# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
path: data/datasets/crowdpose
labels: kp_labels
train: kp_labels/img_txt/trainval.txt
val: kp_labels/img_txt/test.txt

train_annotations: crowdpose_trainval.json
val_annotations: crowdpose_test.json

pose_obj: True  # write pose object labels

nc: 15  # number of classes (person class + 14 keypoint classes)
num_coords: 28 # number of keypoint coordinates (x, y)

# class names
names: [ 'person',
         'left_shoulder', 'right_shoulder',
         'left_elbow', 'right_elbow',
         'left_wrist', 'right_wrist',
         'left_hip', 'right_hip',
         'left_knee', 'right_knee',
         'left_ankle', 'right_ankle',
         'head', 'neck']

kp_bbox: 0.05  # keypoint object size (normalized by longest img dim)
kp_flip: [1, 0, 3, 2, 5, 4, 7, 6, 9, 8, 11, 10, 12, 13]  # for left-right keypoint flipping
kp_left: [0, 2, 4, 6, 8, 10]  # left keypoints

kp_names_short:
  0: 'ls'
  1: 'rs'
  2: 'lel'
  3: 'rel'
  4: 'lw'
  5: 'rw'
  6: 'lh'
  7: 'rh'
  8: 'lk'
  9: 'rk'
  10: 'la'
  11: 'ra'
  12: 'h'
  13: 'n'

# segments for plotting
segments:
  1: [0, 13]
  2: [1, 13]
  3: [0, 2]
  4: [2, 4]
  5: [1, 3]
  6: [3, 5]
  7: [0, 6]
  8: [6, 7]
  9: [7, 1]
  10: [6, 8]
  11: [8, 10]
  12: [7, 9]
  13: [9, 11]
  14: [12, 13]