Draw Multiple Rectangles in Image using OpenCV Python

OpenCV is an Open Source Computer Vision Library in Python that provides numerous functions to perform various image and video processing operations. In this article, we will explore different ways to draw multiple rectangles in an image using OpenCV Python.

To draw rectangular shapes on an image, OpenCV provides the cv2.rectangle() method which can be used in various approaches to create multiple rectangles efficiently.

The cv2.rectangle() Function

The cv2.rectangle() method draws rectangular shapes on an image. Following is the syntax ?

cv2.rectangle(img, pt1, pt2, color[, thickness[, lineType[, shift]]])

Parameters

• img: The source image where to draw the rectangle

• pt1: Top-left corner coordinates (x, y) of the rectangle

• pt2: Bottom-right corner coordinates (x, y) of the rectangle

• color: Rectangle color in BGR format

• thickness: Line thickness of the rectangle border (default: 1)

Method 1: Using Pre-defined Coordinates

In this approach, we manually define the coordinates for each rectangle using a list of coordinate pairs.

Example

Here we use a list of coordinate pairs to draw multiple rectangles around specific areas ?

import cv2
import numpy as np

# Create a sample image for demonstration
img = np.ones((400, 600, 3), dtype=np.uint8) * 255

# Define rectangle coordinates as [top-left, bottom-right] pairs
rectangles = [
    [(50, 50), (150, 120)],
    [(200, 80), (350, 180)], 
    [(400, 100), (550, 200)],
    [(100, 250), (250, 350)]
]

# Draw rectangles with different colors
colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0)]
thickness = 2

for i, (pt1, pt2) in enumerate(rectangles):
    color = colors[i % len(colors)]
    cv2.rectangle(img, pt1, pt2, color, thickness)

print("Multiple rectangles drawn successfully")

Multiple rectangles drawn successfully

Method 2: Using Dictionary with Normalized Coordinates

This method uses normalized coordinates (0-1 range) which can be scaled to any image size ?

import cv2
import numpy as np

# Create a sample image
img = np.ones((400, 600, 3), dtype=np.uint8) * 255
img_h, img_w = img.shape[:2]

# Define objects with normalized coordinates
objects = {
    'object1': {'center_x': 0.25, 'center_y': 0.3, 'width': 0.2, 'height': 0.25},
    'object2': {'center_x': 0.75, 'center_y': 0.3, 'width': 0.15, 'height': 0.2},
    'object3': {'center_x': 0.5, 'center_y': 0.7, 'width': 0.3, 'height': 0.2}
}

# Convert normalized coordinates to pixel coordinates
for obj_name, coords in objects.items():
    center_x = coords['center_x']
    center_y = coords['center_y'] 
    width = coords['width']
    height = coords['height']
    
    # Calculate corner points
    x_min = int((center_x - width/2) * img_w)
    y_min = int((center_y - height/2) * img_h)
    x_max = int((center_x + width/2) * img_w)
    y_max = int((center_y + height/2) * img_h)
    
    # Draw rectangle
    cv2.rectangle(img, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)

print("Rectangles drawn using normalized coordinates")

Rectangles drawn using normalized coordinates

Method 3: Using cv2.findContours() for Object Detection

This method automatically detects objects in binary images and draws bounding rectangles around them ?

import cv2
import numpy as np

# Create a binary image with white circles on black background
img = np.zeros((300, 400), dtype=np.uint8)
cv2.circle(img, (100, 100), 30, 255, -1)
cv2.circle(img, (250, 150), 25, 255, -1)
cv2.circle(img, (350, 200), 20, 255, -1)

# Convert to color image for rectangle drawing
result = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)

# Find contours
contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Draw bounding rectangles around each contour
for contour in contours:
    x, y, w, h = cv2.boundingRect(contour)
    cv2.rectangle(result, (x, y), (x+w, y+h), (0, 255, 0), 2)

print(f"Found {len(contours)} objects and drew bounding rectangles")

Found 3 objects and drew bounding rectangles

Comparison of Methods

Conclusion

OpenCV provides flexible methods to draw multiple rectangles on images. Use pre-defined coordinates for precise manual control, normalized coordinates for scalable applications, and contour detection for automatic object detection in binary images.