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# Copyright (C) 2023 Deforum LLC
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, version 3 of the License.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
# Contact the authors: https://deforum.github.io/
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