metadata
license: apache-2.0
language:
- en
base_model:
- facebook/dinov2-large
- jadohu/LlamaGen-T2I
- facebook/sam2.1-hiera-large
library_name: transformers
This is the Offical weights of ConFiDeNet
Installation
pip3 install git+https://github.com/Onkarsus13/transformers.git@confidenet
from PIL import Image
import torch
from transformers import ConFiDeNetForDepthEstimation, ConFiDeNetImageProcessor
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
image = Image.open("<Image Path>").convert("RGB")
print(image.size)
# image.save("image.jpg")
image_processor = ConFiDeNetImageProcessor.from_pretrained("onkarsus13/ConFiDeNet-Large-VQ-32")
model = ConFiDeNetForDepthEstimation.from_pretrained("onkarsus13/ConFiDeNet-Large-VQ-32").to(device)
inputs = image_processor(images=image, return_tensors="pt").to(device)
with torch.no_grad():
outputs = model(**inputs)
post_processed_output = image_processor.post_process_depth_estimation(
outputs, target_sizes=[(image.height, image.width)],
)
depth = post_processed_output[0]["predicted_depth_uint16"].detach().cpu().numpy()
depth = Image.fromarray(depth, mode="I;16")
depth.save("depth.png")