mirror of
https://github.com/gosticks/body-pose-animation.git
synced 2025-10-16 11:45:42 +00:00
147 lines
3.9 KiB
Python
147 lines
3.9 KiB
Python
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from modules.camera import CameraProjSimple
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from modules.transform import Transform
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from renderer import Renderer
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import yaml
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import torch
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import torch.nn.functional as F
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import torch.nn as nn
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from math import tan, radians
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from model import *
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import time
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# from renderer import *
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from dataset import *
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from utils import get_named_joints, estimate_scale
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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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dtype = torch.float
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# torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
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device = torch.device('cpu')
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def load_config():
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with open('./config.yaml') as file:
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# The FullLoader parameter handles the conversion from YAML
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# scalar values to Python the dictionary format
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config = yaml.load(file, Loader=yaml.FullLoader)
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return config
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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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# ------------------------------
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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[0]
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print("keypoints shape:", keypoints.shape)
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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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cam_est_joints_names = ["hip-left", "hip-right",
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"shoulder-left", "shoulder-right"]
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est_scale = estimate_scale(joints, keypoints)
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# apply scaling to keypoints
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keypoints = keypoints * est_scale
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init_joints = get_named_joints(joints, cam_est_joints_names)
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init_keypoints = get_named_joints(keypoints, cam_est_joints_names)
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# setup renderer
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r = 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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# render openpose torso markers
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r.render_points(
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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], name="ops_torso", group_name="keypoints")
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r.render_points(
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init_joints,
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radius=0.01,
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color=[0.0, 0.7, 0.0, 1.0], name="body_torso", group_name="body")
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keypoints[:, 2] = 0
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init_keypoints = get_named_joints(keypoints, cam_est_joints_names)
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# start renderer
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r.start()
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# -------------------------------------
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# Optimize for translation and rotation
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# -------------------------------------
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smpl_torso = torch.Tensor(init_joints, device=device)
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keyp_torso = torch.Tensor(init_keypoints, device=device)
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learning_rate = 1e-3
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trans = Transform(dtype, device)
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proj = CameraProjSimple(dtype, device, 1)
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optimizer = torch.optim.Adam(trans.parameters(), lr=learning_rate)
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loss_layer = torch.nn.MSELoss()
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for t in range(50000):
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homog_coord = torch.ones(list(smpl_torso.shape)[:-1] + [1],
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dtype=smpl_torso.dtype,
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device=device)
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# Convert the points to homogeneous coordinates
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points_h = torch.cat([smpl_torso, homog_coord], dim=-1)
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points = trans(points_h)
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points_2d = proj(points)
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# point wise differences
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diff = points_2d - keyp_torso
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# Compute cost function
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# loss = torch.norm(diff)
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loss = loss_layer(keyp_torso, points_2d)
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if t % 100 == 99:
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print(t, loss.item())
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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with torch.no_grad():
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R = trans.get_transform_mat().numpy()
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translation = trans.translation.detach().numpy()
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# update model rendering
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r.set_homog_group_transform("body", R, translation)
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# for t in range(50000):
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# print("do cost evaluation here")
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