def __init__(self, camera_mode='projection', K = None, R = None, t = None,\
smpl_model_path = "/hdd/zen/dev/ActMix/actmix/DataGen/MotionRender/ActmixTorchGenerator/smpl_models/",\
texture_path = "/hdd/zen/data/SURREAL/smpl_data/", dtype = torch.float32, gender = "male", image_size = 256, \
device =(torch.device("cuda", index=0) if torch.cuda.is_available() else torch.device("cpu")), \
background_color=[0,0,0],
):
self.dtype = dtype
self.camera_mode = camera_mode
self.image_size = image_size
r = sciR.from_euler('zxy', [0, 0, 0], degrees = True)
vet_rot = r.as_matrix()
fx = 500.0
fy = 500.0
cx = 512.0
cy = 512.0
def_K = np.array( [ [fx, 0., cx],
[ 0. ,fx, cy],
[0.,0.,1.]])
def_K = def_K[None, :]
def_R = vet_rot[None, :]
def_t = np.array([0,0,1.5])
if K is None:
K = def_K
if R is None:
R = def_R
if t is None:
t = def_t
if camera_mode == "look_at":
self.renderer = nr.Renderer(camera_mode='look_at', viewing_angle = 30, image_size = image_size, background_color= background_color)
self.set_render_angles(2.5, 0, 0)
elif camera_mode == "projection":
self.renderer = nr.Renderer(K = K, R = R, t = t, camera_mode='projection', image_size = image_size, light_intensity_ambient=1, background_color= background_color)
self.renderer = self.renderer.to(device)
self.gender = gender
self.faces = np.load(os.path.join(smpl_model_path, "smpl_faces.npy"))[np.newaxis, :,:]
self.device = device
self.faces_uv = np.load(os.path.join(texture_path, 'final_faces_uv_mapping.npy'))
self.uv_sampler = torch.from_numpy(self.faces_uv.reshape(-1, 2, 2, 2)).to(device).type(self.dtype)
self.uv_sampler = self.uv_sampler.view(-1, 13776, 2*2, 2)
self.male_texture_dataset = TextureDataset(texture_path, "male")
self.male_smpl_p = SMPL_Parser(device = device, gender = "male")
self.female_texture_dataset = TextureDataset(texture_path, "female")
self.female_smpl_p = SMPL_Parser(device = device, gender = "female")
self.smpl_p = self.male_smpl_p if self.gender == 'male' else self.female_smpl_p
self.texture_dataset = self.male_texture_dataset if self.gender == 'male' else self.female_texture_dataset
def render_amass_videos(job_list, image_size, gpu_index):
device = torch.device('cuda', index=gpu_index) if torch.cuda.is_available() else torch.device('cpu')
smpl_p = SMPL_Parser(device = device)
smpl_renderer = SMPL_Renderer(device = device, image_size = image_size)
# smpl_renderer.set_render_angles(3, 89, 0)
for curr_pose, out_file_name in job_list:
import time
t_s = time.time()
curr_pose = vertizalize_smpl_root(curr_pose)
images, _ = smpl_renderer._render_pose_images(curr_pose, smpl_p)
write_frames_to_video(images, out_file_name)
dt = time.time() - t_s
print(out_file_name, "Time {:.4f}".format(dt))
if os.path.isdir("output/rid/same_cloth"):
shutil.rmtree("output/rid/same_cloth")
os.makedirs("output/rid/same_cloth")
if os.path.isdir("output/rid/diff_cloth"):
shutil.rmtree("output/rid/diff_cloth")
os.makedirs("output/rid/diff_cloth")
jobs = [
(idt[0], idt[1], "same_cloth", False) for idt in texture_fixed_ids
] + [(idt[0], idt[1], "diff_cloth", True) for idt in texture_varies_ids]
for i in range(len(jobs)):
p_id, gender, out_dir, texture_fix = jobs[i]
pid_str = "".join(["{:03d}".format(i), str(gender)[0]])
print("Generating for: ", pid_str)
smpl_parser = SMPL_Parser(device=device, gender=gender)
smpl_render = SMPL_Renderer(device=device, gender=gender)
pose_list = []
bg_list = []
for a in range(len(angles)):
smpl_render.set_render_angles(2.5, angles[a], 0)
counter = 0
for posese, trans in seq_generator:
# print(pose.shape, trans.shape)
posese, trans = torch.tensor(posese).to(device).type(
dtype), torch.tensor(trans).to(device).type(dtype)
pose_list.append(posese[0])
counter += 1
bg_list.append((a, angles[a]))
if counter >= pose_num:
import argparse
from zen_renderer.smpl_parser import SMPL_Parser
from zen_renderer.dataloaders.dataset_amass import Dataset_AMASS
from zen_renderer.renderer.smpl_renderer import SMPL_Renderer
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--gpu_index', type=int, default=0)
args = parser.parse_args()
dtype = torch.FloatTensor
device = torch.device('cuda', index=args.gpu_index) if torch.cuda.is_available() else torch.device('cpu')
smpl_p = SMPL_Parser(device = device)
smpl_render = SMPL_Renderer(device = device)
smpl_render.set_render_angles(3, 150, 0)
dsf_res_path = "/hdd/zen/dev/ActMix/actmix/DataGen/MotionSyn/gen.pk"
dsf_ress = pk.load(open(dsf_res_path, "rb"))
org_seq = dsf_ress["org_seq"]
gen_seq =dsf_ress["gen_seq"]
label = np.argmax(dsf_ress["label"], 1)
gen_seq = gen_seq.reshape(org_seq.shape[0], gen_seq.shape[0]//org_seq.shape[0], org_seq.shape[1], org_seq.shape[2])
print(org_seq.shape)
print(gen_seq.shape)
print(label.shape)
for i in range(org_seq.shape[0]):
open("/hdd/zen/data/VIBE_NTU/ntu_class_labels.pk", "rb"))
idx = np.argmax(label)
return label_map[idx]
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--gpu_index', type=int, default=0)
args = parser.parse_args()
dtype = torch.FloatTensor
device = torch.device('cuda',
index=args.gpu_index) if torch.cuda.is_available(
) else torch.device('cpu')
smpl_p = SMPL_Parser(device=device)
smpl_renderer = SMPL_Renderer(device=device)
smpl_renderer.set_render_angles(2, 89, 0)
################## Load BG images ##################
# bg_base = "/hdd/zen/data/lsun/imgs/"
# bg_imgs = [ os.path.join(bg_base, i) for i in os.listdir(bg_base) if i.endswith("jpg")]
bg_imgs = [
"/hdd/zen/data/NTU/images/S010C002P025R001A030_rgb/frame000001.jpg",
"/hdd/zen/data/NTU/images/S012C003P017R001A048_rgb/frame000001.jpg",
"/hdd/zen/data/NTU/images/S006C003P007R001A041_rgb/frame000001.jpg",
]
data_path = "/hdd/zen/data/ActBound/Aug/gen_aug_6d.pk"
pose_data = pk.load(open(data_path, "rb"))
write_frames_to_video(output_frames, ouput_dir)
torch.cuda.empty_cache()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--gpu_index', type=int, default=0)
args = parser.parse_args()
dtype = torch.FloatTensor
device = torch.device('cuda', index=args.gpu_index) if torch.cuda.is_available() else torch.device('cpu')
print(device)
smpl_p = SMPL_Parser(device = device)
smpl_renderer = SMPL_Renderer(device = device, image_size=512)
smpl_renderer.set_render_angles(3, 89, 0)
################## NTU ##################
# from glob import glob
# class_num = "A009"
# vid_names = glob("/hdd/zen/data/HMMR/results/ntu/{}/*.avi".format(class_num))
# for vid_name in vid_names:
# vid_name = vid_name.split("/")[-1][:-4]
# vibe_data_path = "/hdd/zen/data/VIBE_NTU/{}/{}.pkl".format(class_num, vid_name)
# hmmr_path = "/hdd/zen/data/HMMR/results/ntu/{}/{}.avi/hmmr_output/hmmr_output.pkl".format(class_num, vid_name)
# vid_path = "/hdd/zen/data/NTU/videos/{}/{}.avi".format(class_num, vid_name)
# render_compare_video(vibe_data_path, hmmr_path, vid_path)