from transformers import pipeline from PIL import Image import gradio as gr import numpy as np # Load the Hugging Face depth estimation pipelines pipe_base = pipeline(task="depth-estimation", model="LiheYoung/depth-anything-base-hf") pipe_small = pipeline(task="depth-estimation", model="LiheYoung/depth-anything-small-hf") pipe_intel = pipeline(task="depth-estimation", model="Intel/dpt-swinv2-tiny-256") pipe_beit = pipeline(task="depth-estimation", model="Intel/dpt-beit-base-384") def estimate_depths(image): # Perform depth estimation with each pipeline depth_base = pipe_base(image)["depth"] depth_small = pipe_small(image)["depth"] depth_intel = pipe_intel(image)["depth"] depth_beit = pipe_beit(image)["depth"] # Normalize depths for visualization depth_base = normalize_depth(depth_base) depth_small = normalize_depth(depth_small) depth_intel = normalize_depth(depth_intel) depth_beit = normalize_depth(depth_beit) return depth_base, depth_small, depth_intel, depth_beit def normalize_depth(depth_map): # Normalize depth map values to range [0, 255] for visualization normalized_depth = ((depth_map - depth_map.min()) / (depth_map.max() - depth_map.min())) * 255 return normalized_depth.astype(np.uint8) # Create a Gradio interface iface = gr.Interface( fn=estimate_depths, inputs=gr.Image(type="pil"), outputs=[ gr.Image(type="numpy", label="LiheYoung/depth-anything-base-hf"), gr.Image(type="numpy", label="LiheYoung/depth-anything-small-hf"), gr.Image(type="numpy", label="Intel/dpt-swinv2-tiny-256"), gr.Image(type="numpy", label="Intel/dpt-beit-base-384") ], title="Multi-Model Depth Estimation", description="Upload an image to get depth estimation maps from multiple models.", layout="vertical" ) # Launch the Gradio app iface.launch() """ 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"] """