3.real_time_2D_hand_pose_estimation.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import cv2
import torch

from hand_shape_pose.config import cfg
from hand_shape_pose.model.pose_network import PoseNetwork, RESIZE_DIM

from hand_shape_pose.util.vis_pose_only import draw_2d_skeleton

### specify inputs ###
config_file = "configs/eval_webcam.yaml"

######################
 
cfg.merge_from_file(config_file)
cfg.freeze()

# Load trained network model
model = PoseNetwork(cfg)
device = cfg.MODEL.DEVICE
model.to(device)
model.load_model(cfg)
model = model.eval()

# webcam settings - default image size [640x480]
cap = cv2.VideoCapture(0)

while(True):
    ret, frame= cap.read()

    # feed forward the model to obtain 2D hand pose
    with torch.no_grad():
        coord, _, est_pose_uv = model(frame, detect_hand=True)

    if est_pose_uv is not None:
        est_pose_uv = est_pose_uv.to('cpu')
        est_pose_uv[0,:,0] = est_pose_uv[0,:,0] * (coord[2]- coord[0]) /RESIZE_DIM[1]
        est_pose_uv[0,:,1] = est_pose_uv[0,:,1] * (coord[3]- coord[1]) /RESIZE_DIM[0]

        est_pose_uv[0,:,0] += coord[0]
        est_pose_uv[0,:,1] += coord[1]

        est_pose_uv = est_pose_uv[0].detach().numpy()

        # Display the resulting frame
        est_pose_uv += 16    # manual tuning to fit hand pose on top of the hand

        # draw 2D hand pose
        cv2.rectangle(frame, (coord[0], coord[1]),(coord[2], coord[3]), (0, 255, 0), 2)
        frame = draw_2d_skeleton(frame, est_pose_uv)

    # plot hand poses
    cv2.imshow('hand pose estimation', cv2.flip(frame, 1))

    if cv2.waitKey(5) & 0xFF == 27:
        break

# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()