def histogram_areas_AP(dict_areas_AP=None):
if dict_areas_AP is None:
with open('pickles/dict_areas_AP.pkl', 'rb') as cls_pkl:
dict_areas_AP = pickle.load(cls_pkl)
for key, value in dict_areas_AP.items():
AP = np.mean(value)
if math.isnan(AP):
dict_areas_AP[key] = 0
else:
dict_areas_AP[key] = AP
# dict_areas_AP[key] = 0
# dict_areas_AP[key] = np.mean(value)
print(dict_areas_AP)
mAP = dict_areas_AP['mAP']
dict_areas_AP.pop('mAP')
n_classes = len(dict_areas_AP.keys())
window_title = "AP per area"
plot_title = "mAP = {0:.2f}%".format(mAP * 100)
x_label = "Average Precision"
output_path = "output/areas_AP.png"
to_show = False
plot_color = 'royalblue'
draw_plot_func(dict_areas_AP, n_classes, window_title, plot_title, x_label,
output_path, to_show, plot_color, "")
def histogram_areas(dict_areas=None):
if dict_areas is None:
with open('pickles/dict_areas.pkl', 'rb') as areas_pkl:
dict_areas = pickle.load(areas_pkl)
window_title = "areas"
plot_title = "number of objects per areas"
x_label = "number of objects per area"
output_path = "output/areas_wide.png"
to_show = True
plot_color = 'forestgreen'
draw_plot_func(dict_areas, len(dict_areas.keys()), window_title,
plot_title, x_label, output_path, to_show, plot_color, "")
def histogram_resolutions_img(dict_res_img=None):
if dict_res_img is None:
with open('pickles/dict_res_img_images.pkl', 'rb') as gt_pkl:
dict_res_img = pickle.load(gt_pkl)
classes = load_classes('data/autel.resolutions')
window_title = "img/resolution"
plot_title = "num images per resolution"
x_label = "num images"
output_path = "output/resolution_img_08072018.png"
to_show = True
plot_color = 'royalblue'
draw_plot_func(dict_res_img, len(classes), window_title, plot_title,
x_label, output_path, to_show, plot_color, "")
def histogram_resolutions_videos(dict_res_video=None):
if dict_res_video is None:
with open('pickles/dict_res_videos_images.pkl', 'rb') as gt_pkl:
dict_res_video = pickle.load(gt_pkl)
for key, values in dict_res_video.items():
dict_res_video[key] = len(values)
classes = load_classes('data/autel.resolutions')
window_title = "ground truth"
plot_title = "number videos per resolution"
x_label = "num videos"
output_path = "output/resolution_videos_08072018.png"
to_show = True
plot_color = 'royalblue'
draw_plot_func(dict_res_video, len(classes), window_title, plot_title,
x_label, output_path, to_show, plot_color, "")
def histogram_classes_gt(dict_gt=None):
if dict_gt is None:
with open('pickle/dict_gt.pkl', 'rb') as gt_pkl:
dict_gt = pickle.load(gt_pkl)
classes = load_classes('data/autel.names')
gt_path = '/home/alupotto/data/autel/new_labels'
gt_list = glob.glob("{}/*.txt".format(gt_path))
window_title = "ground truth"
plot_title = "Ground-Truth\n"
plot_title += "(" + str(len(gt_list)) + " files and " + str(
len(classes)) + " classes)"
x_label = "number ground truth objects"
output_path = "output/ground_truth.png"
to_show = False
plot_color = 'forestgreen'
draw_plot_func(dict_gt, len(classes), window_title, plot_title, x_label,
output_path, to_show, plot_color, "")
return dict_gt
def histogram_classes_AP(dict_classes=None):
if dict_classes is None:
with open('pickles/dict_classes.pkl', 'rb') as cls_pkl:
dict_classes = pickle.load(cls_pkl)
classes = load_classes('data/autel.names')
for key, value in dict_classes.items():
dict_classes[key] = np.mean(value)
mAP = dict_classes['mAP']
dict_classes.pop('mAP')
window_title = "AP per class"
plot_title = "mAP = {0:.2f}%".format(mAP * 100)
x_label = "Average Precision"
output_path = "output/classes_AP.png"
to_show = True
plot_color = 'royalblue'
draw_plot_func(dict_classes, len(classes), window_title, plot_title,
x_label, output_path, to_show, plot_color, "")
return dict_classes
def histogram_classes_gt(imgs_path, dict_gt=None):
if dict_gt is None:
with open('pickles/dict_part1_autel.pkl', 'rb') as gt_pkl:
dict_gt = pickle.load(gt_pkl)
print(dict_gt)
classes = load_classes('data/autel.names')
# load images
img_files = sorted(glob.glob('%s/*.jpg' % imgs_path))
print(len(img_files))
window_title = "ground truth"
plot_title = "Ground-Truth\n"
plot_title += "(" + str(len(img_files)) + " files and " + str(
len(classes)) + " classes)"
x_label = "number ground truth objects"
output_path = "output/ground_truth_08072018.png"
to_show = True
plot_color = 'forestgreen'
draw_plot_func(dict_gt, len(classes), window_title, plot_title, x_label,
output_path, to_show, plot_color, "")