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import os
import cv2
import numpy as np
import torch
from PIL import Image
from custom_controlnet_aux.util import HWC3, common_input_validate, resize_image_with_pad, custom_hf_download, HF_MODEL_NAME
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=HF_MODEL_NAME, filename="res101.pth", pix2pix_filename="latest_net_G.pth"):
model_path = custom_hf_download(pretrained_model_or_path, filename)
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
pix2pix_model_path = custom_hf_download(pretrained_model_or_path, pix2pix_filename)
opt = TestOptions().parse()
if not torch.cuda.is_available():
opt.gpu_ids = [] # cpu mode
pix2pixmodel = Pix2Pix4DepthModel(opt)
pix2pixmodel.save_dir = os.path.dirname(pix2pix_model_path)
pix2pixmodel.load_networks('latest')
pix2pixmodel.eval()
return cls(model, pix2pixmodel)
def to(self, device):
self.model.to(device)
# TODO - refactor pix2pix implementation to support device migration
# self.pix2pixmodel.to(device)
return self
def __call__(self, input_image, thr_a=0, thr_b=0, boost=False, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", **kwargs):
input_image, output_type = common_input_validate(input_image, output_type, **kwargs)
detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method)
with torch.no_grad():
if boost:
depth = estimateboost(detected_map, self.model, 0, self.pix2pixmodel, max(detected_map.shape[1], detected_map.shape[0]))
else:
depth = estimateleres(detected_map, self.model, detected_map.shape[1], detected_map.shape[0])
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 = HWC3(remove_pad(depth_image))
if output_type == "pil":
detected_map = Image.fromarray(detected_map)
return detected_map |