def parse_detections(format,
input,
identify_fun=identify,
clslabelmap=['person']):
dets = bbb.parse(format, input, identify_fun, class_label_map=clslabelmap)
bbb.modify(dets, [bbb.AspectRatioModifier(.41)])
bbb.filter_discard(dets,
[bbb.HeightRangeFilter((50 / 1.25, float('Inf')))])
return dets
identify = lambda f: os.path.splitext("/".join(f.rsplit('/')[-3:]))[0]
# parse ground truth from all videos in all sets
ground_truth = bbb.parse('anno_dollar',
'annotations/*/*/*.txt',
identify,
occlusion_tag_map=[0.0, 0.25, 0.75])
print(len(ground_truth))
# print(identify)
# filter ground truth by marking boxes with the ignore flag
bbb.filter_ignore(
ground_truth,
[
bbb.ClassLabelFilter(['person']), # only consider 'person' objects
bbb.HeightRangeFilter(
(50, float('Inf'))), # select instances of 50 pixels or higher
bbb.OcclusionAreaFilter((0.65, float('Inf')))
]) # only include objects that are 65% visible or more
for _, annos in ground_truth.items():
for i in range(len(annos)):
annos[i].class_label = 'person'
# modify ground truth aspect ratio
bbb.modify(ground_truth, [bbb.AspectRatioModifier(.41, modify_ignores=False)])
# split and copy to day and night ground truth
ground_truth_day = {
key: values
for key, values in ground_truth.items() if key.startswith('set06')
or key.startswith('set07') or key.startswith('set08')
def meanAP_LogAverageMissRate():
identify = lambda f: os.path.splitext("/".join(f.rsplit('/')[-3:]))[0]
# parse ground truth from all videos in all sets
ground_truth = bbb.parse('anno_dollar', 'annotations/*/*/*.txt', identify, occlusion_tag_map=[0.0, 0.25, 0.75])
# print(len(ground_truth))
# print(identify)
# filter ground truth by marking boxes with the ignore flag
bbb.filter_ignore(ground_truth, [bbb.ClassLabelFilter(['person']), # only consider 'person' objects
bbb.HeightRangeFilter((50, float('Inf'))), # select instances of 50 pixels or higher
bbb.OcclusionAreaFilter(
(0.65, float('Inf')))]) # only include objects that are 65% visible or more
for _, annos in ground_truth.items():
for i in range(len(annos)):
annos[i].class_label = 'person'
# modify ground truth aspect ratio
bbb.modify(ground_truth, [bbb.AspectRatioModifier(.41, modify_ignores=False)]);
# split and copy to day and night ground truth
ground_truth_day = {key: values for key, values in ground_truth.items() if
key.startswith('set06') or key.startswith('set07') or key.startswith('set08')}
ground_truth_night = {key: values for key, values in ground_truth.items() if
key.startswith('set09') or key.startswith('set10') or key.startswith('set11')}
def parse_detections(format, input, identify_fun=identify, clslabelmap=['person']):
dets = bbb.parse(format, input, identify_fun, class_label_map=clslabelmap)
bbb.modify(dets, [bbb.AspectRatioModifier(.41)])
bbb.filter_discard(dets, [bbb.HeightRangeFilter((50 / 1.25, float('Inf')))])
return dets
detections_all = {}
# detections_all['current'] = parse_detections('det_coco', 'results/conditioning/condition86e_mAP.json')
# path_source = os.getcwd()
# path_source = os.path.join(path_source, 'detection_results.json')
path_source = 'results/detection_results.json'
# print(path_source)
detections_all['current'] = parse_detections('det_coco', path_source)
# detections_all['Our: TD(V,V)'] = parse_detections('det_coco','results/adaptation/1_Visible_15e.json')
# detections_all['Our: TD(T,T)'] = parse_detections('det_coco','results/adaptation/1_Thermal_15e.json')
# detections_all['Our: TD(VT,T)'] = parse_detections('det_coco','results/adaptation/1_15e_toFT_34elike30e.json')
# detections_all['Our: BU(VAT,T)'] = parse_detections('det_coco','results/adaptation/1_Adap30layers_From15_000014_best.json')
# detections_all['Our: BU(VLT,T)'] = parse_detections('det_coco','results/adaptation/1_Layerwise5layers_from15e_000020_best.json')
# detections_all['MSDS'] = parse_detections('det_coco','results/SOTA/MSDS.json')
# detections_all['MSDS_sanitized'] = parse_detections('det_coco','results/SOTA/MSDS_sanitized.json')
# split and copy to day and night detections
detections_day = {}
detections_night = {}
for label, detections in detections_all.items():
detections_day[label] = {key: values for key, values in detections.items() if
key.startswith('set06') or key.startswith('set07') or key.startswith('set08')}
detections_night[label] = {key: values for key, values in detections.items() if
key.startswith('set09') or key.startswith('set10') or key.startswith('set11')}
detectors_to_plot = ['current']
# detectors_to_plot = ['Our: BU(VLT,T)', 'condition 86e map','Our: TD(V,V)','Our: TD(T,T)','Our: TD(VT,T)','Our: BU(VAT,T)','MSDS']
def lamr(miss_rate, fppi, num_of_samples=9):
""" Compute the log average miss-rate from a given MR-FPPI curve.
The log average miss-rate is defined as the average of a number of evenly spaced log miss-rate samples
on the :math:`{log}(FPPI)` axis within the range :math:`[10^{-2}, 10^{0}]`
Args:
miss_rate (list): miss-rate values
fppi (list): FPPI values
num_of_samples (int, optional): Number of samples to take from the curve to measure the average precision; Default **9**
Returns:
Number: log average miss-rate
"""
samples = np.logspace(-2., 0., num_of_samples)
m = np.array(miss_rate)
f = np.array(fppi)
interpolated = scipy.interpolate.interp1d(f, m, fill_value=(1., 0.), bounds_error=False)(samples)
# print('interpolated: ')
# print(interpolated)
for i, value in enumerate(interpolated):
if value <= 0:
interpolated[i] = interpolated[i - 1]
log_interpolated = np.log(interpolated)
avg = sum(log_interpolated) / len(log_interpolated)
return np.exp(avg)
def generate_curves(ground_truth, results, pr=True, title="", saveplot="", overlap=0.5, only_plot=None,
linewidth=2, figsize=(8, 6), legendloc=3):
curves = []
scores = {}
# colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
# colors = ['#1919ff', '#ff7f0e', '#ff1919', '#ff19ff', '#19ff19', '#19ff19']
colors = ['#1919ff', '#ff7f0e', '#ff1919', '#ff19ff', '#19ff19']
i = 0
linestyles = ['-', '--', '-.', ':']
for label, detections in results.items():
### because YOLO has stuck in small object, so the paper of this code on CVPRW prefer chose overlap 0.4, but here we choose 0.5 for all.
# if label=='YOLO_TLV' or label=='Ours: TD(V,V)' or label=='Ours: TD(T,T)' or label=='Ours: TD(VT,T)' or label=='Ours: BU(VAT,T)' or label == 'Ours: BU(VLT,T)':
# print(label)
# overlap = 0.4
if pr:
ys, xs = bbb.pr(detections, ground_truth, overlap)
score = round(bbb.ap(ys, xs) * 100, 2)
else:
ys, xs = bbb.mr_fppi(detections, ground_truth, overlap)
score = round(lamr(ys, xs) * 100, 2)
color = colors[i % len(colors)]
linestyle = linestyles[i % len(linestyles)]
if only_plot is None or label in only_plot:
i += 1
curves += [(label, ys, xs, score, color, linestyle)]
scores[label] = score
# if pr:
# # sort from highest ap to lowest
# sorted_curves = sorted(curves, key=lambda curve: curve[3], reverse=True)
# else:
# # sort from lowest to highest
# sorted_curves = sorted(curves, key=lambda curve: curve[3])
# fig, ax = plt.subplots(figsize=figsize)
# for label, ys, xs, score, color, linestyle in sorted_curves:
# # skip curves not mensioned in only_plot
# if only_plot is not None and label not in only_plot:
# continue
# if pr:
# plt.plot(xs, ys, color=color, linestyle=linestyle, label=f"{score}% {label}", linewidth=linewidth)
# else:
# plt.loglog(xs, ys, color=color, linestyle=linestyle, label=f"{score}% {label}", linewidth=linewidth)
#
# plt.legend(loc=legendloc)
#
# plt.gcf().suptitle(title, weight='bold')
# if pr:
# plt.grid(which='major')
# plt.gca().set_ylabel('Precision')
# plt.gca().set_xlabel('Recall')
# plt.gca().set_xlim([0, 1])
# plt.gca().set_ylim([0, 1])
# else:
# # modify the y axis a bit
# from matplotlib.ticker import FormatStrFormatter, LogLocator
# subs = [1.0, 2.0, 3.0, 4.0, 5.0, 6.4, 8.0] # ticks to show per decade
# ax.yaxis.set_minor_locator(LogLocator(subs=subs))
# ax.yaxis.set_minor_formatter(FormatStrFormatter("%.2f"))
# ax.yaxis.grid(which='minor')
# ax.xaxis.grid(which='major')
# plt.setp(ax.get_ymajorticklabels(), visible=False) # disable major labels
#
# plt.gca().set_ylabel('Miss rate')
# plt.gca().set_xlabel('FPPI')
# plt.gca().set_ylim([0.1, 1])
#
# plt.gca().set_xlim([0, 10])
# if saveplot:
# plt.savefig(saveplot, format='eps', dpi=1200)
return scores
scores_all_ap = generate_curves(ground_truth, detections_all, True, title="Day and night time",
saveplot="all_pr.eps", only_plot=detectors_to_plot)
scores_all_lamr = generate_curves(ground_truth, detections_all, False, title="Day and night time",
saveplot="all_mr_fppi.eps", only_plot=detectors_to_plot)
scores_day_ap = generate_curves(ground_truth_day, detections_day, True, title="Day time",
saveplot="day_pr.eps", only_plot=detectors_to_plot, figsize=(8,6))
scores_day_lamr = generate_curves(ground_truth_day, detections_day, False, title="Day time",
saveplot="day_mr_fppi.eps", only_plot=detectors_to_plot, figsize=(8,6))
scores_night_ap = generate_curves(ground_truth_night, detections_night, True, title="Night time",
saveplot="night_pr.eps", only_plot=detectors_to_plot, figsize=(8,6))
scores_night_lamr = generate_curves(ground_truth_night, detections_night, False, title="Night time",
saveplot="night_mr_fppi.eps", only_plot=detectors_to_plot, figsize=(8,6), legendloc='lower left')
# plt.show()
# print(scores_all_ap['current'])
# print(scores_day_ap['current'])
# print(scores_night_ap['current'])
# print(scores_all_lamr['current'])
# print(scores_day_lamr['current'])
# print(scores_night_lamr['current'])
return scores_all_ap['current'],scores_day_ap['current'],scores_night_ap['current'],scores_all_lamr['current'],scores_day_lamr['current'],scores_night_lamr['current']