from typing import Optional
import torch
# TODO: implement width of the line
def _draw_pixel(image: torch.Tensor, x: int, y: int, color: torch.Tensor) -> None:
r"""Draws a pixel into an image.
Args:
image: the input image to where to draw the lines with shape :math`(C,H,W)`.
x: the x coordinate of the pixel.
y: the y coordinate of the pixel.
color: the color of the pixel with :math`(C)` where :math`C` is the number of channels of the image.
Return:
Nothing is returned.
"""
image[:, y, x] = color
[docs]def draw_line(image: torch.Tensor, p1: torch.Tensor, p2: torch.Tensor, color: torch.Tensor) -> torch.Tensor:
r"""Draw a single line into an image.
Args:
image: the input image to where to draw the lines with shape :math`(C,H,W)`.
p1: the start point [x y] of the line with shape (2).
p2: the end point [x y] of the line with shape (2).
color: the color of the line with shape :math`(C)` where :math`C` is the number of channels of the image.
Return:
the image with containing the line.
Examples:
>>> image = torch.zeros(1, 8, 8)
>>> draw_line(image, torch.tensor([6, 4]), torch.tensor([1, 4]), torch.tensor([255]))
tensor([[[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 255., 255., 255., 255., 255., 255., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.]]])
"""
if (len(p1) != 2) or (len(p2) != 2):
raise ValueError("p1 and p2 must have length 2.")
if len(image.size()) != 3:
raise ValueError("image must have 3 dimensions (C,H,W).")
if color.size(0) != image.size(0):
raise ValueError("color must have the same number of channels as the image.")
if (p1[0] >= image.size(2)) or (p1[1] >= image.size(1) or (p1[0] < 0) or (p1[1] < 0)):
raise ValueError("p1 is out of bounds.")
if (p2[0] >= image.size(2)) or (p2[1] >= image.size(1) or (p2[0] < 0) or (p2[1] < 0)):
raise ValueError("p2 is out of bounds.")
# make move arguments to same device and dtype as the input image
p1, p2, color = p1.to(image), p2.to(image), color.to(image)
# assign points
x1, y1 = p1
x2, y2 = p2
# calcullate coefficients A,B,C of line
# from equation Ax + By + C = 0
A = y2 - y1
B = x1 - x2
C = x2 * y1 - x1 * y2
# make sure A is positive to utilize the functiom properly
if A < 0:
A = -A
B = -B
C = -C
# calculate the slope of the line
# check for division by zero
if B != 0:
m = -A / B
# make sure you start drawing in the right direction
x1, x2 = min(x1, x2).long(), max(x1, x2).long()
y1, y2 = min(y1, y2).long(), max(y1, y2).long()
# line equation that determines the distance away from the line
def line_equation(x, y):
return A * x + B * y + C
# vertical line
if B == 0:
image[:, y1 : y2 + 1, x1] = color
# horizontal line
elif A == 0:
image[:, y1, x1 : x2 + 1] = color
# slope between 0 and 1
elif 0 < m < 1:
for i in range(x1, x2 + 1):
_draw_pixel(image, i, y1, color)
if line_equation(i + 1, y1 + 0.5) > 0:
y1 += 1
# slope greater than or equal to 1
elif m >= 1:
for j in range(y1, y2 + 1):
_draw_pixel(image, x1, j, color)
if line_equation(x1 + 0.5, j + 1) < 0:
x1 += 1
# slope less then -1
elif m <= -1:
for j in range(y1, y2 + 1):
_draw_pixel(image, x2, j, color)
if line_equation(x2 - 0.5, j + 1) > 0:
x2 -= 1
# slope between -1 and 0
elif -1 < m < 0:
for i in range(x1, x2 + 1):
_draw_pixel(image, i, y2, color)
if line_equation(i + 1, y2 - 0.5) > 0:
y2 -= 1
return image
[docs]def draw_rectangle(
image: torch.Tensor, rectangle: torch.Tensor, color: Optional[torch.Tensor] = None, fill: Optional[bool] = None
) -> torch.Tensor:
r"""Draw N rectangles on a batch of image tensors.
Args:
image: is tensor of BxCxHxW.
rectangle: represents number of rectangles to draw in BxNx4
N is the number of boxes to draw per batch index[x1, y1, x2, y2]
4 is in (top_left.x, top_left.y, bot_right.x, bot_right.y).
color: a size 1, size 3, BxNx1, or BxNx3 tensor.
If C is 3, and color is 1 channel it will be broadcasted.
fill: is a flag used to fill the boxes with color if True.
Returns:
This operation modifies image inplace but also returns the drawn tensor for
convenience with same shape the of the input BxCxHxW.
Example:
>>> img = torch.rand(2, 3, 10, 12)
>>> rect = torch.tensor([[[0, 0, 4, 4]], [[4, 4, 10, 10]]])
>>> out = draw_rectangle(img, rect)
"""
batch, c, h, w = image.shape
batch_rect, num_rectangle, num_points = rectangle.shape
if batch != batch_rect:
raise AssertionError("Image batch and rectangle batch must be equal")
if num_points != 4:
raise AssertionError("Number of points in rectangle must be 4")
# clone rectangle, in case it's been expanded assignment from clipping causes problems
rectangle = rectangle.long().clone()
# clip rectangle to hxw bounds
rectangle[:, :, 1::2] = torch.clamp(rectangle[:, :, 1::2], 0, h - 1)
rectangle[:, :, ::2] = torch.clamp(rectangle[:, :, ::2], 0, w - 1)
if color is None:
color = torch.tensor([0.0] * c).expand(batch, num_rectangle, c)
if fill is None:
fill = False
if len(color.shape) == 1:
color = color.expand(batch, num_rectangle, c)
b, n, color_channels = color.shape
if color_channels == 1 and c == 3:
color = color.expand(batch, num_rectangle, c)
for b in range(batch):
for n in range(num_rectangle):
if fill:
image[
b,
:,
int(rectangle[b, n, 1]) : int(rectangle[b, n, 3] + 1),
int(rectangle[b, n, 0]) : int(rectangle[b, n, 2] + 1),
] = color[b, n, :, None, None]
else:
image[b, :, int(rectangle[b, n, 1]) : int(rectangle[b, n, 3] + 1), rectangle[b, n, 0]] = color[
b, n, :, None
]
image[b, :, int(rectangle[b, n, 1]) : int(rectangle[b, n, 3] + 1), rectangle[b, n, 2]] = color[
b, n, :, None
]
image[b, :, rectangle[b, n, 1], int(rectangle[b, n, 0]) : int(rectangle[b, n, 2] + 1)] = color[
b, n, :, None
]
image[b, :, rectangle[b, n, 3], int(rectangle[b, n, 0]) : int(rectangle[b, n, 2] + 1)] = color[
b, n, :, None
]
return image