def get_iou_totals_per_image(gt_objects, positive_props, detections_regressed,
file, percentage):
total_props_iou = 0
total_detections_iou = 0
total_positives = 0
total_negatives = 0
total_gt = 0
total_pckh_precisions = 0
outliers = 0
total_fn = 0
list_found_indices = []
for gt in gt_objects:
if (common_functions.check_box_boundaries(gt) == True):
total_gt += 1
for prop in positive_props:
#if(common_functions.check_if_rectangle_contains_another(prop,gt)):
if (common_functions.bb_intersection_over_boxA(gt, prop) >=
0.5):
list_found_indices.append(positive_props.index(prop))
total_positives += 1
total_props_iou += common_functions.bb_intersection_over_union(
prop, gt)
prop_index = positive_props.index(prop)
total_detections_iou += common_functions.bb_intersection_over_union(
detections_regressed[prop_index][0], gt)
pckh_precision = get_shoulder_precisions(
gt, detections_regressed[prop_index][1], file,
percentage)
total_pckh_precisions += pckh_precision
break
for i in range(0, len(positive_props)):
if not i in list_found_indices:
if (common_functions.check_box_boundaries(
positive_props[i]) == True):
total_negatives += 1
else:
outliers += 1
#total_negatives = len(positive_props) - total_positives
average_props_iou = 0
average_detections_iou = 0
average_pckh_precision = 0
if (total_positives != 0):
average_props_iou = total_props_iou / total_positives
average_detections_iou = total_detections_iou / total_positives
average_pckh_precision = total_pckh_precisions / (total_positives * 2)
test = total_gt - (total_positives + total_negatives)
if test > 0:
total_fn = test
return average_props_iou, average_detections_iou, total_gt, total_positives, total_negatives, average_pckh_precision, outliers, total_fn
def get_iou_totals_per_image(gt_objects, positive_props, file, percentage):
total_props_iou = 0
total_positives = 0
total_negatives = 0
total_gt = 0
outliers = 0
total_fn = 0
list_found_indices = []
for gt in gt_objects:
gtbox = [gt[0], gt[1], gt[2], gt[3]]
if (common_functions.check_box_boundaries(gtbox) == True):
total_gt += 1
for prop in positive_props:
#if(common_functions.check_if_rectangle_contains_another(prop,gt)):
if (common_functions.bb_intersection_over_boxA(gtbox, prop[0])
>= 0.5):
total_positives += 1
list_found_indices.append(positive_props.index(prop))
total_props_iou += common_functions.bb_intersection_over_union(
prop[0], gtbox)
prop_index = positive_props.index(prop)
break
for i in range(0, len(positive_props)):
if not i in list_found_indices:
if (common_functions.check_box_boundaries(
positive_props[i][0]) == True):
total_negatives += 1
else:
outliers += 1
#total_negatives = len(positive_props) - total_positives
average_props_iou = 0
if (total_positives != 0):
average_props_iou = total_props_iou / total_positives
test = total_gt - (total_positives + total_negatives)
if test > 0:
total_fn = test
return average_props_iou, 0, total_gt, total_positives, total_negatives, 0, outliers, total_fn, 0
def get_iou_totals_per_image(gt_objects, positive_props, file):
total_props_iou = 0
total_detections_iou = 0
total_positives = 0
total_negatives = 0
total_gt = 0
total_pckh_precisions = 0
list_found_indices = []
for gt in gt_objects:
if (common_functions.check_box_boundaries(gt) == True):
total_gt += 1
for prop in positive_props:
if (common_functions.check_if_rectangle_contains_another(
prop[0], gt)):
list_found_indices.append(positive_props.index(prop))
total_positives += 1
#total_props_iou += common_functions.bb_intersection_over_union(prop,gt)
prop_index = positive_props.index(prop)
#total_detections_iou += common_functions.bb_intersection_over_union(detections_regressed[prop_index][0],gt)
#pckh_precision = get_shoulder_precisions(gt,detections_regressed[prop_index][1],file)
#total_pckh_precisions += pckh_precision
break
for i in range(0, len(positive_props)):
if not i in list_found_indices:
if (common_functions.check_box_boundaries(
positive_props[i][0]) == True):
total_negatives += 1
#total_negatives = len(positive_props) - total_positives
#average_props_iou = 0
#average_detections_iou = 0
#average_pckh_precision = 0
#if(total_positives != 0):
# average_props_iou = total_props_iou/total_positives
# average_detections_iou = total_detections_iou/total_positives
# average_pckh_precision = total_pckh_precisions/(total_positives * 2)
return total_gt, total_positives, total_negatives