image-analysis

Create Accurate Depth Maps with Crestereo

26 Apr 2025
Create Accurate Depth Maps with Crestereo

Crestereo is a powerful image analysis service designed to enhance your applications by generating high accuracy depth maps from stereo images. By leveraging advanced algorithms and a cascaded recurrent network with adaptive correlation, Crestereo simplifies the complex task of depth perception in images, providing developers with a robust tool for various visual applications.

Imagine needing to analyze 3D environments for augmented reality, robotics, or even advanced gaming. With Crestereo, you can easily convert pairs of stereo images into detailed depth maps, which can be used to create immersive experiences, improve object detection, or enhance navigation systems. This capability not only speeds up development time but also allows for greater innovation in how you utilize visual data.

Prerequisites

Before diving into using Crestereo, ensure you have a valid Cognitive Actions API key and a basic understanding of making API calls.

Generate High Accuracy Depth Maps

The primary action offered by Crestereo is the ability to generate highly accurate depth maps. This action takes two stereo images as input and processes them to produce a depth map that reveals the spatial relationships of objects within the images.

Purpose

This action addresses the challenge of deriving depth information from flat images, enabling applications that require understanding of distance and spatial orientation.

Input Requirements

To utilize this action, you need to provide two images:

  • Left Image URI: A valid URL pointing to the left image.
  • Right Image URI: A valid URL pointing to the right image.

For example, the input schema requires the following structure:

{
  "leftImage": "https://example.com/left.png",
  "rightImage": "https://example.com/right.png"
}

Expected Output

The output will be a URI of the generated depth map image, which visually represents the depth information extracted from the input images. An example of the output might look like:

https://assets.cognitiveactions.com/invocations/6414d7e3-1232-43ec-94b7-27c4f99b8d4b/f1347925-586f-453f-8543-f95558c62071.png

Use Cases for this Specific Action

  • Augmented Reality: Enhance AR applications by providing depth information for better object placement and interaction.
  • Robotics: Improve navigation systems for robots by allowing them to understand the spatial layout of their environment.
  • Gaming: Create more immersive gaming experiences where depth perception is crucial for gameplay mechanics.
  • 3D Reconstruction: Aid in the creation of 3D models from real-world scenes by accurately mapping depth.
import requests
import json

# Replace with your actual Cognitive Actions API key and endpoint
# Ensure your environment securely handles the API key
COGNITIVE_ACTIONS_API_KEY = "YOUR_COGNITIVE_ACTIONS_API_KEY"
# This endpoint URL is hypothetical and should be documented for users
COGNITIVE_ACTIONS_EXECUTE_URL = "https://api.cognitiveactions.com/actions/execute"

action_id = "82c46cac-381e-43c5-a891-3af41023f386" # Action ID for: Generate High Accuracy Depth Maps

# Construct the exact input payload based on the action's requirements
# This example uses the predefined example_input for this action:
payload = {
  "leftImage": "https://replicate.delivery/pbxt/IDt4TZu6MiajgPPPvkTSmiWftMCqtDH8aYPXMwxRy19dq6hQ/left.png",
  "rightImage": "https://replicate.delivery/pbxt/IDt4T7PB76wcTqOi4kwMphN58UfyW9dSRKvsBRtfM1Z4d03c/right.png"
}

headers = {
    "Authorization": f"Bearer {COGNITIVE_ACTIONS_API_KEY}",
    "Content-Type": "application/json",
    # Add any other required headers for the Cognitive Actions API
}

# Prepare the request body for the hypothetical execution endpoint
request_body = {
    "action_id": action_id,
    "inputs": payload
}

print(f"--- Calling Cognitive Action: {action.name or action_id} ---")
print(f"Endpoint: {COGNITIVE_ACTIONS_EXECUTE_URL}")
print(f"Action ID: {action_id}")
print("Payload being sent:")
print(json.dumps(request_body, indent=2))
print("------------------------------------------------")

try:
    response = requests.post(
        COGNITIVE_ACTIONS_EXECUTE_URL,
        headers=headers,
        json=request_body
    )
    response.raise_for_status() # Raise an exception for bad status codes (4xx or 5xx)

    result = response.json()
    print("Action executed successfully. Result:")
    print(json.dumps(result, indent=2))

except requests.exceptions.RequestException as e:
    print(f"Error executing action {action_id}: {e}")
    if e.response is not None:
        print(f"Response status: {e.response.status_code}")
        try:
            print(f"Response body: {e.response.json()}")
        except json.JSONDecodeError:
            print(f"Response body (non-JSON): {e.response.text}")
    print("------------------------------------------------")

Conclusion

Crestereo provides developers with an invaluable tool for generating high accuracy depth maps from stereo images, unlocking a myriad of possibilities in fields such as augmented reality, robotics, gaming, and more. By simplifying the process of depth perception, Crestereo enables faster development and encourages innovation. As you explore this action, consider the potential applications in your projects and the enhanced user experiences that depth maps can facilitate. Embrace the power of Crestereo to elevate your applications to new heights!

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