Zi 字媒體

fanfuhan OpenCV 教學028 ~ opencv-028-圖像積分圖算法(應用在邊緣檢測) 資料來源: https://fanfuhan.github.io/ https://fanfuhan.github.io/2019/04/07/opencv-028/ GITHUB:https://github.com/jash-git/fanfuhan_ML_OpenCV 積分圖像是Crow在1984年首次提出，是為了在多尺度透視投影中提高渲染速度，是一種快速計算圖像區域和與平方和的算法。 其核心思想是對每個圖像建立自己的積分圖查找表，在圖像積分處理計算階段根據預先建立的積分圖查找表，直接查找從而實現對均值卷積線性時間計算，做到了卷積執行的時間與尺寸窗口大小的無關聯。 提取HAAR / SURF，二值圖像分析，圖像相似相關性NCC計算，圖像卷積快速計算等方面方面的應用，是圖像處理中的經典算法之一。 C++ #include #include using namespace std; using namespace cv; void blur_demo(Mat &image, Mat &sum); void edge_demo(Mat &image, Mat &sum); int getblockSum(Mat &sum, int x1, int y1, int x2, int y2, int i); /* * 图像积分图算法 */ int main() { Mat src = imread("../images/test.png"); if (src.empty()) { cout << "could not load image.." << endl; } imshow("input", src); // 计算积分图 Mat sum, sqrsum; integral(src, sum, sqrsum); /* * 积分图应用 */ int type = 0; // 模糊应用 blur_demo(src, sum); // 边缘检测 edge_demo(src, sum); waitKey(0); return 0; } void blur_demo(Mat &image, Mat &sum) { int w = image.cols; int h = image.rows; Mat result = Mat::zeros(image.size(), image.type()); int x2 = 0, y2 = 0; int x1 = 0, y1 = 0; int ksize = 5; int radius = ksize / 2; int ch = image.channels(); int cx = 0, cy = 0; for (int row = 0; row < h + radius; row++) { y2 = (row + 1)>h ? h : (row + 1); y1 = (row - ksize) < 0 ? 0 : (row - ksize); for (int col = 0; col < w + radius; col++) { x2 = (col + 1)>w ? w : (col + 1); x1 = (col - ksize) < 0 ? 0 : (col - ksize); cx = (col - radius) < 0 ? 0 : col - radius; cy = (row - radius) < 0 ? 0 : row - radius; int num = (x2 - x1)*(y2 - y1); for (int i = 0; i < ch; i++) { // 积分图查找和表，计算卷积 int s = getblockSum(sum, x1, y1, x2, y2, i); result.at(cy, cx)[i] = saturate_cast(s / num); } } } imshow("blur_demo", result); } /** * 3x3 sobel 垂直边缘检测演示 */ void edge_demo(Mat &image, Mat &sum) { int w = image.cols; int h = image.rows; Mat result = Mat::zeros(image.size(), CV_32SC3); int x2 = 0, y2 = 0; int x1 = 0, y1 = 0; int ksize = 3; // 算子大小，可以修改，越大边缘效应越明显 int radius = ksize / 2; int ch = image.channels(); int cx = 0, cy = 0; for (int row = 0; row < h + radius; row++) { y2 = (row + 1)>h ? h : (row + 1); y1 = (row - ksize) < 0 ? 0 : (row - ksize); for (int col = 0; col < w + radius; col++) { x2 = (col + 1)>w ? w : (col + 1); x1 = (col - ksize) < 0 ? 0 : (col - ksize); cx = (col - radius) < 0 ? 0 : col - radius; cy = (row - radius) < 0 ? 0 : row - radius; int num = (x2 - x1)*(y2 - y1); for (int i = 0; i < ch; i++) { // 积分图查找和表，计算卷积 int s1 = getblockSum(sum, x1, y1, cx, y2, i); int s2 = getblockSum(sum, cx, y1, x2, y2, i); result.at(cy, cx)[i] = saturate_cast(s2 - s1); } } } Mat dst, gray; convertScaleAbs(result, dst); normalize(dst, dst, 0, 255, NORM_MINMAX); cvtColor(dst, gray, COLOR_BGR2GRAY); imshow("edge_demo", gray); } int getblockSum(Mat &sum, int x1, int y1, int x2, int y2, int i) { int tl = sum.at(y1, x1)[i]; int tr = sum.at(y2, x1)[i]; int bl = sum.at(y1, x2)[i]; int br = sum.at(y2, x2)[i]; int s = (br - bl - tr + tl); return s; } Python import cv2 as cv import numpy as np def get_block_sum(ii, x1, y1, x2, y2, index): tl = ii[y1, x1][index] tr = ii[y2, x1][index] bl = ii[y1, x2][index] br = ii[y2, x2][index] s = (br - bl - tr + tl) return s def blur_demo(image, ii): h, w, dims = image.shape result = np.zeros(image.shape, image.dtype) ksize = 15 radius = ksize // 2 for row in range(0, h + radius, 1): y2 = h if (row + 1)> h else (row + 1) y1 = 0 if (row - ksize) < 0 else (row - ksize) for col in range(0, w + radius, 1): x2 = w if (col + 1)>w else (col + 1) x1 = 0 if (col - ksize) < 0 else (col - ksize) cx = 0 if (col - radius) < 0 else (col - radius) cy = 0 if (row - radius) < 0 else (row - radius) num = (x2 - x1)*(y2 - y1) for i in range(0, 3, 1): s = get_block_sum(ii, x1, y1, x2, y2, i) result[cy, cx][i] = s // num cv.imshow("integral fast blur", result) cv.imwrite("D:/result.png", result) src = cv.imread("D:/images/test1.png") cv.namedWindow("input", cv.WINDOW_AUTOSIZE) cv.imshow("input", src) sum_table = cv.integral(src, sdepth=cv.CV_32S) blur_demo(src, sum_table) cv.waitKey(0) cv.destroyAllWindows()