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non-int ticks

This commit is contained in:
kirilllzaitsev 2023-07-05 14:08:39 +02:00
parent c9d2b52900
commit 35d9ee7bad
1 changed files with 64 additions and 29 deletions
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93
supervision/metrics/detection.py
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@ -60,15 +60,15 @@ class ConfusionMatrix:
... [ 0.1, 0.1, 3.0, 3.0 ],
... [ 6.0, 1.0, 8.0, 3.0 ],
... [ 1.0, 6.0, 2.0, 7.0 ],
... ]),
... confidence=array([ 0.9, 0.9, 0.8, 0.8 ]),
... ]),
... confidence=array([ 0.9, 0.9, 0.8, 0.8 ]),
... class_id=array([1, 0, 1, 1])
... ),
... Detections(
... xyxy=array([
... [ 1.0, 1.0, 2.0, 2.0 ]
... ]),
... confidence=array([ 0.8 ]),
... ]),
... confidence=array([ 0.8 ]),
... class_id=array([2])
... )
... ]
@ -89,7 +89,7 @@ class ConfusionMatrix:
```
"""
# TODO: add validation for inputs
num_classes = len(classes)
matrix = np.zeros((num_classes + 1, num_classes + 1))
for true_batch, detection_batch in zip(target, predictions):
@ -116,7 +116,6 @@ class ConfusionMatrix:
conf_threshold: float,
iou_threshold: float,
) -> np.ndarray:
result_matrix = np.zeros((num_classes + 1, num_classes + 1))
detection_batch_filtered = pred_detections[
pred_detections.confidence > conf_threshold
@ -178,16 +177,18 @@ class ConfusionMatrix:
Create confusion matrix plot and save it at selected location.
Args:
target_path: `str` selected target location of confusion matrix plot.
title: `Optional[str]` title displayed at the top of the confusion matrix plot. Default `None`.
class_names: `Optional[List[str]]` list of class names detected my model. If non given class indexes will be used. Default `None`.
normalize: `bool` if set to `False` chart will display absolute number of detections falling into given category. Otherwise percentage of detections will be displayed.
target_path: save location of confusion matrix plot.
title: title displayed at the top of the confusion matrix plot. Default `None`.
class_names: list of class names detected my model. If non given class indexes will be used. Default `None`.
normalize: chart will display absolute number of detections falling into given category. Otherwise percentage of detections will be displayed.
"""
# TODO: replace seaborn with matplotlib
import matplotlib.pyplot as plt
import seaborn as sn
from matplotlib import rcParams
rcParams["font.family"] = "sans-serif"
rcParams["font.sans-serif"] = ["Verdana"]
# rcParams["font.family"] = "sans-serif"
# rcParams["font.sans-serif"] = ["Verdana"]
array = self.matrix.copy()
@ -196,8 +197,8 @@ class ConfusionMatrix:
array[array < 0.005] = np.nan
fig = plt.figure(figsize=(12, 10), tight_layout=True, facecolor="white")
sn.set(font_scale=1.0 if self.num_classes < 50 else 0.8)
fig, ax = plt.subplots(figsize=(12, 10), tight_layout=True, facecolor="white")
# sn.set(font_scale=1.0 if self.num_classes < 50 else 0.8)
labels = (
class_names is not None
@ -206,24 +207,58 @@ class ConfusionMatrix:
)
x_tick_labels = class_names + ["FN"] if labels else "auto"
y_tick_labels = class_names + ["FP"] if labels else "auto"
sn.heatmap(
array,
annot=self.num_classes < 30,
annot_kws={"size": 8},
fmt=".2f",
square=True,
vmin=0,
cmap="Blues",
xticklabels=x_tick_labels,
yticklabels=y_tick_labels,
).set_facecolor((1, 1, 1))
# sn.heatmap(
# array,
# annot=self.num_classes < 30,
# annot_kws={"size": 8},
# fmt=".2f",
# square=True,
# vmin=0,
# cmap="Blues",
# xticklabels=x_tick_labels,
# yticklabels=y_tick_labels,
# ).set_facecolor((1, 1, 1))
im = ax.imshow(array, cmap="Blues")
# Create colorbar
cbar_kw = {}
cbarlabel = ""
cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)
cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom")
cbar.mappable.set_clim(vmin=0, vmax=np.nanmax(array))
# Disable grid lines in the axis
ax.grid(False)
# Adjust the z-order of the grid lines
ax.set_axisbelow(True)
ax.yaxis.grid(color='gray', zorder=0)
ax.xaxis.grid(color='gray', zorder=0)
# Show all ticks and label them with the respective list entries.
ax.set_xticks(np.arange(array.shape[1])-0.5)
ax.set_yticks(np.arange(array.shape[0])-0.5)
# ax.grid(which="major", color="gray", linestyle="-", linewidth=1)
# ax.tick_params(which="minor", bottom=False, left=False)
# Let the horizontal axes labeling appear on top.
# ax.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False)
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=90, ha="right", rotation_mode="anchor")
# Turn spines off and create white grid.
# ax.spines[:].set_visible(False)
# ax.set_xticks(x_tick_labels, minor=True)
# ax.set_yticks(y_tick_labels, minor=True)
# ax.grid(which="minor", color="w", linestyle="-", linewidth=1)
# ax.tick_params(which="minor", bottom=False, left=False)
if title:
fig.axes[0].set_title(title, fontsize=20)
ax.set_title(title, fontsize=20)
fig.axes[0].set_xlabel("Predicted")
fig.axes[0].set_ylabel("True")
fig.axes[0].set_facecolor("white")
ax.set_xlabel("Predicted")
ax.set_ylabel("True")
ax.set_facecolor("white")
fig.savefig(
target_path, dpi=250, facecolor=fig.get_facecolor(), transparent=True
)