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from PIL import Image
import torch
from huggingface_hub import hf_hub_download
# custom installation from this PR: https://github.com/huggingface/transformers/pull/34583
# !pip install git+https://github.com/geetu040/transformers.git@depth-pro-projects#egg=transformers
from transformers import DepthProConfig, DepthProImageProcessorFast, DepthProForDepthEstimation
# load DepthPro model, used as backbone
config = DepthProConfig(
patch_size=192,
patch_embeddings_size=16,
num_hidden_layers=12,
intermediate_hook_ids=[11, 8, 7, 5],
intermediate_feature_dims=[256, 256, 256, 256],
scaled_images_ratios=[0.5, 1.0],
scaled_images_overlap_ratios=[0.5, 0.25],
scaled_images_feature_dims=[1024, 512],
use_fov_model=False,
)
depthpro_for_depth_estimation = DepthProForDepthEstimation(config)
# create DepthPro for super resolution
class DepthProForSuperResolution(torch.nn.Module):
def __init__(self, depthpro_for_depth_estimation):
super().__init__()
self.depthpro_for_depth_estimation = depthpro_for_depth_estimation
hidden_size = self.depthpro_for_depth_estimation.config.fusion_hidden_size
self.image_head = torch.nn.Sequential(
torch.nn.ConvTranspose2d(
in_channels=config.num_channels,
out_channels=hidden_size,
kernel_size=4, stride=2, padding=1
),
torch.nn.ReLU(),
)
self.head = torch.nn.Sequential(
torch.nn.Conv2d(
in_channels=hidden_size,
out_channels=hidden_size,
kernel_size=3, stride=1, padding=1
),
torch.nn.ReLU(),
torch.nn.ConvTranspose2d(
in_channels=hidden_size,
out_channels=hidden_size,
kernel_size=4, stride=2, padding=1
),
torch.nn.ReLU(),
torch.nn.Conv2d(
in_channels=hidden_size,
out_channels=self.depthpro_for_depth_estimation.config.num_channels,
kernel_size=3, stride=1, padding=1
),
)
def forward(self, pixel_values):
# x is the low resolution image
x = pixel_values
encoder_features = self.depthpro_for_depth_estimation.depth_pro(x).features
fused_hidden_state = self.depthpro_for_depth_estimation.fusion_stage(encoder_features)[-1]
x = self.image_head(x)
x = torch.nn.functional.interpolate(x, size=fused_hidden_state.shape[2:])
x = x + fused_hidden_state
x = self.head(x)
return x
# initialize the model
model = DepthProForSuperResolution(depthpro_for_depth_estimation)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
# load weights
weights_path = hf_hub_download(repo_id="geetu040/DepthPro_SR_4x_384p", filename="model_weights.pth")
model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu')))
# load image processor
image_processor = DepthProImageProcessorFast(
do_resize=True,
size={"width": 384, "height": 384},
do_rescale=True,
do_normalize=True
)
# define crop function to ensure square image
def crop_image(image):
"""
Crops the image from the center to make aspect ratio 1:1.
"""
width, height = image.size
min_dim = min(width, height)
left = (width - min_dim) // 2
top = (height - min_dim) // 2
right = left + min_dim
bottom = top + min_dim
image = image.crop((left, top, right, bottom))
return image
def predict(image):
# inference
image = crop_image(image)
image = image.resize((384, 384), Image.Resampling.BICUBIC)
# prepare image for the model
inputs = image_processor(images=image, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model(**inputs)
# convert tensors to PIL.Image
output = outputs[0] # extract the first and only batch
output = output.cpu() # unload from cuda if used
output = torch.permute(output, (1, 2, 0)) # (C, H, W) -> (H, W, C)
output = output * 0.5 + 0.5 # undo normalization
output = output * 255. # undo scaling
output = output.clip(0, 255.) # fix out of range
output = output.numpy() # convert to numpy
output = output.astype('uint8') # convert to PIL.Image compatible format
output = Image.fromarray(output) # create PIL.Image object
return output
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