Open Keronaya opened 3 months ago
Keronaya
commented
3 months ago https://onedrive.live.com/?authkey=%21AGPDvrDlvIFrNfg&id=303FB25922AAD438%2173206&cid=303FB25922AAD438 d Solved, need to download the original dataset(video frame with image)
Keronaya
commented
3 months ago Drawing skeleton for person_id: 1
Traceback (most recent call last):
File "/mnt/i/My Drive/code/TrajREC/visualize_skeleton_bbox.py", line 454, in
P-AEL
commented
2 months ago i am having the same error. Somebody already fixed it ?
Keronaya
commented
2 months ago i am having the same error. Somebody already fixed it ?
`import argparse import math import numpy as np import cv2 import os import tqdm
COLOURS = {(0, 1) : (255, 0, 255), (0, 2) : (255, 0, 255), (1, 3) : (255, 0, 255), (2, 4) : (255, 0, 255), (5, 7) : (0, 127, 255), # left arm (7, 9) : (0, 255, 255), # left arm (6, 8) : (127, 255, 0), # right arm (8, 10) : (0, 255, 0), # right arm (11, 13) : (127, 225, 0), # left leg (13, 15) : (255, 225, 0), # left let (12, 14) : (255, 127, 0), # right leg (14, 16) : (255, 0, 0), # right leg (0, 5) : (192, 127, 192), (0, 6) : (127, 127, 192), (5, 6) : (0, 0, 255), # chest (5, 11) : (0, 0, 255), # left side (6, 12) : (0, 0, 255), # right side (11, 12) : (0, 0, 255) # pelvis } # Dark Green
COLOURS_POINTS = { 0 : (255, 0, 255), 1 : (255, 0, 255), 2 : (255, 0, 255), 3 : (255, 0, 255), 4 : (255, 0, 255), 5 : (0, 64, 255), 7 : (0, 191, 255), 9 : (0, 255, 255), 6 : (191, 255, 0), 8 : (64, 255, 0), 10 : (0, 255, 0), 11 : (127,255,127), 13 : (192, 225, 0), 15 : (255, 255, 0), 12 : (255, 127, 64), 14 : (255, 64, 0), 16 : (255, 0, 0) } # Dark Green
parser = argparse.ArgumentParser(description='Visualize the predicted skeletons with corresponding bounding boxes.')
parser.add_argument('--frames', type=str, help='Directory containing video frames.') parser.add_argument('--gt_trajectories', type=str, help='Directory containing the ground-truth trajectories of people in the video.') parser.add_argument('--draw_gt_skeleton', type=bool,default=True, help='Whether to draw the ground-truth skeletons or not.') parser.add_argument('--draw_gt_bbox',type=bool,default=False, help='Whether to draw the bounding box of the ground-truth skeletons or not.') parser.add_argument('--trajectories', type=str,help='Directory containing the reconstructed/predicted trajectories of people in ' 'the video.') parser.add_argument('--draw_pred_skeleton',type=bool,default=True, help='Whether to draw the reconstructed/predicted skeleton or not.') parser.add_argument('--draw_pred_bbox',type=bool,default=False, help='Whether to draw the bounding box of the reconstructed/predicted trajectories ' 'or not.') parser.add_argument('--person_id', type=int, help='Draw only a specific person in the video.') parser.add_argument('--draw_local_skeleton', action='store_true', help='If specified, draw local skeletons on a white background. It must be used ' 'in conjunction with --person_id, since it is only possible to visualise ' 'one pair (ground-truth, reconstructed/predicted) of local skeletons.') parser.add_argument('--write_dir', default='./visualise', type=str, help='Directory to write rendered frames. If the specified directory does not ' 'exist, it will be created.') parser.add_argument('--generate_gif',type=bool,default=False, help='Render gif from the prediction frames.') parser.add_argument('--scale',type=int,default=1, help='scale of frames.')
def prepare_keypoints(keypoints): keypoints = keypoints * 8 min_x = min([k[0] for k in keypoints if k[0]!=0]) min_y = min([k[1] for k in keypoints if k[1]!=0])
max_x = max([k[0] for k in keypoints if k[0]!=0])
max_y = max([k[1] for k in keypoints if k[1]!=0])
n = 800 / (max_x-min_x)
keypoints = keypoints * n
new_ks = []
for x,y in keypoints:
if 0 in (x, y):
new_ks.append((x,y))
else:
new_ks.append((x-(min_x*n)+40.,y-(min_y*n)+40.))
keypoints = new_ks
frame = np.full((math.floor((max_y-min_y)*n+80.),math.floor((max_x-min_x)*n+80.)),fill_value=255, dtype=np.single)
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
return keypoints,framedef draw_skeleton(frame, keypoints, colour, dotted=False, scale=4, scale_vis=False): connections = [(5, 6), (5, 11), (6, 12), (11, 12), (0, 1), (0, 2), (1, 3), (2, 4), (5, 7), (7, 9), (6, 8), (8, 10), (11, 13), (13, 15), (12, 14), (14, 16), (0, 5), (0, 6)]
keypoints = keypoints * scale
if scale_vis:
line_thickness=4*10
circle_thickness = -1
radius =3*12
else:
line_thickness=4
circle_thickness = 2
radius =3
for i,(keypoint_id1, keypoint_id2) in enumerate(connections):
x1, y1 = keypoints[keypoint_id1]
x2, y2 = keypoints[keypoint_id2]
if 0 in (x1, y1, x2, y2):
continue
pt1 = int(round(x1)), int(round(y1))
pt2 = int(round(x2)), int(round(y2))
if dotted:
draw_line(frame, pt1=pt1, pt2=pt2, color=COLOURS[connections[i]], thickness=line_thickness, gap=5)
else:
cv2.line(frame, pt1=pt1, pt2=pt2, color=COLOURS[connections[i]], thickness=line_thickness)
for i, (x, y) in enumerate(keypoints):
if 0 in (x, y):
continue
center = int(round(x)), int(round(y))
cv2.circle(frame, center=center, radius=radius, color=COLOURS_POINTS[i], thickness=circle_thickness)
return Nonedef draw_rect(img, pt1, pt2, color, thickness=1, style='dotted'): pts = [pt1, (pt2[0], pt1[1]), pt2, (pt1[0], pt2[1])] draw_poly(img, pts, color, thickness, style)
def draw_line(img, pt1, pt2, color, thickness=1, style='dotted', gap=10): dist = ((pt1[0] - pt2[0]) 2 + (pt1[1] - pt2[1]) 2) * .5 pts = [] for i in np.arange(0, dist, gap): r = i / dist x = int((pt1[0] (1 - r) + pt2[0] r) + .5) y = int((pt1[1] (1 - r) + pt2[1] * r) + .5) p = (x, y) pts.append(p)
if style == 'dotted':
for p in pts:
cv2.circle(img, p, thickness, color, -1)
else:
s = pts[0]
e = pts[0]
i = 0
for p in pts:
s = e
e = p
if i % 2 == 1:
cv2.line(img, s, e, color, thickness)
i += 1def draw_poly(img, pts, color, thickness=1, style='dotted'): s = pts[0] e = pts[0] pts.append(pts.pop(0)) for p in pts: s = e e = p draw_line(img, s, e, color, thickness, style)
def compute_simple_bounding_box(skeleton): x = skeleton[::2] x = np.where(x == 0.0, np.nan, x) left, right = int(round(np.nanmin(x))), int(round(np.nanmax(x))) y = skeleton[1::2] y = np.where(y == 0.0, np.nan, y) top, bottom = int(round(np.nanmin(y))), int(round(np.nanmax(y)))
return left, right, top, bottomdef render_trajectories_skeletons(args): try: os.makedirs(args.write_dir) except OSError: print(f' \n directory for the images already exists. IMAGES WILL BE REWRITTEN!!! \n') pass
frames_path = args.frames
gt_trajectories_path = args.gt_trajectories
draw_gt_skeleton = args.draw_gt_skeleton
draw_gt_bounding_box = args.draw_gt_bbox
trajectories_path = args.trajectories
draw_trajectories_skeleton = args.draw_pred_skeleton
draw_trajectories_bounding_box = args.draw_pred_bbox
specific_person_id = args.person_id
draw_local_skeleton = args.draw_local_skeleton
if gt_trajectories_path is None and trajectories_path is None:
raise ValueError('At least one of --ground_truth_trajectories or --trajectories must be specified.')
if not any([draw_gt_skeleton, draw_gt_bounding_box, draw_trajectories_skeleton, draw_trajectories_bounding_box]):
raise ValueError('At least one of --draw_ground_truth_trajectories_skeleton, '
'--draw_ground_truth_trajectories_bounding_box, --draw_trajectories_skeleton or '
'--draw_trajectories_bounding_box must be specified.')
if draw_local_skeleton and specific_person_id is None:
raise ValueError('If --draw_local_skeleton is specified, a --person_id must be chosen as well.')
elif draw_local_skeleton:
draw_gt_skeleton = draw_trajectories_skeleton = True
draw_gt_bounding_box = draw_trajectories_bounding_box = False
_render_trajectories_skeletons(args.write_dir, frames_path, gt_trajectories_path, trajectories_path, specific_person_id, scale=args.scale)
print('Visualisation successfully rendered to %s' % args.write_dir)
return Nonedef fill(frames_path, frame_name,scale,ts): if ts is None or not ts: ts = [None,None] ts[0] = cv2.imread(os.path.join(frames_path, frame_name)) h,w,c = ts[0].shape ts[0] = cv2.resize(ts[0], (wscale,hscale), interpolation = cv2.INTER_AREA) ts[1] = np.full_like(ts[0], fill_value=255) return ts
def fill_multi(frames_path, frame_name,scale,ts,person_ids): if ts is None: ts = {p : [] for p in person_ids} for p in ts.keys(): ts1 = cv2.imread(os.path.join(frames_path, frame_name)) h,w,c = ts1.shape ts1 = cv2.resize(ts1, (wscale,hscale), interpolation = cv2.INTER_AREA) ts2 = np.full_like(ts1, fill_value=255) ts[p] = (ts1,ts2)
for p in person_ids:
if p not in ts.keys():
ts1 = cv2.imread(os.path.join(frames_path, frame_name))
h,w,c = ts1.shape
ts1 = cv2.resize(ts1, (w*scale,h*scale), interpolation = cv2.INTER_AREA)
ts2 = np.full_like(ts1, fill_value=255)
ts[p] = (ts1,ts2)
return tsdef _render_trajectories_skeletons(write_dir, frames_path, gt_trajectories_path, trajectories_path, specific_person_id=None, scale=4):
vid_id = trajectories_path.split('/')[-1]
w_dirs = [os.path.join(write_dir,'frames',s,vid_id) for s in ['ind_pred','ind_gt','all_pred','all_gt']]
for d in w_dirs:
if not os.path.isdir(d):
os.makedirs(d)
wo_dirs = [os.path.join(write_dir,'trajectories',s,vid_id) for s in ['ind_pred','ind_gt','all_pred','all_gt']]
for d in wo_dirs:
if not os.path.isdir(d):
os.makedirs(d)
frames_names = sorted(os.listdir(frames_path)) # 000.jpg, 001.jpg, ...
max_frame_id = len(frames_names)
rendered_pred_frames_all = {}
rendered_pred_frames_ind = {}
rendered_gt_frames_all = {}
rendered_gt_frames_ind = {}
person_ids = []
if trajectories_path is not None:
trajectories_files_names = sorted(os.listdir(trajectories_path)) # 001.csv, 002.csv, ...
for trajectory_file_name in trajectories_files_names:
person_id = int(trajectory_file_name.split('.')[0])
if specific_person_id is not None and specific_person_id != person_id:
continue
print('Drawing skeleton for person_id:',person_id)
if person_id not in person_ids:
person_ids.append(person_id)
colour = COLOURS_POINTS[person_id % len(COLOURS)]
trajectory = np.loadtxt(os.path.join(trajectories_path, trajectory_file_name), delimiter=',', ndmin=2)
trajectory_frames = trajectory[:, 0].astype(np.int32)
trajectory_coordinates = trajectory[:, 1:]
for frame_id, skeleton_coordinates in zip(trajectory_frames, trajectory_coordinates):
if frame_id >= max_frame_id:
break
frame_ind = cv2.imread(os.path.join(frames_path, frames_names[frame_id]))
h,w,c = frame_ind.shape
frame_ind = cv2.resize(frame_ind, (w*scale,h*scale), interpolation = cv2.INTER_AREA)
blank_frame_ind = np.full_like(frame_ind, fill_value=255)
coords, blank_frame_ind = prepare_keypoints(skeleton_coordinates.reshape(-1, 2))
el = rendered_pred_frames_all.get(frame_id)
if el is not None:
frame = el[0]
blank_frame = el[1]
else:
frame = frame_ind.copy()
blank_frame = np.full_like(frame_ind, fill_value=255)
draw_skeleton(frame, keypoints=skeleton_coordinates.reshape(-1, 2), colour=colour, dotted=False, scale=scale)
draw_skeleton(frame_ind, keypoints=skeleton_coordinates.reshape(-1, 2), colour=colour, dotted=False, scale=scale)
#height, width = blank_frame_ind.shape[:2]
#left, right, top, bottom = compute_simple_bounding_box(skeleton_coordinates)
#bb_center = np.array([(left + right) / 2, (top + bottom) / 2], dtype=np.float32)
#target_center = np.array([3 * width / 4, height / 2], dtype=np.float32)
#displacement_vector = target_center - bb_center
draw_skeleton(blank_frame_ind, keypoints=coords,colour=colour, dotted=False, scale=scale, scale_vis=True)
draw_skeleton(blank_frame, keypoints=skeleton_coordinates.reshape(-1, 2),colour=colour, dotted=False, scale=scale)
rendered_pred_frames_all[frame_id] = (frame,blank_frame)
if frame_id not in rendered_pred_frames_ind.keys():
rendered_pred_frames_ind[frame_id] = {}
if person_id not in rendered_pred_frames_ind[frame_id].keys():
rendered_pred_frames_ind[frame_id][person_id] = []
rendered_pred_frames_ind[frame_id][person_id] = (frame_ind,blank_frame_ind)
if gt_trajectories_path is not None:
gt_trajectories_files_names = sorted(os.listdir(gt_trajectories_path))
for gt_trajectory_file_name in gt_trajectories_files_names:
person_id = int(gt_trajectory_file_name.split('.')[0])
if specific_person_id is not None and specific_person_id != person_id:
continue
colour = COLOURS_POINTS[person_id % len(COLOURS)]
gt_trajectory = np.loadtxt(os.path.join(gt_trajectories_path, gt_trajectory_file_name),
delimiter=',', ndmin=2)
gt_trajectory_frames = gt_trajectory[:, 0].astype(np.int32)
gt_trajectory_coordinates = gt_trajectory[:, 1:]
for frame_id, skeleton_coordinates in zip(gt_trajectory_frames, gt_trajectory_coordinates):
skeleton_is_null = np.any(skeleton_coordinates)
if not skeleton_is_null:
continue
frame_ind = cv2.imread(os.path.join(frames_path, frames_names[frame_id]))
h,w,c = frame_ind.shape
frame_ind = cv2.resize(frame_ind, (w*scale,h*scale), interpolation = cv2.INTER_AREA)
blank_frame_ind = np.full_like(frame_ind, fill_value=255)
coords, blank_frame_ind = prepare_keypoints(skeleton_coordinates.reshape(-1, 2))
el = rendered_gt_frames_all.get(frame_id)
if el is not None:
frame = el[0]
blank_frame = el[1]
else:
frame = frame_ind.copy()
blank_frame = np.full_like(frame_ind, fill_value=255)
draw_skeleton(frame, keypoints=skeleton_coordinates.reshape(-1, 2), colour=colour, dotted=False, scale=scale)
draw_skeleton(frame_ind, keypoints=skeleton_coordinates.reshape(-1, 2), colour=colour, dotted=False, scale=scale)
#height, width = blank_frame_ind.shape[:2]
#left, right, top, bottom = compute_simple_bounding_box(skeleton_coordinates)
#bb_center = np.array([(left + right) / 2, (top + bottom) / 2], dtype=np.float32)
#target_center = np.array([3 * width / 4, height / 2], dtype=np.float32)
#displacement_vector = target_center - bb_center
draw_skeleton(blank_frame_ind, keypoints=coords,colour=colour, dotted=False, scale=scale, scale_vis=True)
draw_skeleton(blank_frame, keypoints=skeleton_coordinates.reshape(-1, 2),colour=colour, dotted=False, scale=scale)
rendered_gt_frames_all[frame_id] = (frame,blank_frame)
if frame_id not in rendered_gt_frames_ind.keys():
rendered_gt_frames_ind[frame_id] = {}
if person_id not in rendered_gt_frames_ind[frame_id].keys():
rendered_gt_frames_ind[frame_id][person_id] = []
rendered_gt_frames_ind[frame_id][person_id] = (frame_ind,blank_frame_ind)
for frame_id, frame_name in tqdm.tqdm(enumerate(frames_names),total=len(frames_names)):
pred_frame_ind = rendered_pred_frames_ind.get(frame_id)
pred_frame_all = rendered_pred_frames_all.get(frame_id)
gt_frame_ind = rendered_gt_frames_ind.get(frame_id)
gt_frame_all = rendered_gt_frames_all.get(frame_id)
pred_frame_all = fill(frames_path, frame_name,scale,pred_frame_all)
pred_frame_ind = fill_multi(frames_path, frame_name,scale,pred_frame_ind,person_ids)
gt_frame_all = fill(frames_path, frame_name,scale,gt_frame_all)
gt_frame_ind = fill_multi(frames_path, frame_name,scale,gt_frame_ind,person_ids)
#cv2.imwrite(os.path.join(w_dirs[0],frame_name), pred_frame_ind[0])
#cv2.imwrite(os.path.join(w_dirs[1],frame_name), gt_frame_ind[0])
cv2.imwrite(os.path.join(w_dirs[2],frame_name), pred_frame_all[0])
cv2.imwrite(os.path.join(w_dirs[3],frame_name), gt_frame_all[0])
#cv2.imwrite(os.path.join(wo_dirs[0],frame_name), pred_frame_ind[1])
#cv2.imwrite(os.path.join(wo_dirs[1],frame_name), gt_frame_ind[1])
cv2.imwrite(os.path.join(wo_dirs[2],frame_name), pred_frame_all[1])
cv2.imwrite(os.path.join(wo_dirs[3],frame_name), gt_frame_all[1])
for person_id in pred_frame_ind.keys():
pred_frame_ind_pid = pred_frame_ind.get(person_id)
if not os.path.isdir(os.path.join(w_dirs[0],str(person_id))):
os.makedirs(os.path.join(w_dirs[0],str(person_id)))
if not os.path.isdir(os.path.join(wo_dirs[0],str(person_id))):
os.makedirs(os.path.join(wo_dirs[0],str(person_id)))
cv2.imwrite(os.path.join(w_dirs[0],str(person_id),frame_name), pred_frame_ind_pid[0])
cv2.imwrite(os.path.join(wo_dirs[0],str(person_id),frame_name), pred_frame_ind_pid[1])
for person_id in gt_frame_ind.keys():
gt_frame_ind_pid = gt_frame_ind.get(person_id)
gt_frame_ind_pid = fill(frames_path, frame_name,scale,gt_frame_ind_pid)
if not os.path.isdir(os.path.join(w_dirs[1],str(person_id))):
os.makedirs(os.path.join(w_dirs[1],str(person_id)))
if not os.path.isdir(os.path.join(wo_dirs[1],str(person_id))):
os.makedirs(os.path.join(wo_dirs[1],str(person_id)))
cv2.imwrite(os.path.join(w_dirs[1],str(person_id),frame_name), gt_frame_ind_pid[0])
cv2.imwrite(os.path.join(wo_dirs[1],str(person_id),frame_name), gt_frame_ind_pid[1])def main(): args = parser.parse_args()
if not os.path.exists(args.write_dir):
os.makedirs(args.write_dir)
render_trajectories_skeletons(args)if name == 'main': main() `
Hi, As this topic said, did I miss some step of it?