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import cv2
import numpy as np
import torch
import os
# 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
from annotator.base_annotator import BaseProcessor
# pix2pix/merge net imports
from .pix2pix.options.test_options import TestOptions
from .pix2pix.models.pix2pix4depth_model import Pix2Pix4DepthModel
# 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"
class LeresPix2Pix(BaseProcessor):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.model = None
self.pix2pixmodel = None
self.model_dir = os.path.join(self.models_path, "leres")
def unload_model(self):
if self.model is not None:
self.model = self.model.cpu()
if self.pix2pixmodel is not None:
self.pix2pixmodel = self.pix2pixmodel.unload_network('G')
def load_model(self):
model_path = os.path.join(self.model_dir, "res101.pth")
if 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=self.model_dir)
if torch.cuda.is_available():
checkpoint = torch.load(model_path)
else:
checkpoint = torch.load(model_path, map_location=torch.device('cpu'))
self.model = RelDepthModel(backbone='resnext101')
self.model.load_state_dict(strip_prefix_if_present(checkpoint['depth_model'], "module."), strict=True)
del checkpoint
def load_pix2pix2_model(self):
pix2pixmodel_path = os.path.join(self.model_dir, "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=self.model_dir)
opt = TestOptions().parse()
if not torch.cuda.is_available():
opt.gpu_ids = [] # cpu mode
self.pix2pixmodel = Pix2Pix4DepthModel(opt)
self.pix2pixmodel.save_dir = self.model_dir
self.pix2pixmodel.load_networks('latest')
self.pix2pixmodel.eval()
def __call__(self, input_image, thr_a, thr_b, boost=False, **kwargs):
if self.model is None:
self.load_model()
if boost and self.pix2pixmodel is None:
self.load_pix2pix2_model()
if self.device != 'mps':
self.model = self.model.to(self.device)
assert input_image.ndim == 3
height, width, dim = input_image.shape
with torch.no_grad():
if boost:
depth = estimateboost(input_image, self.model, 0, self.pix2pixmodel, max(width, height))
else:
depth = estimateleres(input_image, self.model, width, height, self.device)
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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