414 lines
18 KiB
Python
414 lines
18 KiB
Python
import numpy as np
|
|
import cv2
|
|
import py3d_tools as p3d # this is actually a file in our /src folder!
|
|
from functools import reduce
|
|
import math
|
|
import torch
|
|
from einops import rearrange
|
|
from modules.shared import state, opts
|
|
from .prompt import check_is_number
|
|
from .general_utils import debug_print
|
|
|
|
def sample_from_cv2(sample: np.ndarray) -> torch.Tensor:
|
|
sample = ((sample.astype(float) / 255.0) * 2) - 1
|
|
sample = sample[None].transpose(0, 3, 1, 2).astype(np.float16)
|
|
sample = torch.from_numpy(sample)
|
|
return sample
|
|
|
|
def sample_to_cv2(sample: torch.Tensor, type=np.uint8) -> np.ndarray:
|
|
sample_f32 = rearrange(sample.squeeze().cpu().numpy(), "c h w -> h w c").astype(np.float32)
|
|
sample_f32 = ((sample_f32 * 0.5) + 0.5).clip(0, 1)
|
|
sample_int8 = (sample_f32 * 255)
|
|
return sample_int8.astype(type)
|
|
|
|
def construct_RotationMatrixHomogenous(rotation_angles):
|
|
assert(type(rotation_angles)==list and len(rotation_angles)==3)
|
|
RH = np.eye(4,4)
|
|
cv2.Rodrigues(np.array(rotation_angles), RH[0:3, 0:3])
|
|
return RH
|
|
|
|
# https://en.wikipedia.org/wiki/Rotation_matrix
|
|
def getRotationMatrixManual(rotation_angles):
|
|
|
|
rotation_angles = [np.deg2rad(x) for x in rotation_angles]
|
|
|
|
phi = rotation_angles[0] # around x
|
|
gamma = rotation_angles[1] # around y
|
|
theta = rotation_angles[2] # around z
|
|
|
|
# X rotation
|
|
Rphi = np.eye(4,4)
|
|
sp = np.sin(phi)
|
|
cp = np.cos(phi)
|
|
Rphi[1,1] = cp
|
|
Rphi[2,2] = Rphi[1,1]
|
|
Rphi[1,2] = -sp
|
|
Rphi[2,1] = sp
|
|
|
|
# Y rotation
|
|
Rgamma = np.eye(4,4)
|
|
sg = np.sin(gamma)
|
|
cg = np.cos(gamma)
|
|
Rgamma[0,0] = cg
|
|
Rgamma[2,2] = Rgamma[0,0]
|
|
Rgamma[0,2] = sg
|
|
Rgamma[2,0] = -sg
|
|
|
|
# Z rotation (in-image-plane)
|
|
Rtheta = np.eye(4,4)
|
|
st = np.sin(theta)
|
|
ct = np.cos(theta)
|
|
Rtheta[0,0] = ct
|
|
Rtheta[1,1] = Rtheta[0,0]
|
|
Rtheta[0,1] = -st
|
|
Rtheta[1,0] = st
|
|
|
|
R = reduce(lambda x,y : np.matmul(x,y), [Rphi, Rgamma, Rtheta])
|
|
|
|
return R
|
|
|
|
def getPoints_for_PerspectiveTranformEstimation(ptsIn, ptsOut, W, H, sidelength):
|
|
|
|
ptsIn2D = ptsIn[0,:]
|
|
ptsOut2D = ptsOut[0,:]
|
|
ptsOut2Dlist = []
|
|
ptsIn2Dlist = []
|
|
|
|
for i in range(0,4):
|
|
ptsOut2Dlist.append([ptsOut2D[i,0], ptsOut2D[i,1]])
|
|
ptsIn2Dlist.append([ptsIn2D[i,0], ptsIn2D[i,1]])
|
|
|
|
pin = np.array(ptsIn2Dlist) + [W/2.,H/2.]
|
|
pout = (np.array(ptsOut2Dlist) + [1.,1.]) * (0.5*sidelength)
|
|
pin = pin.astype(np.float32)
|
|
pout = pout.astype(np.float32)
|
|
|
|
return pin, pout
|
|
|
|
|
|
def warpMatrix(W, H, theta, phi, gamma, scale, fV):
|
|
|
|
# M is to be estimated
|
|
M = np.eye(4, 4)
|
|
|
|
fVhalf = np.deg2rad(fV/2.)
|
|
d = np.sqrt(W*W+H*H)
|
|
sideLength = scale*d/np.cos(fVhalf)
|
|
h = d/(2.0*np.sin(fVhalf))
|
|
n = h-(d/2.0)
|
|
f = h+(d/2.0)
|
|
|
|
# Translation along Z-axis by -h
|
|
T = np.eye(4,4)
|
|
T[2,3] = -h
|
|
|
|
# Rotation matrices around x,y,z
|
|
R = getRotationMatrixManual([phi, gamma, theta])
|
|
|
|
|
|
# Projection Matrix
|
|
P = np.eye(4,4)
|
|
P[0,0] = 1.0/np.tan(fVhalf)
|
|
P[1,1] = P[0,0]
|
|
P[2,2] = -(f+n)/(f-n)
|
|
P[2,3] = -(2.0*f*n)/(f-n)
|
|
P[3,2] = -1.0
|
|
|
|
# pythonic matrix multiplication
|
|
F = reduce(lambda x,y : np.matmul(x,y), [P, T, R])
|
|
|
|
# shape should be 1,4,3 for ptsIn and ptsOut since perspectiveTransform() expects data in this way.
|
|
# In C++, this can be achieved by Mat ptsIn(1,4,CV_64FC3);
|
|
ptsIn = np.array([[
|
|
[-W/2., H/2., 0.],[ W/2., H/2., 0.],[ W/2.,-H/2., 0.],[-W/2.,-H/2., 0.]
|
|
]])
|
|
ptsOut = np.array(np.zeros((ptsIn.shape), dtype=ptsIn.dtype))
|
|
ptsOut = cv2.perspectiveTransform(ptsIn, F)
|
|
|
|
ptsInPt2f, ptsOutPt2f = getPoints_for_PerspectiveTranformEstimation(ptsIn, ptsOut, W, H, sideLength)
|
|
|
|
# check float32 otherwise OpenCV throws an error
|
|
assert(ptsInPt2f.dtype == np.float32)
|
|
assert(ptsOutPt2f.dtype == np.float32)
|
|
M33 = cv2.getPerspectiveTransform(ptsInPt2f,ptsOutPt2f)
|
|
|
|
return M33, sideLength
|
|
|
|
def get_flip_perspective_matrix(W, H, keys, frame_idx):
|
|
perspective_flip_theta = keys.perspective_flip_theta_series[frame_idx]
|
|
perspective_flip_phi = keys.perspective_flip_phi_series[frame_idx]
|
|
perspective_flip_gamma = keys.perspective_flip_gamma_series[frame_idx]
|
|
perspective_flip_fv = keys.perspective_flip_fv_series[frame_idx]
|
|
M,sl = warpMatrix(W, H, perspective_flip_theta, perspective_flip_phi, perspective_flip_gamma, 1., perspective_flip_fv);
|
|
post_trans_mat = np.float32([[1, 0, (W-sl)/2], [0, 1, (H-sl)/2]])
|
|
post_trans_mat = np.vstack([post_trans_mat, [0,0,1]])
|
|
bM = np.matmul(M, post_trans_mat)
|
|
return bM
|
|
|
|
def flip_3d_perspective(anim_args, prev_img_cv2, keys, frame_idx):
|
|
W, H = (prev_img_cv2.shape[1], prev_img_cv2.shape[0])
|
|
return cv2.warpPerspective(
|
|
prev_img_cv2,
|
|
get_flip_perspective_matrix(W, H, keys, frame_idx),
|
|
(W, H),
|
|
borderMode=cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE
|
|
)
|
|
|
|
def anim_frame_warp(prev_img_cv2, args, anim_args, keys, frame_idx, depth_model=None, depth=None, device='cuda', half_precision = False):
|
|
|
|
if anim_args.use_depth_warping:
|
|
if depth is None and depth_model is not None:
|
|
depth = depth_model.predict(prev_img_cv2, anim_args.midas_weight, half_precision)
|
|
|
|
else:
|
|
depth = None
|
|
|
|
if anim_args.animation_mode == '2D':
|
|
prev_img = anim_frame_warp_2d(prev_img_cv2, args, anim_args, keys, frame_idx)
|
|
else: # '3D'
|
|
prev_img = anim_frame_warp_3d(device, prev_img_cv2, depth, anim_args, keys, frame_idx)
|
|
|
|
return prev_img, depth
|
|
|
|
def anim_frame_warp_2d(prev_img_cv2, args, anim_args, keys, frame_idx):
|
|
angle = keys.angle_series[frame_idx]
|
|
zoom = keys.zoom_series[frame_idx]
|
|
translation_x = keys.translation_x_series[frame_idx]
|
|
translation_y = keys.translation_y_series[frame_idx]
|
|
transform_center_x = keys.transform_center_x_series[frame_idx]
|
|
transform_center_y = keys.transform_center_y_series[frame_idx]
|
|
center_point = (args.W * transform_center_x, args.H * transform_center_y)
|
|
rot_mat = cv2.getRotationMatrix2D(center_point, angle, zoom)
|
|
trans_mat = np.float32([[1, 0, translation_x], [0, 1, translation_y]])
|
|
trans_mat = np.vstack([trans_mat, [0,0,1]])
|
|
rot_mat = np.vstack([rot_mat, [0,0,1]])
|
|
if anim_args.enable_perspective_flip:
|
|
bM = get_flip_perspective_matrix(args.W, args.H, keys, frame_idx)
|
|
rot_mat = np.matmul(bM, rot_mat, trans_mat)
|
|
else:
|
|
rot_mat = np.matmul(rot_mat, trans_mat)
|
|
return cv2.warpPerspective(
|
|
prev_img_cv2,
|
|
rot_mat,
|
|
(prev_img_cv2.shape[1], prev_img_cv2.shape[0]),
|
|
borderMode=cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE
|
|
)
|
|
|
|
def anim_frame_warp_3d(device, prev_img_cv2, depth, anim_args, keys, frame_idx):
|
|
TRANSLATION_SCALE = 1.0/200.0 # matches Disco
|
|
translate_xyz = [
|
|
-keys.translation_x_series[frame_idx] * TRANSLATION_SCALE,
|
|
keys.translation_y_series[frame_idx] * TRANSLATION_SCALE,
|
|
-keys.translation_z_series[frame_idx] * TRANSLATION_SCALE
|
|
]
|
|
rotate_xyz = [
|
|
math.radians(keys.rotation_3d_x_series[frame_idx]),
|
|
math.radians(keys.rotation_3d_y_series[frame_idx]),
|
|
math.radians(keys.rotation_3d_z_series[frame_idx])
|
|
]
|
|
if anim_args.enable_perspective_flip:
|
|
prev_img_cv2 = flip_3d_perspective(anim_args, prev_img_cv2, keys, frame_idx)
|
|
rot_mat = p3d.euler_angles_to_matrix(torch.tensor(rotate_xyz, device=device), "XYZ").unsqueeze(0)
|
|
result = transform_image_3d_switcher(device if not device.type.startswith('mps') else torch.device('cpu'), prev_img_cv2, depth, rot_mat, translate_xyz, anim_args, keys, frame_idx)
|
|
torch.cuda.empty_cache()
|
|
return result
|
|
|
|
def transform_image_3d_switcher(device, prev_img_cv2, depth_tensor, rot_mat, translate, anim_args, keys, frame_idx):
|
|
if anim_args.depth_algorithm.lower() in ['midas+adabins (old)', 'zoe+adabins (old)']:
|
|
return transform_image_3d_legacy(device, prev_img_cv2, depth_tensor, rot_mat, translate, anim_args, keys, frame_idx)
|
|
else:
|
|
return transform_image_3d_new(device, prev_img_cv2, depth_tensor, rot_mat, translate, anim_args, keys, frame_idx)
|
|
|
|
def transform_image_3d_legacy(device, prev_img_cv2, depth_tensor, rot_mat, translate, anim_args, keys, frame_idx):
|
|
# adapted and optimized version of transform_image_3d from Disco Diffusion https://github.com/alembics/disco-diffusion
|
|
w, h = prev_img_cv2.shape[1], prev_img_cv2.shape[0]
|
|
|
|
if anim_args.aspect_ratio_use_old_formula:
|
|
aspect_ratio = float(w)/float(h)
|
|
else:
|
|
aspect_ratio = keys.aspect_ratio_series[frame_idx]
|
|
|
|
near = keys.near_series[frame_idx]
|
|
far = keys.far_series[frame_idx]
|
|
fov_deg = keys.fov_series[frame_idx]
|
|
persp_cam_old = p3d.FoVPerspectiveCameras(near, far, aspect_ratio, fov=fov_deg, degrees=True, device=device)
|
|
persp_cam_new = p3d.FoVPerspectiveCameras(near, far, aspect_ratio, fov=fov_deg, degrees=True, R=rot_mat, T=torch.tensor([translate]), device=device)
|
|
|
|
# range of [-1,1] is important to torch grid_sample's padding handling
|
|
y,x = torch.meshgrid(torch.linspace(-1.,1.,h,dtype=torch.float32,device=device),torch.linspace(-1.,1.,w,dtype=torch.float32,device=device))
|
|
if depth_tensor is None:
|
|
z = torch.ones_like(x)
|
|
else:
|
|
z = torch.as_tensor(depth_tensor, dtype=torch.float32, device=device)
|
|
xyz_old_world = torch.stack((x.flatten(), y.flatten(), z.flatten()), dim=1)
|
|
|
|
xyz_old_cam_xy = persp_cam_old.get_full_projection_transform().transform_points(xyz_old_world)[:,0:2]
|
|
xyz_new_cam_xy = persp_cam_new.get_full_projection_transform().transform_points(xyz_old_world)[:,0:2]
|
|
|
|
offset_xy = xyz_new_cam_xy - xyz_old_cam_xy
|
|
# affine_grid theta param expects a batch of 2D mats. Each is 2x3 to do rotation+translation.
|
|
identity_2d_batch = torch.tensor([[1.,0.,0.],[0.,1.,0.]], device=device).unsqueeze(0)
|
|
# coords_2d will have shape (N,H,W,2).. which is also what grid_sample needs.
|
|
coords_2d = torch.nn.functional.affine_grid(identity_2d_batch, [1,1,h,w], align_corners=False)
|
|
offset_coords_2d = coords_2d - torch.reshape(offset_xy, (h,w,2)).unsqueeze(0)
|
|
|
|
image_tensor = rearrange(torch.from_numpy(prev_img_cv2.astype(np.float32)), 'h w c -> c h w').to(device)
|
|
new_image = torch.nn.functional.grid_sample(
|
|
image_tensor.add(1/512 - 0.0001).unsqueeze(0),
|
|
offset_coords_2d,
|
|
mode=anim_args.sampling_mode,
|
|
padding_mode=anim_args.padding_mode,
|
|
align_corners=False
|
|
)
|
|
|
|
# convert back to cv2 style numpy array
|
|
result = rearrange(
|
|
new_image.squeeze().clamp(0,255),
|
|
'c h w -> h w c'
|
|
).cpu().numpy().astype(prev_img_cv2.dtype)
|
|
return result
|
|
|
|
def transform_image_3d_new(device, prev_img_cv2, depth_tensor, rot_mat, translate, anim_args, keys, frame_idx):
|
|
'''
|
|
originally an adapted and optimized version of transform_image_3d from Disco Diffusion https://github.com/alembics/disco-diffusion
|
|
modified by reallybigname to control various incoming tensors
|
|
'''
|
|
if anim_args.depth_algorithm.lower().startswith('midas'): # 'Midas-3-Hybrid' or 'Midas-3.1-BeitLarge'
|
|
depth = 1
|
|
depth_factor = -1
|
|
depth_offset = -2
|
|
elif anim_args.depth_algorithm.lower() == "adabins":
|
|
depth = 1
|
|
depth_factor = 1
|
|
depth_offset = 1
|
|
elif anim_args.depth_algorithm.lower() == "leres":
|
|
depth = 1
|
|
depth_factor = 1
|
|
depth_offset = 1
|
|
elif anim_args.depth_algorithm.lower() == "zoe":
|
|
depth = 1
|
|
depth_factor = 1
|
|
depth_offset = 1
|
|
else:
|
|
raise Exception(f"Unknown depth_algorithm passed to transform_image_3d function: {anim_args.depth_algorithm}")
|
|
|
|
w, h = prev_img_cv2.shape[1], prev_img_cv2.shape[0]
|
|
|
|
# depth stretching aspect ratio (has nothing to do with image dimensions - which is why the old formula was flawed)
|
|
aspect_ratio = float(w)/float(h) if anim_args.aspect_ratio_use_old_formula else keys.aspect_ratio_series[frame_idx]
|
|
|
|
# get projection keys
|
|
near = keys.near_series[frame_idx]
|
|
far = keys.far_series[frame_idx]
|
|
fov_deg = keys.fov_series[frame_idx]
|
|
|
|
# get perspective cams old (still) and new (transformed)
|
|
persp_cam_old = p3d.FoVPerspectiveCameras(near, far, aspect_ratio, fov=fov_deg, degrees=True, device=device)
|
|
persp_cam_new = p3d.FoVPerspectiveCameras(near, far, aspect_ratio, fov=fov_deg, degrees=True, R=rot_mat, T=torch.tensor([translate]), device=device)
|
|
|
|
# make xy meshgrid - range of [-1,1] is important to torch grid_sample's padding handling
|
|
y,x = torch.meshgrid(torch.linspace(-1.,1.,h,dtype=torch.float32,device=device),torch.linspace(-1.,1.,w,dtype=torch.float32,device=device))
|
|
|
|
# test tensor for validity (some are corrupted for some reason)
|
|
depth_tensor_invalid = depth_tensor is None or torch.isnan(depth_tensor).any() or torch.isinf(depth_tensor).any() or depth_tensor.min() == depth_tensor.max()
|
|
|
|
if depth_tensor is not None:
|
|
debug_print(f"Depth_T.min: {depth_tensor.min()}, Depth_T.max: {depth_tensor.max()}")
|
|
# if invalid, create flat z for this frame
|
|
if depth_tensor_invalid:
|
|
# if none, then 3D depth is turned off, so no warning is needed.
|
|
if depth_tensor is not None:
|
|
print("Depth tensor invalid. Generating a Flat depth for this frame.")
|
|
# create flat depth
|
|
z = torch.ones_like(x)
|
|
# create z from depth tensor
|
|
else:
|
|
# prepare tensor between 0 and 1 with optional equalization and autocontrast
|
|
depth_normalized = prepare_depth_tensor(depth_tensor)
|
|
|
|
# Rescale the depth values to depth with offset (depth 2 and offset -1 would be -1 to +11)
|
|
depth_final = depth_normalized * depth + depth_offset
|
|
|
|
# depth factor (1 is normal. -1 is inverted)
|
|
if depth_factor != 1:
|
|
depth_final *= depth_factor
|
|
|
|
# console reporting of depth normalization, min, max, diff
|
|
# will *only* print to console if Dev mode is enabled in general settings of Deforum
|
|
txt_depth_min, txt_depth_max = '{:.2f}'.format(float(depth_tensor.min())), '{:.2f}'.format(float(depth_tensor.max()))
|
|
diff = '{:.2f}'.format(float(depth_tensor.max()) - float(depth_tensor.min()))
|
|
console_txt = f"\033[36mDepth normalized to {depth_final.min()}/{depth_final.max()} from"
|
|
debug_print(f"{console_txt} {txt_depth_min}/{txt_depth_max} diff {diff}\033[0m")
|
|
|
|
# add z from depth
|
|
z = torch.as_tensor(depth_final, dtype=torch.float32, device=device)
|
|
|
|
# calculate offset_xy
|
|
xyz_old_world = torch.stack((x.flatten(), y.flatten(), z.flatten()), dim=1)
|
|
xyz_old_cam_xy = persp_cam_old.get_full_projection_transform().transform_points(xyz_old_world)[:,0:2]
|
|
xyz_new_cam_xy = persp_cam_new.get_full_projection_transform().transform_points(xyz_old_world)[:,0:2]
|
|
offset_xy = xyz_new_cam_xy - xyz_old_cam_xy
|
|
|
|
# affine_grid theta param expects a batch of 2D mats. Each is 2x3 to do rotation+translation.
|
|
identity_2d_batch = torch.tensor([[1.,0.,0.],[0.,1.,0.]], device=device).unsqueeze(0)
|
|
|
|
# coords_2d will have shape (N,H,W,2).. which is also what grid_sample needs.
|
|
coords_2d = torch.nn.functional.affine_grid(identity_2d_batch, [1,1,h,w], align_corners=False)
|
|
offset_coords_2d = coords_2d - torch.reshape(offset_xy, (h,w,2)).unsqueeze(0)
|
|
|
|
# do the hyperdimensional remap
|
|
image_tensor = rearrange(torch.from_numpy(prev_img_cv2.astype(np.float32)), 'h w c -> c h w').to(device)
|
|
new_image = torch.nn.functional.grid_sample(
|
|
image_tensor.unsqueeze(0), # image_tensor.add(1/512 - 0.0001).unsqueeze(0),
|
|
offset_coords_2d,
|
|
mode=anim_args.sampling_mode,
|
|
padding_mode=anim_args.padding_mode,
|
|
align_corners=False
|
|
)
|
|
|
|
# convert back to cv2 style numpy array
|
|
result = rearrange(
|
|
new_image.squeeze().clamp(0,255),
|
|
'c h w -> h w c'
|
|
).cpu().numpy().astype(prev_img_cv2.dtype)
|
|
return result
|
|
|
|
def prepare_depth_tensor(depth_tensor=None):
|
|
# Prepares a depth tensor with normalization & equalization between 0 and 1
|
|
depth_range = depth_tensor.max() - depth_tensor.min()
|
|
depth_tensor = (depth_tensor - depth_tensor.min()) / depth_range
|
|
depth_tensor = depth_equalization(depth_tensor=depth_tensor)
|
|
return depth_tensor
|
|
|
|
def depth_equalization(depth_tensor):
|
|
"""
|
|
Perform histogram equalization on a single-channel depth tensor.
|
|
|
|
Args:
|
|
depth_tensor (torch.Tensor): A 2D depth tensor (H, W).
|
|
|
|
Returns:
|
|
torch.Tensor: Equalized depth tensor (2D).
|
|
"""
|
|
|
|
# Convert the depth tensor to a NumPy array for processing
|
|
depth_array = depth_tensor.cpu().numpy()
|
|
|
|
# Calculate the histogram of the depth values using a specified number of bins
|
|
# Increase the number of bins for higher precision depth tensors
|
|
hist, bin_edges = np.histogram(depth_array, bins=1024, range=(0, 1))
|
|
|
|
# Calculate the cumulative distribution function (CDF) of the histogram
|
|
cdf = hist.cumsum()
|
|
|
|
# Normalize the CDF so that the maximum value is 1
|
|
cdf = cdf / float(cdf[-1])
|
|
|
|
# Perform histogram equalization by mapping the original depth values to the CDF values
|
|
equalized_depth_array = np.interp(depth_array, bin_edges[:-1], cdf)
|
|
|
|
# Convert the equalized depth array back to a PyTorch tensor and return it
|
|
equalized_depth_tensor = torch.from_numpy(equalized_depth_array).to(depth_tensor.device)
|
|
|
|
return equalized_depth_tensor
|