depth_estimator.py

import torch
import numpy as np
from PIL import Image
from transformers import DPTFeatureExtractor, DPTForDepthEstimation

device = None
depth_estimator = None
feature_extractor = None


def init():
    global device, depth_estimator, feature_extractor

    device = "cuda" if torch.cuda.is_available() else "cpu"

    print("Initializing depth estimator...")

    depth_estimator = DPTForDepthEstimation.from_pretrained(
        "Intel/dpt-hybrid-midas").to(device)
    feature_extractor = DPTFeatureExtractor.from_pretrained(
        "Intel/dpt-hybrid-midas")


def get_depth_map(image):
    original_size = image.size

    image = feature_extractor(
        images=image, return_tensors="pt").pixel_values.to(device)

    with torch.no_grad(), torch.autocast(device):
        depth_map = depth_estimator(image).predicted_depth

    depth_map = torch.nn.functional.interpolate(
        depth_map.unsqueeze(1),
        size=original_size[::-1],
        mode="bicubic",
        align_corners=False,
    )

    depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
    depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
    depth_map = (depth_map - depth_min) / (depth_max - depth_min)
    image = torch.cat([depth_map] * 3, dim=1)

    image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
    image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))

    return image