mirror of https://github.com/open-mmlab/mmpose
104 lines
5.3 KiB
Markdown
104 lines
5.3 KiB
Markdown
# Visualization
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- [Single Image](#single-image)
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- [Browse Dataset](#browse-dataset)
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- [Visualizer Hook](#visualizer-hook)
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## Single Image
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`demo/image_demo.py` helps the user to visualize the prediction result of a single image, including the skeleton and heatmaps.
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```shell
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python demo/image_demo.py ${IMG} ${CONFIG} ${CHECKPOINT} [-h] [--out-file OUT_FILE] [--device DEVICE] [--draw-heatmap]
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```
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| ARGS | Description |
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| --------------------- | -------------------------------- |
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| `IMG` | The path to the test image. |
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| `CONFIG` | The path to the config file. |
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| `CHECKPOINT` | The path to the checkpoint file. |
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| `--out-file OUT_FILE` | Path to output file. |
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| `--device DEVICE` | Device used for inference. |
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| `--draw-heatmap` | Visualize the predicted heatmap. |
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Here is an example of Heatmap visualization:
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## Browse Dataset
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`tools/analysis_tools/browse_dataset.py` helps the user to browse a pose dataset visually, or save the image to a designated directory.
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```shell
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python tools/misc/browse_dataset.py ${CONFIG} [-h] [--output-dir ${OUTPUT_DIR}] [--not-show] [--phase ${PHASE}] [--mode ${MODE}] [--show-interval ${SHOW_INTERVAL}]
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```
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| ARGS | Description |
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| -------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `CONFIG` | The path to the config file. |
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| `--output-dir OUTPUT_DIR` | The target folder to save visualization results. If not specified, the visualization results will not be saved. |
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| `--not-show` | Do not show the visualization results in an external window. |
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| `--phase {train, val, test}` | Options for dataset. |
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| `--mode {original, transformed}` | Specify the type of visualized images. `original` means to show images without pre-processing; `transformed` means to show images are pre-processed. |
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| `--show-interval SHOW_INTERVAL` | Time interval between visualizing two images. |
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For instance, users who want to visualize images and annotations in COCO dataset use:
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```shell
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python tools/misc/browse_dataset.py configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_8xb64-e210_coco-256x192.py --mode original
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```
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The bounding boxes and keypoints will be plotted on the original image. Following is an example:
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The original images need to be processed before being fed into models. To visualize pre-processed images and annotations, users need to modify the argument `mode` to `transformed`. For example:
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```shell
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python tools/misc/browse_dataset.py configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_8xb64-e210_coco-256x192.py --mode transformed
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```
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Here is a processed sample
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The heatmap target will be visualized together if it is generated in the pipeline.
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## Visualizer Hook
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During validation and testing, users can specify certain arguments to visualize the output of trained models.
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To visualize in external window during testing:
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```shell
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python tools/test.py ${CONFIG} ${CHECKPOINT} --show
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```
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During validation:
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```shell
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python tools/train.py ${CONFIG} --work-dir ${WORK_DIR} --show --interval ${INTERVAL}
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```
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It is suggested to use large `INTERVAL` (e.g., 50) if users want to visualize during validation, since the wait time for each visualized instance will make the validation process very slow.
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To save visualization results in `SHOW_DIR` during testing:
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```shell
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python tools/test.py ${CONFIG} ${CHECKPOINT} --show-dir=${SHOW_DIR}
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```
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During validation:
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```shell
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python tools/train.py ${CONFIG} --work-dir ${WORK_DIR} --show-dir=${SHOW_DIR}
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```
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More details about visualization arguments can be found in [train_and_test](./train_and_test.md).
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If you use a heatmap-based method and want to visualize predicted heatmaps, you can manually specify `output_heatmaps=True` for `model.test_cfg` in config file. Another way is to add `--cfg-options='model.test_cfg.output_heatmaps=True'` at the end of your command.
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Visualization example (top: decoded keypoints; bottom: predicted heatmap):
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For top-down models, each sample only contains one instance. So there will be multiple visualization results for each image.
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