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Fixes IoU calculation - off by 1 bug #343

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Fixes IoU calculation - off by 1 bug #343

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7a5d6b1
Fixes IoU calculation - off by 1 bug
dsuthar-nvidia May 23, 2017
99f10be
Using correct terminology in scores calculation
dsuthar-nvidia May 25, 2017
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16 changes: 8 additions & 8 deletions python/caffe/layers/detectnet/mean_ap.py
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Original file line number Diff line number Diff line change
Expand Up @@ -103,13 +103,13 @@ def backward(self, top, propagate_down, bottom):


def iou(det, rhs):
x_overlap = max(0, min(det[2], rhs[2]) - max(det[0], rhs[0]))
y_overlap = max(0, min(det[3], rhs[3]) - max(det[1], rhs[1]))
x_overlap = max(0, min(det[2], rhs[2]) - max(det[0], rhs[0]) + 1)
y_overlap = max(0, min(det[3], rhs[3]) - max(det[1], rhs[1]) + 1)
overlap_area = x_overlap * y_overlap
if overlap_area == 0:
return 0
det_area = (det[2]-det[0])*(det[3]-det[1])
rhs_area = (rhs[2]-rhs[0])*(rhs[3]-rhs[1])
det_area = (det[2]-det[0]+1)*(det[3]-det[1]+1)
rhs_area = (rhs[2]-rhs[0]+1)*(rhs[3]-rhs[1]+1)
unionarea = det_area + rhs_area - overlap_area
return overlap_area/unionarea

Expand Down Expand Up @@ -144,10 +144,10 @@ def score_det(gt_bbox_list, det_bbox_list):

tp = np.asarray([np.append(j, 1) for j in cur_det_bbox[np.where(det_matched == 1)]])
fp = np.asarray([np.append(j, 2) for j in cur_det_bbox[np.where(det_matched == 0)]])
tn = np.asarray([np.append(j, 3) for j in cur_gt_bbox[np.where(gt_matched == 0)]])
fn = np.asarray([np.append(j, 3) for j in cur_gt_bbox[np.where(gt_matched == 0)]])

temp = np.append(tp, fp)
temp = np.append(temp, tn)
temp = np.append(temp, fn)
temp = temp.reshape([temp.size/5, 5])
matched_bbox[k, 0:temp.shape[0], :] = temp

Expand All @@ -157,7 +157,7 @@ def score_det(gt_bbox_list, det_bbox_list):
def calcmAP(scored_detections, self):
self.true_positives = np.where(scored_detections[:, :, 4] == 1)[0].size
self.false_positives = np.where(scored_detections[:, :, 4] == 2)[0].size
self.true_negatives = np.where(scored_detections[:, :, 4] == 3)[0].size
self.false_negatives = np.where(scored_detections[:, :, 4] == 3)[0].size
self.precision = divide_zero_is_zero(self.true_positives, self.true_positives+self.false_positives)*100.00
self.recall = divide_zero_is_zero(self.true_positives, self.true_positives+self.true_negatives)*100.00
self.recall = divide_zero_is_zero(self.true_positives, self.true_positives+self.false_negatives)*100.00
self.avp = self.precision * self.recall / 100.0