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from operator import mod
import os
# from cv2 import CAP_PROP_INTELPERC_DEPTH_LOW_CONFIDENCE_VALUE
import imageio
import shutil
import numpy as np
import torch
from tqdm import tqdm
from scipy.spatial.transform import Rotation as R
from mGPT.render.renderer import get_renderer
from mGPT.render.rendermotion import render_video
# from mld.utils.img_utils import convert_img
# from mld.utils.uicap_utils import output_pkl
def parsename(path):
basebane = os.path.basename(path)
base = os.path.splitext(basebane)[0]
strs = base.split('_')
key = strs[-2]
action = strs[-1]
return key, action
def load_anim(path, timesize=None):
data = np.array(imageio.mimread(path, memtest=False)) #[..., :3]
if timesize is None:
return data
# take the last frame and put shadow repeat the last frame but with a little shadow
# lastframe = add_shadow(data[-1])
# alldata = np.tile(lastframe, (timesize, 1, 1, 1))
alldata = data
# debug fix mat dim
if len(data.shape) == 3 and len(alldata.shape) == 4:
data = data[:, None, :, :]
# copy the first frames
lenanim = data.shape[0]
alldata[:lenanim] = data[:lenanim]
return alldata
def plot_3d_motion_dico(x):
motion, length, save_path, params, kargs = x
plot_3d_motion(motion, length, save_path, params, **kargs)
def plot_3d_motion(motion,
length,
save_path,
params,
title="",
interval=50,
pred_cam=None,
imgs=None,
bbox=None,
side=None):
# render smpl
# [nframes, nVs, 3]
if motion.shape[1] == 6890:
# width = 250
# height = 250
width = 600
height = 600
if pred_cam is None:
# cam=(0.75, 0.75, 0, 0.1)
cam = (0.8, 0.8, 0, 0.1)
# cam=(0.9, 0.9, 0, 0.1)
else:
assert bbox is not None
assert imgs is not None
# Tmp visulize
# weak perspective camera parameters in cropped image space (s,tx,ty)
# to
# weak perspective camera parameters in original image space (sx,sy,tx,ty)
cam = np.concatenate(
(pred_cam[:, [0]], pred_cam[:, [0]], pred_cam[:, 1:3]), axis=1)
# ToDo convert to original cam
# load original img?
# calculate cam after padding???
#
# cam = convert_crop_cam_to_orig_img(
# cam=pred_cam,
# bbox=bbox,
# img_width=width,
# img_height=height
# )
cam_pose = np.eye(4)
cam_pose[0:3, 0:3] = R.from_euler('x', -90, degrees=True).as_matrix()
cam_pose[0:3, 3] = [0, 0, 0]
if side:
rz = np.eye(4)
rz[0:3, 0:3] = R.from_euler('z', -90, degrees=True).as_matrix()
cam_pose = np.matmul(rz, cam_pose)
# # reshape input imgs
# if imgs is not None:
# imgs = convert_img(imgs.unsqueeze(0), height)[:,0]
backgrounds = imgs if imgs is not None else np.ones(
(height, width, 3)) * 255
renderer = get_renderer(width, height, cam_pose)
# [nframes, nVs, 3]
meshes = motion
key, action = parsename(save_path)
render_video(meshes,
key,
action,
renderer,
save_path,
backgrounds,
cam_pose,
cams=cam)
return
def stack_images(real, real_gens, gen, real_imgs=None):
# change to 3 channel
# print(real.shape)
# print(real_gens.shape)
# print(real_gens.shape)
# real = real[:3]
# real_gens = real_gens[:3]
# gen = gen[:3]
nleft_cols = len(real_gens) + 1
print("Stacking frames..")
allframes = np.concatenate(
(real[:, None, ...], *[x[:, None, ...] for x in real_gens], gen), 1)
nframes, nspa, nats, h, w, pix = allframes.shape
blackborder = np.zeros((w // 30, h * nats, pix), dtype=allframes.dtype)
# blackborder = np.ones((w//30, h*nats, pix), dtype=allframes.dtype)*255
frames = []
for frame_idx in tqdm(range(nframes)):
columns = np.vstack(allframes[frame_idx].transpose(1, 2, 3, 4,
0)).transpose(
3, 1, 0, 2)
frame = np.concatenate(
(*columns[0:nleft_cols], blackborder, *columns[nleft_cols:]),
0).transpose(1, 0, 2)
frames.append(frame)
if real_imgs is not None:
resize_imgs = convert_img(real_imgs, h)[:nframes, ...]
for i in range(len(frames)):
imgs = np.vstack(resize_imgs[i, ...])
imgs4 = np.ones(
(imgs.shape[0], imgs.shape[1], 4), dtype=np.uint8) * 255
imgs4[:, :, :3] = imgs
#imgs = torch2numpy(imgs)
frames[i] = np.concatenate((imgs4, frames[i]), 1)
return np.stack(frames)
def stack_images_gen(gen, real_imgs=None):
print("Stacking frames..")
allframes = gen
nframes, nspa, nats, h, w, pix = allframes.shape
blackborder = np.zeros((w * nspa, h // 30, pix), dtype=allframes.dtype)
blackborder = blackborder[None, ...].repeat(nats,
axis=0).transpose(0, 2, 1, 3)
frames = []
for frame_idx in tqdm(range(nframes)):
rows = np.vstack(allframes[frame_idx].transpose(0, 3, 2, 4,
1)).transpose(
3, 1, 0, 2)
rows = np.concatenate((rows, blackborder), 1)
frame = np.concatenate(rows, 0)
frames.append(frame)
if real_imgs is not None:
# ToDo Add images
resize_imgs = convert_img(real_imgs, h)[:nframes, ...]
for i in range(len(frames)):
imgs = np.vstack(resize_imgs[i, ...])
#imgs = torch2numpy(imgs)
frames[i] = np.concatenate((imgs, frames[i]), 1)
return np.stack(frames)
def generate_by_video(visualization, reconstructions, generation,
label_to_action_name, params, nats, nspa, tmp_path):
# shape : (17, 3, 4, 480, 640, 3)
# (nframes, row, column, h, w, 3)
fps = params["fps"]
params = params.copy()
gen_only = False
if visualization is None:
gen_only = True
outputkey = "output_vertices"
params["pose_rep"] = "vertices"
elif "output_vertices" in visualization:
outputkey = "output_vertices"
params["pose_rep"] = "vertices"
elif "output_xyz" in visualization:
outputkey = "output_xyz"
params["pose_rep"] = "xyz"
else:
outputkey = "poses"
keep = [outputkey, 'lengths', "y"]
gener = {key: generation[key].data.cpu().numpy() for key in keep}
if not gen_only:
visu = {key: visualization[key].data.cpu().numpy() for key in keep}
recons = {}
# visualize regressor results
if 'vertices_hat' in reconstructions['ntf']:
recons['regressor'] = {
'output_vertices':
reconstructions['ntf']['vertices_hat'].data.cpu().numpy(),
'lengths':
reconstructions['ntf']['lengths'].data.cpu().numpy(),
'y':
reconstructions['ntf']['y'].data.cpu().numpy()
}
recons['regressor_side'] = {
'output_vertices':
reconstructions['ntf']['vertices_hat'].data.cpu().numpy(),
'lengths':
reconstructions['ntf']['lengths'].data.cpu().numpy(),
'y':
reconstructions['ntf']['y'].data.cpu().numpy(),
'side':
True
}
# ToDo rendering overlap results
# recons['overlap'] = {'output_vertices':reconstructions['ntf']['vertices_hat'].data.cpu().numpy(),
# 'lengths':reconstructions['ntf']['lengths'].data.cpu().numpy(),
# 'y':reconstructions['ntf']['y'].data.cpu().numpy(),
# 'imgs':reconstructions['ntf']['imgs'],
# 'bbox':reconstructions['ntf']['bbox'].data.cpu().numpy(),
# 'cam':reconstructions['ntf']['preds'][0]['cam'].data.cpu().numpy()}
for mode, reconstruction in reconstructions.items():
recons[mode] = {
key: reconstruction[key].data.cpu().numpy()
for key in keep
}
recons[mode + '_side'] = {
key: reconstruction[key].data.cpu().numpy()
for key in keep
}
recons[mode + '_side']['side'] = True
# lenmax = max(gener['lengths'].max(), visu['lengths'].max())
# timesize = lenmax + 5 longer visulization
lenmax = gener['lengths'].max()
timesize = lenmax
import multiprocessing
def pool_job_with_desc(pool, iterator, desc, max_, save_path_format, isij):
with tqdm(total=max_, desc=desc.format("Render")) as pbar:
for data in iterator:
plot_3d_motion_dico(data)
# for _ in pool.imap_unordered(plot_3d_motion_dico, iterator):
# pbar.update()
if isij:
array = np.stack([[
load_anim(save_path_format.format(i, j), timesize)
for j in range(nats)
] for i in tqdm(range(nspa), desc=desc.format("Load"))])
return array.transpose(2, 0, 1, 3, 4, 5)
else:
array = np.stack([
load_anim(save_path_format.format(i), timesize)
for i in tqdm(range(nats), desc=desc.format("Load"))
])
return array.transpose(1, 0, 2, 3, 4)
pool = None
# if True:
with multiprocessing.Pool() as pool:
# Generated samples
save_path_format = os.path.join(tmp_path, "gen_{}_{}.gif")
iterator = ((gener[outputkey][i, j], gener['lengths'][i, j],
save_path_format.format(i, j), params, {
"title":
f"gen: {label_to_action_name(gener['y'][i, j])}",
"interval": 1000 / fps
}) for j in range(nats) for i in range(nspa))
gener["frames"] = pool_job_with_desc(pool, iterator,
"{} the generated samples",
nats * nspa, save_path_format,
True)
if not gen_only:
# Real samples
save_path_format = os.path.join(tmp_path, "real_{}.gif")
iterator = ((visu[outputkey][i], visu['lengths'][i],
save_path_format.format(i), params, {
"title":
f"real: {label_to_action_name(visu['y'][i])}",
"interval": 1000 / fps
}) for i in range(nats))
visu["frames"] = pool_job_with_desc(pool, iterator,
"{} the real samples", nats,
save_path_format, False)
for mode, recon in recons.items():
# Reconstructed samples
save_path_format = os.path.join(
tmp_path, f"reconstructed_{mode}_" + "{}.gif")
if mode == 'overlap':
iterator = ((
recon[outputkey][i], recon['lengths'][i],
save_path_format.format(i), params, {
"title":
f"recons: {label_to_action_name(recon['y'][i])}",
"interval": 1000 / fps,
"pred_cam": recon['cam'][i],
"imgs": recon['imgs'][i],
"bbox": recon['bbox'][i]
}) for i in range(nats))
else:
side = True if 'side' in recon.keys() else False
iterator = ((
recon[outputkey][i], recon['lengths'][i],
save_path_format.format(i), params, {
"title":
f"recons: {label_to_action_name(recon['y'][i])}",
"interval": 1000 / fps,
"side": side
}) for i in range(nats))
recon["frames"] = pool_job_with_desc(
pool, iterator, "{} the reconstructed samples", nats,
save_path_format, False)
# vis img in visu
if not gen_only:
input_imgs = visualization["imgs"] if visualization[
"imgs"] is not None else None
vis = visu["frames"] if not gen_only else None
rec = [recon["frames"]
for recon in recons.values()] if not gen_only else None
gen = gener["frames"]
frames = stack_images(vis, rec, gen, input_imgs)
else:
gen = gener["frames"]
frames = stack_images_gen(gen)
return frames
def viz_epoch(model,
dataset,
epoch,
params,
folder,
module=None,
writer=None,
exps=''):
""" Generate & viz samples """
module = model if module is None else module
# visualize with joints3D
model.outputxyz = True
print(f"Visualization of the epoch {epoch}")
noise_same_action = params["noise_same_action"]
noise_diff_action = params["noise_diff_action"]
duration_mode = params["duration_mode"]
reconstruction_mode = params["reconstruction_mode"]
decoder_test = params["decoder_test"]
fact = params["fact_latent"]
figname = params["figname"].format(epoch)
nspa = params["num_samples_per_action"]
nats = params["num_actions_to_sample"]
num_classes = params["num_classes"]
# nats = min(num_classes, nats)
# define some classes
classes = torch.randperm(num_classes)[:nats]
# duplicate same classes when sampling too much
if nats > num_classes:
classes = classes.expand(nats)
meandurations = torch.from_numpy(
np.array([
round(dataset.get_mean_length_label(cl.item())) for cl in classes
]))
if duration_mode == "interpolate" or decoder_test == "diffduration":
points, step = np.linspace(-nspa, nspa, nspa, retstep=True)
# points = np.round(10*points/step).astype(int)
points = np.array([5, 10, 16, 30, 60, 80]).astype(int)
# gendurations = meandurations.repeat((nspa, 1)) + points[:, None]
gendurations = torch.from_numpy(points[:, None]).expand(
(nspa, 1)).repeat((1, nats))
else:
gendurations = meandurations.repeat((nspa, 1))
print("Duration time: ")
print(gendurations[:, 0])
# extract the real samples
# real_samples, real_theta, mask_real, real_lengths, imgs, paths
batch = dataset.get_label_sample_batch(classes.numpy())
# ToDo
# clean these data
# Visualizaion of real samples
visualization = {
"x": batch['x'].to(model.device),
"y": classes.to(model.device),
"mask": batch['mask'].to(model.device),
'lengths': batch['lengths'].to(model.device),
"output": batch['x'].to(model.device),
"theta":
batch['theta'].to(model.device) if 'theta' in batch.keys() else None,
"imgs":
batch['imgs'].to(model.device) if 'imgs' in batch.keys() else None,
"paths": batch['paths'] if 'paths' in batch.keys() else None,
}
# Visualizaion of real samples
if reconstruction_mode == "both":
reconstructions = {
"tf": {
"x":
batch['x'].to(model.device),
"y":
classes.to(model.device),
'lengths':
batch['lengths'].to(model.device),
"mask":
batch['mask'].to(model.device),
"teacher_force":
True,
"theta":
batch['theta'].to(model.device)
if 'theta' in batch.keys() else None
},
"ntf": {
"x":
batch['x'].to(model.device),
"y":
classes.to(model.device),
'lengths':
batch['lengths'].to(model.device),
"mask":
batch['mask'].to(model.device),
"theta":
batch['theta'].to(model.device)
if 'theta' in batch.keys() else None
}
}
else:
reconstructions = {
reconstruction_mode: {
"x":
batch['x'].to(model.device),
"y":
classes.to(model.device),
'lengths':
batch['lengths'].to(model.device),
"mask":
batch['mask'].to(model.device),
"teacher_force":
reconstruction_mode == "tf",
"imgs":
batch['imgs'].to(model.device)
if 'imgs' in batch.keys() else None,
"theta":
batch['theta'].to(model.device)
if 'theta' in batch.keys() else None,
"bbox":
batch['bbox'] if 'bbox' in batch.keys() else None
}
}
print("Computing the samples poses..")
# generate the repr (joints3D/pose etc)
model.eval()
with torch.no_grad():
# Reconstruction of the real data
for mode in reconstructions:
# update reconstruction dicts
reconstructions[mode] = model(reconstructions[mode])
reconstruction = reconstructions[list(reconstructions.keys())[0]]
if decoder_test == "gt":
# Generate the new data
gt_input = {
"x": batch['x'].repeat(nspa, 1, 1, 1).to(model.device),
"y": classes.repeat(nspa).to(model.device),
"mask": batch['mask'].repeat(nspa, 1).to(model.device),
'lengths': batch['lengths'].repeat(nspa).to(model.device)
}
generation = model(gt_input)
if decoder_test == "new":
# Generate the new data
generation = module.generate(gendurations,
classes=classes,
nspa=nspa,
noise_same_action=noise_same_action,
noise_diff_action=noise_diff_action,
fact=fact)
elif decoder_test == "diffaction":
assert nats == nspa
# keep the same noise for each "sample"
z = reconstruction["z"].repeat((nspa, 1))
mask = reconstruction["mask"].repeat((nspa, 1))
lengths = reconstruction['lengths'].repeat(nspa)
# but use other labels
y = classes.repeat_interleave(nspa).to(model.device)
generation = {"z": z, "y": y, "mask": mask, 'lengths': lengths}
model.decoder(generation)
elif decoder_test == "diffduration":
z = reconstruction["z"].repeat((nspa, 1))
lengths = gendurations.reshape(-1).to(model.device)
mask = model.lengths_to_mask(lengths)
y = classes.repeat(nspa).to(model.device)
generation = {"z": z, "y": y, "mask": mask, 'lengths': lengths}
model.decoder(generation)
elif decoder_test == "interpolate_action":
assert nats == nspa
# same noise for each sample
z_diff_action = torch.randn(1,
model.latent_dim,
device=model.device).repeat(nats, 1)
z = z_diff_action.repeat((nspa, 1))
# but use combination of labels and labels below
y = F.one_hot(classes.to(model.device),
model.num_classes).to(model.device)
y_below = F.one_hot(torch.cat((classes[1:], classes[0:1])),
model.num_classes).to(model.device)
convex_factors = torch.linspace(0, 1, nspa, device=model.device)
y_mixed = torch.einsum("nk,m->mnk", y, 1-convex_factors) + \
torch.einsum("nk,m->mnk", y_below, convex_factors)
y_mixed = y_mixed.reshape(nspa * nats, y_mixed.shape[-1])
durations = gendurations[0].to(model.device)
durations_below = torch.cat((durations[1:], durations[0:1]))
gendurations = torch.einsum("l,k->kl", durations, 1-convex_factors) + \
torch.einsum("l,k->kl", durations_below, convex_factors)
gendurations = gendurations.to(dtype=durations.dtype)
lengths = gendurations.to(model.device).reshape(z.shape[0])
mask = model.lengths_to_mask(lengths)
generation = {
"z": z,
"y": y_mixed,
"mask": mask,
'lengths': lengths
}
generation = model.decoder(generation)
visualization = module.prepare(visualization)
visualization["output_xyz"] = visualization["x_xyz"]
visualization["output_vertices"] = visualization["x_vertices"]
# Get xyz for the real ones
# visualization["output_xyz"] = module.rot2xyz(visualization["output"], visualization["mask"], jointstype="smpl")
# # Get smpl vertices for the real ones
# if module.cvae.pose_rep != "xyz":
# visualization["output_vertices"] = module.rot2xyz(visualization["output"], visualization["mask"], jointstype="vertices")
for key, val in generation.items():
if len(generation[key].shape) == 1:
generation[key] = val.reshape(nspa, nats)
else:
generation[key] = val.reshape(nspa, nats, *val.shape[1:])
finalpath = os.path.join(folder, figname + exps + ".gif")
tmp_path = os.path.join(folder, f"subfigures_{figname}")
os.makedirs(tmp_path, exist_ok=True)
print("Generate the videos..")
frames = generate_by_video(visualization, reconstructions, generation,
dataset.label_to_action_name, params, nats,
nspa, tmp_path)
print(f"Writing video {finalpath}")
imageio.mimsave(finalpath.replace('gif', 'mp4'), frames, fps=params["fps"])
shutil.rmtree(tmp_path)
# output npy
output = {
"data_id": batch['id'],
"paths": batch['paths'],
"x": batch['x'].cpu().numpy(),
"x_vertices": visualization["x_vertices"].cpu().numpy(),
"output_vertices":
reconstructions['ntf']["output_vertices"].cpu().numpy(),
"gen_vertices": generation["output_vertices"].cpu().numpy()
}
outputpath = finalpath.replace('gif', 'npy')
np.save(outputpath, output)
# output pkl
batch_recon = reconstructions["ntf"]
outputpath = finalpath.replace('gif', 'pkl')
# output_pkl([batch_recon], outputpath)
if writer is not None:
writer.add_video(f"Video/Epoch {epoch}",
frames.transpose(0, 3, 1, 2)[None],
epoch,
fps=params["fps"])
return finalpath
def viz_dataset(dataset, params, folder):
""" Generate & viz samples """
print("Visualization of the dataset")
nspa = params["num_samples_per_action"]
nats = params["num_actions_to_sample"]
num_classes = params["num_classes"]
figname = "{}_{}_numframes_{}_sampling_{}_step_{}".format(
params["dataset"], params["pose_rep"], params["num_frames"],
params["sampling"], params["sampling_step"])
# define some classes
classes = torch.randperm(num_classes)[:nats]
allclasses = classes.repeat(nspa, 1).reshape(nspa * nats)
# extract the real samples
real_samples, mask_real, real_lengths = dataset.get_label_sample_batch(
allclasses.numpy())
# to visualize directly
# Visualizaion of real samples
visualization = {
"x": real_samples,
"y": allclasses,
"mask": mask_real,
'lengths': real_lengths,
"output": real_samples
}
from mGPT.models.rotation2xyz import Rotation2xyz
device = params["device"]
rot2xyz = Rotation2xyz(device=device)
rot2xyz_params = {
"pose_rep": params["pose_rep"],
"glob_rot": params["glob_rot"],
"glob": params["glob"],
"jointstype": params["jointstype"],
"translation": params["translation"]
}
output = visualization["output"]
visualization["output_xyz"] = rot2xyz(output.to(device),
visualization["mask"].to(device),
**rot2xyz_params)
for key, val in visualization.items():
if len(visualization[key].shape) == 1:
visualization[key] = val.reshape(nspa, nats)
else:
visualization[key] = val.reshape(nspa, nats, *val.shape[1:])
finalpath = os.path.join(folder, figname + ".gif")
tmp_path = os.path.join(folder, f"subfigures_{figname}")
os.makedirs(tmp_path, exist_ok=True)
print("Generate the videos..")
frames = generate_by_video_sequences(visualization,
dataset.label_to_action_name, params,
nats, nspa, tmp_path)
print(f"Writing video {finalpath}..")
imageio.mimsave(finalpath, frames, fps=params["fps"])
def generate_by_video_sequences(visualization, label_to_action_name, params,
nats, nspa, tmp_path):
# shape : (17, 3, 4, 480, 640, 3)
# (nframes, row, column, h, w, 3)
fps = params["fps"]
if "output_vetices" in visualization:
outputkey = "output_vetices"
params["pose_rep"] = "vertices"
elif "output_xyz" in visualization:
outputkey = "output_xyz"
params["pose_rep"] = "xyz"
else:
outputkey = "poses"
keep = [outputkey, 'lengths', "y"]
visu = {key: visualization[key].data.cpu().numpy() for key in keep}
lenmax = visu['lengths'].max()
timesize = lenmax + 5
# import multiprocessing
def pool_job_with_desc(pool, iterator, desc, max_, save_path_format):
for data in iterator:
plot_3d_motion_dico(data)
# with tqdm(total=max_, desc=desc.format("Render")) as pbar:
# for _ in pool.imap_unordered(plot_3d_motion_dico, iterator):
# pbar.update()
array = np.stack([[
load_anim(save_path_format.format(i, j), timesize)
for j in range(nats)
] for i in tqdm(range(nspa), desc=desc.format("Load"))])
return array.transpose(2, 0, 1, 3, 4, 5)
pool = None
# with multiprocessing.Pool() as pool:
# Real samples
save_path_format = os.path.join(tmp_path, "real_{}_{}.gif")
iterator = ((visu[outputkey][i, j], visu['lengths'][i, j],
save_path_format.format(i, j), params, {
"title": f"real: {label_to_action_name(visu['y'][i, j])}",
"interval": 1000 / fps
}) for j in range(nats) for i in range(nspa))
visu["frames"] = pool_job_with_desc(pool, iterator, "{} the real samples",
nats, save_path_format)
frames = stack_images_sequence(visu["frames"])
return frames
def stack_images_sequence(visu):
print("Stacking frames..")
allframes = visu
nframes, nspa, nats, h, w, pix = allframes.shape
frames = []
for frame_idx in tqdm(range(nframes)):
columns = np.vstack(allframes[frame_idx].transpose(1, 2, 3, 4,
0)).transpose(
3, 1, 0, 2)
frame = np.concatenate(columns).transpose(1, 0, 2)
frames.append(frame)
return np.stack(frames)
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