detectron/projects/DensePose/doc/TOOL_APPLY_NET.md

• Apply Net
• • Dump Mode
  • Visualization Mode

Apply Net

apply_net is a tool to print or visualize DensePose results on a set of images.
It has two modes: dump to save DensePose model results to a pickle file
and show to visualize them on images.

Dump Mode

The general command form is:

python apply_net.py dump [-h] [-v] [--output <dump_file>] <config> <model> <input>

There are three mandatory arguments:

• <config>, configuration file for a given model;
• <model>, model file with trained parameters
• <input>, input image file name, pattern or folder

One can additionally provide --output argument to define the output file name,
which defaults to output.pkl.

Examples:

1. Dump results of a DensePose model with ResNet-50 FPN backbone for images
   in a folder images to file dump.pkl:

python apply_net.py dump configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl images --output dump.pkl -v

2. Dump results of a DensePose model with ResNet-50 FPN backbone for images
   with file name matching a pattern image*.jpg to file results.pkl:

python apply_net.py dump configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl "image*.jpg" --output results.pkl -v

Visualization Mode

The general command form is:

python apply_net.py show [-h] [-v] [--min_score <score>] [--nms_thresh <threshold>] [--output <image_file>] <config> <model> <input> <visualizations>

There are four mandatory arguments:

• <config>, configuration file for a given model;
• <model>, model file with trained parameters
• <input>, input image file name, pattern or folder
• <visualizations>, visualizations specifier; currently available visualizations are:
  • bbox - bounding boxes of detected persons;
  • dp_segm - segmentation masks for detected persons;
  • dp_u - each body part is colored according to the estimated values of the
    U coordinate in part parameterization;
  • dp_v - each body part is colored according to the estimated values of the
    V coordinate in part parameterization;
  • dp_contour - plots contours with color-coded U and V coordinates

One can additionally provide the following optional arguments:

• --min_score to only show detections with sufficient scores that are not lower than provided value
• --nms_thresh to additionally apply non-maximum suppression to detections at a given threshold
• --output to define visualization file name template, which defaults to output.png.
  To distinguish output file names for different images, the tool appends 1-based entry index,
  e.g. output.0001.png, output.0002.png, etc...

The following examples show how to output results of a DensePose model
with ResNet-50 FPN backbone using different visualizations for image image.jpg:

1. Show bounding box and segmentation:

python apply_net.py show configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl image.jpg bbox,dp_segm -v

2. Show bounding box and estimated U coordinates for body parts:

python apply_net.py show configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl image.jpg bbox,dp_u -v

3. Show bounding box and estimated V coordinates for body parts:

python apply_net.py show configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl image.jpg bbox,dp_v -v

4. Show bounding box and estimated U and V coordinates via contour plots:

python apply_net.py show configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl image.jpg dp_contour,bbox -v