mmpose/docs/en/visualization.md

Visualization

• Single Image
• Browse Dataset
• Visualizer Hook

Single Image

demo/image_demo.py helps the user to visualize the prediction result of a single image, including the skeleton and heatmaps.

python demo/image_demo.py ${IMG} ${CONFIG} ${CHECKPOINT} [-h] [--out-file OUT_FILE] [--device DEVICE] [--draw-heatmap]

ARGS	Description
IMG	The path to the test image.
CONFIG	The path to the config file.
CHECKPOINT	The path to the checkpoint file.
--out-file OUT_FILE	Path to output file.
--device DEVICE	Device used for inference.
--draw-heatmap	Visualize the predicted heatmap.

Here is an example of Heatmap visualization:

Browse Dataset

tools/analysis_tools/browse_dataset.py helps the user to browse a pose dataset visually, or save the image to a designated directory.

python tools/misc/browse_dataset.py ${CONFIG} [-h] [--output-dir ${OUTPUT_DIR}] [--not-show] [--phase ${PHASE}] [--mode ${MODE}] [--show-interval ${SHOW_INTERVAL}]

ARGS	Description
CONFIG	The path to the config file.
--output-dir OUTPUT_DIR	The target folder to save visualization results. If not specified, the visualization results will not be saved.
--not-show	Do not show the visualization results in an external window.
--phase {train, val, test}	Options for dataset.
--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.
--show-interval SHOW_INTERVAL	Time interval between visualizing two images.

For instance, users who want to visualize images and annotations in COCO dataset use:

python tools/misc/browse_dataset.py configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_8xb64-e210_coco-256x192.py --mode original

The bounding boxes and keypoints will be plotted on the original image. Following is an example:

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:

python tools/misc/browse_dataset.py configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_8xb64-e210_coco-256x192.py --mode transformed

Here is a processed sample

The heatmap target will be visualized together if it is generated in the pipeline.

Visualizer Hook

During validation and testing, users can specify certain arguments to visualize the output of trained models.

To visualize in external window during testing:

python tools/test.py ${CONFIG} ${CHECKPOINT} --show

During validation:

python tools/train.py ${CONFIG} --work-dir ${WORK_DIR} --show --interval ${INTERVAL}

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.

To save visualization results in SHOW_DIR during testing:

python tools/test.py ${CONFIG} ${CHECKPOINT} --show-dir=${SHOW_DIR}

During validation:

python tools/train.py ${CONFIG} --work-dir ${WORK_DIR} --show-dir=${SHOW_DIR}

More details about visualization arguments can be found in train_and_test.

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.

Visualization example (top: decoded keypoints; bottom: predicted heatmap):

For top-down models, each sample only contains one instance. So there will be multiple visualization results for each image.