OpenCV尋找火花交叉點解決方案
- 2019 年 12 月 23 日
- 筆記
來源：土鹽
接地裝置衝擊特性及土壤火花放電形貌特徵研究
採用接地裝置暫態特性的時域電網絡模型對變電站集中接地裝置與接地網互連/獨立時,變電站地網上暫態電位升進行仿真計算,評估變電站地網電位升對變壓器中性點的反擊風險,並分別計算了兩種情況下雷電流入地點(集中集中裝置引下線)與變壓器中性點之間的防反擊安全距離,以及集中接地裝置衝擊阻抗的安全限值。採用感光膠片對平板電極進行了火花放電形貌特徵觀測試驗,結合平板電極衝擊試驗,將土壤電離過程分為四個階段：局部放電階段、電離延遲階段、貫穿電離階段與電離恢復階段,並研究了四個電離階段電氣參數的變化特徵,分析了電極的瞬時電阻與電離過程的對應關係。對不同尺寸的單根水平接地極與接地網的火花放電形貌特徵進行觀測試驗,研究了接地裝置周圍土壤中火花放電的種類與特徵、放電發展過程與放電點分佈規律。火花放電觀測結果表明,接地極周圍土壤中火花放電主要分為局部放電與樹枝狀放電。兩種形式放電點分佈規律為：局部放電的放電點較多,沿導體表面密集分佈,放電強度較弱；當土壤中場強超過其臨界擊穿場強,局部放電就發展成為強烈的樹枝放電。
我是看到下面的代碼，再搜知網上的這篇論文的，願有緣人能結合這個順利畢業，祝好。
下面是乾貨代碼：
#include <opencv2/opencv.hpp> using namespace cv; #pragma comment(lib, "opencv_world410.lib") #include <iostream> #include <vector> using namespace std; int main(void) { Mat frame = imread("sparks.png"); if (frame.empty()) { cout << "Error loading image file" << endl; return -1; } Mat colour_frame = frame.clone(); cvtColor(frame, frame, COLOR_BGR2GRAY); threshold(frame, frame, 127, 255, THRESH_BINARY); vector<Point2i> branch_locations; // Start with the second column for (int i = 1; i < frame.cols; i++) { bool lit = false; vector<int> begin_black_regions; vector<int> end_black_regions; // Start with the first row if (255 == frame.at<unsigned char>(0, i)) { lit = true; } else { lit = false; begin_black_regions.push_back(0); } // Start with the second row for (int j = 1; j < frame.rows - 1; j++) { if (255 == frame.at<unsigned char>(j, i) && lit == false) { lit = true; end_black_regions.push_back(j - 1); } else if (0 == frame.at<unsigned char>(j, i) && lit == true) { lit = false; begin_black_regions.push_back(j); } } // End with the last row if (0 == frame.at<unsigned char>(frame.rows - 1, i) && lit == false) { end_black_regions.push_back(frame.rows - 1); } else if (0 == frame.at<unsigned char>(frame.rows - 1, i) && lit == true) { begin_black_regions.push_back(frame.rows - 1); end_black_regions.push_back(frame.rows - 1); } else if (255 == frame.at<unsigned char>(frame.rows - 1, i) && lit == false) { end_black_regions.push_back(frame.rows - 2); } if(begin_black_regions.size() != end_black_regions.size()) cout << begin_black_regions.size() << " " << end_black_regions.size() << endl; // for each black region begin/end pair for (size_t k = 0; k < begin_black_regions.size(); k++) { bool found_branch = true; for (int l = begin_black_regions[k]; l <= end_black_regions[k]; l++) { if (0 == frame.at<unsigned char>(l, i - 1)) { found_branch = false; break; } } if (found_branch == true) { Point2i location(i - 1, begin_black_regions[k]); branch_locations.push_back(location); } } } for (size_t i = 0; i < branch_locations.size(); i++) circle(colour_frame, branch_locations[i], 2, Scalar(255, 127, 0), 2); imshow("frame", colour_frame); waitKey(); return 0; }
import cv2 import numpy frame = cv2.imread('sparks.png') if frame is None: print('Error loading image') exit() colour_frame = frame frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) ret, frame = cv2.threshold(frame, 127, 255, cv2.THRESH_BINARY) rows = frame.shape[0] cols = frame.shape[1] branch_locations = [] # start with second column for i in range(1, cols): lit = False begin_black_regions = [] end_black_regions = [] # start with first row if 255 == frame[0, i]: lit = True else: lit = False begin_black_regions.append(0) # start with second row for j in range(1, rows - 1): if 255 == frame[j, i] and lit == False: lit = True end_black_regions.append(j - 1) elif 0 == frame[j, i] and lit == True: lit = False begin_black_regions.append(j) # end with last row if 0 == frame[rows - 1, i] and lit == False: end_black_regions.append(rows - 1) elif 0 == frame[rows - 1, i] and lit == True: begin_black_regions.append(rows - 1) end_black_regions.append(rows - 1) elif 255 == frame[rows - 1, i] and lit == False: end_black_regions.append(rows - 2) for k in range(0, len(begin_black_regions)): found_branch = True for l in range(begin_black_regions[k], end_black_regions[k] + 1): if 0 == frame[l, i - 1]: found_branch = False break if found_branch == True: branch_locations.append(complex(i - 1, begin_black_regions[k])) for i in range(0, len(branch_locations)): cv2.circle(colour_frame, (int(branch_locations[i].real), int(branch_locations[i].imag)), 2, (255, 127, 0), 2) cv2.imshow("Frame", colour_frame) cv2.waitKey(0)
