Welcome to the World of OpenCV: Exploring Image Processing Techniques
OpenCV (short for Open Source Computer Vision) is a powerful library that encompasses Computer Vision, Machine Learning, and Image Processing. It enables users to identify patterns in images, extract features, and conduct intricate mathematical operations. In this article, we will delve into the basics of OpenCV image processing and explore various techniques to manipulate images.
Installing the OpenCV Python Package
pip install opencv-pythonHow to Load an Image
The first step in image processing is to load an image file and convert it into an OpenCV matrix. This matrix can then be manipulated and displayed using OpenCV functions.
# Reading an image
import cv2 as cv
import numpy as np
original = cv.imread("")
cv.imshow("original", original)Manipulating Images: Drawing Shapes and Text
Once an image is loaded, various shapes and text can be drawn on it. This snippet demonstrates how to draw lines, circles, rectangles, and text on an image.
# Drawing a line
line = cv.line(original, (original.shape[1]//2, original.shape[0]//2), (0,0) , (0,255,0), thickness=2)
cv.imshow("line", line)
# Drawing other shapes
circle = cv.circle(line, (line.shape[1]//2, line.shape[0]//2), 50, (0,0,255), thickness=2)
rect = cv.rectangle(circle, (10,10), (circle.shape[1]//2, circle.shape[0]//2), (255,0,0), thickness=2)
text = cv.putText(rect, "Hi", (rect.shape[1]//2, rect.shape[0]//2), cv.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), thickness=2)
cv.imshow("all shapes", text)Detecting Contours and Cropping Images
Contour detection is crucial for identifying shapes within images. By detecting contours and cropping images, we can isolate specific objects and eliminate unnecessary areas.
# Convert image to grayscale and add blur for better contour detection
gray = cv.cvtConvert(original, cv.COLOR_BGR2GRAY)
contours, _ = cv.findContours(gray, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)Final Steps of Image Processing
After detecting contours, the image is cropped to focus on the puzzle grid. This process reduces noise and facilitates efficient grid detection.
# Crop the grid area from the original image
grid = original[y:y+h, x:x+w]
cv.imwrite("solution/grid.png", grid)
# Find contours again in the cropped grayscale grid
contours, _ = cv.findContours(gray, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)
contours = sorted(contours, key=cv.contourArea)By iteratively processing each cell within the grid, we can assign unique numerical values based on color intensity. This methodology helps to create a comprehensive 2D array representation of the puzzle grid, enabling further analysis and manipulation.