def estimate_paf(peaks, heat_mat, paf_mat):
pafprocess.process_paf(peaks, heat_mat, paf_mat)
humans = []
for human_id in range(pafprocess.get_num_humans()):
human = Human([])
is_added = False
for part_idx in range(18):
c_idx = int(pafprocess.get_part_cid(human_id, part_idx))
if c_idx < 0:
continue
is_added = True
human.body_parts[part_idx] = BodyPart(
'%d-%d' % (human_id, part_idx), part_idx,
float(pafprocess.get_part_x(c_idx)) / heat_mat.shape[1],
float(pafprocess.get_part_y(c_idx)) / heat_mat.shape[0],
pafprocess.get_part_score(c_idx))
if is_added:
score = pafprocess.get_score(human_id)
human.score = score
humans.append(human)
return humans
peaks[local_max, i] = heatMat[local_max, i]
plt.figure()
plt.imshow(np.sum(peaks[:, :, i] for i in range(19)))
plt.show()
#Particule Affinity Field to separate people
pafprocess.process_paf(peaks.astype('float32'),
heatMat.astype('float32'),
pafMat.astype('float32'))
humans = []
for human_id in range(pafprocess.get_num_humans()):
body_parts = [(0.0, 0.0)] * 18
for part_idx in range(18):
c_idx = int(pafprocess.get_part_cid(human_id, part_idx))
if c_idx < 0:
continue
is_added = True
body_parts[part_idx] = (pafprocess.get_part_x(c_idx),
pafprocess.get_part_y(c_idx))
body_parts = np.array(
[[body_parts[i][0] * scale, body_parts[i][1] * scale]
for i in range(18)]).flatten()
humans.append(body_parts)
# Draw human body parts
d = ImageDraw.Draw(resized)
for _data in humans:
for pair in connections: