| Pipeline API | |
| The pipeline allows to use the model in a few lines of code: | |
| thon | |
| from transformers import pipeline | |
| from PIL import Image | |
| import requests | |
| load pipe | |
| pipe = pipeline(task="depth-estimation", model="LiheYoung/depth-anything-small-hf") | |
| load image | |
| url = 'http://images.cocodataset.org/val2017/000000039769.jpg' | |
| image = Image.open(requests.get(url, stream=True).raw) | |
| inference | |
| depth = pipe(image)["depth"] | |
| Using the model yourself | |
| If you want to do the pre- and postprocessing yourself, here's how to do that: | |
| thon | |
| from transformers import AutoImageProcessor, AutoModelForDepthEstimation | |
| import torch | |
| import numpy as np | |
| from PIL import Image | |
| import requests | |
| url = "http://images.cocodataset.org/val2017/000000039769.jpg" | |
| image = Image.open(requests.get(url, stream=True).raw) | |
| image_processor = AutoImageProcessor.from_pretrained("LiheYoung/depth-anything-small-hf") | |
| model = AutoModelForDepthEstimation.from_pretrained("LiheYoung/depth-anything-small-hf") | |
| prepare image for the model | |
| inputs = image_processor(images=image, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| predicted_depth = outputs.predicted_depth | |
| interpolate to original size | |
| prediction = torch.nn.functional.interpolate( | |
| predicted_depth.unsqueeze(1), | |
| size=image.size[::-1], | |
| mode="bicubic", | |
| align_corners=False, | |
| ) | |
| visualize the prediction | |
| output = prediction.squeeze().cpu().numpy() | |
| formatted = (output * 255 / np.max(output)).astype("uint8") | |
| depth = Image.fromarray(formatted) | |
| Resources | |
| A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Depth Anything. |