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import gradio as gr
import numpy as np
import cv2
from tqdm import tqdm
import torch
from pytorch3d.io.obj_io import load_obj
import tempfile
import main_mcc
import mcc_model
import util.misc as misc
from engine_mcc import prepare_data
from plyfile import PlyData, PlyElement
def run_inference(model, samples, device, temperature, args):
model.eval()
seen_xyz, valid_seen_xyz, unseen_xyz, unseen_rgb, labels, seen_images = prepare_data(
samples, device, is_train=False, args=args, is_viz=True
)
pred_occupy = []
pred_colors = []
max_n_unseen_fwd = 2000
model.cached_enc_feat = None
num_passes = int(np.ceil(unseen_xyz.shape[1] / max_n_unseen_fwd))
for p_idx in range(num_passes):
p_start = p_idx * max_n_unseen_fwd
p_end = (p_idx + 1) * max_n_unseen_fwd
cur_unseen_xyz = unseen_xyz[:, p_start:p_end]
cur_unseen_rgb = unseen_rgb[:, p_start:p_end].zero_()
cur_labels = labels[:, p_start:p_end].zero_()
with torch.no_grad():
_, pred = model(
seen_images=seen_images,
seen_xyz=seen_xyz,
unseen_xyz=cur_unseen_xyz,
unseen_rgb=cur_unseen_rgb,
unseen_occupy=cur_labels,
cache_enc=True,
valid_seen_xyz=valid_seen_xyz,
)
if device == "cuda":
pred_occupy.append(pred[..., 0].cuda())
else:
pred_occupy.append(pred[..., 0].cpu())
if args.regress_color:
pred_colors.append(pred[..., 1:].reshape((-1, 3)))
else:
pred_colors.append(
(
torch.nn.Softmax(dim=2)(
pred[..., 1:].reshape((-1, 3, 256)) / temperature
) * torch.linspace(0, 1, 256, device=pred.device)
).sum(axis=2)
)
pred_occupy = torch.cat(pred_occupy, dim=1)
pred_occupy = torch.nn.Sigmoid()(pred_occupy)
return torch.cat(pred_colors, dim=0).cpu().numpy(), pred_occupy.cpu().numpy(), unseen_xyz.cpu().numpy()
def pad_image(im, value):
if im.shape[0] > im.shape[1]:
diff = im.shape[0] - im.shape[1]
return torch.cat([im, (torch.zeros((im.shape[0], diff, im.shape[2])) + value)], dim=1)
else:
diff = im.shape[1] - im.shape[0]
return torch.cat([im, (torch.zeros((diff, im.shape[1], im.shape[2])) + value)], dim=0)
def normalize(seen_xyz):
seen_xyz = seen_xyz / (seen_xyz[torch.isfinite(seen_xyz.sum(dim=-1))].var(dim=0) ** 0.5).mean()
seen_xyz = seen_xyz - seen_xyz[torch.isfinite(seen_xyz.sum(dim=-1))].mean(axis=0)
return seen_xyz
def infer(
image,
point_cloud,
seg,
granularity,
temperature,
):
score_thresholds = [0.1, 0.2, 0.3, 0.4, 0.5]
device = "cuda" if torch.cuda.is_available() else "cpu"
parser = main_mcc.get_args_parser()
parser.set_defaults(eval=True)
args = parser.parse_args()
model = mcc_model.get_mcc_model(
occupancy_weight=1.0,
rgb_weight=0.01,
args=args,
)
if device == "cuda":
model = model.cuda()
misc.load_model(args=args, model_without_ddp=model, optimizer=None, loss_scaler=None)
rgb = image
obj = load_obj(point_cloud.name)
seen_rgb = (torch.tensor(rgb).float() / 255)[..., [2, 1, 0]]
H, W = seen_rgb.shape[:2]
seen_rgb = torch.nn.functional.interpolate(
seen_rgb.permute(2, 0, 1)[None],
size=[H, W],
mode="bilinear",
align_corners=False,
)[0].permute(1, 2, 0)
seen_xyz = obj[0].reshape(H, W, 3)
seg = cv2.imread(seg.name, cv2.IMREAD_UNCHANGED)
mask = torch.tensor(cv2.resize(seg, (W, H))).bool()
seen_xyz[~mask] = float('inf')
seen_xyz = normalize(seen_xyz)
bottom, right = mask.nonzero().max(dim=0)[0]
top, left = mask.nonzero().min(dim=0)[0]
bottom = bottom + 40
right = right + 40
top = max(top - 40, 0)
left = max(left - 40, 0)
seen_xyz = seen_xyz[top:bottom+1, left:right+1]
seen_rgb = seen_rgb[top:bottom+1, left:right+1]
seen_xyz = pad_image(seen_xyz, float('inf'))
seen_rgb = pad_image(seen_rgb, 0)
seen_rgb = torch.nn.functional.interpolate(
seen_rgb.permute(2, 0, 1)[None],
size=[800, 800],
mode="bilinear",
align_corners=False,
)
seen_xyz = torch.nn.functional.interpolate(
seen_xyz.permute(2, 0, 1)[None],
size=[112, 112],
mode="bilinear",
align_corners=False,
).permute(0, 2, 3, 1)
samples = [
[seen_xyz, seen_rgb],
[torch.zeros((20000, 3)), torch.zeros((20000, 3))],
]
pred_colors, pred_occupy, unseen_xyz = run_inference(model, samples, device, temperature, args)
_masks = pred_occupy > 0.1
unseen_xyz = unseen_xyz[_masks]
pred_colors = pred_colors[None, ...][_masks] * 255
# Prepare data for PlyElement
vertex = np.core.records.fromarrays(np.hstack((unseen_xyz, pred_colors)).transpose(),
names='x, y, z, red, green, blue',
formats='f8, f8, f8, u1, u1, u1')
# Create PlyElement
element = PlyElement.describe(vertex, 'vertex')
# Save point cloud data to a temporary file
with tempfile.NamedTemporaryFile(suffix=".ply", delete=False) as f:
PlyData([element], text=True).write(f)
temp_file_name = f.name
return temp_file_name
demo = gr.Interface(fn=infer,
inputs=[gr.Image(label="Input Image"),
gr.File(label="Pointcloud File"),
gr.File(label="Segmentation File"),
gr.Slider(minimum=0.05, maximum=0.5, step=0.05, value=0.2, label="Granularity"),
gr.Slider(minimum=0, maximum=1.0, step=0.1, value=0.1, label="Temperature")
],
outputs=[gr.outputs.File(label="Point Cloud Json")],
examples=[["demo/quest2.jpg", "demo/quest2.obj", "demo/quest2_seg.png", 0.2, 0.1]],
cache_examples=True)
demo.launch(server_name="0.0.0.0", server_port=7860)
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