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A10G
Running
on
A10G
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import cv2
import numpy as np
import torch
import os
from annotator.annotator_path import models_path, DEVICE
from torchvision.transforms import transforms
# AdelaiDepth/LeReS imports
from .leres.depthmap import estimateleres, estimateboost
from .leres.multi_depth_model_woauxi import RelDepthModel
from .leres.net_tools import strip_prefix_if_present
# pix2pix/merge net imports
from .pix2pix.options.test_options import TestOptions
from .pix2pix.models.pix2pix4depth_model import Pix2Pix4DepthModel
base_model_path = os.path.join(models_path, "leres")
old_modeldir = os.path.dirname(os.path.realpath(__file__))
remote_model_path_leres = "https://huggingface.co/lllyasviel/Annotators/resolve/main/res101.pth"
remote_model_path_pix2pix = "https://huggingface.co/lllyasviel/Annotators/resolve/main/latest_net_G.pth"
model = None
pix2pixmodel = None
def unload_leres_model():
global model, pix2pixmodel
if model is not None:
model = model.cpu()
if pix2pixmodel is not None:
pix2pixmodel = pix2pixmodel.unload_network('G')
def apply_leres(input_image, thr_a, thr_b, boost=False):
global model, pix2pixmodel
if model is None:
model_path = os.path.join(base_model_path, "res101.pth")
old_model_path = os.path.join(old_modeldir, "res101.pth")
if os.path.exists(old_model_path):
model_path = old_model_path
elif not os.path.exists(model_path):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path_leres, model_dir=base_model_path)
if torch.cuda.is_available():
checkpoint = torch.load(model_path)
else:
checkpoint = torch.load(model_path, map_location=torch.device('cpu'))
model = RelDepthModel(backbone='resnext101')
model.load_state_dict(strip_prefix_if_present(checkpoint['depth_model'], "module."), strict=True)
del checkpoint
if boost and pix2pixmodel is None:
pix2pixmodel_path = os.path.join(base_model_path, "latest_net_G.pth")
if not os.path.exists(pix2pixmodel_path):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path_pix2pix, model_dir=base_model_path)
opt = TestOptions().parse()
if not torch.cuda.is_available():
opt.gpu_ids = [] # cpu mode
pix2pixmodel = Pix2Pix4DepthModel(opt)
pix2pixmodel.save_dir = base_model_path
pix2pixmodel.load_networks('latest')
pix2pixmodel.eval()
if DEVICE.type != 'mps':
model = model.to(DEVICE)
assert input_image.ndim == 3
height, width, dim = input_image.shape
with torch.no_grad():
if boost:
depth = estimateboost(input_image, model, 0, pix2pixmodel, max(width, height))
else:
depth = estimateleres(input_image, model, width, height)
numbytes=2
depth_min = depth.min()
depth_max = depth.max()
max_val = (2**(8*numbytes))-1
# check output before normalizing and mapping to 16 bit
if depth_max - depth_min > np.finfo("float").eps:
out = max_val * (depth - depth_min) / (depth_max - depth_min)
else:
out = np.zeros(depth.shape)
# single channel, 16 bit image
depth_image = out.astype("uint16")
# convert to uint8
depth_image = cv2.convertScaleAbs(depth_image, alpha=(255.0/65535.0))
# remove near
if thr_a != 0:
thr_a = ((thr_a/100)*255)
depth_image = cv2.threshold(depth_image, thr_a, 255, cv2.THRESH_TOZERO)[1]
# invert image
depth_image = cv2.bitwise_not(depth_image)
# remove bg
if thr_b != 0:
thr_b = ((thr_b/100)*255)
depth_image = cv2.threshold(depth_image, thr_b, 255, cv2.THRESH_TOZERO)[1]
return depth_image
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