def computeMetrics(scoresAll, labelsAll, nGTall):
apAll = np.zeros((nGTall.shape[0] + 1, 1))
recAll = np.zeros((nGTall.shape[0] + 1, 1))
preAll = np.zeros((nGTall.shape[0] + 1, 1))
# iterate over joints
for j in range(nGTall.shape[0]):
scores = np.zeros([0, 0], dtype=np.float32)
labels = np.zeros([0, 0], dtype=np.int8)
# iterate over images
for imgidx in range(nGTall.shape[1]):
scores = np.append(scores, scoresAll[j][imgidx])
labels = np.append(labels, labelsAll[j][imgidx])
# compute recall/precision values
nGT = sum(nGTall[j, :])
precision, recall, scoresSortedIdxs = eval_helpers.computeRPC(scores, labels, nGT)
if (len(precision) > 0):
apAll[j] = eval_helpers.VOCap(recall, precision) * 100
preAll[j] = precision[len(precision) - 1] * 100
recAll[j] = recall[len(recall) - 1] * 100
idxs = np.argwhere(~np.isnan(apAll[:nGTall.shape[0],0]))
apAll[nGTall.shape[0]] = apAll[idxs, 0].mean()
idxs = np.argwhere(~np.isnan(recAll[:nGTall.shape[0],0]))
recAll[nGTall.shape[0]] = recAll[idxs, 0].mean()
idxs = np.argwhere(~np.isnan(preAll[:nGTall.shape[0],0]))
preAll[nGTall.shape[0]] = preAll[idxs, 0].mean()
return apAll, preAll, recAll
def computeMetrics(scoresAll, labelsAll, nGTall):
apAll = np.zeros((nGTall.shape[0] + 1, 1))
recAll = np.zeros((nGTall.shape[0] + 1, 1))
preAll = np.zeros((nGTall.shape[0] + 1, 1))
# iterate over joints
for j in range(nGTall.shape[0]):
scores = np.zeros([0, 0], dtype=np.float32)
labels = np.zeros([0, 0], dtype=np.int8)
# iterate over images
for imgidx in range(nGTall.shape[1]):
scores = np.append(scores, scoresAll[j][imgidx])
labels = np.append(labels, labelsAll[j][imgidx])
# compute recall/precision values
nGT = sum(nGTall[j, :])
precision, recall, scoresSortedIdxs = eval_helpers.computeRPC(scores, labels, nGT)
if (len(precision) > 0):
apAll[j] = eval_helpers.VOCap(recall, precision) * 100
preAll[j] = precision[len(precision) - 1] * 100
recAll[j] = recall[len(recall) - 1] * 100
apAll[nGTall.shape[0]] = apAll[:nGTall.shape[0], 0].mean()
recAll[nGTall.shape[0]] = recAll[:nGTall.shape[0], 0].mean()
preAll[nGTall.shape[0]] = preAll[:nGTall.shape[0], 0].mean()
return apAll, preAll, recAll