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WIP: scale keypoints to match

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Wlad Meixner 2021-01-23 13:45:05 +01:00 committed by Wlad
parent 8018877fa6
commit f0b2015f53
5 changed files with 12 additions and 14 deletions
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4
camera_estimation.py
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@ -124,7 +124,7 @@ class CameraEstimate:
init_points_3d = torch.from_numpy(init_points_3d) init_points_3d = torch.from_numpy(init_points_3d)
params = [translation, rotation] params = [translation, rotation]
opt = torch.optim.Adam(params, lr=0.1) opt = torch.optim.Adam(params, lr=0.001)
def C(params, X): def C(params, X):
translation = params[0] translation = params[0]
@ -146,7 +146,7 @@ class CameraEstimate:
loss.float() loss.float()
loss.backward() loss.backward()
opt.step() opt.step()
stop = loss > 3e-4 stop = loss > 3e-2
current_pose = self.torch_params_to_pose(params) current_pose = self.torch_params_to_pose(params)
current_pose = current_pose.detach().numpy() current_pose = current_pose.detach().numpy()
self.renderer.scene.set_pose(self.transformed_points, current_pose) self.renderer.scene.set_pose(self.transformed_points, current_pose)

10
example_fitter.py
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@ -12,7 +12,7 @@ from model import *
import time import time
# from renderer import * # from renderer import *
from dataset import * from dataset import *
from utils import get_named_joints, estimate_depth from utils import get_named_joints, estimate_scale
ascii_logo = """\ ascii_logo = """\
/$$$$$$ /$$ /$$ /$$$$$$$ /$$ /$$ /$$ /$$$$$$ /$$ /$$ /$$$$$$$ /$$ /$$ /$$
@ -66,10 +66,10 @@ joints = model_out.joints.detach().cpu().numpy().squeeze()
# --------------------------------- # ---------------------------------
cam_est_joints_names = ["hip-left", "hip-right", cam_est_joints_names = ["hip-left", "hip-right",
"shoulder-left", "shoulder-right"] "shoulder-left", "shoulder-right"]
est_depth = estimate_depth(joints, keypoints) est_scale = estimate_scale(joints, keypoints)
# apply depth to keypoints # apply scaling to keypoints
keypoints[:, 2] = -est_depth keypoints = keypoints * est_scale
init_joints = get_named_joints(joints, cam_est_joints_names) init_joints = get_named_joints(joints, cam_est_joints_names)
init_keypoints = get_named_joints(keypoints, cam_est_joints_names) init_keypoints = get_named_joints(keypoints, cam_est_joints_names)
@ -108,7 +108,7 @@ keyp_torso = torch.Tensor(init_keypoints, device=device)
learning_rate = 1e-3 learning_rate = 1e-3
trans = Transform(dtype, device) trans = Transform(dtype, device)
proj = CameraProjSimple(dtype, device, -est_depth) proj = CameraProjSimple(dtype, device, 1)
optimizer = torch.optim.Adam(trans.parameters(), lr=learning_rate) optimizer = torch.optim.Adam(trans.parameters(), lr=learning_rate)
loss_layer = torch.nn.MSELoss() loss_layer = torch.nn.MSELoss()

2
modules/transform.py
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@ -66,5 +66,5 @@ class Transform(nn.Module):
return transform return transform
def forward(self, joints): def forward(self, joints):
R = self.get_transform_mat().squeeze() R = self.get_transform_mat()
return joints @ R + F.pad(self.translation, (0,1), value=1) return joints @ R + F.pad(self.translation, (0,1), value=1)

2
renderer.py
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@ -18,8 +18,6 @@ class Renderer:
if camera is None: if camera is None:
camera = pyrender.OrthographicCamera(ymag=1, xmag=1) camera = pyrender.OrthographicCamera(ymag=1, xmag=1)
if camera_pose is None: if camera_pose is None:
camera_pose = np.eye(4) camera_pose = np.eye(4)
camera_pose[:3, :3] = R.from_rotvec(np.pi/2 * np.array([0, 0, 0])).as_matrix() camera_pose[:3, :3] = R.from_rotvec(np.pi/2 * np.array([0, 0, 0])).as_matrix()

8
utils.py
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@ -130,8 +130,8 @@ def openpose_to_opengl_coords(
points = np.array([ points = np.array([
[ [
x / real_width * 2 - 1, x / real_width * 5 - 1,
-y / real_height * 2 + 1, -y / real_height * 5 + 1,
0 0
] for (x, y, z) in input_data]) ] for (x, y, z) in input_data])
@ -176,11 +176,11 @@ def render_points(scene, points, radius=0.005, color=[0.0, 0.0, 1.0, 1.0], name=
tfs = np.tile(np.eye(4), (len(points), 1, 1)) tfs = np.tile(np.eye(4), (len(points), 1, 1))
tfs[:, :3, 3] = points tfs[:, :3, 3] = points
pcl = pyrender.Mesh.from_trimesh(sm, poses=tfs) pcl = pyrender.Mesh.from_trimesh(sm, poses=tfs)
# return the render scene node # return the render scsene node
return scene.add(pcl, name=name) return scene.add(pcl, name=name)
def estimate_depth(joints, keypoints, pairs=[ def estimate_scale(joints, keypoints, pairs=[
("shoulder-right", "hip-right"), ("shoulder-right", "hip-right"),
("shoulder-left", "hip-left") ("shoulder-left", "hip-left")
], cam_fy=1): ], cam_fy=1):