display_img.shape[:2])
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visualizer.draw_texts(
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texts,
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positions=np.array(text_positions),
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font_sizes=img_scale * 7,
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colors='black',
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horizontal_alignments='center',
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font_families='monospace')
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return visualizer.get_image()
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class InspectCompose(Compose):
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"""Compose multiple transforms sequentially.
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And record "img" field of all results in one list.
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"""
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def __init__(self, transforms, intermediate_imgs):
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super().__init__(transforms=transforms)
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self.intermediate_imgs = intermediate_imgs
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def __call__(self, data):
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if 'img' in data:
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self.intermediate_imgs.append({
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'name': 'original',
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'img': data['img'].copy()
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})
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self.ptransforms = [
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self.transforms[i] for i in range(len(self.transforms) - 1)
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]
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for t in self.ptransforms:
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data = t(data)
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# Keep the same meta_keys in the PackDetInputs
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self.transforms[-1].meta_keys = [key for key in data]
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data_sample = self.transforms[-1](data)
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if data is None:
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return None
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if 'img' in data:
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self.intermediate_imgs.append({
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'name':
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t.__class__.__name__,
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'dataset_sample':
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data_sample['data_samples']
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})
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return data
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def main():
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args = parse_args()
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cfg = Config.fromfile(args.config)
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if args.cfg_options is not None:
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cfg.merge_from_dict(args.cfg_options)
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# register all modules in mmdet into the registries
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# register all modules in mmyolo into the registries
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register_all_modules()
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dataset = DATASETS.build(cfg.train_dataloader.dataset)
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dataset_cfg = cfg.get(args.phase + '_dataloader').get('dataset')
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dataset = DATASETS.build(dataset_cfg)
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visualizer = VISUALIZERS.build(cfg.visualizer)
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visualizer.dataset_meta = dataset.metainfo
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progress_bar = ProgressBar(len(dataset))
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for item in dataset:
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img = item['inputs'].permute(1, 2, 0).numpy()
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data_samples = item['data_samples'].numpy()
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gt_instances = data_samples.gt_instances
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img_path = osp.basename(item['data_samples'].img_path)
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intermediate_imgs = []
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# TODO: The dataset wrapper occasion is not considered here
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dataset.pipeline = InspectCompose(dataset.pipeline.transforms,
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intermediate_imgs)
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out_file = osp.join(
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args.output_dir,
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osp.basename(img_path)) if args.output_dir is not None else None
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# init visualization image number
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assert args.show_number > 0
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display_number = min(args.show_number, len(dataset))
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img = img[..., [2, 1, 0]] # bgr to rgb
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gt_bboxes = gt_instances.get('bboxes', None)
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if gt_bboxes is not None and isinstance(gt_bboxes, BaseBoxes):
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gt_instances.bboxes = gt_bboxes.tensor
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gt_masks = gt_instances.get('masks', None)
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if gt_masks is not None:
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masks = mask2ndarray(gt_masks)
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gt_instances.masks = masks.astype(np.bool)
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data_samples.gt_instances = gt_instances
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progress_bar = ProgressBar(display_number)
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for i, item in zip(range(display_number), dataset):
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image_i = []
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result_i = [result['dataset_sample'] for result in intermediate_imgs]
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for k, datasample in enumerate(result_i):
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image = datasample.img
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gt_instances = datasample.gt_instances
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image = image[..., [2, 1, 0]] # bgr to rgb
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gt_bboxes = gt_instances.get('bboxes', None)
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if gt_bboxes is not None and isinstance(gt_bboxes, BaseBoxes):
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gt_instances.bboxes = gt_bboxes.tensor
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gt_masks = gt_instances.get('masks', None)
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if gt_masks is not None:
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masks = mask2ndarray(gt_masks)
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gt_instances.masks = masks.astype(np.bool)
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datasample.gt_instances = gt_instances
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# get filename from dataset or just use index as filename
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visualizer.add_datasample(
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'result',
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image,
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datasample,
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draw_pred=False,
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draw_gt=True,
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show=False)
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image_show = visualizer.get_image()
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image_i.append(image_show)
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visualizer.add_datasample(
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osp.basename(img_path),
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img,
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data_samples,
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draw_pred=False,
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show=not args.not_show,
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wait_time=args.show_interval,
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out_file=out_file)
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if args.mode == 'original':
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image = image_i[0]
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elif args.mode == 'transformed':
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image = image_i[-1]
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else:
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image = make_grid([result for result in image_i],
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[result['name'] for result in intermediate_imgs])
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if hasattr(datasample, 'img_path'):
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filename = osp.basename(datasample.img_path)
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else:
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# some dataset have not image path
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filename = f'{i}.jpg'
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out_file = osp.join(args.output_dir,
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filename) if args.output_dir is not None else None
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if out_file is not None:
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mmcv.imwrite(image[..., ::-1], out_file)
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if not args.not_show:
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visualizer.show(
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image, win_name=filename, wait_time=args.show_interval)
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intermediate_imgs.clear()
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progress_bar.update()
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