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