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fit_vid_dataset.py
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fit_vid_dataset.py
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# -*- coding: utf-8 -*-
# pylint: disable=C0411,broad-except,too-many-statements,too-many-branches,logging-fstring-interpolation,import-error
import argparse
from collections import defaultdict
import logging
import os
import pickle
import cv2
import numpy as np
import torch
from libyana.exputils import argutils
from libyana.randomutils import setseeds
from homan import getdataset
from homan.eval import evalviz, pointmetrics, saveresults
from homan.jointopt import optimize_hand_object
from homan.lib2d import maskutils
from homan.pointrend import MaskExtractor
from homan.pose_optimization import find_optimal_poses
from homan.prepare.frameinfos import get_frame_infos, get_gt_infos
from homan.tracking import preprocess
from homan.utils.bbox import bbox_xy_to_wh, make_bbox_square
from homan.visualize import visualize_hand_object
from homan.viz import cliputils
from homan.viz.viz_gtpred_points import viz_gtpred_points
from handmocap.hand_mocap_api import HandMocap
logger = logging.getLogger(__file__)
logging.basicConfig(level=logging.INFO,
format="%(asctime)s %(levelname)-8s %(message)s")
def get_args():
parser = argparse.ArgumentParser(
description="Optimize object meshes w.r.t. hand.")
parser.add_argument("--dataset",
default="ho3d",
choices=[
"contactpose", "ho3d", "fhb", "epic", "core50",
"inhandycb", "ourycb"
],
help="Dataset name")
parser.add_argument("--chunk_step",
default=4,
type=int,
help="Step between consecutive frames")
parser.add_argument("--frame_nb",
default=10,
type=int,
help="Number of video frames to process in a batch")
parser.add_argument("--data_step", default=100, type=int)
parser.add_argument("--data_offset", default=0, type=int)
parser.add_argument("--seed", default=0, type=int)
parser.add_argument("--split",
default="val",
choices=["train", "val", "trainval", "test"],
help="Dataset name")
parser.add_argument("--box_mode", choices=["gt", "track"], default="gt")
parser.add_argument("--output_dir",
default="output",
help="Output directory.")
parser.add_argument("--num_obj_iterations", default=50, type=int)
parser.add_argument("--num_joint_iterations", default=201, type=int)
parser.add_argument("--num_initializations", default=500, type=int)
parser.add_argument("--mesh_path", type=str, help="Index of mesh ")
parser.add_argument("--result_root", default="results/tmp")
parser.add_argument(
"--resume",
help="Path to root folder of previously computed optimization results")
parser.add_argument("--resume_indep", action="store_true")
parser.add_argument("--debug", action="store_true")
parser.add_argument("--viz_step", default=20, type=int)
parser.add_argument("--save_indep", action="store_true")
parser.add_argument("--only_missing", choices=[0, 1], type=int)
parser.add_argument("--gt_masks", choices=[0, 1], default=0, type=int)
parser.add_argument("--optimize_mano", choices=[0, 1], default=1, type=int)
parser.add_argument("--optimize_mano_beta",
choices=[0, 1],
default=1,
type=int)
parser.add_argument("--optimize_object_scale",
choices=[0, 1],
default=0,
type=int)
parser.add_argument("--hand_proj_mode",
default="persp",
choices=["ortho", "persp"])
parser.add_argument(
"--lw_smooth",
type=float,
default=2000,
help="Loss weight for smoothness.",
)
parser.add_argument(
"--lw_v2d_hand",
type=float,
default=50,
help="Loss weight for 2D vertices reprojection loss.",
)
parser.add_argument(
"--lw_inter",
type=float,
default=1,
help="Loss weight for coarse interaction loss.",
)
parser.add_argument(
"--lw_contact",
type=float,
default=0,
choices=[0, 1],
help="Loss contact heuristic",
)
parser.add_argument(
"--lw_depth",
type=float,
default=0,
help="Loss weight for ordinal depth loss.",
)
parser.add_argument(
"--lw_pca",
type=float,
default=0.004,
help="Loss weight for PCA loss.",
)
parser.add_argument(
"--lw_sil_obj",
type=float,
default=1,
help="Loss weight for object mask loss.",
)
parser.add_argument(
"--lw_sil_hand",
type=float,
default=0,
help="Loss weight for hand mask loss.",
)
parser.add_argument(
"--lw_collision",
type=float,
default=0,
choices=[0, 0.001],
help="Loss weight for collision loss. (None: default weight)",
)
parser.add_argument(
"--lw_scale_obj",
type=float,
default=0.001,
help="Loss weight for object scale loss. (None: default weight)",
)
parser.add_argument(
"--lw_scale_hand",
type=float,
default=0.001,
help="Loss weight for hand scale loss. (None: default weight)",
)
parser.add_argument("--hand_checkpoint",
default="extra_data/hand_module/pretrained_weights/"
"pose_shape_best.pth")
parser.add_argument("--smpl_path", default="extra_data/smpl")
args = parser.parse_args()
args.lw_smooth_obj = args.lw_smooth
args.lw_smooth_hand = args.lw_smooth
argutils.print_args(args)
if args.gt_masks and args.box_mode == "track":
raise ValueError("gt_masks should not be used with bbox_mode 'track'")
logger.info(f"Calling with args: {str(args)}")
return args
def main(args):
setseeds.set_all_seeds(args.seed)
# Update defaults based on commandline args.
dataset, image_size = getdataset.get_dataset(
args.dataset,
split=args.split,
frame_nb=args.frame_nb,
box_mode=args.box_mode,
chunk_step=args.chunk_step,
)
print(f"Processing {len(dataset)} samples")
# Get pretrained networks
mask_extractor = MaskExtractor()
hand_predictor = HandMocap(args.hand_checkpoint, args.smpl_path)
all_metrics = defaultdict(list)
for sample_idx in range(args.data_offset, len(dataset), args.data_step):
# Prepare sample folder
sample_folder = os.path.join(args.result_root, "samples",
f"{sample_idx:08d}")
os.makedirs(sample_folder, exist_ok=True)
save_path = os.path.join(args.result_root, "results.pkl")
sample_path = os.path.join(sample_folder, "results.pkl")
check_path = os.path.join(sample_folder, "joint_fit.pt")
if args.only_missing and os.path.exists(check_path):
print(f"Skipping existing {sample_path}")
continue
annots = dataset[sample_idx]
print("Pre-processing detections")
images = annots["images"]
right_hands = [
hand for hand in annots["hands"] if hand["label"] == "right_hand"
]
left_hands = [
hand for hand in annots["hands"] if hand["label"] == "left_hand"
]
setup = annots["setup"]
# Get hand detections and make them square
hand_bboxes = {}
hand_expansion = 0.1
if len(left_hands) > 0:
hand_bboxes["left_hand"] = make_bbox_square(
bbox_xy_to_wh(left_hands[0]['bbox']),
bbox_expansion=hand_expansion)
else:
hand_bboxes["left_hand"] = None
if len(right_hands) > 0:
hand_bboxes["right_hand"] = make_bbox_square(
bbox_xy_to_wh(right_hands[0]['bbox']),
bbox_expansion=hand_expansion)
else:
hand_bboxes["right_hand"] = None
camintr = annots["camera"]["K"].copy()
# Get object bboxes and add padding
obj_bboxes = np.array([annots["objects"][0]['bbox']])
obj_bbox_padding = 5
obj_bboxes = obj_bboxes + np.array([
-obj_bbox_padding, -obj_bbox_padding, obj_bbox_padding,
obj_bbox_padding
])
# Preprocess images
images_np = [
preprocess.get_image(image, image_size) for image in images
]
print("Regressing hands")
camintr_nc = camintr.copy()
camintr_nc[:, :2] = camintr_nc[:, :2] / image_size
indep_fit_path = os.path.join(sample_folder, "indep_fit.pkl")
# Collect 2D and 3D evidence
if not args.resume:
person_parameters, obj_mask_infos, super2d_imgs = get_frame_infos(
images_np,
hand_predictor,
mask_extractor,
sample_folder=sample_folder,
hand_bboxes=hand_bboxes,
obj_bboxes=obj_bboxes,
camintr=camintr,
debug=args.debug,
image_size=image_size,
)
super2d_img_path = os.path.join(sample_folder,
"detections_masks.png")
cv2.imwrite(super2d_img_path,
super2d_imgs[:, :, :3].astype(np.uint8)[:, :, ::-1])
# For ablations, render ground truth object and hand masks
if args.gt_masks:
gt2d_imgs = get_gt_infos(images_np,
annots,
person_parameters=person_parameters,
obj_mask_infos=obj_mask_infos,
image_size=image_size,
sample_folder=sample_folder)
gt2d_img_path = os.path.join(sample_folder,
"gt_detections_masks.png")
cv2.imwrite( # pylint: disable=E1101
gt2d_img_path,
gt2d_imgs[:, :, :3].astype(np.uint8)[:, :, ::-1])
obj_verts_can = annots["objects"][0]['canverts3d']
obj_faces = annots["objects"][0]['faces']
# Compute object pose initializations
object_parameters = find_optimal_poses(
images=images_np,
image_size=images_np[0].shape,
vertices=obj_verts_can[0],
faces=obj_faces[0],
annotations=obj_mask_infos,
num_initializations=args.num_initializations,
num_iterations=args.num_obj_iterations,
Ks=camintr,
viz_path=os.path.join(sample_folder, "optimal_pose.png"),
debug=args.debug,
)
# Populate person_parameters target_masks and K_roi given
# object occlusions
for person_param, obj_param, cam in zip(person_parameters,
object_parameters,
camintr):
maskutils.add_target_hand_occlusions(
person_param,
obj_param,
cam,
debug=args.debug,
sample_folder=sample_folder)
indep_fit_res = {
"person_parameters": person_parameters,
"object_parameters": object_parameters,
"obj_verts_can": obj_verts_can,
"obj_faces": obj_faces,
"super2d_img_path": super2d_img_path
}
# Save initial optimization results
with open(indep_fit_path, "wb") as p_f:
pickle.dump(indep_fit_res, p_f)
state_dict = None
else:
# Load from previous computation
resume_folder = os.path.join(args.resume, "samples",
f"{sample_idx:08d}")
resume_indep_path = os.path.join(resume_folder, "indep_fit.pkl")
if args.resume_indep:
state_dict = None
else:
resume_joint_path = os.path.join(resume_folder, "joint_fit.pt")
state_dict = torch.load(resume_joint_path)["state_dict"]
state_dict = {
key: val.cuda()
for key, val in state_dict.items()
}
with open(resume_indep_path, "rb") as p_f:
indep_fit_res = pickle.load(p_f)
super2d_img_path = indep_fit_res["super2d_img_path"]
# Extract weight dictionary from arguments
loss_weights = {
key: val
for key, val in vars(args).items() if "lw_" in key
}
# Run joint optimization
model, loss_evolution, imgs = optimize_hand_object(
person_parameters=indep_fit_res["person_parameters"],
object_parameters=indep_fit_res["object_parameters"],
hand_proj_mode=args.hand_proj_mode,
objvertices=indep_fit_res["obj_verts_can"],
objfaces=indep_fit_res["obj_faces"],
optimize_mano=args.optimize_mano,
optimize_mano_beta=args.optimize_mano_beta,
optimize_object_scale=args.optimize_object_scale,
loss_weights=loss_weights,
image_size=image_size,
num_iterations=args.num_joint_iterations,
images=images_np,
camintr=camintr_nc,
state_dict=state_dict,
viz_step=args.viz_step,
viz_folder=os.path.join(sample_folder, "jointoptim"),
)
save_dict = {
"state_dict": {
key: val.contiguous().cpu()
for key, val in model.state_dict().items()
if ("mano_model" not in key)
}
}
torch.save(save_dict, os.path.join(sample_folder, "joint_fit.pt"))
# Save initial optimization results
with open(indep_fit_path, "wb") as p_f:
pickle.dump(indep_fit_res, p_f)
init_obj_verts = model.verts_object_init
init_hand_verts = model.verts_hand_init
fit_obj_verts, _ = model.get_verts_object()
fit_hand_verts, _ = model.get_verts_hand()
if "objects" in setup:
gt_obj_verts = np.concatenate(
[annot["verts3d"] for annot in annots["objects"]], 1)
gt_hand_verts = []
if "right_hand" in setup:
gt_hand_verts.append(
np.concatenate([
hand["verts3d"] for hand in annots["hands"]
if hand["label"] == "right_hand"
], 1))
if "left_hand" in setup:
gt_hand_verts.append(
np.concatenate([
hand["verts3d"]
for hand in annots["hands"] if hand["label"] == "left_hand"
], 1))
gt_hand_verts = fit_hand_verts.new(gt_hand_verts)
gt_obj_verts = fit_obj_verts.new(gt_obj_verts)
# viz_len = min(5, args.frame_nb)
viz_len = min(5, args.frame_nb)
with torch.no_grad():
frontal, top_down = visualize_hand_object(model,
images_np,
dist=4,
viz_len=args.frame_nb,
image_size=image_size)
frontal_gt_only, top_down_gt_only = visualize_hand_object(
model,
images_np,
dist=4,
viz_len=args.frame_nb,
image_size=image_size,
verts_hand_gt=gt_hand_verts,
verts_object_gt=gt_obj_verts,
gt_only=True)
with torch.no_grad():
# GT + pred overlay renders
frontal_gt, top_down_gt = visualize_hand_object(
model,
images_np,
dist=4,
verts_hand_gt=gt_hand_verts,
verts_object_gt=gt_obj_verts,
viz_len=args.frame_nb,
image_size=image_size,
)
# GT + init overlay renders
frontal_init_gt, top_down_init_gt = visualize_hand_object(
model,
images_np[:viz_len],
dist=4,
verts_hand_gt=gt_hand_verts,
verts_object_gt=gt_obj_verts,
viz_len=viz_len,
init=True,
image_size=image_size,
)
# pred verts need to be brought back from square image space
viz_path = os.path.join(sample_folder, "final_points.png")
viz_gtpred_points(images=images_np[:viz_len],
pred_images={
"frontal_pred": frontal[:viz_len],
"topdown_pred": top_down[:viz_len],
"frontal_pred+gt": frontal_gt[:viz_len],
"topdown_pred+gt": top_down_gt[:viz_len],
"frontal_init+gt": frontal_init_gt,
"topdown_init+gt": top_down_init_gt
},
save_path=viz_path)
# Save predicted video clip
top_down = cliputils.add_clip_text(top_down, "Pred")
top_down_gt = cliputils.add_clip_text(top_down_gt, "Pred + GT")
top_down_gt_only = cliputils.add_clip_text(top_down_gt_only,
"Ground Truth")
clip = np.concatenate([
np.concatenate([np.stack(images_np), frontal, frontal_gt_only], 2),
np.concatenate([top_down_gt, top_down, top_down_gt_only], 2)
], 1)
evalviz.make_video_np(clip,
viz_path.replace(".png", ".webm"),
resize_factor=0.5)
optim_vid_path = os.path.join(sample_folder, "final_points.webm")
evalviz.make_video_np(clip,
optim_vid_path.replace(".webm", ".mp4"),
resize_factor=0.5)
with torch.no_grad():
sample_obj_metrics = pointmetrics.get_point_metrics(
gt_obj_verts, fit_obj_verts)
sample_hand_metrics = pointmetrics.get_point_metrics(
gt_hand_verts.view(-1, 778, 3),
fit_hand_verts.view(-1, 778, 3))
init_obj_metrics = pointmetrics.get_point_metrics(
gt_obj_verts, init_obj_verts)
init_hand_metrics = pointmetrics.get_point_metrics(
gt_hand_verts.view(-1, 778, 3),
init_hand_verts.view(-1, 778, 3))
aligned_metrics = pointmetrics.get_align_metrics(
gt_hand_verts.view(-1, 778, 3),
fit_hand_verts.view(-1, 778, 3), gt_obj_verts, fit_obj_verts)
init_aligned_metrics = pointmetrics.get_align_metrics(
gt_hand_verts.view(-1, 778, 3),
fit_hand_verts.view(-1, 778, 3), gt_obj_verts, fit_obj_verts)
inter_metrics = pointmetrics.get_inter_metrics(
fit_hand_verts, fit_obj_verts, model.faces_hand,
model.faces_object)
init_inter_metrics = pointmetrics.get_inter_metrics(
init_hand_verts, init_obj_verts, model.faces_hand,
model.faces_object)
sample_metrics = {}
# Metrics before joint optimization
for key, vals in init_obj_metrics.items():
sample_metrics[f"{key}_obj_init"] = vals
for key, vals in init_hand_metrics.items():
if key == "verts_dists":
sample_metrics[f"{key}_hand_init"] = vals
# Metrics after joint optimizations
for key, vals in sample_obj_metrics.items():
sample_metrics[f"{key}_obj"] = vals
for key, vals in sample_hand_metrics.items():
if key == "verts_dists":
sample_metrics[f"{key}_hand"] = vals
for key, vals in aligned_metrics.items():
sample_metrics[key] = vals
for key, vals in init_aligned_metrics.items():
sample_metrics[f"{key}_init"] = vals
for key, vals in inter_metrics.items():
sample_metrics[f"{key}"] = vals
for key, vals in init_inter_metrics.items():
sample_metrics[f"{key}_init"] = vals
for key, vals in sample_metrics.items():
all_metrics[key].extend(vals)
with open(sample_path, "wb") as p_f:
pickle.dump(
{
"opts": vars(args),
"losses": loss_evolution,
"metrics": sample_metrics,
"imgs": imgs,
"show_img_paths": {
"pred_gt": viz_path,
"super2d": super2d_img_path,
"last": imgs[max(list(imgs.keys()))]
},
}, p_f)
saveresults.dump(args, all_metrics, save_path)
if __name__ == "__main__":
main(get_args())