5 Best Ways to Find Patterns in a Chessboard Using OpenCV Python

💡 Problem Formulation: In computer vision tasks, detecting chessboard patterns is crucial for applications such as camera calibration, robotics, and 3D reconstruction. The input typically is an image of a chessboard, while the desired output is the detected chessboard pattern, often interpreted through the positions of the corners of the squares within the image.

Method 1: Using the findChessboardCorners Function

OpenCV’s findChessboardCorners function is specifically designed to detect the corners of a chessboard pattern, which is a common pre-requisite in camera calibration processes. It utilizes an adaptive thresholding approach that works well under varying lighting conditions and even when the chessboard has partial occlusions. The function searches for corner junctions in a grayscale image and returns the detected corners’ locations.

Here’s an example:

import cv2
import numpy as np

# Load the image
image = cv2.imread('chessboard.jpg')
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray_image, (7, 7))

# If found, draw corners
if ret:
    image = cv2.drawChessboardCorners(image, (7, 7), corners, ret)
    cv2.imshow('Detected Chessboard', image)
    cv2.waitKey(0)

Output: The image with detected chessboard corners drawn on it.

The code snippet reads an image of a chessboard, converts it to grayscale, and then applies the findChessboardCorners function to detect the corners of the pattern. The detected corners are visualized using drawChessboardCorners.

Method 2: Harris Corner Detection

Harris Corner Detection algorithm, provided by OpenCV through the cornerHarris function, is another powerful method for feature detection, which can be used to identify the corners on a chessboard. It detects corners based on the changes in intensity, which make it an excellent choice for detecting chessboard patterns where high contrast corners are present.

Here’s an example:

import cv2
import numpy as np

# Load image
image = cv2.imread("chessboard.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Harris corner detection
gray = np.float32(gray)
dst = cv2.cornerHarris(gray, 2, 3, 0.04)

# Result is dilated for marking the corners
dst = cv2.dilate(dst, None)

# Only keep areas with large corner responses
image[dst > 0.01 * dst.max()] = [0, 0, 255]

# Show result
cv2.imshow('Harris Corners', image)
cv2.waitKey(0)

Output: The image with Harris corners marked in red.

This code snippet uses the Harris Corner Detection method to identify corners within the chessboard. The areas with significant corner responses are marked in red, showing the corner points of the chessboard pattern.

Method 3: FAST Algorithm for Corner Detection

FAST (Features from Accelerated Segment Test) is a corner detection algorithm that is known for its high-speed performance. While not specific to chessboard patterns, its efficiency makes it a viable option for detecting chessboard corners when computational speed is a concern. OpenCV’s implementation makes it accessible and simple to apply.

Here’s an example:

import cv2

# Load image
image = cv2.imread('chessboard.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# FAST corner detection
fast = cv2.FastFeatureDetector_create()
keypoints = fast.detect(gray, None)

# Draw keypoints
image = cv2.drawKeypoints(image, keypoints, None, color=(255, 0, 0))

# Show result
cv2.imshow('FAST Corners', image)
cv2.waitKey(0)

Output: The image with FAST corners annotated.

By applying the FAST algorithm, we swiftly identify and draw keypoints on the chessboard image, which correspond to the corners of the squares. This demonstrates FAST’s rapid corner detection capability, which can be particularly beneficial when performing real-time analysis.

Method 4: Shi-Tomasi Corner Detector

The Shi-Tomasi corner detector is an enhancement of Harris corner detection that changes the way the response is calculated, often leading to better results in detecting corners. Like Harris, it is suitable for a variety of image feature detection use-cases, including chessboard pattern recognition.

Here’s an example:

import cv2

# Load image
image = cv2.imread('chessboard.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Shi-Tomasi corner detection
corners = cv2.goodFeaturesToTrack(gray, 50, 0.01, 10)
corners = np.int0(corners)

# Draw corners
for corner in corners:
    x, y = corner.ravel()
    cv2.circle(image, (x, y), 3, 255, -1)

# Show result
cv2.imshow('Shi-Tomasi Corners', image)
cv2.waitKey(0)

Output: The image with Shi-Tomasi corners drawn as small circles.

This snippet employs the goodFeaturesToTrack function, which implements the Shi-Tomasi corner detection method. The most prominent corners are then highlighted in the image using circles, effectively showcasing each corner detected within the chessboard pattern.

Bonus One-Liner Method 5: Convolutional Neural Networks (CNNs)

Modern Convolutional Neural Networks are highly effective in pattern recognition and can be trained to detect chessboard patterns. Libraries like TensorFlow and PyTorch make deploying these models more practical. However, it’s worth noting that this method requires a significant amount of data and computational resources to train the network.

Here’s an example:

print("Please see a relevant TensorFlow or PyTorch tutorial for an actual implementation of this method.")

Output: This is not an actual code snippet but a pointer towards extensive tutorials and resources related to CNN implementations.

While using CNNs for chessboard pattern detection is beyond the scope of a simple code example, this approach is a powerful one in the AI domain for detecting complex patterns and can give exceptionally accurate results with the right model and dataset.

Summary/Discussion

• Method 1: findChessboardCorners Function. Tailor-made for chessboard detection. Strength: High accuracy in ideal conditions. Weakness: Might struggle with partial occlusions or distortions.
• Method 2: Harris Corner Detection. General-purpose corner detection method. Strength: Good with high-contrast corners. Weakness: Can require additional filtering to focus on chessboard patterns.
• Method 3: FAST Algorithm for Corner Detection. Very fast, useful for real-time analysis. Strength: Speed. Weakness: Less accurate than other methods; may detect extra features as corners.
• Method 4: Shi-Tomasi Corner Detector. Improved response calculation over Harris. Strength: Produces better corner detection in practice. Weakness: Still not specialized for chessboard patterns.
• Method 5: Convolutional Neural Networks (CNNs). Powerful for complex pattern recognition. Strength: High accuracy and adaptability. Weakness: Requires extensive data and computational power for training.