|
|
|
|
@ -21,7 +21,16 @@ from .ops import clip_boxes, scale_image, xywh2xyxy, xyxy2xywh |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class Colors: |
|
|
|
|
"""Ultralytics color palette https://ultralytics.com/.""" |
|
|
|
|
"""Ultralytics default color palette https://ultralytics.com/. |
|
|
|
|
|
|
|
|
|
This class provides methods to work with the Ultralytics color palette, including converting hex color codes to |
|
|
|
|
RGB values. |
|
|
|
|
|
|
|
|
|
Attributes: |
|
|
|
|
palette (list of tuple): List of RGB color values. |
|
|
|
|
n (int): The number of colors in the palette. |
|
|
|
|
pose_palette (np.array): A specific color palette array with dtype np.uint8. |
|
|
|
|
""" |
|
|
|
|
|
|
|
|
|
def __init__(self): |
|
|
|
|
"""Initialize colors as hex = matplotlib.colors.TABLEAU_COLORS.values().""" |
|
|
|
|
@ -36,12 +45,13 @@ class Colors: |
|
|
|
|
dtype=np.uint8) |
|
|
|
|
|
|
|
|
|
def __call__(self, i, bgr=False): |
|
|
|
|
"""Converts hex color codes to rgb values.""" |
|
|
|
|
"""Converts hex color codes to RGB values.""" |
|
|
|
|
c = self.palette[int(i) % self.n] |
|
|
|
|
return (c[2], c[1], c[0]) if bgr else c |
|
|
|
|
|
|
|
|
|
@staticmethod |
|
|
|
|
def hex2rgb(h): # rgb order (PIL) |
|
|
|
|
def hex2rgb(h): |
|
|
|
|
"""Converts hex color codes to RGB values (i.e. default PIL order).""" |
|
|
|
|
return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@ -49,7 +59,17 @@ colors = Colors() # create instance for 'from utils.plots import colors' |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class Annotator: |
|
|
|
|
"""YOLOv8 Annotator for train/val mosaics and jpgs and detect/hub inference annotations.""" |
|
|
|
|
"""Ultralytics Annotator for train/val mosaics and JPGs and predictions annotations. |
|
|
|
|
|
|
|
|
|
Attributes: |
|
|
|
|
im (Image.Image or numpy array): The image to annotate. |
|
|
|
|
pil (bool): Whether to use PIL or cv2 for drawing annotations. |
|
|
|
|
font (ImageFont.truetype or ImageFont.load_default): Font used for text annotations. |
|
|
|
|
lw (float): Line width for drawing. |
|
|
|
|
skeleton (List[List[int]]): Skeleton structure for keypoints. |
|
|
|
|
limb_color (List[int]): Color palette for limbs. |
|
|
|
|
kpt_color (List[int]): Color palette for keypoints. |
|
|
|
|
""" |
|
|
|
|
|
|
|
|
|
def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'): |
|
|
|
|
"""Initialize the Annotator class with image and line width along with color palette for keypoints and limbs.""" |
|
|
|
|
@ -113,6 +133,7 @@ class Annotator: |
|
|
|
|
|
|
|
|
|
def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False): |
|
|
|
|
"""Plot masks at once. |
|
|
|
|
|
|
|
|
|
Args: |
|
|
|
|
masks (tensor): predicted masks on cuda, shape: [n, h, w] |
|
|
|
|
colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n] |
|
|
|
|
@ -292,7 +313,37 @@ def plot_labels(boxes, cls, names=(), save_dir=Path(''), on_plot=None): |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True): |
|
|
|
|
"""Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop.""" |
|
|
|
|
"""Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop. |
|
|
|
|
|
|
|
|
|
This function takes a bounding box and an image, and then saves a cropped portion of the image according |
|
|
|
|
to the bounding box. Optionally, the crop can be squared, and the function allows for gain and padding |
|
|
|
|
adjustments to the bounding box. |
|
|
|
|
|
|
|
|
|
Args: |
|
|
|
|
xyxy (torch.Tensor or list): A tensor or list representing the bounding box in xyxy format. |
|
|
|
|
im (numpy.ndarray): The input image. |
|
|
|
|
file (Path, optional): The path where the cropped image will be saved. Defaults to 'im.jpg'. |
|
|
|
|
gain (float, optional): A multiplicative factor to increase the size of the bounding box. Defaults to 1.02. |
|
|
|
|
pad (int, optional): The number of pixels to add to the width and height of the bounding box. Defaults to 10. |
|
|
|
|
square (bool, optional): If True, the bounding box will be transformed into a square. Defaults to False. |
|
|
|
|
BGR (bool, optional): If True, the image will be saved in BGR format, otherwise in RGB. Defaults to False. |
|
|
|
|
save (bool, optional): If True, the cropped image will be saved to disk. Defaults to True. |
|
|
|
|
|
|
|
|
|
Returns: |
|
|
|
|
(numpy.ndarray): The cropped image. |
|
|
|
|
|
|
|
|
|
Examples: |
|
|
|
|
```python |
|
|
|
|
from ultralytics.utils.plotting import save_one_box |
|
|
|
|
|
|
|
|
|
xyxy = [50, 50, 150, 150] |
|
|
|
|
im = cv2.imread('image.jpg') |
|
|
|
|
cropped_im = save_one_box(xyxy, im, file='cropped.jpg', square=True) |
|
|
|
|
``` |
|
|
|
|
""" |
|
|
|
|
|
|
|
|
|
if not isinstance(xyxy, torch.Tensor): # may be list |
|
|
|
|
xyxy = torch.stack(xyxy) |
|
|
|
|
b = xyxy2xywh(xyxy.view(-1, 4)) # boxes |
|
|
|
|
if square: |
|
|
|
|
b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square |
|
|
|
|
|
Glenn Jocher
1 year ago
committed by
GitHub