5 Effective Ways to Check If Two Rectangles Overlap in Python

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5 Effective Ways to Check If Two Rectangles Overlap in Python

πŸ’‘ Problem Formulation: Detecting the overlap between two rectangles is a common computational geometry problem. Suppose you’re given two rectangles, each defined by the coordinates of the lower-left and upper-right corners. The task is to write a program in Python to determine if these two rectangles overlap. The result should be a boolean value indicating overlap (True) or no overlap (False).

Method 1: Checking Edge Conditions

This method involves comparing the bounding coordinates of both rectangles. We assume that if one rectangle is to the left, right, above, or below the other, then they do not overlap. This method checks for these conditions and returns False if any are true, otherwise True.

Here’s an example:

def do_rectangles_overlap(rect1, rect2):
    if (rect1['right'] <= rect2['left'] or rect1['left'] >= rect2['right'] or
        rect1['top'] <= rect2['bottom'] or rect1['bottom'] >= rect2['top']):
        return False
    return True

# Define rectangles with (left, bottom, right, top)
rectA = {'left': 0, 'bottom': 0, 'right': 2, 'top': 2}
rectB = {'left': 1, 'bottom': 1, 'right': 3, 'top': 3}

# Check overlap
print(do_rectangles_overlap(rectA, rectB))

Output:

True

This code snippet defines a function do_rectangles_overlap(rect1, rect2) that takes as input dictionaries with coordinates for two rectangles and returns True if they overlap. It checks if one rectangle is completely on a specific side of the other by comparing their edges. If none of these conditions is met, the rectangles overlap.

Method 2: Separating Axis Theorem

The Separating Axis Theorem (SAT) states that two convex shapes do not overlap if and only if there is a line (axis) onto which their projections do not overlap. For rectangles aligned with the axes, checking overlap can be simplified to comparing their projections onto the x and y axes.

Here’s an example:

def overlap(rect1, rect2):
    return not (rect1['right'] <= rect2['left'] or rect1['left'] >= rect2['right'] or
                rect1['top'] <= rect2['bottom'] or rect1['bottom'] >= rect2['top'])

# Define rectangles with (left, bottom, right, top)
rectA = {'left': 0, 'bottom': 0, 'right': 2, 'top': 2}
rectB = {'left': 3, 'bottom': 3, 'right': 4, 'top': 4}

# Apply the Separating Axis Theorem
print(overlap(rectA, rectB))

Output:

False

This code uses the SAT by defining a function overlap(rect1, rect2) which compares the projections of two rectangles onto the coordinate axes. The absence of an overlapping axis means the rectangles do not overlap; this is detected by checking if the edges of one rectangle are completely beyond the edges of another.

Method 3: Geometry Library (Shapely)

Shapely is a Python package for the manipulation and analysis of planar geometric objects. It can be used to quickly check for rectangle overlaps using object-oriented concepts, which hide the underlying procedural logic from the user, enabling high-level and intuitive interface for geometric operations.

Here’s an example:

from shapely.geometry import box

def overlap_shapely(rect1, rect2):
    return rect1.intersects(rect2)

# Create rectangles using Shapely
rectA = box(0, 0, 2, 2)
rectB = box(1, 1, 3, 3)

# Check overlap
print(overlap_shapely(rectA, rectB))

Output:

True

The code uses Shapely’s box() function to create two rectangle objects, and the intersects() method to check for an overlap. The function overlap_shapely(rect1, rect2) simply returns the result of that intersection test, providing a very high-level approach to solving the problem.

Method 4: Computational Geometry Approach

In a computational geometry approach, you may use mathematical formulas and algorithms to find the intersection of two rectangles. This method is more complex and requires understanding of geometry and algorithmic concepts but can be more accurate for certain applications, especially when dealing with floating point numbers or non-axis-aligned rectangles.

Here’s an example:

def do_overlap(rect1, rect2):
    # Calculate the overlap on each axis
    x_overlap = max(0, min(rect1['right'], rect2['right']) - max(rect1['left'], rect2['left']))
    y_overlap = max(0, min(rect1['top'], rect2['top']) - max(rect1['bottom'], rect2['bottom']))
    # Overlap area
    overlap_area = x_overlap * y_overlap
    return overlap_area > 0

# Define rectangles with (left, bottom, right, top)
rectA = {'left': 0, 'bottom': 0, 'right': 2, 'top': 2}
rectB = {'left': 1, 'bottom': 1, 'right': 3, 'top': 3}

# Check overlap
print(do_overlap(rectA, rectB))

Output:

True

The do_overlap function calculates the overlap on each axis separately and then computes the area of the overlapping region. This area is greater than zero if the rectangles overlap. This approach provides a more detailed look at the nature of the overlap, such as calculating overlap area, which can be useful in more complex applications.

Bonus One-Liner Method 5: Using Lambda

For python enthusiasts who love concise code, here’s a one-liner function that uses a lambda function to check for rectangle overlap. It embodies the first method but in a more compact form.

Here’s an example:

overlap = lambda r1, r2: not (r1['right'] <= r2['left'] or r1['left'] >= r2['right'] or
                             r1['top'] <= r2['bottom'] or r1['bottom'] >= r2['top'])

# Define rectangles with (left, bottom, right, top)
rectA = {'left': 0, 'bottom': 0, 'right': 2, 'top': 2}
rectB = {'left': 3, 'bottom': 3, 'right': 4, 'top': 4}

# Check overlap
print(overlap(rectA, rectB))

Output:

False

This lambda function overlap() instantly shows the overlap between two rectangles in a single line of code, providing a succinct and elegant approach. Even though it’s compact, it’s not the most readable approach especially for beginners.

Summary/Discussion

  • Method 1: Checking Edge Conditions. Simple and fast. Limited to axis-aligned rectangles. Very straightforward and easy to understand.
  • Method 2: Separating Axis Theorem. Versatile and effective. Can be used for other convex shapes. May require more geometric knowledge.
  • Method 3: Geometry Library (Shapely). Very high-level and easy to use. Requires external library. May not be suitable for all environments.
  • Method 4: Computational Geometry Approach. Offers additional information (like area of overlap). More complex, better for advanced users. Can handle floating point precision well.
  • Bonus One-Liner Method 5: Using Lambda. Concise, but can sacrifice readability. Pithy solution for one-off checks or seasoned Python coders.