DPT Model for Depth Estimation

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Let’s say you want to create a self-driving car that navigates through complex environments. One crucial component of this system is the ability to accurately estimate depth from images captured by cameras mounted on the vehicle. This information can be used to calculate distances between objects and plan safe paths for the car to follow.

To achieve this, you could use a DPT Model for Depth Estimation with a pre-trained model that has been fine-tuned specifically for your self-driving application. The input images would first pass through an image processor (such as AutoImageProcessor) which performs any necessary preprocessing steps like resizing and normalization.

The processed images are then fed into the DPT Model, which uses a combination of convolutional layers and attention mechanisms to estimate depth for each pixel in the image. The output is a predicted depth map that can be used by your self-driving system to navigate through complex environments safely and efficiently.

Here’s an example code snippet using PyTorch:

# Import necessary libraries
from transformers import AutoImageProcessor, DPTModel
import torch
import numpy as np
from PIL import Image
import requests

# Define URL for image to be used for depth estimation
url = "https://images.cocodataset.org/val2017/00000039769.jpg"

# Open image from URL and store it in a variable
image = Image.open(requests.get(url, stream=True).raw)

# Load pre-trained DPT Model for Depth Estimation
model = DPTModel.from_pretrained("Intel/dpt-large")

# Load image processor to perform necessary preprocessing steps
image_processor = AutoImageProcessor.from_pretrained("Intel/dpt-large")

# Preprocess input images using the image processor
inputs = image_processor(images=image, return_tensors="pt")

# Run DPT Model for Depth Estimation and store the outputs in a variable
outputs = model(**inputs)

# Get predicted depth map from the outputs
depths = outputs.get('pred_depth')['sample']

In this example, we first load the pre-trained DPT Model and image processor using their respective `from_pretrained()` functions. We then use the `AutoImageProcessor` to perform any necessary preprocessing steps on our input images before passing them through the DPT Model for depth estimation. The output is a predicted depth map which can be used by your self-driving system to navigate safely and efficiently through complex environments.

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