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Image Transformation

🔄 3. Image Transformations & Contours

This section dives into geometry-based manipulation of images and shape-based processing using contours.

🧮 3.1 Morphological Operations

Used for shape-based processing after thresholding (e.g., removing noise).

import cv2
import numpy as np

img = cv2.imread('image.jpg', 0)
_, binary = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)

kernel = np.ones((5,5), np.uint8)
erosion = cv2.erode(binary, kernel, iterations=1)
dilation = cv2.dilate(binary, kernel, iterations=1)

cv2.imshow('Erosion', erosion)
cv2.imshow('Dilation', dilation)
cv2.waitKey(0)

opening = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)

cv2.imshow('Opening', opening)
cv2.imshow('Closing', closing)
cv2.waitKey(0)

📐 3.2 Geometric Transformations

Perform affine and perspective warping.

rows, cols, ch = img.shape
pts1 = np.float32([[50,50], [200,50], [50,200]])
pts2 = np.float32([[10,100], [200,50], [100,250]])
M = cv2.getAffineTransform(pts1, pts2)
dst = cv2.warpAffine(img, M, (cols, rows))
cv2.imshow('Affine', dst)
cv2.waitKey(0)

pts1 = np.float32([[56,65], [368,52], [28,387], [389,390]])
pts2 = np.float32([[0,0], [300,0], [0,300], [300,300]])
M = cv2.getPerspectiveTransform(pts1, pts2)
warped = cv2.warpPerspective(img, M, (300, 300))
cv2.imshow('Perspective', warped)
cv2.waitKey(0)

✏️ 3.3 Contours and Shape Analysis

Contours are useful for shape detection and measurement.

img = cv2.imread('image.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 127, 255, 0)
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, contours, -1, (0,255,0), 2)
cv2.imshow("Contours", img)
cv2.waitKey(0)

for cnt in contours:
    area = cv2.contourArea(cnt)
    perimeter = cv2.arcLength(cnt, True)
    print("Area:", area, "Perimeter:", perimeter)

📊 3.4 Histogram & Equalization

Enhance contrast or analyze pixel distribution.

import matplotlib.pyplot as plt

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
hist = cv2.calcHist([gray], [0], None, [256], [0,256])
plt.plot(hist)
plt.title("Grayscale Histogram")
plt.show()

equalized = cv2.equalizeHist(gray)
cv2.imshow("Equalized", equalized)
cv2.waitKey(0)