mirror of
https://github.com/vladmandic/sdnext.git
synced 2026-01-29 05:02:09 +03:00
97 lines
4.2 KiB
Python
97 lines
4.2 KiB
Python
import os
|
|
import cv2
|
|
import numpy as np
|
|
import torch
|
|
from huggingface_hub import hf_hub_download
|
|
from PIL import Image
|
|
from modules import devices
|
|
from modules.shared import opts
|
|
from modules.control.util import HWC3, resize_image
|
|
from .leres.depthmap import estimateboost, estimateleres
|
|
from .leres.multi_depth_model_woauxi import RelDepthModel
|
|
from .leres.net_tools import strip_prefix_if_present
|
|
from .pix2pix.models.pix2pix4depth_model import Pix2Pix4DepthModel
|
|
from .pix2pix.options.test_options import TestOptions
|
|
|
|
|
|
class LeresDetector:
|
|
def __init__(self, model, pix2pixmodel):
|
|
self.model = model
|
|
self.pix2pixmodel = pix2pixmodel
|
|
|
|
@classmethod
|
|
def from_pretrained(cls, pretrained_model_or_path, filename=None, pix2pix_filename=None, cache_dir=None, local_files_only=False):
|
|
filename = filename or "res101.pth"
|
|
pix2pix_filename = pix2pix_filename or "latest_net_G.pth"
|
|
if os.path.isdir(pretrained_model_or_path):
|
|
model_path = os.path.join(pretrained_model_or_path, filename)
|
|
else:
|
|
model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir, local_files_only=local_files_only)
|
|
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 os.path.isdir(pretrained_model_or_path):
|
|
model_path = os.path.join(pretrained_model_or_path, pix2pix_filename)
|
|
else:
|
|
model_path = hf_hub_download(pretrained_model_or_path, pix2pix_filename, cache_dir=cache_dir, local_files_only=local_files_only)
|
|
opt = TestOptions().parse()
|
|
if not torch.cuda.is_available():
|
|
opt.gpu_ids = [] # cpu mode
|
|
pix2pixmodel = Pix2Pix4DepthModel(opt)
|
|
pix2pixmodel.save_dir = os.path.dirname(model_path)
|
|
pix2pixmodel.load_networks('latest')
|
|
pix2pixmodel.eval()
|
|
return cls(model, pix2pixmodel)
|
|
|
|
def to(self, device):
|
|
self.model.to(device)
|
|
return self
|
|
|
|
def __call__(self, input_image, thr_a=0, thr_b=0, boost=False, detect_resolution=512, image_resolution=512, output_type="pil"):
|
|
self.model.to(devices.device)
|
|
# device = next(iter(self.model.parameters())).device
|
|
if not isinstance(input_image, np.ndarray):
|
|
input_image = np.array(input_image, dtype=np.uint8)
|
|
input_image = HWC3(input_image)
|
|
input_image = resize_image(input_image, detect_resolution)
|
|
assert input_image.ndim == 3
|
|
height, width, _dim = input_image.shape
|
|
if boost:
|
|
depth = estimateboost(input_image, self.model, 0, self.pix2pixmodel, max(width, height))
|
|
else:
|
|
depth = estimateleres(input_image, self.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]
|
|
detected_map = depth_image
|
|
detected_map = HWC3(detected_map)
|
|
img = resize_image(input_image, image_resolution)
|
|
H, W, _C = img.shape
|
|
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
|
|
if opts.control_move_processor:
|
|
self.model.to('cpu')
|
|
if output_type == "pil":
|
|
detected_map = Image.fromarray(detected_map)
|
|
return detected_map
|