Decoding Visual Relationships: Analyzing Stacked Boxes with OpenCV Edge Detection

The Setup: Stacked Boxes and Edge Detection

Visualizing the Stack

Imagine a scenario where boxes are stacked atop one another. Each box has distinct edges, and our objective is to decipher the spatial relationships between the bottom and top boxes using computer vision techniques.

Edge Detection with OpenCV

OpenCV, a versatile computer vision library, provides robust edge detection algorithms. In our scenario, we leverage edge detection to identify and extract the edges of each box, forming the basis for subsequent analysis.

Angle Detection: Unraveling Spatial Relationships


We focus on the bottom-left edge point of each box and measure the angles with respect to the bottom-left edge point of the bottom box. This approach allows us to quantify the spatial orientation of each stacked box in relation to the base.


The angles obtained serve as valuable metrics, offering insights into how each box is positioned concerning the one beneath it. This information can be crucial in scenarios such as automated sorting or robotic manipulation.

Color Coding: Quantifying Distances Between Edges

To further enhance our analysis, we introduce color coding to denote distances between left and right edges of the top boxes concerning the left and right edges of the bottom box.

Distance Thresholding

We set a threshold value, categorizing the relationships into two colors:

  • Green: Denotes a distance less than or equal to the threshold.
  • Red: Denotes a distance greater than the threshold.

This visual representation facilitates quick interpretation of spatial relationships, aiding in the identification of stable and unstable stacking configurations.

Practical Applications and Extensions

Industrial Automation

Understanding spatial relationships and stability in stacked objects is pivotal in industrial settings. Automated systems can use this information for efficient sorting, packaging, and quality control.


Robotic systems, equipped with vision capabilities, can utilize the analyzed angles and distances for precise manipulation of stacked objects. This has implications in logistics, warehouse automation, and beyond.

In this exploration of stacked boxes through OpenCV edge detection, we've unveiled a method to quantify spatial relationships and distances. The combination of angle detection and color-coded distance representation offers a comprehensive understanding of the stacking configuration.

As we delve deeper into the realms of computer vision, the applications of such analyses become increasingly impactful, shaping the future of automation and robotics. Through innovative approaches like these, we bridge the gap between the physical and digital worlds, opening doors to a multitude of possibilities.

Feel free to share your thoughts, insights, or any specific details you'd like to include in this exploration of stacked boxes and edge detection.

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