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import numpy as np
import cv2
from basicsr.utils import img2tensor
import torch
import torch.nn.functional as F
def resize_numpy_image(image, max_resolution=768 * 768, resize_short_edge=None):
h, w = image.shape[:2]
w_org = image.shape[1]
if resize_short_edge is not None:
k = resize_short_edge / min(h, w)
else:
k = max_resolution / (h * w)
k = k**0.5
h = int(np.round(h * k / 64)) * 64
w = int(np.round(w * k / 64)) * 64
image = cv2.resize(image, (w, h), interpolation=cv2.INTER_LANCZOS4)
scale = w/w_org
return image, scale
def split_ldm(ldm):
x = []
y = []
for p in ldm:
x.append(p[0])
y.append(p[1])
return x,y
def process_move(path_mask, h, w, dx, dy, scale, input_scale, resize_scale, up_scale, up_ft_index, w_edit, w_content, w_contrast, w_inpaint, precision, path_mask_ref=None):
dx, dy = dx*input_scale, dy*input_scale
if isinstance(path_mask, str):
mask_x0 = cv2.imread(path_mask)
else:
mask_x0 = path_mask
mask_x0 = cv2.resize(mask_x0, (h, w))
if path_mask_ref is not None:
if isinstance(path_mask_ref, str):
mask_x0_ref = cv2.imread(path_mask_ref)
else:
mask_x0_ref = path_mask_ref
mask_x0_ref = cv2.resize(mask_x0_ref, (h, w))
else:
mask_x0_ref=None
mask_x0 = img2tensor(mask_x0)[0]
mask_x0 = (mask_x0>0.5).float().to('cuda', dtype=precision)
if mask_x0_ref is not None:
mask_x0_ref = img2tensor(mask_x0_ref)[0]
mask_x0_ref = (mask_x0_ref>0.5).float().to('cuda', dtype=precision)
mask_org = F.interpolate(mask_x0[None,None], (int(mask_x0.shape[-2]//scale), int(mask_x0.shape[-1]//scale)))>0.5
mask_tar = F.interpolate(mask_x0[None,None], (int(mask_x0.shape[-2]//scale*resize_scale), int(mask_x0.shape[-1]//scale*resize_scale)))>0.5
mask_cur = torch.roll(mask_tar, (int(dy//scale*resize_scale), int(dx//scale*resize_scale)), (-2,-1))
pad_size_x = abs(mask_tar.shape[-1]-mask_org.shape[-1])//2
pad_size_y = abs(mask_tar.shape[-2]-mask_org.shape[-2])//2
if resize_scale>1:
sum_before = torch.sum(mask_cur)
mask_cur = mask_cur[:,:,pad_size_y:pad_size_y+mask_org.shape[-2],pad_size_x:pad_size_x+mask_org.shape[-1]]
sum_after = torch.sum(mask_cur)
if sum_after != sum_before:
raise ValueError('Resize out of bounds, exiting.')
else:
temp = torch.zeros(1,1,mask_org.shape[-2], mask_org.shape[-1]).to(mask_org.device)
temp[:,:,pad_size_y:pad_size_y+mask_cur.shape[-2],pad_size_x:pad_size_x+mask_cur.shape[-1]]=mask_cur
mask_cur =temp>0.5
mask_other = (1-((mask_cur+mask_org)>0.5).float())>0.5
mask_overlap = ((mask_cur.float()+mask_org.float())>1.5).float()
mask_non_overlap = (mask_org.float()-mask_overlap)>0.5
return {
"mask_x0":mask_x0,
"mask_x0_ref":mask_x0_ref,
"mask_tar":mask_tar,
"mask_cur":mask_cur,
"mask_other":mask_other,
"mask_overlap":mask_overlap,
"mask_non_overlap":mask_non_overlap,
"up_scale":up_scale,
"up_ft_index":up_ft_index,
"resize_scale":resize_scale,
"w_edit":w_edit,
"w_content":w_content,
"w_contrast":w_contrast,
"w_inpaint":w_inpaint,
}
def process_drag_face(h, w, x, y, x_cur, y_cur, scale, input_scale, up_scale, up_ft_index, w_edit, w_inpaint, precision):
for i in range(len(x)):
x[i] = int(x[i]*input_scale)
y[i] = int(y[i]*input_scale)
x_cur[i] = int(x_cur[i]*input_scale)
y_cur[i] = int(y_cur[i]*input_scale)
mask_tar = []
for p_idx in range(len(x)):
mask_i = torch.zeros(int(h//scale), int(w//scale)).cuda()
y_clip = int(np.clip(y[p_idx]//scale, 1, mask_i.shape[0]-2))
x_clip = int(np.clip(x[p_idx]//scale, 1, mask_i.shape[1]-2))
mask_i[y_clip-1:y_clip+2,x_clip-1:x_clip+2]=1
mask_i = mask_i>0.5
mask_tar.append(mask_i)
mask_cur = []
for p_idx in range(len(x_cur)):
mask_i = torch.zeros(int(h//scale), int(w//scale)).cuda()
y_clip = int(np.clip(y_cur[p_idx]//scale, 1, mask_i.shape[0]-2))
x_clip = int(np.clip(x_cur[p_idx]//scale, 1, mask_i.shape[1]-2))
mask_i[y_clip-1:y_clip+2,x_clip-1:x_clip+2]=1
mask_i=mask_i>0.5
mask_cur.append(mask_i)
return {
"mask_tar":mask_tar,
"mask_cur":mask_cur,
"up_scale":up_scale,
"up_ft_index":up_ft_index,
"w_edit": w_edit,
"w_inpaint": w_inpaint,
}
def process_drag(path_mask, h, w, x, y, x_cur, y_cur, scale, input_scale, up_scale, up_ft_index, w_edit, w_inpaint, w_content, precision, latent_in):
if isinstance(path_mask, str):
mask_x0 = cv2.imread(path_mask)
else:
mask_x0 = path_mask
mask_x0 = cv2.resize(mask_x0, (h, w))
mask_x0 = img2tensor(mask_x0)[0]
dict_mask = {}
dict_mask['base'] = mask_x0
mask_x0 = (mask_x0>0.5).float().to('cuda', dtype=precision)
mask_other = F.interpolate(mask_x0[None,None], (int(mask_x0.shape[-2]//scale), int(mask_x0.shape[-1]//scale)))<0.5
mask_tar = []
mask_cur = []
for p_idx in range(len(x)):
mask_tar_i = torch.zeros(int(mask_x0.shape[-2]//scale), int(mask_x0.shape[-1]//scale)).to('cuda', dtype=precision)
mask_cur_i = torch.zeros(int(mask_x0.shape[-2]//scale), int(mask_x0.shape[-1]//scale)).to('cuda', dtype=precision)
y_tar_clip = int(np.clip(y[p_idx]//scale, 1, mask_tar_i.shape[0]-2))
x_tar_clip = int(np.clip(x[p_idx]//scale, 1, mask_tar_i.shape[0]-2))
y_cur_clip = int(np.clip(y_cur[p_idx]//scale, 1, mask_cur_i.shape[0]-2))
x_cur_clip = int(np.clip(x_cur[p_idx]//scale, 1, mask_cur_i.shape[0]-2))
mask_tar_i[y_tar_clip-1:y_tar_clip+2,x_tar_clip-1:x_tar_clip+2]=1
mask_cur_i[y_cur_clip-1:y_cur_clip+2,x_cur_clip-1:x_cur_clip+2]=1
mask_tar_i = mask_tar_i>0.5
mask_cur_i=mask_cur_i>0.5
mask_tar.append(mask_tar_i)
mask_cur.append(mask_cur_i)
latent_in[:,:,y_cur_clip//up_scale-1:y_cur_clip//up_scale+2, x_cur_clip//up_scale-1:x_cur_clip//up_scale+2] = latent_in[:,:, y_tar_clip//up_scale-1:y_tar_clip//up_scale+2, x_tar_clip//up_scale-1:x_tar_clip//up_scale+2]
return {
"dict_mask":dict_mask,
"mask_x0":mask_x0,
"mask_tar":mask_tar,
"mask_cur":mask_cur,
"mask_other":mask_other,
"up_scale":up_scale,
"up_ft_index":up_ft_index,
"w_edit": w_edit,
"w_inpaint": w_inpaint,
"w_content": w_content,
"latent_in":latent_in,
}
def process_appearance(path_mask, path_mask_replace, h, w, scale, input_scale, up_scale, up_ft_index, w_edit, w_content, precision):
if isinstance(path_mask, str):
mask_base = cv2.imread(path_mask)
else:
mask_base = path_mask
mask_base = cv2.resize(mask_base, (h, w))
if isinstance(path_mask_replace, str):
mask_replace = cv2.imread(path_mask_replace)
else:
mask_replace = path_mask_replace
mask_replace = cv2.resize(mask_replace, (h, w))
dict_mask = {}
mask_base = img2tensor(mask_base)[0]
dict_mask['base'] = mask_base
mask_base = (mask_base>0.5).to('cuda', dtype=precision)
mask_replace = img2tensor(mask_replace)[0]
dict_mask['replace'] = mask_replace
mask_replace = (mask_replace>0.5).to('cuda', dtype=precision)
mask_base_cur = F.interpolate(mask_base[None,None], (int(mask_base.shape[-2]//scale), int(mask_base.shape[-1]//scale)))>0.5
mask_replace_cur = F.interpolate(mask_replace[None,None], (int(mask_replace.shape[-2]//scale), int(mask_replace.shape[-1]//scale)))>0.5
return {
"dict_mask":dict_mask,
"mask_base_cur":mask_base_cur,
"mask_replace_cur":mask_replace_cur,
"up_scale":up_scale,
"up_ft_index":up_ft_index,
"w_edit":w_edit,
"w_content":w_content,
}
def process_paste(path_mask, h, w, dx, dy, scale, input_scale, up_scale, up_ft_index, w_edit, w_content, precision, resize_scale=None):
dx, dy = dx*input_scale, dy*input_scale
if isinstance(path_mask, str):
mask_base = cv2.imread(path_mask)
else:
mask_base = path_mask
mask_base = cv2.resize(mask_base, (h, w))
dict_mask = {}
mask_base = img2tensor(mask_base)[0][None, None]
mask_base = (mask_base>0.5).to('cuda', dtype=precision)
if resize_scale is not None and resize_scale!=1:
hi, wi = mask_base.shape[-2], mask_base.shape[-1]
mask_base = F.interpolate(mask_base, (int(hi*resize_scale), int(wi*resize_scale)))
pad_size_x = np.abs(mask_base.shape[-1]-wi)//2
pad_size_y = np.abs(mask_base.shape[-2]-hi)//2
if resize_scale>1:
mask_base = mask_base[:,:,pad_size_y:pad_size_y+hi,pad_size_x:pad_size_x+wi]
else:
temp = torch.zeros(1,1,hi, wi).to(mask_base.device)
temp[:,:,pad_size_y:pad_size_y+mask_base.shape[-2],pad_size_x:pad_size_x+mask_base.shape[-1]]=mask_base
mask_base = temp
mask_replace = mask_base.clone()
mask_base = torch.roll(mask_base, (int(dy), int(dx)), (-2,-1))
dict_mask['base'] = mask_base[0,0]
dict_mask['replace'] = mask_replace[0,0]
mask_replace = (mask_replace>0.5).to('cuda', dtype=precision)
mask_base_cur = F.interpolate(mask_base, (int(mask_base.shape[-2]//scale), int(mask_base.shape[-1]//scale)))>0.5
mask_replace_cur = torch.roll(mask_base_cur, (-int(dy/scale), -int(dx/scale)), (-2,-1))
return {
"dict_mask":dict_mask,
"mask_base_cur":mask_base_cur,
"mask_replace_cur":mask_replace_cur,
"up_scale":up_scale,
"up_ft_index":up_ft_index,
"w_edit":w_edit,
"w_content":w_content,
"w_edit":w_edit,
"w_content":w_content,
} |