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demo.py
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from pathlib import Path
import torch
import argparse
import os
import cv2
import numpy as np
import math
from tqdm import tqdm
from hamer.configs import CACHE_DIR_HAMER
from hamer.models import HAMER, download_models, load_hamer, DEFAULT_CHECKPOINT
from hamer.utils import recursive_to
from hamer.datasets.vitdet_dataset import ViTDetDataset, DEFAULT_MEAN, DEFAULT_STD
from hamer.utils.renderer import Renderer, cam_crop_to_full
from decord import VideoReader
LIGHT_BLUE=(0.65098039, 0.74117647, 0.85882353)
# from vitpose_model import ViTPoseModel
from DWPose.ControlNet.annotator.dwpose import DWposeDetector
import json
from typing import Dict, Optional
def main():
parser = argparse.ArgumentParser(description='HaMeR demo code')
parser.add_argument('--checkpoint', type=str, default=DEFAULT_CHECKPOINT, help='Path to pretrained model checkpoint')
parser.add_argument('--img_folder', type=str, default='images', help='Folder with input images')
parser.add_argument('--out_folder', type=str, default='out_demo', help='Output folder to save rendered results')
parser.add_argument('--side_view', dest='side_view', action='store_true', default=False, help='If set, render side view also')
parser.add_argument('--full_frame', dest='full_frame', action='store_true', default=False, help='If set, render all people together also')
parser.add_argument('--save_mesh', dest='save_mesh', action='store_true', default=False, help='If set, save meshes to disk also')
parser.add_argument('--batch_size', type=int, default=1, help='Batch size for inference/fitting')
parser.add_argument('--rescale_factor', type=float, default=2.0, help='Factor for padding the bbox')
parser.add_argument('--file_type', nargs='+', default=['*.jpg', '*.png'], help='List of file extensions to consider')
args = parser.parse_args()
# Download and load checkpoints
download_models(CACHE_DIR_HAMER)
model, model_cfg = load_hamer(args.checkpoint)
# Setup HaMeR model
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model = model.to(device)
model.eval()
# Load detector
from hamer.utils.utils_detectron2 import DefaultPredictor_Lazy
from detectron2.config import LazyConfig
import hamer
cfg_path = Path(hamer.__file__).parent/'configs'/'cascade_mask_rcnn_vitdet_h_75ep.py'
detectron2_cfg = LazyConfig.load(str(cfg_path))
detectron2_cfg.train.init_checkpoint = "/mnt/workspace/workgroup/wangbenzhi.wbz/RealisHuman/submodules/hamer-main/model_final_f05665.pkl"
for i in range(3):
detectron2_cfg.model.roi_heads.box_predictors[i].test_score_thresh = 0.25
detector = DefaultPredictor_Lazy(detectron2_cfg)
# keypoint detector
# cpm = ViTPoseModel(device)
# Setup the renderer
renderer = Renderer(model_cfg, faces=model.mano.faces)
# Make output directory if it does not exist
# os.makedirs(args.out_folder, exist_ok=True)
path = args.img_folder
iter_path = os.listdir(path)
pose = DWposeDetector()
for i, dirname in tqdm(enumerate(iter_path)):
img_folder = os.path.join(args.img_folder, dirname, 'image')
# Get all demo images ends with .jpg or .png
img_paths = [img for end in args.file_type for img in Path(img_folder).glob(end)]
# Iterate over all images in folder
for img_path in img_paths:
img_cv2 = cv2.imread(str(img_path))
# Detect humans in image
det_out = detector(img_cv2) #检测人
img = img_cv2.copy()[:, :, ::-1]
det_instances = det_out['instances']
valid_idx = (det_instances.pred_classes==0) & (det_instances.scores > 0.5)
pred_bboxes=det_instances.pred_boxes.tensor[valid_idx].cpu().numpy()
pred_scores=det_instances.scores[valid_idx].cpu().numpy()
# Detect human keypoints for each person
# vitposes_out = cpm.predict_pose(
# img_cv2,
# [np.concatenate([pred_bboxes, pred_scores[:, None]], axis=1)],
# ) #预测关键点， 可以替换成dwpose
vitposes_out = pose(img_cv2)
bboxes = []
is_right = []
# Use hands based on hand keypoint detections
for vitposes in vitposes_out:
# left_hand_keyp = vitposes['keypoints'][-42:-21]
# right_hand_keyp = vitposes['keypoints'][-21:]
left_hand_keyp = vitposes[-42:-21]
right_hand_keyp = vitposes[-21:]
#定位手部
# Rejecting not confident detections
keyp = left_hand_keyp
# valid = keyp[:,2] > 0.5
# if sum(valid) > 3:
# bbox = [keyp[valid,0].min(), keyp[valid,1].min(), keyp[valid,0].max(), keyp[valid,1].max()]
if (keyp==-1).sum()==0:
bbox = [keyp[:,0].min(), keyp[:,1].min(), keyp[:,0].max(), keyp[:,1].max()]
bboxes.append(bbox)
is_right.append(0)
keyp = right_hand_keyp
# valid = keyp[:,2] > 0.5
# if sum(valid) > 3:
# bbox = [keyp[valid,0].min(), keyp[valid,1].min(), keyp[valid,0].max(), keyp[valid,1].max()]
if (keyp==-1).sum()==0:
bbox = [keyp[:,0].min(), keyp[:,1].min(), keyp[:,0].max(), keyp[:,1].max()]
bboxes.append(bbox)
is_right.append(1)
if len(bboxes) == 0:
continue
boxes = np.stack(bboxes)
right = np.stack(is_right)
# Run reconstruction on all detected hands
dataset = ViTDetDataset(model_cfg, img_cv2, boxes, right, rescale_factor=args.rescale_factor)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=8, shuffle=False, num_workers=0)
all_verts = []
all_cam_t = []
all_right = []
for batch in dataloader:
batch = recursive_to(batch, device)
with torch.no_grad():
out = model(batch)
multiplier = (2*batch['right']-1)
pred_cam = out['pred_cam']
pred_cam[:,1] = multiplier*pred_cam[:,1]
box_center = batch["box_center"].float()
box_size = batch["box_size"].float()
img_size = batch["img_size"].float()
multiplier = (2*batch['right']-1)
scaled_focal_length = model_cfg.EXTRA.FOCAL_LENGTH / model_cfg.MODEL.IMAGE_SIZE * img_size.max()
pred_cam_t_full = cam_crop_to_full(pred_cam, box_center, box_size, img_size, scaled_focal_length).detach().cpu().numpy()
# Render the result
batch_size = batch['img'].shape[0]
for n in range(batch_size):
# Get filename from path img_path
img_fn, _ = os.path.splitext(os.path.basename(img_path))
person_id = int(batch['personid'][n])
white_img = (torch.ones_like(batch['img'][n]).cpu() - DEFAULT_MEAN[:,None,None]/255) / (DEFAULT_STD[:,None,None]/255)
input_patch = batch['img'][n].cpu() * (DEFAULT_STD[:,None,None]/255) + (DEFAULT_MEAN[:,None,None]/255)
input_patch = input_patch.permute(1,2,0).numpy()
regression_img = renderer(out['pred_vertices'][n].detach().cpu().numpy(),
out['pred_cam_t'][n].detach().cpu().numpy(),
batch['img'][n],
mesh_base_color=LIGHT_BLUE,
scene_bg_color=(1, 1, 1),
)
if args.side_view:
side_img = renderer(out['pred_vertices'][n].detach().cpu().numpy(),
out['pred_cam_t'][n].detach().cpu().numpy(),
white_img,
mesh_base_color=LIGHT_BLUE,
scene_bg_color=(1, 1, 1),
side_view=True)
final_img = np.concatenate([input_patch, regression_img, side_img], axis=1)
else:
# mask = np.where(np.all(regression_img == 1., axis=-1), 0, 1)
# final_img = np.where(mask[:, :, np.newaxis] == 1, regression_img, input_patch)
hand_mask_img = regression_img
hand_img = input_patch
# final_img = np.concatenate([input_patch, regression_img], axis=1)
is_right = batch['right'][n]
if is_right:
mask_subdir = 'mask_right_hand'
subdir = 'right_hand'
else:
mask_subdir = 'mask_left_hand'
subdir = 'left_hand'
image_out_folder = os.path.join(args.out_folder, dirname, subdir)
mask_out_folder = os.path.join(args.out_folder, dirname, mask_subdir)
os.makedirs(image_out_folder, exist_ok=True)
os.makedirs(mask_out_folder, exist_ok=True)
cv2.imwrite(os.path.join(image_out_folder, os.path.basename(img_path).replace('image', subdir)), 255*hand_img[:, :, ::-1])
cv2.imwrite(os.path.join(mask_out_folder, os.path.basename(img_path).replace('image', mask_subdir)), 255*hand_mask_img[:, :, ::-1])
# cv2.imwrite(os.path.join(mask_out_folder, os.path.basename(img_path).replace('image', 'final')), 255*final_img[:, :, ::-1])
# Add all verts and cams to list
verts = out['pred_vertices'][n].detach().cpu().numpy()
is_right = batch['right'][n].cpu().numpy()
verts[:,0] = (2*is_right-1)*verts[:,0]
cam_t = pred_cam_t_full[n]
all_verts.append(verts)
all_cam_t.append(cam_t)
all_right.append(is_right)
# # Save all meshes to disk
# if args.save_mesh:
# camera_translation = cam_t.copy()
# tmesh = renderer.vertices_to_trimesh(verts, camera_translation, LIGHT_BLUE, is_right=is_right)
# tmesh.export(os.path.join(args.out_folder, f'{img_fn}_{person_id}.obj'))
# Render front view
if args.full_frame and len(all_verts) > 0:
misc_args = dict(
mesh_base_color=LIGHT_BLUE,
scene_bg_color=(1, 1, 1),
focal_length=scaled_focal_length,
)
cam_view = renderer.render_rgba_multiple(all_verts, cam_t=all_cam_t, render_res=img_size[n], is_right=all_right, **misc_args)
# Overlay image
input_img = img_cv2.astype(np.float32)[:,:,::-1]/255.0
input_img = np.concatenate([input_img, np.ones_like(input_img[:,:,:1])], axis=2) # Add alpha channel
# input_img_overlay = input_img[:,:,:3] * (1-cam_view[:,:,3:]) + cam_view[:,:,:3] * cam_view[:,:,3:]
# cv2.imwrite(os.path.join(args.out_folder, f'{img_fn}_all.jpg'), 255*input_img_overlay[:, :, ::-1])
fullview_out_folder = os.path.join(args.out_folder, dirname, 'full_view')
os.makedirs(fullview_out_folder, exist_ok=True)
cv2.imwrite(os.path.join(fullview_out_folder, os.path.basename(img_path).replace('image', 'full_view')), 255*cam_view[:, :, ::-1])
if __name__ == '__main__':
main()