_start = time()
# for frame in range(0, 214):
end_frame = 100
for frame in range(0, end_frame):
Im, calib, pos2d, pos3d = kth.get(
data_root, seq1_zipname, seq1_dir, frame, player=2)
Calib.append(calib)
Pos3d.append(pos3d)
Ims.append(Im)
txt_add = str(peak_threshold)
if 0.099 < peak_threshold < 0.101:
txt_add = ''
name = 'cvpr_kth_' + seq1_zipname + txt_add
predictions = pe.predict(Im, name, frame)
poses_per_frame.append(predictions)
_end = time()
print('elapsed', _end - _start)
tracks = tracking(Calib, poses_per_frame,
epi_threshold=110,
scale_to_mm=1000,
max_distance_between_tracks=200,
distance_threshold=200,
correct_limb_size=False,
merge_distance=200)
track = tracks[0]
from mvpose.baseline.tracking import Track
track = Track.smoothing(track, sigma=2.3, interpolation_range=5)
ax.set_ylim([1080, 0])
for pid, points3d_weighted in Y:
pts3d = points3d_weighted[:, 0:3].astype('float32')
pts2d = cam.projectPoints(pts3d)
pts2dT = pts2d.transpose()
ax.scatter(pts2d[:, 0], pts2d[:, 1], color=colors[pid])
for edge in body_edges:
ax.plot(pts2dT[0, edge], pts2dT[1, edge], color=colors[pid])
plt.tight_layout()
plt.pause(2)
pe = OpenPose(tmp=tmp)
predictions = pe.predict(Im, 'cvpr_cmu', FRAME)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
fig = plt.figure(figsize=(16, 8))
for idx, (im, pred) in enumerate(zip(Im, predictions)):
ax = fig.add_subplot(2, 3, idx + 1)
ax.axis('off')
ax.imshow(im)
ax.set_xlim([0, 1920])
ax.set_ylim([1080, 0])
for human in pred:
draw_mscoco_human2d(ax, human[:, 0:2], color='red')
plt.tight_layout()
plt.pause(2)
pe = OpenPose(tmp=tmp, peak_threshold=0.05)
valid_frames = list(range(350, 470)) + list(range(650, 750))
Calib = []
poses_per_frame = []
Pos3d = {}
Imgs = {}
peak_threshold = 0.05
_start = time()
for frame in valid_frames:
Im, Y, calib = epfl_campus.get(root, frame)
Imgs[frame] = Im
Calib.append(calib)
Pos3d[frame] = Y
predictions = pe.predict(Im, 'cvpr_campus' + str(peak_threshold),
frame)
poses_per_frame.append(predictions)
_end = time()
print('elapsed', _end - _start)
from mvpose.baseline.tracking import tracking, Track
_start = time()
tracks = tracking(Calib, poses_per_frame,
epi_threshold=20,
scale_to_mm=1000,
max_distance_between_tracks=200,
actual_frames=valid_frames,
min_track_length=10,
merge_distance=80,
last_seen_delay=5)
_end = time()
from mvpose.algorithm.candidate_selection import project_human_to_2d
from mvpose.plot.limbs import draw_mscoco_human, draw_mscoco_human2d
# heatmaps, pafs = paf_loader.load_confidence_map_and_paf(
# 'kth' + seq1_zipname[0:-4], Im, FRAME, dir=tmp)
# _start = time()
# Debug, detections = pose.estimate(Calib, heatmaps, pafs,
# settings=params, debug=True)
# _end = time()
# print('elapsed', _end - _start)
from openpose import OpenPose
pe = OpenPose(tmp=tmp)
name = 'cvpr_kth_' + seq1_zipname
predictions = pe.predict(Im, name, FRAME)
Humans = predictions
from mvpose.baseline.baseline import estimate
H, Hypothesis = estimate(Calib,
predictions,
epi_threshold=100,
scale_to_mm=1000,
get_hypothesis=True)
fig = plt.figure(figsize=(16, 12))
colors = ['red', 'blue', 'green', 'teal']
from openpose import OpenPose
pe = OpenPose(tmp=tmp)
poses_per_frame = []
start_frame = 300
end_frame = 600
actual_frames = list(range(start_frame, end_frame))
calib = None
_start = time()
Imgs = {}
for t in range(start_frame, end_frame):
Im, _, calib = shelf.get(root, t)
Imgs[t] = Im
predictions = pe.predict(Im, 'cvpr_shelf', t)
poses_per_frame.append(predictions)
print('extract poses from ' +\
str(end_frame - start_frame) + ' frames')
print('\telapsed:', time() - _start)
_start = time()
tracks = tracking(calib, poses_per_frame,
actual_frames=actual_frames,
epi_threshold=70,
scale_to_mm=1000,
max_distance_between_tracks=200,
min_track_length=10,
merge_distance=80,
last_seen_delay=15)
print('extract ' + str(len(tracks)) + ' tracks')