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| import os |
| import cv2 |
| import torch |
| import numpy as np |
| from .general_utils import download_file_with_checksum |
| from midas.dpt_depth import DPTDepthModel |
| from midas.transforms import Resize, NormalizeImage, PrepareForNet |
| import torchvision.transforms as T |
|
|
| class MidasDepth: |
| def __init__(self, models_path, device, half_precision=True, midas_model_type='Midas-3-Hybrid'): |
| if midas_model_type.lower() == 'midas-3.1-beitlarge': |
| self.midas_model_filename = 'dpt_beit_large_512.pt' |
| self.midas_model_checksum='66cbb00ea7bccd6e43d3fd277bd21002d8d8c2c5c487e5fcd1e1d70c691688a19122418b3ddfa94e62ab9f086957aa67bbec39afe2b41c742aaaf0699ee50b33' |
| self.midas_model_url = 'https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_512.pt' |
| self.resize_px = 512 |
| self.backbone = 'beitl16_512' |
| else: |
| self.midas_model_filename = 'dpt_large-midas-2f21e586.pt' |
| self.midas_model_checksum = 'fcc4829e65d00eeed0a38e9001770676535d2e95c8a16965223aba094936e1316d569563552a852d471f310f83f597e8a238987a26a950d667815e08adaebc06' |
| self.midas_model_url = 'https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt' |
| self.resize_px = 384 |
| self.backbone = 'vitl16_384' |
| self.device = device |
| self.normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) |
| self.midas_transform = T.Compose([ |
| Resize(self.resize_px, self.resize_px, resize_target=None, keep_aspect_ratio=True, ensure_multiple_of=32, |
| resize_method="minimal", image_interpolation_method=cv2.INTER_CUBIC), |
| self.normalization, |
| PrepareForNet() |
| ]) |
| |
| download_file_with_checksum(url=self.midas_model_url, expected_checksum=self.midas_model_checksum, dest_folder=models_path, dest_filename=self.midas_model_filename) |
| |
| self.load_midas_model(models_path, self.midas_model_filename) |
| if half_precision: |
| self.midas_model = self.midas_model.half() |
| |
| def load_midas_model(self, models_path, midas_model_filename): |
| model_file = os.path.join(models_path, midas_model_filename) |
| print(f"Loading MiDaS model from {midas_model_filename}...") |
| self.midas_model = DPTDepthModel( |
| path=model_file, |
| backbone=self.backbone, |
| non_negative=True, |
| ) |
| self.midas_model.eval().to(self.device, memory_format=torch.channels_last if self.device == torch.device("cuda") else None) |
| |
| def predict(self, prev_img_cv2, half_precision): |
| img_midas = prev_img_cv2.astype(np.float32) / 255.0 |
| img_midas_input = self.midas_transform({"image": img_midas})["image"] |
| sample = torch.from_numpy(img_midas_input).float().to(self.device).unsqueeze(0) |
|
|
| if self.device.type == "cuda" or self.device.type == "mps": |
| sample = sample.to(memory_format=torch.channels_last) |
| if half_precision: |
| sample = sample.half() |
|
|
| with torch.no_grad(): |
| midas_depth = self.midas_model.forward(sample) |
| midas_depth = torch.nn.functional.interpolate( |
| midas_depth.unsqueeze(1), |
| size=img_midas.shape[:2], |
| mode="bicubic", |
| align_corners=False, |
| ).squeeze().cpu().numpy() |
|
|
| torch.cuda.empty_cache() |
| depth_tensor = torch.from_numpy(np.expand_dims(midas_depth, axis=0)).squeeze().to(self.device) |
|
|
| return depth_tensor |
| |
| def to(self, device): |
| self.device = device |
| self.midas_model = self.midas_model.to(device, memory_format=torch.channels_last if device == torch.device("cuda") else None) |
