Depth estimation basics in Computer Vision

Introduction

Depth estimation helps computers understand how far things are in a picture. It turns flat images into 3D views.

To help robots know how far objects are so they don't bump into things.

In self-driving cars to see how close other cars or people are.

For augmented reality apps to place virtual objects correctly in the real world.

In video games to create realistic scenes with depth.

To improve photo effects like background blur by knowing object distances.

Syntax

Computer Vision

model = DepthEstimationModel()
depth_map = model.predict(image)

This is a simple example showing how to use a depth estimation model.

The model takes an image and outputs a depth map showing distance for each pixel.

Examples

Estimate depth for one image.

Computer Vision

depth_map = model.predict(single_image)

Estimate depth for many images at once.

Computer Vision

depth_maps = model.predict(batch_of_images)

Resize image before depth estimation if model needs fixed size.

Computer Vision

depth_map = model.predict(resize(image, (224, 224)))

Sample Model

This code creates a simple fake depth estimation model that assumes depth increases from top to bottom of the image. It then shows the depth map and prints depth values at some points.

Computer Vision

import numpy as np
import matplotlib.pyplot as plt

# Fake depth estimation model for demo
class DepthEstimationModel:
    def predict(self, image):
        # Simple fake depth: distance increases with pixel row
        height, width, _ = image.shape
        depth_map = np.tile(np.linspace(0, 1, height).reshape(height, 1), (1, width))
        return depth_map

# Create a fake image (100x100 with 3 color channels)
image = np.zeros((100, 100, 3))

model = DepthEstimationModel()
depth_map = model.predict(image)

# Show depth map as image
plt.imshow(depth_map, cmap='plasma')
plt.colorbar(label='Depth')
plt.title('Estimated Depth Map')
plt.show()

# Print some depth values
print(f"Depth at top-left: {depth_map[0, 0]:.2f}")
print(f"Depth at center: {depth_map[50, 50]:.2f}")
print(f"Depth at bottom-right: {depth_map[-1, -1]:.2f}")

Important Notes

Real depth estimation models use complex neural networks trained on many images with known distances.

Depth maps show distance per pixel, often normalized between 0 (near) and 1 (far).

Depth estimation can be done from one image (monocular) or two images (stereo).

Summary

Depth estimation helps computers see how far things are in pictures.

It is useful in robots, cars, games, and AR apps.

Models take images and output depth maps showing distance per pixel.