def dist_pr_curve(md_list: MetricDataList,
metrics: DetectionMetrics,
dist_th: float,
min_precision: float,
min_recall: float,
savepath: str = None) -> None:
fig, (ax, lax) = plt.subplots(ncols=2,
gridspec_kw={"width_ratios": [4, 1]},
figsize=(7.5, 5))
ax = setup_axis(xlabel='Recall',
ylabel='Precision',
xlim=1,
ylim=1,
min_precision=min_precision,
min_recall=min_recall,
ax=ax)
# Plot the recall vs. precision curve for each detection class.
data = md_list.get_dist_data(dist_th)
for md, detection_name in data:
md = md_list[(detection_name, dist_th)]
ap = metrics.get_label_ap(detection_name, dist_th)
ax.plot(md.recall,
md.precision,
label='{}: {:.1f}%'.format(
PRETTY_DETECTION_NAMES[detection_name], ap * 100),
color=DETECTION_COLORS[detection_name])
hx, lx = ax.get_legend_handles_labels()
lax.legend(hx, lx, borderaxespad=0)
lax.axis("off")
plt.tight_layout()
if savepath is not None:
plt.savefig(savepath)
plt.close()
def class_pr_curve(md_list: MetricDataList,
metrics: DetectionMetrics,
detection_name: str,
min_precision: float,
min_recall: float,
savepath: str = None,
ax=None):
if ax is None:
ax = setup_axis(title=PRETTY_DETECTION_NAMES[detection_name],
xlabel='Recall',
ylabel='Precision',
xlim=1,
ylim=1,
min_precision=min_precision,
min_recall=min_recall)
# Get recall vs precision values of given class for each distance threshold.
data = md_list.get_class_data(detection_name)
# Plot the recall vs. precision curve for each distance threshold.
for md, dist_th in data:
ap = metrics.get_label_ap(detection_name, dist_th)
ax.plot(md.recall,
md.precision,
label='Dist. : {}, AP: {:.1f}'.format(dist_th, ap * 100))
ax.legend(loc='best')
if savepath is not None:
plt.savefig(savepath)
plt.close()
def dist_pr_curve(md_list: DetectionMetricDataList,
metrics: DetectionMetrics,
dist_th: float,
min_precision: float,
min_recall: float,
savepath: str = None) -> None:
"""
Plot the PR curves for different distance thresholds.
:param md_list: DetectionMetricDataList instance.
:param metrics: DetectionMetrics instance.
:param dist_th: Distance threshold for matching.
:param min_precision: Minimum precision value.
:param min_recall: Minimum recall value.
:param savepath: If given, saves the the rendering here instead of displaying.
"""
# Prepare axis.
fig, (ax, lax) = plt.subplots(ncols=2,
gridspec_kw={"width_ratios": [4, 1]},
figsize=(7.5, 5))
ax = setup_axis(xlabel='Recall',
ylabel='Precision',
xlim=1,
ylim=1,
min_precision=min_precision,
min_recall=min_recall,
ax=ax)
# Plot the recall vs. precision curve for each detection class.
data = md_list.get_dist_data(dist_th)
for md, detection_name in data:
md = md_list[(detection_name, dist_th)]
ap = metrics.get_label_ap(detection_name, dist_th)
ax.plot(md.recall,
md.precision,
label='{}: {:.1f}%'.format(
PRETTY_DETECTION_NAMES[detection_name], ap * 100),
color=DETECTION_COLORS[detection_name])
hx, lx = ax.get_legend_handles_labels()
lax.legend(hx, lx, borderaxespad=0)
lax.axis("off")
plt.tight_layout()
if savepath is not None:
plt.savefig(savepath)
plt.close()
def class_pr_curve(md_list: DetectionMetricDataList,
metrics: DetectionMetrics,
detection_name: str,
min_precision: float,
min_recall: float,
savepath: str = None,
ax: Axis = None) -> None:
"""
Plot a precision recall curve for the specified class.
:param md_list: DetectionMetricDataList instance.
:param metrics: DetectionMetrics instance.
:param detection_name: The detection class.
:param min_precision:
:param min_recall: Minimum recall value.
:param savepath: If given, saves the the rendering here instead of displaying.
:param ax: Axes onto which to render.
"""
# Prepare axis.
if ax is None:
ax = setup_axis(title=PRETTY_DETECTION_NAMES[detection_name],
xlabel='Recall',
ylabel='Precision',
xlim=1,
ylim=1,
min_precision=min_precision,
min_recall=min_recall)
# Get recall vs precision values of given class for each distance threshold.
data = md_list.get_class_data(detection_name)
# Plot the recall vs. precision curve for each distance threshold.
for md, dist_th in data:
md: DetectionMetricData
ap = metrics.get_label_ap(detection_name, dist_th)
ax.plot(md.recall,
md.precision,
label='Dist. : {}, AP: {:.1f}'.format(dist_th, ap * 100))
ax.legend(loc='best')
if savepath is not None:
plt.savefig(savepath)
plt.close()