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| dependencies = ["torch"] | |
| import torch | |
| from midas.dpt_depth import DPTDepthModel | |
| from midas.midas_net import MidasNet | |
| from midas.midas_net_custom import MidasNet_small | |
| def DPT_Large(pretrained=True, **kwargs): | |
| """ # This docstring shows up in hub.help() | |
| MiDaS DPT-Large model for monocular depth estimation | |
| pretrained (bool): load pretrained weights into model | |
| """ | |
| model = DPTDepthModel( | |
| path=None, | |
| backbone="vitl16_384", | |
| non_negative=True, | |
| ) | |
| if pretrained: | |
| checkpoint = ( | |
| "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt" | |
| ) | |
| state_dict = torch.hub.load_state_dict_from_url( | |
| checkpoint, map_location=torch.device('cpu'), progress=True, check_hash=True | |
| ) | |
| model.load_state_dict(state_dict) | |
| return model | |
| def DPT_Hybrid(pretrained=True, **kwargs): | |
| """ # This docstring shows up in hub.help() | |
| MiDaS DPT-Hybrid model for monocular depth estimation | |
| pretrained (bool): load pretrained weights into model | |
| """ | |
| model = DPTDepthModel( | |
| path=None, | |
| backbone="vitb_rn50_384", | |
| non_negative=True, | |
| ) | |
| if pretrained: | |
| checkpoint = ( | |
| "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt" | |
| ) | |
| state_dict = torch.hub.load_state_dict_from_url( | |
| checkpoint, map_location=torch.device('cpu'), progress=True, check_hash=True | |
| ) | |
| model.load_state_dict(state_dict) | |
| return model | |
| def MiDaS(pretrained=True, **kwargs): | |
| """ # This docstring shows up in hub.help() | |
| MiDaS v2.1 model for monocular depth estimation | |
| pretrained (bool): load pretrained weights into model | |
| """ | |
| model = MidasNet() | |
| if pretrained: | |
| checkpoint = ( | |
| "https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-f6b98070.pt" | |
| ) | |
| state_dict = torch.hub.load_state_dict_from_url( | |
| checkpoint, map_location=torch.device('cpu'), progress=True, check_hash=True | |
| ) | |
| model.load_state_dict(state_dict) | |
| return model | |
| def MiDaS_small(pretrained=True, **kwargs): | |
| """ # This docstring shows up in hub.help() | |
| MiDaS small model for monocular depth estimation on resource-constrained devices | |
| pretrained (bool): load pretrained weights into model | |
| """ | |
| model = MidasNet_small(None, features=64, backbone="efficientnet_lite3", exportable=True, non_negative=True, blocks={'expand': True}) | |
| if pretrained: | |
| checkpoint = ( | |
| "https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-small-70d6b9c8.pt" | |
| ) | |
| state_dict = torch.hub.load_state_dict_from_url( | |
| checkpoint, map_location=torch.device('cpu'), progress=True, check_hash=True | |
| ) | |
| model.load_state_dict(state_dict) | |
| return model | |
| def transforms(): | |
| import cv2 | |
| from torchvision.transforms import Compose | |
| from midas.transforms import Resize, NormalizeImage, PrepareForNet | |
| from midas import transforms | |
| transforms.default_transform = Compose( | |
| [ | |
| lambda img: {"image": img / 255.0}, | |
| Resize( | |
| 384, | |
| 384, | |
| resize_target=None, | |
| keep_aspect_ratio=True, | |
| ensure_multiple_of=32, | |
| resize_method="upper_bound", | |
| image_interpolation_method=cv2.INTER_CUBIC, | |
| ), | |
| NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), | |
| PrepareForNet(), | |
| lambda sample: torch.from_numpy(sample["image"]).unsqueeze(0), | |
| ] | |
| ) | |
| transforms.small_transform = Compose( | |
| [ | |
| lambda img: {"image": img / 255.0}, | |
| Resize( | |
| 256, | |
| 256, | |
| resize_target=None, | |
| keep_aspect_ratio=True, | |
| ensure_multiple_of=32, | |
| resize_method="upper_bound", | |
| image_interpolation_method=cv2.INTER_CUBIC, | |
| ), | |
| NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), | |
| PrepareForNet(), | |
| lambda sample: torch.from_numpy(sample["image"]).unsqueeze(0), | |
| ] | |
| ) | |
| transforms.dpt_transform = Compose( | |
| [ | |
| lambda img: {"image": img / 255.0}, | |
| Resize( | |
| 384, | |
| 384, | |
| resize_target=None, | |
| keep_aspect_ratio=True, | |
| ensure_multiple_of=32, | |
| resize_method="minimal", | |
| image_interpolation_method=cv2.INTER_CUBIC, | |
| ), | |
| NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), | |
| PrepareForNet(), | |
| lambda sample: torch.from_numpy(sample["image"]).unsqueeze(0), | |
| ] | |
| ) | |
| return transforms | |