def get_dis_i_j(gtFramesAll, prFramesAll, imgidx, joint_indx,
FN_match_exc_thre_i):
from eval_helpers import getHeadSize
for i, pair_i in enumerate(FN_match_exc_thre_i):
gt_i = pair_i["gt"]
pred_i = pair_i["predict"]
# print("imgidx,gt_i,joint_indx:",imgidx,gt_i,joint_indx)
# print( get_kps_i(gtFramesAll,imgidx,gt_i,joint_indx,gt_flag=True) )
pointGT, head_coordinate = get_kps_i(gtFramesAll,
imgidx,
gt_i,
joint_indx,
gt_flag=True)
pointPr = get_kps_i(prFramesAll, imgidx, pred_i, joint_indx)
headSize = getHeadSize(head_coordinate[0], head_coordinate[1],
head_coordinate[2], head_coordinate[3])
dist_i_j = np.linalg.norm(np.subtract(pointGT, pointPr)) / headSize
FN_match_exc_thre_i[i]["gt"] = {
"x": pointGT[0],
"y": pointGT[1],
"id": joint_indx,
"head_size": headSize
}
FN_match_exc_thre_i[i]["predict"] = {
"x": pointPr[0],
"y": pointPr[1],
"id": joint_indx
}
FN_match_exc_thre_i[i]["dis"] = dist_i_j
return FN_match_exc_thre_i
def computeDist(preFrames, gtFrames):
distAll = {}
dist = {}
keypoints_gt, label = eval_helpers.eval1(gtFrames)
keypoints_pre = eval_helpers.eval(preFrames)
for imgidx in keypoints_gt.keys():
gt = keypoints_gt[imgidx]
pre = keypoints_pre[imgidx]
head = gt[0]
neck = ((gt[1][0] + gt[2][0]) / 2, (gt[1][1] + gt[2][1]) / 2)
label_gt = label[imgidx]
if imgidx in keypoints_pre.keys():
for index in range(len(label_gt)):
if label_gt[index] == 0:
dist[index] = 0
else:
pointGT = gt[index]
pointPre = pre[index]
d = np.linalg.norm(np.subtract(pointGT, pointPre))
headSize = eval_helpers.getHeadSize(
head[0], neck[0], head[1], neck[1])
dnormal = d / headSize * 0.1
dist[index] = dnormal
distAll[imgidx] = dist
dist = {}
return distAll
def computeDist(gtFrames, prFrames):
assert (len(gtFrames) == len(prFrames))
nJoints = eval_helpers.Joint().count
distAll = {}
for pidx in range(nJoints):
distAll[pidx] = np.zeros([0, 0])
for imgidx in range(len(gtFrames)):
# ground truth
gtFrame = gtFrames[imgidx]
# prediction
detFrame = prFrames[imgidx]
if (gtFrames[imgidx]["annorect"] != None):
for ridx in range(len(gtFrames[imgidx]["annorect"])):
rectGT = gtFrames[imgidx]["annorect"][ridx]
rectPr = prFrames[imgidx]["annorect"][ridx]
if ("annopoints" in rectGT.keys()
and rectGT["annopoints"] != None):
pointsGT = rectGT["annopoints"][0]["point"]
pointsPr = rectPr["annopoints"][0]["point"]
for pidx in range(len(pointsGT)):
pointGT = [
pointsGT[pidx]["x"][0], pointsGT[pidx]["y"][0]
]
idxGT = pointsGT[pidx]["id"][0]
p = eval_helpers.getPointGTbyID(pointsPr, idxGT)
if (len(p) > 0 and (type(p["x"][0]) == int
or type(p["x"][0]) == float)
and (type(p["y"][0]) == int
or type(p["y"][0]) == float)):
pointPr = [p["x"][0], p["y"][0]]
# compute distance between GT and prediction
d = np.linalg.norm(np.subtract(pointGT, pointPr))
# compute head size for distance normalization
headSize = eval_helpers.getHeadSize(
rectGT["x1"][0], rectGT["y1"][0],
rectGT["x2"][0], rectGT["y2"][0])
# normalize distance
dNorm = d / headSize
else:
dNorm = np.inf
distAll[idxGT] = np.append(distAll[idxGT], [[dNorm]])
return distAll
def computeDist(gtFrames,prFrames):
assert(len(gtFrames) == len(prFrames))
nJoints = eval_helpers.Joint().count
distAll = {}
for pidx in range(nJoints):
distAll[pidx] = np.zeros([0,0])
for imgidx in range(len(gtFrames)):
# ground truth
gtFrame = gtFrames[imgidx]
# prediction
detFrame = prFrames[imgidx]
if (gtFrames[imgidx]["annorect"] != None):
for ridx in range(len(gtFrames[imgidx]["annorect"])):
rectGT = gtFrames[imgidx]["annorect"][ridx]
rectPr = prFrames[imgidx]["annorect"][ridx]
if ("annopoints" in rectGT.keys() and rectGT["annopoints"] != None):
pointsGT = rectGT["annopoints"][0]["point"]
pointsPr = rectPr["annopoints"][0]["point"]
for pidx in range(len(pointsGT)):
pointGT = [pointsGT[pidx]["x"][0],pointsGT[pidx]["y"][0]]
idxGT = pointsGT[pidx]["id"][0]
p = eval_helpers.getPointGTbyID(pointsPr,idxGT)
if (len(p) > 0 and
(type(p["x"][0]) == int or type(p["x"][0]) == float) and
(type(p["y"][0]) == int or type(p["y"][0]) == float)):
pointPr = [p["x"][0],p["y"][0]]
# compute distance between GT and prediction
d = np.linalg.norm(np.subtract(pointGT,pointPr))
# compute head size for distance normalization
headSize = eval_helpers.getHeadSize(rectGT["x1"][0],rectGT["y1"][0],
rectGT["x2"][0],rectGT["y2"][0])
# normalize distance
dNorm = d/headSize
else:
dNorm = np.inf
distAll[idxGT] = np.append(distAll[idxGT],[[dNorm]])
return distAll