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import gradio as gr
import cv2
import numpy as np
import os
from PIL import Image
import torch
import torch.nn.functional as F
from torchvision.transforms import Compose
from depth_anything.dpt import DepthAnything
from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
transform = Compose([
Resize(
width=518,
height=518,
resize_target=False,
keep_aspect_ratio=True,
ensure_multiple_of=14,
resize_method='lower_bound',
image_interpolation_method=cv2.INTER_CUBIC,
),
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
PrepareForNet(),
])
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
model = DepthAnything.from_pretrained('LiheYoung/depth_anything_vitl14').to(DEVICE).eval()
def predict_depthmap(image):
original_image = image.copy()
h, w = image.shape[:2]
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
image = transform({'image': image})['image']
image = torch.from_numpy(image).unsqueeze(0).to(DEVICE)
with torch.no_grad():
depth = model(image)
depth = F.interpolate(depth[None], (h, w), mode='bilinear', align_corners=False)[0, 0]
depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
depth = depth.cpu().numpy().astype(np.uint8)
colored_depth = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)[:, :, ::-1]
# colored_depth = Image.fromarray(cv2.cvtColor(colored_depth, cv2.COLOR_BGR2RGB))
corlored_depth = Image.fromarray(colored_depth)
return colored_depth
demo = gr.Interface(fn=predict_depthmap, inputs=[gr.Image()],
outputs=[gr.Image(type="pil")]
)
demo.launch()
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