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gradio_app.py
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gradio_app.py
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from pathlib import Path
import tempfile
import torch
import argparse
import os
import numpy as np
import gradio as gr
from PIL import Image
from hmr2.configs import CACHE_DIR_4DHUMANS
from hmr2.models import HMR2, download_models, load_hmr2, DEFAULT_CHECKPOINT
from hmr2.utils import recursive_to
from hmr2.datasets.vitdet_dataset import ViTDetDataset, DEFAULT_MEAN, DEFAULT_STD
from hmr2.utils.renderer import Renderer, cam_crop_to_full
LIGHT_BLUE=(0.65098039, 0.74117647, 0.85882353)
def main():
parser = argparse.ArgumentParser(description='HMR2 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='example_data/images', help='Folder with input images')
parser.add_argument('--out_folder', type=str, default='demo_out', 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=64, help='Batch size for inference/fitting')
args = parser.parse_args()
# Download and load checkpoints
download_models(CACHE_DIR_4DHUMANS)
model, model_cfg = load_hmr2(args.checkpoint)
# Setup HMR2.0 model
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model = model.to(device)
model.eval()
# Load detector
from hmr2.utils.utils_detectron2 import DefaultPredictor_Lazy
from detectron2.config import LazyConfig
import hmr2
cfg_path = Path(hmr2.__file__).parent/'configs'/'cascade_mask_rcnn_vitdet_h_75ep.py'
detectron2_cfg = LazyConfig.load(str(cfg_path))
detectron2_cfg.train.init_checkpoint = "https://dl.fbaipublicfiles.com/detectron2/ViTDet/COCO/cascade_mask_rcnn_vitdet_h/f328730692/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)
# Setup the renderer
renderer = Renderer(model_cfg, faces=model.smpl.faces)
# Make output directory if it does not exist
os.makedirs(args.out_folder, exist_ok=True)
# # Iterate over all images in folder
# for img_path in Path(args.img_folder).glob('*.jpg'):
# img_cv2 = cv2.imread(str(img_path))
def infer(in_pil_img, in_threshold=0.8, out_pil_img=None):
# Convert RGB to BGR
img_cv2 = np.array(in_pil_img)[:, :, ::-1].copy()
# Detect humans in image
det_out = detector(img_cv2)
det_instances = det_out['instances']
valid_idx = (det_instances.pred_classes==0) & (det_instances.scores > 0.5)
boxes=det_instances.pred_boxes.tensor[valid_idx].cpu().numpy()
# Run HMR2.0 on all detected humans
dataset = ViTDetDataset(model_cfg, img_cv2, boxes)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=8, shuffle=False, num_workers=0)
all_verts = []
all_cam_t = []
all_mesh_paths = []
temp_name = next(tempfile._get_candidate_names())
for batch in dataloader:
batch = recursive_to(batch, device)
with torch.no_grad():
out = model(batch)
pred_cam = out['pred_cam']
box_center = batch["box_center"].float()
box_size = batch["box_size"].float()
img_size = batch["img_size"].float()
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:
# final_img = np.concatenate([input_patch, regression_img], axis=1)
# cv2.imwrite(os.path.join(args.out_folder, f'{img_fn}_{person_id}.png'), 255*final_img[:, :, ::-1])
# Add all verts and cams to list
verts = out['pred_vertices'][n].detach().cpu().numpy()
cam_t = pred_cam_t_full[n]
all_verts.append(verts)
all_cam_t.append(cam_t)
# Save all meshes to disk
# if args.save_mesh:
if True:
camera_translation = cam_t.copy()
tmesh = renderer.vertices_to_trimesh(verts, camera_translation, LIGHT_BLUE)
temp_path = os.path.join(args.out_folder, f'{temp_name}_{person_id}.obj')
tmesh.export(temp_path)
all_mesh_paths.append(temp_path)
# Render front view
if 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], **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.png'), 255*input_img_overlay[:, :, ::-1])
# convert to PIL image
out_pil_img = Image.fromarray((input_img_overlay*255).astype(np.uint8))
return out_pil_img, all_mesh_paths
else:
return None, []
with gr.Blocks(title="4DHumans", css=".gradio-container") as demo:
gr.HTML("""<div style="font-weight:bold; text-align:center; color:royalblue;">HMR 2.0</div>""")
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Input image", type="pil")
with gr.Column():
output_image = gr.Image(label="Reconstructions", type="pil")
output_meshes = gr.File(label="3D meshes")
gr.HTML("""<br/>""")
with gr.Row():
threshold = gr.Slider(0, 1.0, value=0.6, label='Detection Threshold')
send_btn = gr.Button("Infer")
send_btn.click(fn=infer, inputs=[input_image, threshold], outputs=[output_image, output_meshes])
gr.Examples([
['example_data/images/pexels-anete-lusina-4793258.jpg'],
['example_data/images/skates.png'],
],
inputs=[input_image, 0.6])
demo.launch(debug=True)
if __name__ == '__main__':
main()