Image Processing Module

The Image Processing (imgproc) module provides a comprehensive suite of image processing functions for filtering, geometric transformations, color space conversions, and feature detection.

Overview

From opencv2/imgproc.hpp:48-52:

This module offers a comprehensive suite of image processing functions, enabling tasks such as filtering, geometric transformations, color space conversions, histograms, structural analysis, and feature detection.

Filtering

Linear and non-linear image filtering operations

Transforms

Geometric transformations like resize, rotate, warp

Color Spaces

Conversions between BGR, HSV, Lab, and other formats

Feature Detection

Edge detection, corner detection, and shape analysis

Image Filtering

From imgproc.hpp:54-84, OpenCV provides various filtering operations:

Linear Filters

#include <opencv2/imgproc.hpp>
using namespace cv;

// Gaussian blur
GaussianBlur(src, dst, Size(5, 5), 1.5);

// Box filter (average)
boxFilter(src, dst, -1, Size(5, 5));
blur(src, dst, Size(5, 5));

// Median filter (non-linear)
medianBlur(src, dst, 5);

// Bilateral filter (edge-preserving)
bilateralFilter(src, dst, 9, 75, 75);

// Custom filter with kernel
Mat kernel = (Mat_<float>(3,3) << -1, -1, -1,
                                   -1,  9, -1,
                                   -1, -1, -1);
filter2D(src, dst, -1, kernel);

Morphological Operations

From imgproc.hpp:216-241:

// Morphological operation types
enum MorphTypes {
    MORPH_ERODE = 0,    // Erosion
    MORPH_DILATE = 1,   // Dilation
    MORPH_OPEN = 2,     // Opening: dilate(erode(src))
    MORPH_CLOSE = 3,    // Closing: erode(dilate(src))
    MORPH_GRADIENT = 4, // Morphological gradient
    MORPH_TOPHAT = 5,   // Top hat
    MORPH_BLACKHAT = 6, // Black hat
    MORPH_HITMISS = 7   // Hit-or-miss
};

// Create structuring element
Mat element = getStructuringElement(
    MORPH_RECT,     // Shape: RECT, CROSS, ELLIPSE
    Size(5, 5),     // Size
    Point(-1, -1)   // Anchor point
);

// Apply morphological operations
erode(src, dst, element);
dilate(src, dst, element);
morphologyEx(src, dst, MORPH_OPEN, element);
morphologyEx(src, dst, MORPH_CLOSE, element);

Derivatives and Gradients

// Sobel derivatives
Sobel(src, dst, CV_16S, 1, 0);  // x-derivative
Sobel(src, dst, CV_16S, 0, 1);  // y-derivative

// Scharr (more accurate for 3x3)
Scharr(src, dx, CV_16S, 1, 0);
Scharr(src, dy, CV_16S, 0, 1);

// Laplacian
Laplacian(src, dst, CV_16S, 3);

// Canny edge detector
Canny(src, edges, 50, 150, 3);

Geometric Transformations

From imgproc.hpp:90-127, geometric transformations deform the pixel grid:

Resizing and Interpolation

// Interpolation flags (imgproc.hpp:249-280)
enum InterpolationFlags {
    INTER_NEAREST = 0,        // Nearest neighbor
    INTER_LINEAR = 1,         // Bilinear interpolation
    INTER_CUBIC = 2,          // Bicubic interpolation
    INTER_AREA = 3,           // Area interpolation (best for decimation)
    INTER_LANCZOS4 = 4,       // Lanczos interpolation over 8x8
    INTER_LINEAR_EXACT = 5,   // Bit exact bilinear
    INTER_NEAREST_EXACT = 6   // Bit exact nearest neighbor
};

// Resize image
resize(src, dst, Size(640, 480), 0, 0, INTER_LINEAR);
resize(src, dst, Size(), 0.5, 0.5, INTER_AREA);  // Scale by 0.5

// Pyramid operations
pyrDown(src, dst);  // Downsample
pyrUp(src, dst);    // Upsample

Affine Transformations

// Rotation
Point2f center(width/2.0, height/2.0);
Mat rotMat = getRotationMatrix2D(center, 45, 1.0);  // 45° rotation
warpAffine(src, dst, rotMat, src.size());

// Translation
Mat transMat = (Mat_<double>(2,3) << 1, 0, 50,  // tx=50
                                      0, 1, 30); // ty=30
warpAffine(src, dst, transMat, src.size());

// Affine from 3 points
Point2f srcPts[3], dstPts[3];
// ... set points ...
Mat affineMat = getAffineTransform(srcPts, dstPts);
warpAffine(src, dst, affineMat, src.size());

Perspective Transformations

// Perspective transform from 4 points
Point2f srcQuad[4], dstQuad[4];
// ... define source and destination points ...
Mat perspMat = getPerspectiveTransform(srcQuad, dstQuad);
warpPerspective(src, dst, perspMat, dst.size());

// Find homography from point correspondences
vector<Point2f> srcPoints, dstPoints;
// ... fill points ...
Mat H = findHomography(srcPoints, dstPoints, RANSAC);
warpPerspective(src, dst, H, dst.size());

Remapping

// General remapping
Mat mapX, mapY;
// ... create mapping functions ...
remap(src, dst, mapX, mapY, INTER_LINEAR);

// Polar transformations
linearPolar(src, dst, center, maxRadius, INTER_LINEAR);
logPolar(src, dst, center, M, INTER_LINEAR);

Color Space Conversions

From imgproc.hpp:158-172:

// Common color conversions
cvtColor(src, dst, COLOR_BGR2GRAY);     // BGR to Grayscale
cvtColor(src, dst, COLOR_BGR2HSV);      // BGR to HSV
cvtColor(src, dst, COLOR_BGR2Lab);      // BGR to Lab
cvtColor(src, dst, COLOR_BGR2YCrCb);    // BGR to YCrCb
cvtColor(src, dst, COLOR_GRAY2BGR);     // Grayscale to BGR
cvtColor(src, dst, COLOR_HSV2BGR);      // HSV to BGR

// RGB vs BGR
cvtColor(src, dst, COLOR_BGR2RGB);      // Swap R and B channels

// Alpha channel
cvtColor(src, dst, COLOR_BGR2BGRA);     // Add alpha channel
cvtColor(src, dst, COLOR_BGRA2BGR);     // Remove alpha channel

Thresholding

// Simple thresholding
threshold(src, dst, 127, 255, THRESH_BINARY);
threshold(src, dst, 127, 255, THRESH_BINARY_INV);
threshold(src, dst, 127, 255, THRESH_TRUNC);
threshold(src, dst, 127, 255, THRESH_TOZERO);

// Otsu's method (automatic threshold)
threshold(src, dst, 0, 255, THRESH_BINARY | THRESH_OTSU);

// Adaptive thresholding
adaptiveThreshold(src, dst, 255,
                  ADAPTIVE_THRESH_MEAN_C,
                  THRESH_BINARY, 11, 2);
                  
adaptiveThreshold(src, dst, 255,
                  ADAPTIVE_THRESH_GAUSSIAN_C,
                  THRESH_BINARY, 11, 2);

Edge Detection

Example from samples/cpp/edge.cpp:

#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>

Mat image, gray, blurImage, edges;

// Load and convert to grayscale
image = imread("image.jpg");
cvtColor(image, gray, COLOR_BGR2GRAY);

// Blur to reduce noise (edge.cpp:22)
blur(gray, blurImage, Size(3, 3));

// Canny edge detection (edge.cpp:25)
int threshold1 = 50;
int threshold2 = 150;
Canny(blurImage, edges, threshold1, threshold2, 3);

// Using Scharr gradient (edge.cpp:32-35)
Mat dx, dy;
Scharr(blurImage, dx, CV_16S, 1, 0);
Scharr(blurImage, dy, CV_16S, 0, 1);
Canny(dx, dy, edges, threshold1, threshold2);

Histograms

// Calculate histogram
Mat hist;
int histSize = 256;
float range[] = {0, 256};
const float* histRange = {range};
calcHist(&src, 1, 0, Mat(), hist, 1, &histSize, &histRange);

// Histogram equalization
equalizeHist(src, dst);

// CLAHE (Contrast Limited Adaptive Histogram Equalization)
Ptr<CLAHE> clahe = createCLAHE();
clahe->setClipLimit(4.0);
clahe->apply(src, dst);

// Back projection
Mat backProj;
calcBackProject(&src, 1, channels, hist, backProj, ranges);

Contours and Shapes

// Find contours
vector<vector<Point>> contours;
vector<Vec4i> hierarchy;
findContours(binary, contours, hierarchy,
             RETR_TREE, CHAIN_APPROX_SIMPLE);

// Draw contours
Mat drawing = Mat::zeros(binary.size(), CV_8UC3);
for (size_t i = 0; i < contours.size(); i++) {
    drawContours(drawing, contours, i, Scalar(0, 255, 0), 2);
}

// Contour properties
double area = contourArea(contours[0]);
double perimeter = arcLength(contours[0], true);

// Approximate contour
vector<Point> approx;
approxPolyDP(contours[0], approx, epsilon, true);

// Convex hull
vector<Point> hull;
convexHull(contours[0], hull);

// Bounding shapes
Rect bbox = boundingRect(contours[0]);
RotatedRect minRect = minAreaRect(contours[0]);
Point2f center;
float radius;
minEnclosingCircle(contours[0], center, radius);

Drawing Functions

From imgproc.hpp:130-156:

// Lines
line(img, pt1, pt2, Scalar(0, 255, 0), 2);

// Arrows
arrowedLine(img, pt1, pt2, Scalar(255, 0, 0), 2);

// Rectangles
rectangle(img, pt1, pt2, Scalar(0, 0, 255), 2);
rectangle(img, rect, Scalar(0, 255, 255), -1);  // Filled

// Circles
circle(img, center, radius, Scalar(255, 255, 0), 2);

// Ellipses
ellipse(img, center, Size(100, 50), 45, 0, 360,
        Scalar(255, 0, 255), 2);

// Polygons
vector<Point> pts = {Point(10,10), Point(100,50), Point(50,100)};
polylines(img, pts, true, Scalar(0, 255, 0), 2);
fillPoly(img, pts, Scalar(255, 255, 255));

// Text
putText(img, "OpenCV", Point(50, 50),
        FONT_HERSHEY_SIMPLEX, 1.0, Scalar(0, 0, 0), 2);

Distance Transform

// Distance to nearest zero pixel
Mat dist;
distanceTransform(binary, dist, DIST_L2, 5);

// Distance transform with labels
Mat labels;
distanceTransform(binary, dist, labels, DIST_L2, 5, LABEL_CCOMP);

Connected Components

// Label connected components
Mat labels;
int nLabels = connectedComponents(binary, labels);

// With statistics
Mat stats, centroids;
int nLabels = connectedComponentsWithStats(binary, labels,
                                            stats, centroids);

// Access statistics
for (int i = 1; i < nLabels; i++) {
    int area = stats.at<int>(i, CC_STAT_AREA);
    int left = stats.at<int>(i, CC_STAT_LEFT);
    int top = stats.at<int>(i, CC_STAT_TOP);
    int width = stats.at<int>(i, CC_STAT_WIDTH);
    int height = stats.at<int>(i, CC_STAT_HEIGHT);
    double cx = centroids.at<double>(i, 0);
    double cy = centroids.at<double>(i, 1);
}

Image Moments

// Calculate moments
Moments m = moments(contour);

// Centroid
double cx = m.m10 / m.m00;
double cy = m.m01 / m.m00;

// Hu moments (rotation invariant)
double hu[7];
HuMoments(m, hu);

Hough Transforms

// Hough Line Transform
vector<Vec2f> lines;
HoughLines(edges, lines, 1, CV_PI/180, 100);

// Probabilistic Hough Line Transform
vector<Vec4i> linesP;
HoughLinesP(edges, linesP, 1, CV_PI/180, 50, 50, 10);

// Hough Circle Transform
vector<Vec3f> circles;
HoughCircles(gray, circles, HOUGH_GRADIENT,
             1, 50, 200, 100, 0, 0);

Watershed Segmentation

// Prepare markers
Mat markers;
// ... initialize markers ...

// Apply watershed
watershed(image, markers);

// Markers now contain segment labels
// -1 indicates boundaries between segments

Template Matching

// Match template
Mat result;
matchTemplate(image, templ, result, TM_CCOEFF_NORMED);

// Find best match
double minVal, maxVal;
Point minLoc, maxLoc;
minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc);

// Draw rectangle at match location
rectangle(image, maxLoc,
          Point(maxLoc.x + templ.cols, maxLoc.y + templ.rows),
          Scalar(0, 255, 0), 2);

Practical Example: Image Enhancement

#include <opencv2/imgproc.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>

using namespace cv;

int main() {
    // Load image
    Mat img = imread("photo.jpg");
    Mat result;
    
    // Denoise
    fastNlMeansDenoisingColored(img, result, 10, 10, 7, 21);
    
    // Convert to Lab for better color processing
    Mat lab;
    cvtColor(result, lab, COLOR_BGR2Lab);
    
    // Split channels
    vector<Mat> channels;
    split(lab, channels);
    
    // Apply CLAHE to L channel
    Ptr<CLAHE> clahe = createCLAHE(2.0, Size(8, 8));
    clahe->apply(channels[0], channels[0]);
    
    // Merge and convert back
    merge(channels, lab);
    cvtColor(lab, result, COLOR_Lab2BGR);
    
    // Sharpen
    Mat blurred;
    GaussianBlur(result, blurred, Size(0, 0), 3);
    addWeighted(result, 1.5, blurred, -0.5, 0, result);
    
    // Save result
    imwrite("enhanced.jpg", result);
    
    return 0;
}

Best Practices

Border Handling: Most filtering functions need to extrapolate pixels outside image boundaries. Choose the appropriate border type:

BORDER_REPLICATE - Good for most filtering
BORDER_REFLECT_101 - Better for derivatives
BORDER_CONSTANT - When you need specific padding values

Interpolation: Choose interpolation based on your needs:

INTER_NEAREST - Fastest, but lowest quality
INTER_LINEAR - Good balance of speed and quality
INTER_AREA - Best for downsampling
INTER_CUBIC - Best quality for upsampling
INTER_LANCZOS4 - Highest quality, slowest

Core Module - Provides Mat and basic operations
Image I/O - Reading and writing images
Feature Detection - Advanced feature detection

Source Reference

Key header: ~/workspace/source/modules/imgproc/include/opencv2/imgproc.hpp

Getting Started

Core Concepts

Modules

Tutorials

Language Bindings

Platform Support

Image Processing Module

Overview

Filtering

Transforms

Color Spaces

Feature Detection

Image Filtering

Linear Filters

Morphological Operations

Derivatives and Gradients

Geometric Transformations

Resizing and Interpolation

Affine Transformations

Perspective Transformations

Remapping

Color Space Conversions

Thresholding

Edge Detection

Histograms

Contours and Shapes

Drawing Functions

Distance Transform

Connected Components

Image Moments

Hough Transforms

Watershed Segmentation

Template Matching

Practical Example: Image Enhancement

Best Practices

Source Reference

Getting Started

Core Concepts

Modules

Tutorials

Language Bindings

Platform Support

​Overview

Filtering

Transforms

Color Spaces

Feature Detection

​Image Filtering

​Linear Filters

​Morphological Operations

​Derivatives and Gradients

​Geometric Transformations

​Resizing and Interpolation

​Affine Transformations

​Perspective Transformations

​Remapping

​Color Space Conversions

​Thresholding

​Edge Detection

​Histograms

​Contours and Shapes

​Drawing Functions

​Distance Transform

​Connected Components

​Image Moments

​Hough Transforms

​Watershed Segmentation

​Template Matching

​Practical Example: Image Enhancement

​Best Practices

​Related Modules

​Source Reference

Overview

Image Filtering

Linear Filters

Morphological Operations

Derivatives and Gradients

Geometric Transformations

Resizing and Interpolation

Affine Transformations

Perspective Transformations

Remapping

Color Space Conversions

Thresholding

Edge Detection

Histograms

Contours and Shapes

Drawing Functions

Distance Transform

Connected Components

Image Moments

Hough Transforms

Watershed Segmentation

Template Matching

Practical Example: Image Enhancement

Best Practices

Related Modules

Source Reference