File size: 1,305 Bytes
e132a83 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 |
import torch
import gradio as gr
import numpy as np
from PIL import Image
import cv2
# Загрузка модели
midas = torch.hub.load("intel-isl/MiDaS", "MiDaS_small")
midas.eval()
midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
transform = midas_transforms.small_transform
def predict_depth(image):
img = np.array(image)
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
input_tensor = transform(img_rgb).unsqueeze(0)
with torch.no_grad():
prediction = midas(input_tensor)
prediction = torch.nn.functional.interpolate(
prediction.unsqueeze(1),
size=img_rgb.shape[:2],
mode="bicubic",
align_corners=False,
).squeeze()
depth_map = prediction.cpu().numpy()
depth_map = (depth_map - depth_map.min()) / (depth_map.max() - depth_map.min())
depth_map = (depth_map * 255).astype(np.uint8)
depth_img = Image.fromarray(depth_map)
return depth_img
# Интерфейс Gradio
iface = gr.Interface(
fn=predict_depth,
inputs=gr.Image(type="pil"),
outputs=gr.Image(type="pil"),
title="MiDaS Depth Estimation",
description="Загрузите изображение и получите карту глубины."
)
if __name__ == "__main__":
iface.launch() |