File size: 7,296 Bytes
8377658 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 |
# modified from https://github.com/skimai/DragGAN
import copy
import math
import os
import urllib.request
from typing import List, Optional, Tuple
import numpy as np
import PIL
import PIL.Image
import PIL.ImageDraw
import torch
import torch.optim
from tqdm import tqdm
BASE_DIR = os.environ.get(
'DRAGGAN_HOME',
os.path.join(os.path.expanduser('~'), 'draggan', 'checkpoints')
)
class DownloadProgressBar(tqdm):
def update_to(self, b=1, bsize=1, tsize=None):
if tsize is not None:
self.total = tsize
self.update(b * bsize - self.n)
def download_url(url, output_path):
with DownloadProgressBar(unit='B', unit_scale=True,
miniters=1, desc=url.split('/')[-1]) as t:
urllib.request.urlretrieve(url, filename=output_path, reporthook=t.update_to)
def get_path(base_path):
save_path = os.path.join(BASE_DIR, base_path)
if not os.path.exists(save_path):
url = f"https://huggingface.co/aaronb/StyleGAN2/resolve/main/{base_path}"
print(f'{base_path} not found')
print('Try to download from huggingface: ', url)
os.makedirs(os.path.dirname(save_path), exist_ok=True)
download_url(url, save_path)
print('Downloaded to ', save_path)
return save_path
def tensor_to_PIL(img: torch.Tensor) -> PIL.Image.Image:
"""
Converts a tensor image to a PIL Image.
Args:
img (torch.Tensor): The tensor image of shape [batch_size, num_channels, height, width].
Returns:
A PIL Image object.
"""
img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
return PIL.Image.fromarray(img[0].cpu().numpy(), "RGB")
def get_ellipse_coords(
point: Tuple[int, int], radius: int = 5
) -> Tuple[int, int, int, int]:
"""
Returns the coordinates of an ellipse centered at the given point.
Args:
point (Tuple[int, int]): The center point of the ellipse.
radius (int): The radius of the ellipse.
Returns:
A tuple containing the coordinates of the ellipse in the format (x_min, y_min, x_max, y_max).
"""
center = point
return (
center[0] - radius,
center[1] - radius,
center[0] + radius,
center[1] + radius,
)
def draw_handle_target_points(
img: PIL.Image.Image,
handle_points: List[Tuple[int, int]],
target_points: List[Tuple[int, int]],
radius: int = 5):
"""
Draws handle and target points with arrow pointing towards the target point.
Args:
img (PIL.Image.Image): The image to draw on.
handle_points (List[Tuple[int, int]]): A list of handle [x,y] points.
target_points (List[Tuple[int, int]]): A list of target [x,y] points.
radius (int): The radius of the handle and target points.
"""
if not isinstance(img, PIL.Image.Image):
img = PIL.Image.fromarray(img)
if len(handle_points) == len(target_points) + 1:
target_points = copy.deepcopy(target_points) + [None]
draw = PIL.ImageDraw.Draw(img)
for handle_point, target_point in zip(handle_points, target_points):
handle_point = [handle_point[1], handle_point[0]]
# Draw the handle point
handle_coords = get_ellipse_coords(handle_point, radius)
draw.ellipse(handle_coords, fill="red")
if target_point is not None:
target_point = [target_point[1], target_point[0]]
# Draw the target point
target_coords = get_ellipse_coords(target_point, radius)
draw.ellipse(target_coords, fill="blue")
# Draw arrow head
arrow_head_length = 10.0
# Compute the direction vector of the line
dx = target_point[0] - handle_point[0]
dy = target_point[1] - handle_point[1]
angle = math.atan2(dy, dx)
# Shorten the target point by the length of the arrowhead
shortened_target_point = (
target_point[0] - arrow_head_length * math.cos(angle),
target_point[1] - arrow_head_length * math.sin(angle),
)
# Draw the arrow (main line)
draw.line([tuple(handle_point), shortened_target_point], fill='white', width=3)
# Compute the points for the arrowhead
arrow_point1 = (
target_point[0] - arrow_head_length * math.cos(angle - math.pi / 6),
target_point[1] - arrow_head_length * math.sin(angle - math.pi / 6),
)
arrow_point2 = (
target_point[0] - arrow_head_length * math.cos(angle + math.pi / 6),
target_point[1] - arrow_head_length * math.sin(angle + math.pi / 6),
)
# Draw the arrowhead
draw.polygon([tuple(target_point), arrow_point1, arrow_point2], fill='white')
return np.array(img)
def create_circular_mask(
h: int,
w: int,
center: Optional[Tuple[int, int]] = None,
radius: Optional[int] = None,
) -> torch.Tensor:
"""
Create a circular mask tensor.
Args:
h (int): The height of the mask tensor.
w (int): The width of the mask tensor.
center (Optional[Tuple[int, int]]): The center of the circle as a tuple (y, x). If None, the middle of the image is used.
radius (Optional[int]): The radius of the circle. If None, the smallest distance between the center and image walls is used.
Returns:
A boolean tensor of shape [h, w] representing the circular mask.
"""
if center is None: # use the middle of the image
center = (int(h / 2), int(w / 2))
if radius is None: # use the smallest distance between the center and image walls
radius = min(center[0], center[1], h - center[0], w - center[1])
Y, X = np.ogrid[:h, :w]
dist_from_center = np.sqrt((Y - center[0]) ** 2 + (X - center[1]) ** 2)
mask = dist_from_center <= radius
mask = torch.from_numpy(mask).bool()
return mask
def create_square_mask(
height: int, width: int, center: list, radius: int
) -> torch.Tensor:
"""Create a square mask tensor.
Args:
height (int): The height of the mask.
width (int): The width of the mask.
center (list): The center of the square mask as a list of two integers. Order [y,x]
radius (int): The radius of the square mask.
Returns:
torch.Tensor: The square mask tensor of shape (1, 1, height, width).
Raises:
ValueError: If the center or radius is invalid.
"""
if not isinstance(center, list) or len(center) != 2:
raise ValueError("center must be a list of two integers")
if not isinstance(radius, int) or radius <= 0:
raise ValueError("radius must be a positive integer")
if (
center[0] < radius
or center[0] >= height - radius
or center[1] < radius
or center[1] >= width - radius
):
raise ValueError("center and radius must be within the bounds of the mask")
mask = torch.zeros((height, width), dtype=torch.float32)
x1 = int(center[1]) - radius
x2 = int(center[1]) + radius
y1 = int(center[0]) - radius
y2 = int(center[0]) + radius
mask[y1: y2 + 1, x1: x2 + 1] = 1.0
return mask.bool()
|