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import os
import cv2
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from ..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)
# 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, image_resolution=512, output_type="pil"):
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
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)
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 output_type == "pil":
detected_map = Image.fromarray(detected_map)
return detected_map
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