170 lines
7.8 KiB
Python
170 lines
7.8 KiB
Python
import math, os, subprocess
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import cv2
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import hashlib
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import numpy as np
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import torch
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import gc
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import torchvision.transforms as T
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from einops import rearrange, repeat
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from PIL import Image
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from infer import InferenceHelper
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from midas.dpt_depth import DPTDepthModel
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from midas.transforms import Resize, NormalizeImage, PrepareForNet
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import torchvision.transforms.functional as TF
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from .general_utils import checksum
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class DepthModel():
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def __init__(self, device):
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self.adabins_helper = None
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self.depth_min = 1000
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self.depth_max = -1000
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self.device = device
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self.midas_model = None
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self.midas_transform = None
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def load_adabins(self, models_path):
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if not os.path.exists(os.path.join(models_path,'AdaBins_nyu.pt')):
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from basicsr.utils.download_util import load_file_from_url
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load_file_from_url(r"https://cloudflare-ipfs.com/ipfs/Qmd2mMnDLWePKmgfS8m6ntAg4nhV5VkUyAydYBp8cWWeB7/AdaBins_nyu.pt", models_path)
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if checksum(os.path.join(models_path,'AdaBins_nyu.pt')) != "643db9785c663aca72f66739427642726b03acc6c4c1d3755a4587aa2239962746410d63722d87b49fc73581dbc98ed8e3f7e996ff7b9c0d56d0fbc98e23e41a":
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raise Exception(r"Error while downloading AdaBins_nyu.pt. Please download from here: https://drive.google.com/file/d/1lvyZZbC9NLcS8a__YPcUP7rDiIpbRpoF and place in: " + models_path)
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self.adabins_helper = InferenceHelper(models_path=models_path, dataset='nyu', device=self.device)
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def load_midas(self, models_path, half_precision=True):
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if not os.path.exists(os.path.join(models_path, 'dpt_large-midas-2f21e586.pt')):
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from basicsr.utils.download_util import load_file_from_url
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load_file_from_url(r"https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt", models_path)
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if checksum(os.path.join(models_path,'dpt_large-midas-2f21e586.pt')) != "fcc4829e65d00eeed0a38e9001770676535d2e95c8a16965223aba094936e1316d569563552a852d471f310f83f597e8a238987a26a950d667815e08adaebc06":
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raise Exception(r"Error while downloading dpt_large-midas-2f21e586.pt. Please download from here: https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt and place in: " + models_path)
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self.midas_model = DPTDepthModel(
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path=f"{models_path}/dpt_large-midas-2f21e586.pt",
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backbone="vitl16_384",
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non_negative=True,
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)
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normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
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self.midas_transform = T.Compose([
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Resize(
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384, 384,
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resize_target=None,
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keep_aspect_ratio=True,
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ensure_multiple_of=32,
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resize_method="minimal",
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image_interpolation_method=cv2.INTER_CUBIC,
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),
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normalization,
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PrepareForNet()
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])
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self.midas_model.eval()
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if self.device == torch.device("cuda"):
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self.midas_model = self.midas_model.to(memory_format=torch.channels_last)
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if half_precision:
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self.midas_model = self.midas_model.half()
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self.midas_model.to(self.device)
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def predict(self, prev_img_cv2, midas_weight, half_precision) -> torch.Tensor:
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w, h = prev_img_cv2.shape[1], prev_img_cv2.shape[0]
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# predict depth with AdaBins
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use_adabins = midas_weight < 1.0 and self.adabins_helper is not None
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if use_adabins:
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MAX_ADABINS_AREA = 500000
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MIN_ADABINS_AREA = 448*448
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# resize image if too large or too small
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img_pil = Image.fromarray(cv2.cvtColor(prev_img_cv2.astype(np.uint8), cv2.COLOR_RGB2BGR))
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image_pil_area = w*h
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resized = True
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if image_pil_area > MAX_ADABINS_AREA:
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scale = math.sqrt(MAX_ADABINS_AREA) / math.sqrt(image_pil_area)
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depth_input = img_pil.resize((int(w*scale), int(h*scale)), Image.LANCZOS) # LANCZOS is good for downsampling
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print(f" resized to {depth_input.width}x{depth_input.height}")
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elif image_pil_area < MIN_ADABINS_AREA:
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scale = math.sqrt(MIN_ADABINS_AREA) / math.sqrt(image_pil_area)
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depth_input = img_pil.resize((int(w*scale), int(h*scale)), Image.BICUBIC)
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print(f" resized to {depth_input.width}x{depth_input.height}")
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else:
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depth_input = img_pil
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resized = False
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# predict depth and resize back to original dimensions
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try:
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with torch.no_grad():
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_, adabins_depth = self.adabins_helper.predict_pil(depth_input)
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if resized:
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adabins_depth = TF.resize(
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torch.from_numpy(adabins_depth),
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torch.Size([h, w]),
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interpolation=TF.InterpolationMode.BICUBIC
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)
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adabins_depth = adabins_depth.cpu().numpy()
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adabins_depth = adabins_depth.squeeze()
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except:
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print(f" exception encountered, falling back to pure MiDaS")
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use_adabins = False
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torch.cuda.empty_cache()
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if self.midas_model is not None:
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# convert image from 0->255 uint8 to 0->1 float for feeding to MiDaS
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img_midas = prev_img_cv2.astype(np.float32) / 255.0
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img_midas_input = self.midas_transform({"image": img_midas})["image"]
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# MiDaS depth estimation implementation
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sample = torch.from_numpy(img_midas_input).float().to(self.device).unsqueeze(0)
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if self.device == torch.device("cuda"):
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sample = sample.to(memory_format=torch.channels_last)
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if half_precision:
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sample = sample.half()
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with torch.no_grad():
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midas_depth = self.midas_model.forward(sample)
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midas_depth = torch.nn.functional.interpolate(
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midas_depth.unsqueeze(1),
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size=img_midas.shape[:2],
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mode="bicubic",
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align_corners=False,
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).squeeze()
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midas_depth = midas_depth.cpu().numpy()
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torch.cuda.empty_cache()
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# MiDaS makes the near values greater, and the far values lesser. Let's reverse that and try to align with AdaBins a bit better.
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midas_depth = np.subtract(50.0, midas_depth)
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midas_depth = midas_depth / 19.0
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# blend between MiDaS and AdaBins predictions
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if use_adabins:
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depth_map = midas_depth*midas_weight + adabins_depth*(1.0-midas_weight)
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else:
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depth_map = midas_depth
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depth_map = np.expand_dims(depth_map, axis=0)
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depth_tensor = torch.from_numpy(depth_map).squeeze().to(self.device)
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else:
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depth_tensor = torch.ones((h, w), device=self.device)
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return depth_tensor
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def to_image(self, depth: torch.Tensor):
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depth = depth.cpu().numpy()
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if len(depth.shape) == 2:
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depth = np.expand_dims(depth, axis=0)
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self.depth_min = min(self.depth_min, depth.min())
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self.depth_max = max(self.depth_max, depth.max())
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print(f" depth min:{depth.min()} max:{depth.max()}")
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denom = max(1e-8, self.depth_max - self.depth_min)
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temp = rearrange((depth - self.depth_min) / denom * 255, 'c h w -> h w c')
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temp = repeat(temp, 'h w 1 -> h w c', c=3)
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return Image.fromarray(temp.astype(np.uint8))
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def save(self, filename: str, depth: torch.Tensor):
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self.to_image(depth).save(filename)
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def to(self, device):
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self.device = device
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self.midas_model.to(device)
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if self.adabins_helper is not None:
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self.adabins_helper.to(device)
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gc.collect()
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torch.cuda.empty_cache()
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