vhad/body-pose-animation
1
0
Fork 0
mirror of https://github.com/gosticks/body-pose-animation.git synced 2025-10-16 11:45:42 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

example cleanup

Browse Source
This commit is contained in:
Wlad Meixner 2021-01-26 16:03:09 +01:00 committed by Wlad
parent 8d3fe9b533
commit 9c526344f7
1 changed files with 27 additions and 23 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

50
example_fit.py
View File

@ -29,13 +29,37 @@ print("config loaded")
dataset = SMPLyDataset()
sample_index = 2
sample_transforms = [
[
[0.929741, -0.01139284, 0.36803687, 0.68193704],
[0.01440641, 0.999881, -0.00544171, 0.35154277],
[-0.36793125, 0.01036147, 0.9297949, 0.52250534],
[0, 0, 0, 1]
],
[
[0.9993728, -0.00577453, 0.03493736, 0.9268496],
[0.00514091, 0.9998211, 0.01819922, -0.07861858],
[-0.0350362, -0.0180082, 0.99922377, 0.00451744],
[0, 0, 0, 1]
],
[
[4.9928, 0.0169, 0.5675, 0.3011],
[-0.0289, 4.9951, 0.5460, 0.1138],
[-0.0135, -0.0093, 0.9999, 5.4520],
[0.0000, 0.0000, 0.0000, 1.0000]
]
]
# ------------------------------
# Load data
# ------------------------------
l = SMPLyModel(conf['modelPath'])
model = l.create_model()
keypoints, conf = dataset[2]
img = cv2.imread("samples/003.png")
img_path = "./samples/" + str(sample_index + 1).zfill(3) + ".png"
# ---------------------------------
# Generate model and get joints
@ -61,7 +85,7 @@ r = Renderer()
r.render_model(model, model_out)
r.render_joints(joints)
r.render_keypoints(keypoints)
r.render_image(img)
r.render_image_from_path(img_path)
# render openpose torso markers
r.render_points(
@ -83,28 +107,8 @@ r.start()
dtype = torch.float
device = torch.device('cpu')
# torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
# camera_transformation = torch.tensor([
# [0.929741, -0.01139284, 0.36803687, 0.68193704],
# [0.01440641, 0.999881, -0.00544171, 0.35154277],
# [-0.36793125, 0.01036147, 0.9297949, 0.52250534],
# [0, 0, 0, 1]
# ]).to(device=device, dtype=dtype)
camera_transformation = torch.tensor(
[[0.9993728, -0.00577453, 0.03493736, 0.9268496],
[0.00514091, 0.9998211, 0.01819922, -0.07861858],
[-0.0350362, -0.0180082, 0.99922377, 0.00451744],
[0, 0, 0, 1]]
).to(device=device, dtype=dtype)
# camera_transformation = torch.tensor(
# [[ 4.9928, 0.0169, 0.5675, 0.3011],
# [-0.0289, 4.9951, 0.5460, 0.1138],
# [-0.0135, -0.0093, 0.9999, 5.4520],
# [ 0.0000, 0.0000, 0.0000, 1.0000]]
# ).to(device=device, dtype=dtype)
#camera_transformation = torch.from_numpy(np.eye(4)).to(device=device, dtype=dtype)
sample_transforms[1]).to(device=device, dtype=dtype)
camera = SimpleCamera(dtype, device, z_scale=1,
transform_mat=camera_transformation)