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Scope scipy import and only plot first 1M labels (#4203)

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Glenn Jocher 2023-08-07 03:57:16 +02:00 committed by GitHub
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1 changed files with 27 additions and 13 deletions
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ultralytics/utils/plotting.py
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@ -11,7 +11,6 @@ import numpy as np
import torch import torch
from PIL import Image, ImageDraw, ImageFont from PIL import Image, ImageDraw, ImageFont
from PIL import __version__ as pil_version from PIL import __version__ as pil_version
from scipy.ndimage import gaussian_filter1d
from ultralytics.utils import LOGGER, TryExcept, plt_settings, threaded from ultralytics.utils import LOGGER, TryExcept, plt_settings, threaded
@ -21,7 +20,8 @@ from .ops import clip_boxes, scale_image, xywh2xyxy, xyxy2xywh
class Colors: class Colors:
"""Ultralytics default 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 This class provides methods to work with the Ultralytics color palette, including converting hex color codes to
RGB values. RGB values.
@ -59,7 +59,8 @@ colors = Colors() # create instance for 'from utils.plots import colors'
class Annotator: class Annotator:
"""Ultralytics Annotator for train/val mosaics and JPGs and predictions annotations. """
Ultralytics Annotator for train/val mosaics and JPGs and predictions annotations.
Attributes: Attributes:
im (Image.Image or numpy array): The image to annotate. im (Image.Image or numpy array): The image to annotate.
@ -132,13 +133,15 @@ class Annotator:
lineType=cv2.LINE_AA) lineType=cv2.LINE_AA)
def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False): def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False):
"""Plot masks at once. """
Plot masks on image.
Args: Args:
masks (tensor): predicted masks on cuda, shape: [n, h, w] masks (tensor): Predicted masks on cuda, shape: [n, h, w]
colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n] colors (List[List[Int]]): Colors for predicted masks, [[r, g, b] * n]
im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1] im_gpu (tensor): Image is in cuda, shape: [3, h, w], range: [0, 1]
alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque 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: if self.pil:
# Convert to numpy first # Convert to numpy first
@ -166,7 +169,8 @@ class Annotator:
self.fromarray(self.im) self.fromarray(self.im)
def kpts(self, kpts, shape=(640, 640), radius=5, kpt_line=True): def kpts(self, kpts, shape=(640, 640), radius=5, kpt_line=True):
"""Plot keypoints on the image. """
Plot keypoints on the image.
Args: Args:
kpts (tensor): Predicted keypoints with shape [17, 3]. Each keypoint has (x, y, confidence). kpts (tensor): Predicted keypoints with shape [17, 3]. Each keypoint has (x, y, confidence).
@ -260,7 +264,7 @@ class Annotator:
@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395 @TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395
@plt_settings() @plt_settings()
def plot_labels(boxes, cls, names=(), save_dir=Path(''), on_plot=None): def plot_labels(boxes, cls, names=(), save_dir=Path(''), on_plot=None):
"""Save and plot image with no axis or spines.""" """Plot training labels including class histograms and box statistics."""
import pandas as pd import pandas as pd
import seaborn as sn import seaborn as sn
@ -269,9 +273,9 @@ def plot_labels(boxes, cls, names=(), save_dir=Path(''), on_plot=None):
# Plot dataset labels # Plot dataset labels
LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ") LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
b = boxes.transpose() # classes, boxes
nc = int(cls.max() + 1) # number of classes nc = int(cls.max() + 1) # number of classes
x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height']) boxes = boxes[:1000000] # limit to 1M boxes
x = pd.DataFrame(boxes, columns=['x', 'y', 'width', 'height'])
# Seaborn correlogram # Seaborn correlogram
sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9)) sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
@ -493,8 +497,18 @@ def plot_images(images,
@plt_settings() @plt_settings()
def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False, classify=False, on_plot=None): def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False, classify=False, on_plot=None):
"""Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv').""" """
Plot training results from results CSV file.
Example:
```python
from ultralytics.utils.plotting import plot_results
plot_results('path/to/results.csv')
```
"""
import pandas as pd import pandas as pd
from scipy.ndimage import gaussian_filter1d
save_dir = Path(file).parent if file else Path(dir) save_dir = Path(file).parent if file else Path(dir)
if classify: if classify:
fig, ax = plt.subplots(2, 2, figsize=(6, 6), tight_layout=True) fig, ax = plt.subplots(2, 2, figsize=(6, 6), tight_layout=True)