def eval_graph(sm, detections):
time_start = time.time()
unary_prob = detections.conf
coordinates = detections.coord
pairwise_regr = detections.pairwise
cidx_list = range(0, sm.num_keypoints)
#cidx_list = [0, 1, 2]
unary_counts = []
for idx, cidx in enumerate(cidx_list):
unary_counts.append(unary_prob[cidx].shape[0])
num_unary = sum(unary_counts)
num_pairwise = 0
for idx1, cidx1 in enumerate(cidx_list):
for cidx2 in cidx_list[idx1:]:
if cidx1 == cidx2:
num_pairwise += unary_prob[cidx1].shape[0] * (
unary_prob[cidx1].shape[0] - 1) // 2
else:
num_pairwise += unary_prob[cidx1].shape[0] * unary_prob[
cidx2].shape[0]
pos_array = np.zeros([num_unary, 2], dtype=np.float64)
unary_array = np.zeros([num_unary, 1], dtype=np.float64)
pw_array = np.zeros([num_pairwise, 1], dtype=np.float64)
pwidx_array = np.zeros([num_pairwise, 2], dtype=np.uint16)
firstidx = 0
firstidx_list = []
for idx, cidx in enumerate(cidx_list):
lastidx = firstidx + unary_prob[cidx].shape[0]
unary_array[firstidx:lastidx] = unary_prob[cidx]
pos_array[firstidx:lastidx] = coordinates[cidx]
firstidx_list.append(firstidx)
firstidx = lastidx
firstidx = 0
for idx1, cidx1 in enumerate(cidx_list):
for idx2 in range(idx1, len(cidx_list)):
if coordinates[cidx1].shape[0] > 0:
cidx2 = cidx_list[idx2]
if coordinates[cidx2].shape[0] > 0:
if not sm.need_this_pairwise(cidx1, cidx2):
continue
cur_prob, ptidx = sm.eval(cidx1, cidx2, detections)
lastidx = firstidx + cur_prob.shape[0]
ptidx[:, 0] += firstidx_list[idx1]
ptidx[:, 1] += firstidx_list[idx2]
pw_array[firstidx:lastidx, 0] = cur_prob
pwidx_array[firstidx:lastidx, :] = ptidx
firstidx = lastidx
is_sparse_graph = True
solver_type = False
do_suppression = True
logit_in_solver = False
if unary_array.shape[0] > 0:
unary_array_solver = unary_array if logit_in_solver else logit_transform(
unary_array)
pw_array_solver = pw_array if logit_in_solver else logit_transform(
pw_array)
time_start = time.time()
res = solve_nl_lmp(unary_array_solver, pwidx_array, pw_array_solver,
is_sparse_graph, solver_type, do_suppression,
logit_in_solver)
unLab = np.array(res, dtype=np.uint64)
firstidx = 0
for cidx in cidx_list:
lastidx = firstidx + unary_prob[cidx].shape[0]
unLab[firstidx:lastidx, 0] = cidx
firstidx = lastidx
else:
unLab = np.array([])
return unLab, pos_array, unary_array, pwidx_array, pw_array
def eval_graph(sm, detections):
time_start = time.time()
unary_prob = detections.conf
coordinates = detections.coord
pairwise_regr = detections.pairwise
cidx_list = range(0, sm.num_keypoints)
#cidx_list = [0, 1, 2]
unary_counts = []
for idx, cidx in enumerate(cidx_list):
unary_counts.append(unary_prob[cidx].shape[0])
num_unary = sum(unary_counts)
num_pairwise = 0
for idx1, cidx1 in enumerate(cidx_list):
for cidx2 in cidx_list[idx1:]:
if cidx1 == cidx2:
num_pairwise += unary_prob[cidx1].shape[0]*(unary_prob[cidx1].shape[0] - 1) // 2
else:
num_pairwise += unary_prob[cidx1].shape[0]*unary_prob[cidx2].shape[0]
pos_array = np.zeros([num_unary, 2], dtype=np.float64)
unary_array = np.zeros([num_unary, 1], dtype=np.float64)
pw_array = np.zeros([num_pairwise, 1], dtype=np.float64)
pwidx_array = np.zeros([num_pairwise, 2], dtype=np.uint16)
firstidx = 0
firstidx_list = []
for idx, cidx in enumerate(cidx_list):
lastidx = firstidx + unary_prob[cidx].shape[0]
unary_array[firstidx:lastidx] = unary_prob[cidx]
pos_array[firstidx:lastidx] = coordinates[cidx]
firstidx_list.append(firstidx)
firstidx = lastidx
firstidx = 0
for idx1, cidx1 in enumerate(cidx_list):
for idx2 in range(idx1, len(cidx_list)):
if coordinates[cidx1].shape[0] > 0:
cidx2 = cidx_list[idx2]
if coordinates[cidx2].shape[0] > 0:
if not sm.need_this_pairwise(cidx1, cidx2):
continue
cur_prob, ptidx = sm.eval(cidx1, cidx2, detections)
lastidx = firstidx + cur_prob.shape[0]
ptidx[:, 0] += firstidx_list[idx1]
ptidx[:, 1] += firstidx_list[idx2]
pw_array[firstidx:lastidx, 0] = cur_prob
pwidx_array[firstidx:lastidx, :] = ptidx
firstidx = lastidx
is_sparse_graph = True
solver_type = False
do_suppression = True
logit_in_solver = False
if unary_array.shape[0] > 0:
unary_array_solver = unary_array if logit_in_solver else logit_transform(unary_array)
pw_array_solver = pw_array if logit_in_solver else logit_transform(pw_array)
time_start = time.time()
res = solve_nl_lmp(unary_array_solver, pwidx_array, pw_array_solver,
is_sparse_graph, solver_type, do_suppression, logit_in_solver)
unLab = np.array(res, dtype=np.uint64)
firstidx = 0
for cidx in cidx_list:
lastidx = firstidx + unary_prob[cidx].shape[0]
unLab[firstidx:lastidx, 0] = cidx
firstidx = lastidx
else:
unLab = np.array([])
return unLab, pos_array, unary_array, pwidx_array, pw_array