ultralytics 8.0.75 fixes and updates (#1967)

Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: Jonathan Rayner <jonathan.j.rayner@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/utils/__init__.py

@@ -17,6 +17,7 @@ from types import SimpleNamespace
 from typing import Union
 
 import cv2
+import matplotlib.pyplot as plt
 import numpy as np
 import torch
 import yaml
@@ -116,7 +117,7 @@ class SimpleClass:
         attr = []
         for a in dir(self):
             v = getattr(self, a)
-            if not callable(v) and not a.startswith('__'):
+            if not callable(v) and not a.startswith('_'):
                 if isinstance(v, SimpleClass):
                     # Display only the module and class name for subclasses
                     s = f'{a}: {v.__module__}.{v.__class__.__name__} object'
@@ -164,6 +165,39 @@ class IterableSimpleNamespace(SimpleNamespace):
         return getattr(self, key, default)
 
 
+def plt_settings(rcparams={'font.size': 11}, backend='Agg'):
+    """
+    Decorator to temporarily set rc parameters and the backend for a plotting function.
+
+    Usage:
+        decorator: @plt_settings({"font.size": 12})
+        context manager: with plt_settings({"font.size": 12}):
+
+    Args:
+        rcparams (dict): Dictionary of rc parameters to set.
+        backend (str, optional): Name of the backend to use. Defaults to 'Agg'.
+
+    Returns:
+        callable: Decorated function with temporarily set rc parameters and backend.
+    """
+
+    def decorator(func):
+
+        def wrapper(*args, **kwargs):
+            original_backend = plt.get_backend()
+            plt.switch_backend(backend)
+
+            with plt.rc_context(rcparams):
+                result = func(*args, **kwargs)
+
+            plt.switch_backend(original_backend)
+            return result
+
+        return wrapper
+
+    return decorator
+
+
 def set_logging(name=LOGGING_NAME, verbose=True):
     # sets up logging for the given name
     rank = int(os.getenv('RANK', -1))  # rank in world for Multi-GPU trainings

ultralytics/yolo/utils/checks.py

@@ -128,7 +128,8 @@ def check_latest_pypi_version(package_name='ultralytics'):
     Returns:
         str: The latest version of the package.
     """
-    response = requests.get(f'https://pypi.org/pypi/{package_name}/json')
+    requests.packages.urllib3.disable_warnings()  # Disable the InsecureRequestWarning
+    response = requests.get(f'https://pypi.org/pypi/{package_name}/json', verify=False)
     if response.status_code == 200:
         return response.json()['info']['version']
     return None

ultralytics/yolo/utils/metrics.py

@@ -11,7 +11,7 @@ import numpy as np
 import torch
 import torch.nn as nn
 
-from ultralytics.yolo.utils import LOGGER, SimpleClass, TryExcept
+from ultralytics.yolo.utils import LOGGER, SimpleClass, TryExcept, plt_settings
 
 OKS_SIGMA = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89]) / 10.0
 
@@ -234,6 +234,7 @@ class ConfusionMatrix:
         return tp[:-1], fp[:-1]  # remove background class
 
     @TryExcept('WARNING ⚠️ ConfusionMatrix plot failure')
+    @plt_settings()
     def plot(self, normalize=True, save_dir='', names=()):
         import seaborn as sn
 
@@ -277,6 +278,7 @@ def smooth(y, f=0.05):
     return np.convolve(yp, np.ones(nf) / nf, mode='valid')  # y-smoothed
 
 
+@plt_settings()
 def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
     # Precision-recall curve
     fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
@@ -299,6 +301,7 @@ def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
     plt.close(fig)
 
 
+@plt_settings()
 def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric'):
     # Metric-confidence curve
     fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)

ultralytics/yolo/utils/plotting.py

@@ -5,22 +5,18 @@ import math
 from pathlib import Path
 
 import cv2
-import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
 from PIL import Image, ImageDraw, ImageFont
 from PIL import __version__ as pil_version
 
-from ultralytics.yolo.utils import LOGGER, TryExcept, threaded
+from ultralytics.yolo.utils import LOGGER, TryExcept, plt_settings, threaded
 
 from .checks import check_font, check_version, is_ascii
 from .files import increment_path
 from .ops import clip_boxes, scale_image, xywh2xyxy, xyxy2xywh
 
-matplotlib.rc('font', **{'size': 11})
-matplotlib.use('Agg')  # for writing to files only
-
 
 class Colors:
     # Ultralytics color palette https://ultralytics.com/
@@ -212,6 +208,7 @@ class Annotator:
 
 
 @TryExcept()  # known issue https://github.com/ultralytics/yolov5/issues/5395
+@plt_settings()
 def plot_labels(boxes, cls, names=(), save_dir=Path('')):
     import pandas as pd
     import seaborn as sn
@@ -228,7 +225,6 @@ def plot_labels(boxes, cls, names=(), save_dir=Path('')):
     plt.close()
 
     # matplotlib labels
-    matplotlib.use('svg')  # faster
     ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
     y = ax[0].hist(cls, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
     with contextlib.suppress(Exception):  # color histogram bars by class
@@ -244,9 +240,9 @@ def plot_labels(boxes, cls, names=(), save_dir=Path('')):
 
     # rectangles
     boxes[:, 0:2] = 0.5  # center
-    boxes = xywh2xyxy(boxes) * 2000
-    img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)
-    for cls, box in zip(cls[:1000], boxes[:1000]):
+    boxes = xywh2xyxy(boxes) * 1000
+    img = Image.fromarray(np.ones((1000, 1000, 3), dtype=np.uint8) * 255)
+    for cls, box in zip(cls[:500], boxes[:500]):
         ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls))  # plot
     ax[1].imshow(img)
     ax[1].axis('off')
@@ -256,7 +252,6 @@ def plot_labels(boxes, cls, names=(), save_dir=Path('')):
             ax[a].spines[s].set_visible(False)
 
     plt.savefig(save_dir / 'labels.jpg', dpi=200)
-    matplotlib.use('Agg')
     plt.close()
 
 
@@ -400,6 +395,7 @@ def plot_images(images,
     annotator.im.save(fname)  # save
 
 
+@plt_settings()
 def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False):
     # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
     import pandas as pd