def get_pose_bbox(img, human):
height, width = img.shape[:2]
kp_preds = human['keypoints']
kp_scores = human['kp_score']
kp_preds = torch.cat(
(kp_preds, torch.unsqueeze((kp_preds[5, :] + kp_preds[6, :]) / 2,
0)))
kp_scores = torch.cat(
(kp_scores,
torch.unsqueeze((kp_scores[5, :] + kp_scores[6, :]) / 2, 0)))
keypoints = []
for n in range(kp_scores.shape[0]):
keypoints.append(float(kp_preds[n, 0]))
keypoints.append(float(kp_preds[n, 1]))
keypoints.append(float(kp_scores[n]))
bbox = get_box(keypoints, height, width)
return bbox
def vis_frame(frame, im_res, add_bbox=False, format='coco'):
'''
frame: frame image
im_res: im_res of predictions
format: coco or mpii
return rendered image
'''
if format == 'coco':
l_pair = [
(0, 1),
(0, 2),
(1, 3),
(2, 4), # Head
(5, 6),
(5, 7),
(7, 9),
(6, 8),
(8, 10),
(17, 11),
(17, 12), # Body
(11, 13),
(12, 14),
(13, 15),
(14, 16)
]
p_color = [
(0, 255, 255),
(0, 191, 255),
(0, 255, 102),
(0, 77, 255),
(0, 255, 0), # Nose, LEye, REye, LEar, REar
(77, 255, 255),
(77, 255, 204),
(77, 204, 255),
(191, 255, 77),
(77, 191, 255),
(191, 255,
77), # LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
(204, 77, 255),
(77, 255, 204),
(191, 77, 255),
(77, 255, 191),
(127, 77, 255),
(77, 255, 127),
(0, 255, 255)
] # LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck
line_color = [(0, 215, 255), (0, 255, 204), (0, 134, 255),
(0, 255, 50), (77, 255, 222), (77, 196, 255),
(77, 135, 255), (191, 255, 77), (77, 255, 77),
(77, 222, 255), (255, 156, 127), (0, 127, 255),
(255, 127, 77), (0, 77, 255), (255, 77, 36)]
elif format == 'coco_with_foot':
l_pair = [
(0, 1),
(0, 2),
(1, 3),
(2, 4), # Head
(5, 6),
(5, 7),
(7, 9),
(6, 8),
(8, 10),
(23, 11),
(23, 12), # Body
(11, 13),
(12, 14),
(13, 15),
(14, 16),
(17, 19),
(18, 19),
(20, 22),
(21, 22), # Foot
(15, 19),
(16, 22)
] # Foot
p_color = [
(0, 255, 255),
(0, 191, 255),
(0, 255, 102),
(0, 77, 255),
(0, 255, 0), # Nose, LEye, REye, LEar, REar
(77, 255, 255),
(77, 255, 204),
(77, 204, 255),
(191, 255, 77),
(77, 191, 255),
(191, 255,
77), # LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
(204, 77, 255),
(77, 255, 204),
(191, 77, 255),
(77, 255, 191),
(127, 77, 255),
(77, 255, 127),
(0, 255, 255), # LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck
(0, 255, 255),
(0, 191, 255),
(0, 255, 102),
(0, 77, 255),
(0, 255, 0),
(77, 255, 255)
] # Foot
line_color = [(0, 215, 255), (0, 255, 204), (0, 134, 255),
(0, 255, 50), (77, 255, 222), (77, 196, 255),
(77, 135, 255), (191, 255, 77), (77, 255, 77),
(77, 222, 255), (255, 156, 127), (0, 127, 255),
(255, 127, 77), (0, 77, 255), (255, 77, 36),
(0, 77, 255), (0, 77, 255), (0, 77, 255), (0, 77, 255),
(255, 156, 127), (255, 156, 127)] # Foot
elif format == 'mpii':
l_pair = [(8, 9), (11, 12), (11, 10), (2, 1), (1, 0), (13, 14),
(14, 15), (3, 4), (4, 5), (8, 7), (7, 6), (6, 2), (6, 3),
(8, 12), (8, 13)]
p_color = [
PURPLE, BLUE, BLUE, RED, RED, BLUE, BLUE, RED, RED, PURPLE, PURPLE,
PURPLE, RED, RED, BLUE, BLUE
]
line_color = [
PURPLE, BLUE, BLUE, RED, RED, BLUE, BLUE, RED, RED, PURPLE, PURPLE,
RED, RED, BLUE, BLUE
]
else:
raise NotImplementedError
# im_name = os.path.basename(im_res['imgname'])
img = frame.copy()
height, width = img.shape[:2]
img = cv2.resize(img, (int(width / 2), int(height / 2)))
for human in im_res['result']:
part_line = {}
kp_preds = human['keypoints']
kp_scores = human['kp_score']
kp_preds = torch.cat(
(kp_preds, torch.unsqueeze((kp_preds[5, :] + kp_preds[6, :]) / 2,
0)))
kp_scores = torch.cat(
(kp_scores,
torch.unsqueeze((kp_scores[5, :] + kp_scores[6, :]) / 2, 0)))
# Draw bboxes
if add_bbox:
from PoseFlow.poseflow_infer import get_box
keypoints = []
for n in range(kp_scores.shape[0]):
keypoints.append(float(kp_preds[n, 0]))
keypoints.append(float(kp_preds[n, 1]))
keypoints.append(float(kp_scores[n]))
bbox = get_box(keypoints, height, width)
bg = img.copy()
# color = get_color_fast(int(abs(human['idx'][0][0])))
cv2.rectangle(bg, (int(bbox[0] / 2), int(bbox[2] / 2)),
(int(bbox[1] / 2), int(bbox[3] / 2)), BLUE, 1)
transparency = 0.8
img = cv2.addWeighted(bg, transparency, img, 1 - transparency, 0)
# Draw indexes of humans
if 'idx' in human.keys():
bg = img.copy()
cv2.putText(bg, ''.join(str(human['idx'])),
(int(bbox[0] / 2), int((bbox[2] + 26) / 2)),
DEFAULT_FONT, 0.5, BLACK, 1)
transparency = 0.8
img = cv2.addWeighted(bg, transparency, img, 1 - transparency,
0)
# Draw keypoints
for n in range(kp_scores.shape[0]):
if kp_scores[n] <= 0.35:
continue
cor_x, cor_y = int(kp_preds[n, 0]), int(kp_preds[n, 1])
part_line[n] = (int(cor_x / 2), int(cor_y / 2))
bg = img.copy()
cv2.circle(bg, (int(cor_x / 2), int(cor_y / 2)), 2, p_color[n], -1)
# Now create a mask of logo and create its inverse mask also
transparency = max(0, min(1, kp_scores[n]))
img = cv2.addWeighted(bg, transparency, img, 1 - transparency, 0)
# Draw limbs
for i, (start_p, end_p) in enumerate(l_pair):
if start_p in part_line and end_p in part_line:
start_xy = part_line[start_p]
end_xy = part_line[end_p]
bg = img.copy()
X = (start_xy[0], end_xy[0])
Y = (start_xy[1], end_xy[1])
mX = np.mean(X)
mY = np.mean(Y)
length = ((Y[0] - Y[1])**2 + (X[0] - X[1])**2)**0.5
angle = math.degrees(math.atan2(Y[0] - Y[1], X[0] - X[1]))
stickwidth = (kp_scores[start_p] + kp_scores[end_p]) + 1
polygon = cv2.ellipse2Poly((int(mX), int(mY)),
(int(length / 2), stickwidth),
int(angle), 0, 360, 1)
cv2.fillConvexPoly(bg, polygon, line_color[i])
# cv2.line(bg, start_xy, end_xy, line_color[i], (2 * (kp_scores[start_p] + kp_scores[end_p])) + 1)
transparency = max(
0, min(1, 0.5 * (kp_scores[start_p] + kp_scores[end_p])))
img = cv2.addWeighted(bg, transparency, img, 1 - transparency,
0)
img = cv2.resize(img, (width, height), interpolation=cv2.INTER_CUBIC)
return img
def vis_frame_dense(frame, im_res, add_bbox=False, format='coco'):
'''
frame: frame image
im_res: im_res of predictions
format: coco or mpii
return rendered image
'''
l_pair = [
(0, 1),
(0, 2),
(1, 3),
(2, 4), # Head
(5, 19),
(19, 21),
(21, 17),
(17, 22),
(22, 20),
(20, 6), # Shoulder
(6, 28),
(28, 30),
(30, 8),
(8, 32),
(32, 34),
(34, 10), # right arm
(5, 27),
(27, 29),
(29, 7),
(7, 31),
(31, 33),
(33, 9), #left arm
(17, 47),
(47, 48),
(48, 18), #spine
(20, 24),
(24, 26),
(26, 12), #right spine
(19, 23),
(23, 25),
(25, 11), #left spine
(12, 36),
(36, 38),
(38, 18),
(18, 37),
(37, 35),
(35, 11), #row
(12, 40),
(40, 42),
(42, 14),
(14, 44),
(44, 46),
(46, 16), #right leg
(11, 39),
(39, 41),
(41, 13),
(13, 43),
(43, 45),
(45, 15) #left leg
]
p_color = [
(0, 255, 255),
(0, 191, 255),
(0, 255, 102),
(0, 77, 255),
(0, 255, 0), # Nose, LEye, REye, LEar, REar
(77, 255, 255),
(77, 255, 204),
(77, 204, 255),
(191, 255, 77),
(77, 191, 255),
(191, 255, 77), # LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
(204, 77, 255),
(77, 255, 204),
(191, 77, 255),
(77, 255, 191),
(127, 77, 255),
(77, 255, 127),
(0, 255, 255), # LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck
(0, 255, 255),
(0, 191, 255),
(0, 255, 102),
(0, 77, 255),
(0, 255, 0), # unkonw
(77, 255, 255),
(77, 255, 204),
(77, 204, 255),
(191, 255, 77),
(77, 191, 255),
(191, 255, 77), # unknown
(204, 77, 255),
(77, 255, 204),
(191, 77, 255),
(77, 255, 191),
(127, 77, 255),
(77, 255, 127),
(0, 255, 255), #unknown
(0, 255, 255),
(0, 191, 255),
(0, 255, 102),
(0, 77, 255),
(0, 255, 0), # unkonw
(77, 255, 255),
(77, 255, 204),
(77, 204, 255),
(191, 255, 77),
(77, 191, 255),
(191, 255, 77), # unknown
(204, 77, 255),
(77, 255, 204),
(191, 77, 255),
(77, 255, 191),
(127, 77, 255),
(77, 255, 127),
(0, 255, 255)
] #unknown
line_color = [
(0, 215, 255),
(0, 255, 204),
(0, 134, 255),
(0, 255, 50),
(77, 255, 222),
(77, 255, 222),
(77, 255, 222),
(77, 196, 255),
(77, 196, 255),
(77, 196, 255), # Shoulder
(77, 135, 255),
(77, 135, 255),
(77, 135, 255),
(191, 255, 77),
(191, 255, 77),
(191, 255, 77),
(77, 255, 77),
(77, 255, 77),
(77, 255, 77),
(77, 222, 255),
(77, 222, 255),
(77, 222, 255),
(255, 156, 127),
(255, 156, 127),
(255, 156, 127),
(255, 156, 127),
(255, 156, 127),
(255, 156, 127),
(255, 156, 127),
(255, 156, 127),
(255, 156, 127),
(255, 156, 127),
(255, 156, 127),
(255, 156, 127),
(255, 156, 127),
(255, 156, 127),
(255, 156, 127),
(0, 127, 255),
(0, 127, 255),
(0, 127, 255),
(255, 127, 77),
(255, 127, 77),
(255, 127, 77),
(0, 77, 255),
(0, 77, 255),
(0, 77, 255),
(255, 77, 36),
(255, 77, 36),
(255, 77, 36)
]
# im_name = os.path.basename(im_res['imgname'])
img = frame.copy()
height, width = img.shape[:2]
for human in im_res['result']:
part_line = {}
kp_preds = human['keypoints']
kp_scores = human['kp_score']
kp_preds = torch.cat(
(kp_preds, torch.unsqueeze((kp_preds[5, :] + kp_preds[6, :]) / 2,
0)))
kp_scores = torch.cat(
(kp_scores,
torch.unsqueeze((kp_scores[5, :] + kp_scores[6, :]) / 2, 0)))
# Draw bboxes
if add_bbox:
from PoseFlow.poseflow_infer import get_box
keypoints = []
for n in range(kp_scores.shape[0]):
keypoints.append(float(kp_preds[n, 0]))
keypoints.append(float(kp_preds[n, 1]))
keypoints.append(float(kp_scores[n]))
bbox = get_box(keypoints, height, width)
# color = get_color_fast(int(abs(human['idx'][0])))
cv2.rectangle(img, (int(bbox[0]), int(bbox[2])),
(int(bbox[1]), int(bbox[3])), BLUE, 2)
# Draw indexes of humans
if 'idx' in human.keys():
cv2.putText(img, ''.join(str(human['idx'])),
(int(bbox[0]), int(
(bbox[2] + 26))), DEFAULT_FONT, 1, BLACK, 2)
# Draw keypoints
for n in range(kp_scores.shape[0]):
if kp_scores[n] <= 0.35:
continue
cor_x, cor_y = int(kp_preds[n, 0]), int(kp_preds[n, 1])
part_line[n] = (cor_x, cor_y)
cv2.circle(img, (cor_x, cor_y), 3, p_color[n], -1)
# Draw limbs
for i, (start_p, end_p) in enumerate(l_pair):
if start_p in part_line and end_p in part_line:
start_xy = part_line[start_p]
end_xy = part_line[end_p]
cv2.line(img, start_xy, end_xy, line_color[i],
(kp_scores[start_p] + kp_scores[end_p]) + 1)
return img
def vis_frame_fast(frame, im_res, add_bbox=False, format='coco'):
'''
frame: frame image
im_res: im_res of predictions
format: coco or mpii
return rendered image
'''
if format == 'coco':
l_pair = [
(0, 1),
(0, 2),
(1, 3),
(2, 4), # Head
(5, 6),
(5, 7),
(7, 9),
(6, 8),
(8, 10),
(17, 11),
(17, 12), # Body
(11, 13),
(12, 14),
(13, 15),
(14, 16)
]
p_color = [
(0, 255, 255),
(0, 191, 255),
(0, 255, 102),
(0, 77, 255),
(0, 255, 0), # Nose, LEye, REye, LEar, REar
(77, 255, 255),
(77, 255, 204),
(77, 204, 255),
(191, 255, 77),
(77, 191, 255),
(191, 255,
77), # LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
(204, 77, 255),
(77, 255, 204),
(191, 77, 255),
(77, 255, 191),
(127, 77, 255),
(77, 255, 127),
(0, 255, 255)
] # LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck
line_color = [(0, 215, 255), (0, 255, 204), (0, 134, 255),
(0, 255, 50), (77, 255, 222), (77, 196, 255),
(77, 135, 255), (191, 255, 77), (77, 255, 77),
(77, 222, 255), (255, 156, 127), (0, 127, 255),
(255, 127, 77), (0, 77, 255), (255, 77, 36)]
elif format == 'coco_with_foot':
l_pair = [
(0, 1),
(0, 2),
(1, 3),
(2, 4), # Head
(5, 6),
(5, 7),
(7, 9),
(6, 8),
(8, 10),
(23, 11),
(23, 12), # Body
(11, 13),
(12, 14),
(13, 15),
(14, 16),
(17, 19),
(18, 19),
(20, 22),
(21, 22), # Foot
(15, 19),
(16, 22)
] # Foot
p_color = [
(0, 255, 255),
(0, 191, 255),
(0, 255, 102),
(0, 77, 255),
(0, 255, 0), # Nose, LEye, REye, LEar, REar
(77, 255, 255),
(77, 255, 204),
(77, 204, 255),
(191, 255, 77),
(77, 191, 255),
(191, 255,
77), # LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
(204, 77, 255),
(77, 255, 204),
(191, 77, 255),
(77, 255, 191),
(127, 77, 255),
(77, 255, 127),
(0, 255, 255), # LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck
(0, 255, 255),
(0, 191, 255),
(0, 255, 102),
(0, 77, 255),
(0, 255, 0),
(77, 255, 255)
] # Foot
line_color = [(0, 215, 255), (0, 255, 204), (0, 134, 255),
(0, 255, 50), (77, 255, 222), (77, 196, 255),
(77, 135, 255), (191, 255, 77), (77, 255, 77),
(77, 222, 255), (255, 156, 127), (0, 127, 255),
(255, 127, 77), (0, 77, 255), (255, 77, 36),
(0, 77, 255), (0, 77, 255), (0, 77, 255), (0, 77, 255),
(255, 156, 127), (255, 156, 127)] # Foot
elif format == 'mpii':
l_pair = [(8, 9), (11, 12), (11, 10), (2, 1), (1, 0), (13, 14),
(14, 15), (3, 4), (4, 5), (8, 7), (7, 6), (6, 2), (6, 3),
(8, 12), (8, 13)]
p_color = [
PURPLE, BLUE, BLUE, RED, RED, BLUE, BLUE, RED, RED, PURPLE, PURPLE,
PURPLE, RED, RED, BLUE, BLUE
]
else:
NotImplementedError
# im_name = os.path.basename(im_res['imgname'])
img = frame.copy()
height, width = img.shape[:2]
for human in im_res['result']:
part_line = {}
kp_preds = human['keypoints']
kp_scores = human['kp_score']
kp_preds = torch.cat(
(kp_preds, torch.unsqueeze((kp_preds[5, :] + kp_preds[6, :]) / 2,
0)))
kp_scores = torch.cat(
(kp_scores,
torch.unsqueeze((kp_scores[5, :] + kp_scores[6, :]) / 2, 0)))
# Draw bboxes
if add_bbox:
from PoseFlow.poseflow_infer import get_box
keypoints = []
for n in range(kp_scores.shape[0]):
keypoints.append(float(kp_preds[n, 0]))
keypoints.append(float(kp_preds[n, 1]))
keypoints.append(float(kp_scores[n]))
bbox = get_box(keypoints, height, width)
# color = get_color_fast(int(abs(human['idx'][0])))
cv2.rectangle(img, (int(bbox[0]), int(bbox[2])),
(int(bbox[1]), int(bbox[3])), BLUE, 2)
# Draw indexes of humans
if 'idx' in human.keys():
cv2.putText(img, ''.join(str(human['idx'])),
(int(bbox[0]), int(
(bbox[2] + 26))), DEFAULT_FONT, 1, BLACK, 2)
# Draw keypoints
for n in range(kp_scores.shape[0]):
if kp_scores[n] <= 0.35:
continue
cor_x, cor_y = int(kp_preds[n, 0]), int(kp_preds[n, 1])
part_line[n] = (cor_x, cor_y)
cv2.circle(img, (cor_x, cor_y), 3, p_color[n], -1)
# Draw limbs
for i, (start_p, end_p) in enumerate(l_pair):
if start_p in part_line and end_p in part_line:
start_xy = part_line[start_p]
end_xy = part_line[end_p]
cv2.line(img, start_xy, end_xy, line_color[i],
2 * (kp_scores[start_p] + kp_scores[end_p]) + 1)
return img