# Copyright (C) 2017-2023 Cleanlab Inc.
# This file is part of cleanlab.
#
# cleanlab is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# cleanlab is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with cleanlab. If not, see <https://www.gnu.org/licenses/>.
"""Methods to display examples and their label issues in an object detection dataset."""
from typing import Optional, Any, Dict, Tuple, Union, TYPE_CHECKING, TypeVar
import numpy as np
from cleanlab.internal.constants import MAX_CLASS_TO_SHOW
from cleanlab.object_detection.rank import (
_separate_prediction,
_separate_label,
_get_prediction_type,
)
from cleanlab.internal.object_detection_utils import bbox_xyxy_to_xywh
if TYPE_CHECKING:
from PIL.Image import Image as Image # pragma: no cover
else:
Image = TypeVar("Image")
[docs]def visualize(
image: Union[str, Image],
*,
label: Optional[Dict[str, Any]] = None,
prediction: Optional[np.ndarray] = None,
prediction_threshold: Optional[float] = None,
overlay: bool = True,
class_names: Optional[Dict[Any, Any]] = None,
figsize: Optional[Tuple[int, int]] = None,
save_path: Optional[str] = None,
) -> None:
"""Display the annotated bounding boxes (given labels) and predicted bounding boxes (model predictions) for a particular image.
Given labels are shown in red, model predictions in blue.
Parameters
----------
image:
Image object loaded into memory or full path to the image file. If path is provided, image is loaded into memory.
label:
The given label for a single image in the format ``{'bboxes': np.ndarray((L,4)), 'labels': np.ndarray((L,))}`` where
``L`` is the number of bounding boxes for the `i`-th image and ``bboxes[j]`` is in the format ``[x1,y1,x2,y2]`` with given label ``labels[j]``.
Note: Here, ``[x1,y1]`` corresponds to the coordinates of the bottom-left corner of the bounding box, while ``[x2,y2]`` corresponds to the coordinates of the top-right corner of the bounding box. The last column, pred_prob, represents the predicted probability that the bounding box contains an object of the class k.
prediction:
A prediction for a single image in the format ``np.ndarray((K,))`` and ``prediction[k]`` is of shape ``np.ndarray(N,5)``
where ``M`` is the number of predicted bounding boxes for class ``k`` and the five columns correspond to ``[x,y,x,y,pred_prob]`` where
``[x,y,x,y]`` are the bounding box coordinates predicted by the model and ``pred_prob`` is the model's confidence in ``predictions[i]``.
prediction_threshold:
All model-predicted bounding boxes with confidence (`pred_prob`)
below this threshold are omitted from the visualization.
overlay: bool
If True, display a single image with given labels and predictions overlaid.
If False, display two images (side by side) with the left image showing the model predictions and the right image showing the given label.
class_names:
Optional dictionary mapping one-hot-encoded class labels back to their original class names in the format ``{"integer-label": "original-class-name"}``.
save_path:
Path to save figure at. If a path is provided, the figure is saved. To save in a specific image format, add desired file extension to the end of `save_path`. Allowed file extensions are: 'png', 'pdf', 'ps', 'eps', and 'svg'.
figsize:
Optional figure size for plotting the image.
Corresponds to ``matplotlib.figure.figsize``.
"""
try:
import matplotlib.pyplot as plt
except ImportError as e:
raise ImportError(
"This functionality requires matplotlib. Install it via: `pip install matplotlib`"
)
# Create figure and axes
if isinstance(image, str):
image = plt.imread(image)
if prediction is not None:
prediction_type = _get_prediction_type(prediction)
pbbox, plabels, pred_probs = _separate_prediction(
prediction, prediction_type=prediction_type
)
if prediction_threshold is not None:
keep_idx = np.where(pred_probs > prediction_threshold)
pbbox = pbbox[keep_idx]
plabels = plabels[keep_idx]
if label is not None:
abbox, alabels = _separate_label(label)
if overlay:
figsize = (8, 5) if figsize is None else figsize
fig, ax = plt.subplots(frameon=False, figsize=figsize)
plt.axis("off")
ax.imshow(image)
if label is not None:
fig, ax = _draw_boxes(
fig, ax, abbox, alabels, edgecolor="r", linestyle="-", linewidth=1
)
if prediction is not None:
_, _ = _draw_boxes(fig, ax, pbbox, plabels, edgecolor="b", linestyle="-.", linewidth=1)
else:
figsize = (14, 10) if figsize is None else figsize
fig, axes = plt.subplots(nrows=1, ncols=2, frameon=False, figsize=figsize)
axes[0].axis("off")
axes[0].imshow(image)
axes[1].axis("off")
axes[1].imshow(image)
if label is not None:
fig, ax = _draw_boxes(
fig, axes[0], abbox, alabels, edgecolor="r", linestyle="-", linewidth=1
)
if prediction is not None:
_, _ = _draw_boxes(
fig, axes[1], pbbox, plabels, edgecolor="b", linestyle="-.", linewidth=1
)
bbox_extra_artists = None
if label or prediction is not None:
legend, plt = _plot_legend(class_names, label, prediction)
bbox_extra_artists = (legend,)
if save_path:
allowed_image_formats = set(["png", "pdf", "ps", "eps", "svg"])
image_format: Optional[str] = None
if save_path.split(".")[-1] in allowed_image_formats and "." in save_path:
image_format = save_path.split(".")[-1]
plt.savefig(
save_path,
format=image_format,
bbox_extra_artists=bbox_extra_artists,
bbox_inches="tight",
transparent=True,
pad_inches=0.5,
)
plt.show()
def _plot_legend(class_names, label, prediction):
colors = ["black"]
colors.extend(["red"] if label is not None else [])
colors.extend(["blue"] if prediction is not None else [])
markers = [None]
markers.extend(["s"] if label is not None else [])
markers.extend(["s"] if prediction is not None else [])
labels = [r"$\bf{Legend}$"]
labels.extend(["given label"] if label is not None else [])
labels.extend(["predicted label"] if prediction is not None else [])
if class_names:
colors += ["black"] + ["black"] * min(len(class_names), MAX_CLASS_TO_SHOW)
markers += [None] + [f"${class_key}$" for class_key in class_names.keys()]
labels += [r"$\bf{classes}$"] + list(class_names.values())
try:
import matplotlib.pyplot as plt
except ImportError as e:
raise ImportError(
"This functionality requires matplotlib. Install it via: `pip install matplotlib`"
)
f = lambda m, c: plt.plot([], [], marker=m, color=c, ls="none")[0]
handles = [f(marker, color) for marker, color in zip(markers, colors)]
legend = plt.legend(
handles, labels, bbox_to_anchor=(1.04, 0.05), loc="lower left", borderaxespad=0
)
return legend, plt
def _draw_labels(ax, rect, label, edgecolor):
"""Helper function to draw labels on an axis."""
rx, ry = rect.get_xy()
c_xleft = rx + 10
c_xright = rx + rect.get_width() - 10
c_ytop = ry + 12
if edgecolor == "r":
cx, cy = c_xleft, c_ytop
else: # edgecolor == b
cx, cy = c_xright, c_ytop
l = ax.annotate(
label, (cx, cy), fontsize=8, fontweight="bold", color="white", ha="center", va="center"
)
l.set_bbox(dict(facecolor=edgecolor, alpha=0.35, edgecolor=edgecolor, pad=2))
return ax
def _draw_boxes(fig, ax, bboxes, labels, edgecolor="g", linestyle="-", linewidth=3):
"""Helper function to draw bboxes and labels on an axis."""
bboxes = [bbox_xyxy_to_xywh(box) for box in bboxes]
try:
from matplotlib.patches import Rectangle
except Exception as e:
raise ImportError(
"This functionality requires matplotlib. Install it via: `pip install matplotlib`"
)
for (x, y, w, h), label in zip(bboxes, labels):
rect = Rectangle(
(x, y),
w,
h,
linewidth=linewidth,
linestyle=linestyle,
edgecolor=edgecolor,
facecolor="none",
)
ax.add_patch(rect)
if labels is not None:
ax = _draw_labels(ax, rect, label, edgecolor)
return fig, ax