HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

Ruff Docstring formatting (#15793)

Browse source 
Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher 2024-08-25 04:27:55 +08:00 • committed by GitHub
parent d27664216b
commit 776ca86369
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
60 changed files with 241 additions and 309 deletions
Download patch file Download diff file Expand all files Collapse all files

52
ultralytics/utils/plotting.py
View file

@ -204,7 +204,6 @@ class Annotator:
txt_color (tuple, optional): The color of the text (R, G, B).
margin (int, optional): The margin between the text and the rectangle border.
"""
# If label have more than 3 characters, skip other characters, due to circle size
if len(label) > 3:
print(
@ -246,7 +245,6 @@ class Annotator:
txt_color (tuple, optional): The color of the text (R, G, B).
margin (int, optional): The margin between the text and the rectangle border.
"""
# Calculate the center of the bounding box
x_center, y_center = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
# Get the size of the text
@ -284,7 +282,6 @@ class Annotator:
txt_color (tuple, optional): The color of the text (R, G, B).
rotated (bool, optional): Variable used to check if task is OBB
"""
txt_color = self.get_txt_color(color, txt_color)
if isinstance(box, torch.Tensor):
box = box.tolist()
@ -343,7 +340,6 @@ class Annotator:
alpha (float): Mask transparency: 0.0 fully transparent, 1.0 opaque
retina_masks (bool): Whether to use high resolution masks or not. Defaults to False.
"""
if self.pil:
# Convert to numpy first
self.im = np.asarray(self.im).copy()
@ -374,17 +370,18 @@ class Annotator:
Plot keypoints on the image.
Args:
kpts (tensor): Predicted keypoints with shape [17, 3]. Each keypoint has (x, y, confidence).
shape (tuple): Image shape as a tuple (h, w), where h is the height and w is the width.
radius (int, optional): Radius of the drawn keypoints. Default is 5.
kpt_line (bool, optional): If True, the function will draw lines connecting keypoints
for human pose. Default is True.
kpt_color (tuple, optional): The color of the keypoints (B, G, R).
kpts (torch.Tensor): Keypoints, shape [17, 3] (x, y, confidence).
shape (tuple, optional): Image shape (h, w). Defaults to (640, 640).
radius (int, optional): Keypoint radius. Defaults to 5.
kpt_line (bool, optional): Draw lines between keypoints. Defaults to True.
conf_thres (float, optional): Confidence threshold. Defaults to 0.25.
kpt_color (tuple, optional): Keypoint color (B, G, R). Defaults to None.
Note:
`kpt_line=True` currently only supports human pose plotting.
- `kpt_line=True` currently only supports human pose plotting.
- Modifies self.im in-place.
- If self.pil is True, converts image to numpy array and back to PIL.
"""
if self.pil:
# Convert to numpy first
self.im = np.asarray(self.im).copy()
@ -488,7 +485,6 @@ class Annotator:
Returns:
angle (degree): Degree value of angle between three points
"""
x_min, y_min, x_max, y_max = bbox
width = x_max - x_min
height = y_max - y_min
@ -503,7 +499,6 @@ class Annotator:
color (tuple): Region Color value
thickness (int): Region area thickness value
"""
cv2.polylines(self.im, [np.array(reg_pts, dtype=np.int32)], isClosed=True, color=color, thickness=thickness)
def draw_centroid_and_tracks(self, track, color=(255, 0, 255), track_thickness=2):
@ -515,7 +510,6 @@ class Annotator:
color (tuple): tracks line color
track_thickness (int): track line thickness value
"""
points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
cv2.polylines(self.im, [points], isClosed=False, color=color, thickness=track_thickness)
cv2.circle(self.im, (int(track[-1][0]), int(track[-1][1])), track_thickness * 2, color, -1)
@ -530,7 +524,6 @@ class Annotator:
region_color (RGB): queue region color
txt_color (RGB): text display color
"""
x_values = [point[0] for point in points]
y_values = [point[1] for point in points]
center_x = sum(x_values) // len(points)
@ -574,7 +567,6 @@ class Annotator:
y_center (float): y position center point for bounding box
margin (int): gap between text and rectangle for better display
"""
text_size = cv2.getTextSize(text, 0, fontScale=self.sf, thickness=self.tf)[0]
text_x = x_center - text_size[0] // 2
text_y = y_center + text_size[1] // 2
@ -597,7 +589,6 @@ class Annotator:
bg_color (bgr color): display color for text background
margin (int): gap between text and rectangle for better display
"""
horizontal_gap = int(im0.shape[1] * 0.02)
vertical_gap = int(im0.shape[0] * 0.01)
text_y_offset = 0
@ -629,7 +620,6 @@ class Annotator:
Returns:
angle (degree): Degree value of angle between three points
"""
a, b, c = np.array(a), np.array(b), np.array(c)
radians = np.arctan2(c[1] - b[1], c[0] - b[0]) - np.arctan2(a[1] - b[1], a[0] - b[0])
angle = np.abs(radians * 180.0 / np.pi)
@ -642,12 +632,19 @@ class Annotator:
Draw specific keypoints for gym steps counting.
Args:
keypoints (list): list of keypoints data to be plotted
indices (list): keypoints ids list to be plotted
shape (tuple): imgsz for model inference
radius (int): Keypoint radius value
"""
keypoints (list): Keypoints data to be plotted.
indices (list, optional): Keypoint indices to be plotted. Defaults to [2, 5, 7].
shape (tuple, optional): Image size for model inference. Defaults to (640, 640).
radius (int, optional): Keypoint radius. Defaults to 2.
conf_thres (float, optional): Confidence threshold for keypoints. Defaults to 0.25.
Returns:
(numpy.ndarray): Image with drawn keypoints.
Note:
Keypoint format: [x, y] or [x, y, confidence].
Modifies self.im in-place.
"""
if indices is None:
indices = [2, 5, 7]
for i, k in enumerate(keypoints):
@ -675,7 +672,6 @@ class Annotator:
color (tuple): text background color for workout monitoring
txt_color (tuple): text foreground color for workout monitoring
"""
angle_text, count_text, stage_text = (f" {angle_text:.2f}", f"Steps : {count_text}", f" {stage_text}")
# Draw angle
@ -744,7 +740,6 @@ class Annotator:
label (str): Detection label text
txt_color (RGB): text color
"""
cv2.polylines(self.im, [np.int32([mask])], isClosed=True, color=mask_color, thickness=2)
text_size, _ = cv2.getTextSize(label, 0, self.sf, self.tf)
@ -772,7 +767,6 @@ class Annotator:
line_color (RGB): Distance line color.
centroid_color (RGB): Bounding box centroid color.
"""
(text_width_m, text_height_m), _ = cv2.getTextSize(f"Distance M: {distance_m:.2f}m", 0, self.sf, self.tf)
cv2.rectangle(self.im, (15, 25), (15 + text_width_m + 10, 25 + text_height_m + 20), line_color, -1)
cv2.putText(
@ -813,7 +807,6 @@ class Annotator:
color (tuple): object centroid and line color value
pin_color (tuple): visioneye point color value
"""
center_bbox = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
cv2.circle(self.im, center_point, self.tf * 2, pin_color, -1)
cv2.circle(self.im, center_bbox, self.tf * 2, color, -1)
@ -906,7 +899,6 @@ def save_one_box(xyxy, im, file=Path("im.jpg"), gain=1.02, pad=10, square=False,
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 = ops.xyxy2xywh(xyxy.view(-1, 4)) # boxes
@ -1171,7 +1163,6 @@ def plt_color_scatter(v, f, bins=20, cmap="viridis", alpha=0.8, edgecolors="none
>>> f = np.random.rand(100)
>>> plt_color_scatter(v, f)
"""
# Calculate 2D histogram and corresponding colors
hist, xedges, yedges = np.histogram2d(v, f, bins=bins)
colors = [
@ -1197,7 +1188,6 @@ def plot_tune_results(csv_file="tune_results.csv"):
Examples:
>>> plot_tune_results("path/to/tune_results.csv")
"""
import pandas as pd # scope for faster 'import ultralytics'
from scipy.ndimage import gaussian_filter1d