mirror of
https://github.com/gosticks/body-pose-animation.git
synced 2025-10-16 11:45:42 +00:00
184 lines
4.8 KiB
Python
184 lines
4.8 KiB
Python
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from modules.camera import SimpleCamera
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from modules.transform import Transform
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from modules.pose import BodyPose, train_pose
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from renderer import Renderer
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import torch
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import torchgeometry as tgm
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from model import *
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# from renderer import *
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from dataset import *
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from utils.mapping import *
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from utils.general import *
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from camera_estimation import TorchCameraEstimate
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# mapping = [55, 12, 17, 19, 21, 16, 18, 20, 0, 2, 5,
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# 8, 1, 4, 7, 56, 57, 58, 59] # 7, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34]
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# arr = np.ones(127) * -1 # arr = np.ones(45) * -1
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# for i, v in enumerate(mapping): # for i, v in enumerate(mapping):
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# arr[v] = i # arr[v] = i
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# print(v, i) # print(v, i)
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# for v in arr: # for v in arr:
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# print( # print(
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# int(v), "," # int(v), ","
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# ) # )
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# print(arr) # print(arr)
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ascii_logo = """\
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/$$$$$$ /$$ /$$ /$$$$$$$ /$$ /$$ /$$
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/$$__ $$| $$$ /$$$| $$__ $$| $$ | $$ /$$/
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| $$ \__/| $$$$ /$$$$| $$ \ $$| $$ \ $$ /$$/
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| $$$$$$ | $$ $$/$$ $$| $$$$$$$/| $$ \ $$$$/
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\____ $$| $$ $$$| $$| $$____/ | $$ \ $$/
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/$$ \ $$| $$\ $ | $$| $$ | $$ | $$
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| $$$$$$/| $$ \/ | $$| $$ | $$$$$$$$| $$
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\______/ |__/ |__/|__/ |________/|__/
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"""
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print(ascii_logo)
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conf = load_config()
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print("config loaded")
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dataset = SMPLyDataset()
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sample_index = 2
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sample_transforms = [
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[
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[0.929741, -0.01139284, 0.36803687, 0.68193704],
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[0.01440641, 0.999881, -0.00544171, 0.35154277],
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[-0.36793125, 0.01036147, 0.9297949, 0.52250534],
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[0, 0, 0, 1]
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],
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[
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[9.9901e-01, -3.7266e-02, -2.4385e-02, 7.6932e-01],
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[3.5270e-02, 9.9635e-01, -7.7715e-02, 3.0069e-01],
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[2.7193e-02, 7.6778e-02, 9.9668e-01, -7.6563e-04],
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[0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00]
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],
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[
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[9.99947985e-01, - 7.05885983e-03, -7.36209961e-03, 8.18256989e-01],
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[7.58265353e-03, 9.97249329e-01, 7.37311259e-02, - 6.41522022e-02],
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[6.82139121e-03, - 7.37831150e-02, 9.97250982e-01, 6.04774204e-04],
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[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]
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],
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[
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[4.9928, 0.0169, 0.5675, 0.3011],
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[-0.0289, 4.9951, 0.5460, 0.1138],
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[-0.0135, -0.0093, 0.9999, 5.4520],
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[0.0000, 0.0000, 0.0000, 1.0000]
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]
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]
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# ------------------------------
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# Load data
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# ------------------------------
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l = SMPLyModel(conf['modelPath'])
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model = l.create_model()
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keypoints, conf = dataset[sample_index]
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img_path = "./samples/" + str(sample_index) + ".png"
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print(img_path)
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# ---------------------------------
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# Generate model and get joints
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# ---------------------------------
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model_out = model()
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joints = model_out.joints.detach().cpu().numpy().squeeze()
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# ---------------------------------
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# Draw in the joints of interest
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# ---------------------------------
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est_scale = estimate_scale(joints, keypoints)
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print("ESTIMATED SCALE:", est_scale)
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# apply scaling to keypoints
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keypoints = keypoints * est_scale
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r = Renderer()
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# integrating Camera Estimation
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init_joints = get_torso(joints)
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init_keypoints = get_torso(keypoints)
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print("image path:", img_path)
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# setup renderer
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r.render_model(model, model_out)
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# r.render_joints(joints)
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# r.render_keypoints(keypoints)
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r.render_image_from_path(img_path, est_scale)
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# render openpose torso markers
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render_keypoints = r.render_points(
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keypoints,
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radius=0.005,
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color=[1.0, 1.0, 1.0, 1.0])
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render_keypoints = r.render_keypoints(
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init_keypoints,
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radius=0.01,
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color=[1.0, 0.0, 1.0, 1.0])
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render_points = r.render_points(
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init_joints,
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radius=0.01,
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color=[0.0, 0.1, 0.0, 1.0], name="torso", group_name="body")
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camera = TorchCameraEstimate(
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model,
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dataset=dataset,
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keypoints=keypoints,
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renderer=r,
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device=torch.device('cpu'),
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dtype=torch.float32,
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image_path=img_path,
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est_scale=est_scale
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)
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pose, transform, cam_trans = camera.estimate_camera_pos()
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camera.setup_visualization(render_points, render_keypoints)
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# start renderer
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# r.start()
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dtype = torch.float
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device = torch.device('cpu')
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camera_transformation = transform.clone().detach().to(device=device, dtype=dtype)
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camera_int = pose.clone().detach().to(device=device, dtype=dtype)
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camera_params = cam_trans.clone().detach().to(device=device, dtype=dtype)
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camera = SimpleCamera(dtype, device,
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transform_mat=camera_transformation,
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# camera_intrinsics=camera_int, camera_trans_rot=camera_params
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)
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r.set_group_pose("body", camera_transformation.detach().cpu().numpy())
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print("using device", device)
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train_pose(
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model,
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learning_rate=1e-1,
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keypoints=keypoints,
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keypoint_conf=conf,
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# TODO: use camera_estimation camera here
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camera=camera,
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renderer=r,
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device=device,
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iterations=10
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)
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