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from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
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
# initialize model
config = DepthProConfig(use_fov_model=False)
model = DepthProForDepthEstimation(config)
features = config.fusion_hidden_size
semantic_classifier_dropout = 0.1
num_labels = 1
model.head.head = nn.Sequential(
nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(features),
nn.ReLU(),
nn.Dropout(semantic_classifier_dropout),
nn.Conv2d(features, features, kernel_size=1),
nn.ConvTranspose2d(features, num_labels, kernel_size=2, stride=2, padding=0, bias=True),
)
# load weights
weights_path = hf_hub_download(repo_id="geetu040/DepthPro_Segmentation_Human", filename="model_weights.pth")
model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu'), weights_only=True))
# load to device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
# load image processor
image_processor = DepthProImageProcessorFast()
def predict(image):
# inference
image = image.convert("RGB")
# prepare image for the model
inputs = image_processor(images=image, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}
# inference
with torch.no_grad():
output = model(**inputs)
# convert tensors to PIL.Image
output = output[0] # get output logits
output = F.interpolate(
output.unsqueeze(0),
size=(image.height, image.width)
) # interpolate to match size
output = output.squeeze() # get first and only batch and channel
output = output.sigmoid() # apply sigmoid for binary segmentation
output = (output > 0.5).float() # threshold to create binary mask
output = output.cpu() # unload from cuda if used
output = output * 255 # convert [0, 1] to [0, 255]
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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