diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..cd20be32fa7bddc1ece8f9354845d795786a436c 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +1_19_Idle/1_19_Idle_view2.mp4 filter=lfs diff=lfs merge=lfs -text diff --git a/.ipynb_checkpoints/process-checkpoint.py b/.ipynb_checkpoints/process-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..2db5b11066686c0d26765962acd697942daf8862 --- /dev/null +++ b/.ipynb_checkpoints/process-checkpoint.py @@ -0,0 +1,126 @@ +import numpy as np +import torch +import os +import re +from packaging import version as pver + +def custom_meshgrid(*args): + # ref: https://pytorch.org/docs/stable/generated/torch.meshgrid.html?highlight=meshgrid#torch.meshgrid + if pver.parse(torch.__version__) < pver.parse('1.10'): + return torch.meshgrid(*args) + else: + return torch.meshgrid(*args, indexing='ij') + + +def ray_condition(K, c2w, H, W, device, flip_flag=None): + # c2w: B, V, 4, 4 + # K: B, V, 4 + + B, V = K.shape[:2] + + j, i = custom_meshgrid( + torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype), + torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype), + ) + i = i.reshape([1, 1, H * W]).expand([B, V, H * W]) + 0.5 # [B, V, HxW] + j = j.reshape([1, 1, H * W]).expand([B, V, H * W]) + 0.5 # [B, V, HxW] + + n_flip = torch.sum(flip_flag).item() if flip_flag is not None else 0 + if n_flip > 0: + j_flip, i_flip = custom_meshgrid( + torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype), + torch.linspace(W - 1, 0, W, device=device, dtype=c2w.dtype) + ) + i_flip = i_flip.reshape([1, 1, H * W]).expand(B, 1, H * W) + 0.5 + j_flip = j_flip.reshape([1, 1, H * W]).expand(B, 1, H * W) + 0.5 + i[:, flip_flag, ...] = i_flip + j[:, flip_flag, ...] = j_flip + + fx, fy, cx, cy = K.chunk(4, dim=-1) # B,V, 1 + + zs = torch.ones_like(i) # [B, V, HxW] + xs = (i - cx) / fx * zs + ys = (j - cy) / fy * zs + zs = zs.expand_as(ys) + + directions = torch.stack((xs, ys, zs), dim=-1) # B, V, HW, 3 + directions = directions / directions.norm(dim=-1, keepdim=True) # B, V, HW, 3 + + rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2) # B, V, HW, 3 + rays_o = c2w[..., :3, 3] # B, V, 3 + rays_o = rays_o[:, :, None].expand_as(rays_d) # B, V, HW, 3 + # c2w @ dirctions + rays_dxo = torch.cross(rays_o, rays_d) # B, V, HW, 3 + plucker = torch.cat([rays_dxo, rays_d], dim=-1) + plucker = plucker.reshape(B, c2w.shape[1], H, W, 6) # B, V, H, W, 6 + # plucker = plucker.permute(0, 1, 4, 2, 3) + return plucker + +def plucker_from_camera_params( + K, R, t, W0, H0, cw_np, + H, W, + device='cpu', + normalize_intrinsics=True +): + """ + K, R, t, W0, H0 同 get_camera_params 返回， + cw_np 是 camera.matrix_world 转成的 numpy.ndarray (4×4)。 + H, W 是你要的光线网格尺寸。 + """ + # 1) 归一化 intrinsics + if normalize_intrinsics: + fx = K[0,0] / W0 + fy = K[1,1] / H0 + cx = K[0,2] / W0 + cy = K[1,2] / H0 + else: + fx, fy = K[0,0], K[1,1] + cx, cy = K[0,2], K[1,2] + + intrinsics = torch.tensor( + [[[fx, fy, cx, cy]]], + dtype=torch.float32, + device=device + ) # [1,1,4] + + # 2) cw_np 就是 c2w + # 直接转成张量，不要再调用 Matrix 方法 + c2w = torch.tensor(cw_np, dtype=torch.float32, device=device) + c2w = c2w.unsqueeze(0).unsqueeze(0) # [1,1,4,4] + + # 3) 调用 ray_condition + flip_flag = torch.zeros(1, dtype=torch.bool, device=device) + plucker = ray_condition( + intrinsics, + c2w, + H, W, + device=device, + flip_flag=flip_flag + ) # [1,1,H,W,6] + + return plucker[0,0] # [6, H, W] + + +dir_path = "1_19_Idle" +dict_files = sorted([os.path.join(dir_path, file) for file in os.listdir(dir_path) if file.endswith(".npy") and "dict" in file]) + + +for dict_file in dict_files: + params = np.load(dict_file, allow_pickle=True).item() + match_view = re.search(r'view(\d+)', str(dict_file)) + if match_view: + view = int(match_view.group(1)) + else: + view = None + + plucker_embedding = plucker_from_camera_params( + params['K'], params['R'], params['t'], params['W0'], params['H0'], params['cw'], + 480, 720, + device='cpu', + normalize_intrinsics=True + ) + plucker_embedding = torch.tensor(plucker_embedding).permute(2,0,1) + o = params['cw'][:3, 3] + r = np.linalg.norm(o) + plucker_embedding[:3] = plucker_embedding[:3] / r + torch.save(plucker_embedding, f"{view}.pt") \ No newline at end of file diff --git a/0.pt b/0.pt new file mode 100644 index 0000000000000000000000000000000000000000..d8c655b1d57bd43819e5a335aa01d24c1fa0b274 --- /dev/null +++ b/0.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4d29c274d32b0692f5bbac360d85141b110ffe2554f899a4691f09a4b72be45a +size 8295422 diff --git a/1.pt b/1.pt new file mode 100644 index 0000000000000000000000000000000000000000..ff888beb87af8ee85a2cc709aa76a113e3aba624 --- /dev/null +++ b/1.pt @@ -0,0 +1,3 @@ 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0000000000000000000000000000000000000000..2db5b11066686c0d26765962acd697942daf8862 --- /dev/null +++ b/process.py @@ -0,0 +1,126 @@ +import numpy as np +import torch +import os +import re +from packaging import version as pver + +def custom_meshgrid(*args): + # ref: https://pytorch.org/docs/stable/generated/torch.meshgrid.html?highlight=meshgrid#torch.meshgrid + if pver.parse(torch.__version__) < pver.parse('1.10'): + return torch.meshgrid(*args) + else: + return torch.meshgrid(*args, indexing='ij') + + +def ray_condition(K, c2w, H, W, device, flip_flag=None): + # c2w: B, V, 4, 4 + # K: B, V, 4 + + B, V = K.shape[:2] + + j, i = custom_meshgrid( + torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype), + torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype), + ) + i = i.reshape([1, 1, H * W]).expand([B, V, H * W]) + 0.5 # [B, V, HxW] + j = j.reshape([1, 1, H * W]).expand([B, V, H * W]) + 0.5 # [B, V, HxW] + + n_flip = torch.sum(flip_flag).item() if flip_flag is not None else 0 + if n_flip > 0: + j_flip, i_flip = custom_meshgrid( + torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype), + torch.linspace(W - 1, 0, W, device=device, dtype=c2w.dtype) + ) + i_flip = i_flip.reshape([1, 1, H * W]).expand(B, 1, H * W) + 0.5 + j_flip = j_flip.reshape([1, 1, H * W]).expand(B, 1, H * W) + 0.5 + i[:, flip_flag, ...] = i_flip + j[:, flip_flag, ...] = j_flip + + fx, fy, cx, cy = K.chunk(4, dim=-1) # B,V, 1 + + zs = torch.ones_like(i) # [B, V, HxW] + xs = (i - cx) / fx * zs + ys = (j - cy) / fy * zs + zs = zs.expand_as(ys) + + directions = torch.stack((xs, ys, zs), dim=-1) # B, V, HW, 3 + directions = directions / directions.norm(dim=-1, keepdim=True) # B, V, HW, 3 + + rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2) # B, V, HW, 3 + rays_o = c2w[..., :3, 3] # B, V, 3 + rays_o = rays_o[:, :, None].expand_as(rays_d) # B, V, HW, 3 + # c2w @ dirctions + rays_dxo = torch.cross(rays_o, rays_d) # B, V, HW, 3 + plucker = torch.cat([rays_dxo, rays_d], dim=-1) + plucker = plucker.reshape(B, c2w.shape[1], H, W, 6) # B, V, H, W, 6 + # plucker = plucker.permute(0, 1, 4, 2, 3) + return plucker + +def plucker_from_camera_params( + K, R, t, W0, H0, cw_np, + H, W, + device='cpu', + normalize_intrinsics=True +): + """ + K, R, t, W0, H0 同 get_camera_params 返回， + cw_np 是 camera.matrix_world 转成的 numpy.ndarray (4×4)。 + H, W 是你要的光线网格尺寸。 + """ + # 1) 归一化 intrinsics + if normalize_intrinsics: + fx = K[0,0] / W0 + fy = K[1,1] / H0 + cx = K[0,2] / W0 + cy = K[1,2] / H0 + else: + fx, fy = K[0,0], K[1,1] + cx, cy = K[0,2], K[1,2] + + intrinsics = torch.tensor( + [[[fx, fy, cx, cy]]], + dtype=torch.float32, + device=device + ) # [1,1,4] + + # 2) cw_np 就是 c2w + # 直接转成张量，不要再调用 Matrix 方法 + c2w = torch.tensor(cw_np, dtype=torch.float32, device=device) + c2w = c2w.unsqueeze(0).unsqueeze(0) # [1,1,4,4] + + # 3) 调用 ray_condition + flip_flag = torch.zeros(1, dtype=torch.bool, device=device) + plucker = ray_condition( + intrinsics, + c2w, + H, W, + device=device, + flip_flag=flip_flag + ) # [1,1,H,W,6] + + return plucker[0,0] # [6, H, W] + + +dir_path = "1_19_Idle" +dict_files = sorted([os.path.join(dir_path, file) for file in os.listdir(dir_path) if file.endswith(".npy") and "dict" in file]) + + +for dict_file in dict_files: + params = np.load(dict_file, allow_pickle=True).item() + match_view = re.search(r'view(\d+)', str(dict_file)) + if match_view: + view = int(match_view.group(1)) + else: + view = None + + plucker_embedding = plucker_from_camera_params( + params['K'], params['R'], params['t'], params['W0'], params['H0'], params['cw'], + 480, 720, + device='cpu', + normalize_intrinsics=True + ) + plucker_embedding = torch.tensor(plucker_embedding).permute(2,0,1) + o = params['cw'][:3, 3] + r = np.linalg.norm(o) + plucker_embedding[:3] = plucker_embedding[:3] / r + torch.save(plucker_embedding, f"{view}.pt") \ No newline at end of file