predictor.py

import torch
from PIL import Image

from misc import colorize


class DepthEstimationModel:
    """
    DepthMap'te daha yakın bölgeler beyaz renkte
    daha uzak bölgeler ise siyah veya ona yakın renkte gözükür
    """

    def __init__(self) -> None:
        self.device = self._get_device()
        self.model = self._initialize_model(
            model_repo="isl-org/ZoeDepth", model_Name="ZoeD_N"
        ).to(self.device)

    def _get_device(self):
        return "cuda" if torch.cuda.is_available() else "cpu"

    def _initialize_model(
        self, model_repo="isl-org/ZoeDepth", model_Name="ZoeD_N"
    ):  # it will work automaticaly when the class is called it will download and returned
        torch.hub.help(
            "intel-isl/MiDaS", "DPT_BEiT_L_384", force_reload=True
        )  # Triggers fresh download of MiDaS repo
        model = torch.hub.load(
            model_repo, model_Name, pretrained=True, skip_validation=False
        )
        model.eval()
        print("Model initialized.")
        return model

    def save_colored_depth(self, depth_numpy, output_path):
        colored = colorize(depth_numpy)
        Image.fromarray(colored).save(output_path)
        print("Image saved.")

    def calculate_depthmap(self, image_path, output_path):
        image = Image.open(image_path).convert("RGB")
        print("Image read.")
        depth_numpy = self.model.infer_pil(image)
        self.save_colored_depth(depth_numpy, output_path)
        return f"Image saved to {output_path}"


model = DepthEstimationModel()  # Create a instance from a class.  Nesne  veya Object
model.calculate_depthmap("./test_image.png", "output_image.png")