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import numpy as np
import imageio
import os
import argparse
from tqdm import tqdm
from .renderer import get_renderer
def get_rotation(theta=np.pi / 3):
import mGPT.utils.rotation_conversions as geometry
import torch
axis = torch.tensor([0, 1, 0], dtype=torch.float)
axisangle = theta * axis
matrix = geometry.axis_angle_to_matrix(axisangle)
return matrix.numpy()
def render_video(meshes,
key,
action,
renderer,
savepath,
backgrounds,
cam_pose,
cams=(0.75, 0.75, 0, 0.10),
color=[0.11, 0.53, 0.8]):
# cams=(0.75, 0.75, 0, 0.10), color=[165.0/255,112/255,140/255]):
# center the first frame
if key not in ["real", "ntf", "side"]:
w = int(key) / 6.0
# purpole to green
# color = w*np.array([0.9,102/255,120/255]) + (1-w)*np.array([0.11, 0.9, 0.11])
# color = (1-w)*np.array([165.0/255,112/255,140/255]) + w*np.array([0.11, 0.8, 0.11])
color = (1 - w) * np.array([0.75, 0.13, 0.7]) + w * np.array(
[0.12, 0.7, 0.14])
meshes = meshes - meshes[0].mean(axis=0)
imgs = []
idx = 0
# for mesh in meshes:
for mesh in tqdm(meshes, desc=f"Visualize {key}, action {action}"):
# file_name = '3dpw_rot-90_glob_trimesh.ply' mesh_filename=file_name,
# prepare background
if len(backgrounds.shape) == 3:
background = backgrounds
cam = cams
elif len(backgrounds.shape) == 4:
background = backgrounds[idx]
cam = cams[idx]
idx += 1
# prepare cams
img = renderer.render(background,
mesh,
cam,
color=color,
cam_pose=cam_pose)
imgs.append(img)
# show(img)
imgs = np.array(imgs)
# masks = ~(imgs/255. > 0.96).all(-1)
# coords = np.argwhere(masks.sum(axis=0))
# y1, x1 = coords.min(axis=0)
# y2, x2 = coords.max(axis=0)
# writer = imageio.get_writer(savepath, fps=30)
# for cimg in imgs[:, y1:y2, x1:x2]:
# writer.append_data(cimg)
# writer.close()
# from mld.utils.uicap_utils import write_rgba_seqs
# write_rgba_seqs(imgs, savepath)
writer = imageio.get_writer(savepath, fps=30)
for cimg in imgs:
writer.append_data(cimg)
writer.close()
def main():
parser = argparse.ArgumentParser()
parser.add_argument("filename")
opt = parser.parse_args()
filename = opt.filename
savefolder = os.path.splitext(filename)[0]
os.makedirs(savefolder, exist_ok=True)
output = np.load(filename)
if output.shape[0] == 3:
visualization, generation, reconstruction = output
output = {
"visualization": visualization,
"generation": generation,
"reconstruction": reconstruction
}
else:
# output = {f"generation_{key}": output[key] for key in range(2)} # len(output))}
# output = {f"generation_{key}": output[key] for key in range(len(output))}
output = {
f"generation_{key}": output[key]
for key in range(len(output))
}
width = 1024
height = 1024
background = np.zeros((height, width, 3))
renderer = get_renderer(width, height)
# if duration mode, put back durations
if output["generation_3"].shape[-1] == 100:
output["generation_0"] = output["generation_0"][:, :, :, :40]
output["generation_1"] = output["generation_1"][:, :, :, :60]
output["generation_2"] = output["generation_2"][:, :, :, :80]
output["generation_3"] = output["generation_3"][:, :, :, :100]
elif output["generation_3"].shape[-1] == 160:
print("160 mode")
output["generation_0"] = output["generation_0"][:, :, :, :100]
output["generation_1"] = output["generation_1"][:, :, :, :120]
output["generation_2"] = output["generation_2"][:, :, :, :140]
output["generation_3"] = output["generation_3"][:, :, :, :160]
# if str(action) == str(1) and str(key) == "generation_4":
for key in output:
vidmeshes = output[key]
for action in range(len(vidmeshes)):
meshes = vidmeshes[action].transpose(2, 0, 1)
path = os.path.join(savefolder,
"action{}_{}.mp4".format(action, key))
render_video(meshes, key, action, renderer, path, background)
if __name__ == "__main__":
main()
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