1 | /*
|
---|
2 | StartAndEndPoint l1 = parseArray[i];
|
---|
3 | StartAndEndPoint l2 = parseArray[i+ 1];
|
---|
4 | //CHECK WHICH line starts after the other
|
---|
5 | //If l1 is starting after, then comparisons are based around l1.s
|
---|
6 | //System.out.println("l1: " + l1.getP1().x);
|
---|
7 | //System.out.println("l2: " + l2.getP1().x);
|
---|
8 |
|
---|
9 | System.out.println("1.0: L1S: " + l1.getP1().x + " larger than L2S: " + l2.getP1().x);
|
---|
10 | if(l1.getP1().x > l2.getP1().x) {
|
---|
11 | System.out.println("1.1: Comparing L1S: " + l1.getP1().x + " less than L2E: " + l2.getP2().x);
|
---|
12 | if (l1.getP1().x < l2.getP2().x) {
|
---|
13 | //AND
|
---|
14 | System.out.println("1.2: Comparing L1S: " + l1.getP1().x + " larger than L2S: " + l2.getP1().x);
|
---|
15 | if (l1.getP1().x > l2.getP1().x) {
|
---|
16 | System.out.println("1: Success. NEXT");
|
---|
17 | //IT IS INTERSECTED
|
---|
18 | continue;
|
---|
19 | }
|
---|
20 | else {
|
---|
21 | //FAILED SECOND COMPARISON
|
---|
22 | System.out.println("1: Fail");
|
---|
23 | }
|
---|
24 | }
|
---|
25 | else {
|
---|
26 | System.out.println("Checking other line");
|
---|
27 | }
|
---|
28 | System.out.println("2.0: L2S: " + l2.getP1().x + " larger than L1S: " + l1.getP1().x);
|
---|
29 | }
|
---|
30 | //If l2 is starting after, then comparisons are based around l2.s
|
---|
31 | else if(l2.getP1().x > l1.getP1().x) {
|
---|
32 | System.out.println("2.1: Comparing L2S: " + l1.getP1().x + " less than L1E: " + l2.getP2().x);
|
---|
33 | if (l2.getP1().x < l1.getP2().x) {
|
---|
34 | //AND
|
---|
35 | System.out.println("2.2: Comparing L2S: " + l2.getP1().x + " larger than L1S: " + l1.getP1().x);
|
---|
36 | if (l2.getP1().x > l1.getP1().x) {
|
---|
37 | System.out.println("2: Success");
|
---|
38 | //IT IS INTERSECTED
|
---|
39 | //continue;
|
---|
40 | }
|
---|
41 | else {
|
---|
42 | //FAILED SECOND COMPARISON
|
---|
43 | System.out.println("2: Fail");
|
---|
44 | //return false;
|
---|
45 | }
|
---|
46 | }
|
---|
47 | else {
|
---|
48 | System.out.println("Failed second comparison RETURN FALSE");
|
---|
49 | return false;
|
---|
50 | }
|
---|
51 | //return false;
|
---|
52 | }
|
---|
53 | else{
|
---|
54 | System.out.println("NEITHER RETURN FALSE");
|
---|
55 | return false;
|
---|
56 | }
|
---|
57 | */
|
---|
58 |
|
---|
59 | import org.opencv.core.*;
|
---|
60 | import org.opencv.core.Point;
|
---|
61 |
|
---|
62 | import org.opencv.highgui.HighGui;
|
---|
63 | import org.opencv.imgcodecs.Imgcodecs;
|
---|
64 | import org.opencv.imgproc.Imgproc;
|
---|
65 |
|
---|
66 | import static org.opencv.core.Core.FILLED;
|
---|
67 | import static org.opencv.core.CvType.CV_8UC3;
|
---|
68 | import static org.opencv.highgui.HighGui.imshow;
|
---|
69 | import static org.opencv.imgcodecs.Imgcodecs.imwrite;
|
---|
70 |
|
---|
71 | import java.io.File;
|
---|
72 | import java.util.ArrayList;
|
---|
73 |
|
---|
74 | //REFERENCES:
|
---|
75 | //https://docs.opencv.org/3.4.3/d9/db0/tutorial_hough_lines.
|
---|
76 | //https://stackoverflow.com/questions/43443309/count-red-pixel-in-a-given-image
|
---|
77 | //https://www.wikihow.com/Calculate-Percentage-in-Java
|
---|
78 | //https://riptutorial.com/opencv/example/21963/converting-an-mat-object-to-an-bufferedimage-object
|
---|
79 | //https://beginnersbook.com/2013/12/java-arraylist-of-object-sort-example-comparable-and-comparator/
|
---|
80 | //https://www.programiz.com/java-programming/examples/standard-deviation
|
---|
81 | //https://www.geeksforgeeks.org/how-to-remove-duplicates-from-arraylist-in-java/
|
---|
82 | //https://stackoverflow.com/questions/7988486/how-do-you-calculate-the-variance-median-and-standard-deviation-in-c-or-java/7988556
|
---|
83 | //https://stackoverflow.com/questions/10396970/sort-a-list-that-contains-a-custom-class
|
---|
84 | //https://stackoverflow.com/questions/37946482/crop-images-area-with-opencv-java
|
---|
85 | //https://docs.opencv.org/3.4/dd/dd7/tutorial_morph_lines_detection.html
|
---|
86 | //https://docs.opencv.org/3.4/d0/d49/tutorial_moments.html
|
---|
87 | //https://docs.opencv.org/2.4/doc/tutorials/imgproc/shapedescriptors/moments/moments.html
|
---|
88 | //https://docs.opencv.org/3.3.1/d3/dc0/group__imgproc__shape.html#ga17ed9f5d79ae97bd4c7cf18403e1689a
|
---|
89 | //http://androiderstuffs.blogspot.com/2016/06/detecting-rectangle-using-opencv-java.html
|
---|
90 | //https://stackoverflow.com/questions/23327502/opencv-how-to-draw-minarearect-in-java
|
---|
91 | //https://stackoverflow.com/questions/30056910/opencv-java-modify-pixel-values
|
---|
92 |
|
---|
93 |
|
---|
94 | //GOAL for 21st
|
---|
95 |
|
---|
96 |
|
---|
97 | //Classifier 01
|
---|
98 | //Have args so can call "java image-identification-classifier01 XX XX"
|
---|
99 | //args can be parameters in algorthim such as threshold or theta?
|
---|
100 | //Run on 5000 images.
|
---|
101 | //Record success rates
|
---|
102 | //All done with makefile
|
---|
103 |
|
---|
104 |
|
---|
105 | //But first understand houghline transform
|
---|
106 | //Know what the algorithm being used is doing.
|
---|
107 | //MAke constants for this classifier
|
---|
108 | //Make java be able to run on CMD line
|
---|
109 |
|
---|
110 | public class MainMorph {
|
---|
111 | //CODE VERSIONS
|
---|
112 | static int CODE_VERSION = 8;
|
---|
113 | static int IMAGE_VERSION = 3;
|
---|
114 | //GLOBAL_CONSTANTS
|
---|
115 |
|
---|
116 | static double THRESHOLD_C = 4;
|
---|
117 | static double THRESHOLD_AREA_SIZE = 1000;
|
---|
118 | static double THRESHOLD_AREA_COUNT = 2;
|
---|
119 |
|
---|
120 | //
|
---|
121 |
|
---|
122 | private static void imageViewer(String winName, Mat img) {
|
---|
123 | try {
|
---|
124 | //Internal display - Overview - Will Distort High Res images
|
---|
125 | if(IMAGE_VERSION == 1) {
|
---|
126 | HighGui.namedWindow(winName, HighGui.WINDOW_NORMAL);
|
---|
127 | HighGui.resizeWindow(winName, 1000, 1000);
|
---|
128 | imshow(winName, img);
|
---|
129 |
|
---|
130 | HighGui.moveWindow(winName, 500, 0);
|
---|
131 | HighGui.waitKey(0);
|
---|
132 |
|
---|
133 | HighGui.destroyWindow(winName);
|
---|
134 | }
|
---|
135 | //Internal display - Segmented - Will _NOT_ Distort High Res images
|
---|
136 | if(IMAGE_VERSION == 2) {
|
---|
137 | HighGui.namedWindow(winName, HighGui.WINDOW_AUTOSIZE);
|
---|
138 | HighGui.resizeWindow(winName, 1000, 1000);
|
---|
139 | imshow(winName, img);
|
---|
140 | HighGui.moveWindow(winName, 500, 0);
|
---|
141 | HighGui.waitKey(0);
|
---|
142 |
|
---|
143 | HighGui.destroyWindow(winName);
|
---|
144 | }
|
---|
145 | //External display - Save Images for manual viewing
|
---|
146 | if(IMAGE_VERSION == 3) {
|
---|
147 | //save file (testing purposes)
|
---|
148 | imwrite(winName+".jpg", img);
|
---|
149 | }
|
---|
150 | }
|
---|
151 | catch (Exception e){
|
---|
152 | e.printStackTrace();
|
---|
153 | }
|
---|
154 | }
|
---|
155 | //MAIN
|
---|
156 | public static void main(String[] args) {
|
---|
157 | System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
|
---|
158 | try {
|
---|
159 | //Variables
|
---|
160 | System.out.println("Running code version: " + CODE_VERSION);
|
---|
161 | Mat edgesDetected = new Mat();
|
---|
162 | Mat mid = new Mat();
|
---|
163 | Mat edgesDetectedRGB = new Mat();
|
---|
164 | Mat clustersFoundRGB = new Mat();
|
---|
165 |
|
---|
166 | String testDirectory = "/Scratch/cpb16/is-sheet-music-encore/image-identification-dev-02/image-identification-development/";
|
---|
167 | String directory = "/Scratch/cpb16/is-sheet-music-encore/download-images/MU/";
|
---|
168 | String hiresDirectory = "/Scratch/cpb16/is-sheet-music-encore/hires-download-images/";
|
---|
169 |
|
---|
170 | //!!!!!!!!!!!!!!!!!!!!!!!!!!!NOT!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
---|
171 | //mdp.39015097852365-2.png 176 lines Contents page.
|
---|
172 | //mdp.39015097852555-3.png 76 lines
|
---|
173 | //!!!!!!!!!!!!!!!!!!!!!!!!!!!NOTNOT!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
---|
174 | //coo.31924062612282-9.png 8 lines
|
---|
175 | //String default_file = directory+"NotSheetMusic/coo.31924062612282-9.png";
|
---|
176 | //String default_file = directory+"NotSheetMusic/mdp.39015097852365-2.png";
|
---|
177 | //String default_file =testDirectory+"TestImages/NotNot/mdp.39015080972303-3.png"; //WHY GREY?
|
---|
178 | //String default_file =hiresDirectory+"BK/NotSheetMusic/aeu.ark+=13960=t2q53nq6w-6.png";
|
---|
179 | //String default_file =hiresDirectory+"BK/NotSheetMusic/aeu.ark+=13960=t9z03w65z-4.png";
|
---|
180 | //String default_file =hiresDirectory+"MU/NotSheetMusic/aeu.ark+=13960=t0dv28v9r-1.png";
|
---|
181 | //String default_file =hiresDirectory+"MU/SheetMusic/emu.010001066823-9.png";
|
---|
182 | //String default_file =hiresDirectory+"MU/NotSheetMusic/mdp.39015096363935-1.png";
|
---|
183 | String default_file =hiresDirectory+"MU/SheetMusic/coo.31924062612282-9.png";
|
---|
184 |
|
---|
185 | //System.out.println(default_file);
|
---|
186 | //String default_file = "/Scratch/cpb16/is-sheet-music-encore/image-identification-terminal/TestImages/test-coo.31924062612282-9.png";
|
---|
187 | //String default_file = testDirectory+"TestImages/MorphTester.png";
|
---|
188 | //String default_file = testDirectory+"TestImages/NotSheetMusic01.png";
|
---|
189 | //String default_file = testDirectory+"TestImages/NotSheetMusic02.png";
|
---|
190 | //String default_file = testDirectory+"TestImages/SheetMusic01.png";
|
---|
191 | //String default_file = testDirectory+"TestImages/SheetMusic02.png";
|
---|
192 | //String default_file = testDirectory+"TestImages/vLine.png";
|
---|
193 | String filename = ((args.length > 0) ? args[0] : default_file);
|
---|
194 | File file = new File(filename);
|
---|
195 | if(!file.exists()){System.err.println("Image not found: "+ filename);}
|
---|
196 |
|
---|
197 | int horizontalLineCount =0;
|
---|
198 |
|
---|
199 | // Load an image
|
---|
200 | Mat original1 = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
|
---|
201 | System.out.println("Width: " + original1.width() + " Height: " + original1.height());
|
---|
202 | Mat original = original1.clone();
|
---|
203 |
|
---|
204 | Imgproc.adaptiveThreshold(original1, original,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, THRESHOLD_C);
|
---|
205 | //TEST PARAMETERSImgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 531,1);
|
---|
206 | //Imgproc.threshold(original,original, 127, 255, Imgproc.THRESH_BINARY);
|
---|
207 |
|
---|
208 |
|
---|
209 | //****************MORPHOLOGY****************************************************************************************
|
---|
210 | //ADDIOTIONAL FILTERING TO STOP STREAKS
|
---|
211 | //LOOK INTO STREAKS MORPHOGOLY.
|
---|
212 | //****************MORPHOLOGY****************************************************************************************
|
---|
213 |
|
---|
214 | // Create the images that will use to extract the horizontal and vertical lines
|
---|
215 |
|
---|
216 | //dynamic morphology??
|
---|
217 | if(CODE_VERSION == 1) {
|
---|
218 | int hori = original.width();
|
---|
219 | int vert = original.height();
|
---|
220 | //Find ratio between 100 and width and 100 and height
|
---|
221 | int divX = hori/10;
|
---|
222 | int divY = vert/10;
|
---|
223 | int sizeX = (hori/divX) * 10;
|
---|
224 | int sizeY = (vert/divY) * 10;
|
---|
225 |
|
---|
226 | Mat test = original.clone();
|
---|
227 | imageViewer("Original", test);
|
---|
228 |
|
---|
229 | System.out.println("hori: " + hori + '\t' + "vert: " + vert);
|
---|
230 | System.out.println("sizeX: " + sizeX + '\t' + "sizeY: " + sizeY);
|
---|
231 |
|
---|
232 | Mat kernelErode = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(sizeX, (sizeY/100)));
|
---|
233 | Imgproc.erode(test,test,kernelErode);
|
---|
234 | imageViewer("01 Erode", test);
|
---|
235 |
|
---|
236 | Mat kernelDialate = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(sizeX,(sizeY/10)));
|
---|
237 | Imgproc.dilate(test, test, kernelDialate);
|
---|
238 | imageViewer("02 Dialate", test);
|
---|
239 |
|
---|
240 | Mat kernelErodeAgain = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size((sizeX/10),(sizeY/5)));
|
---|
241 | Imgproc.erode(test,test,kernelErodeAgain);
|
---|
242 | imageViewer(" 03 Erode Again", test);
|
---|
243 |
|
---|
244 | Mat kernelClose = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size((sizeX/10)*3,(sizeY/10)*3));
|
---|
245 | Imgproc.morphologyEx(test,test,Imgproc.MORPH_CLOSE, kernelClose);
|
---|
246 | imageViewer("04 Close", test);
|
---|
247 |
|
---|
248 | Imgproc.adaptiveThreshold(test, test,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, THRESHOLD_C);
|
---|
249 | imageViewer("05 Binarized", test);
|
---|
250 |
|
---|
251 | Mat kernelOpen = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size((sizeX/10),(sizeY/20)));
|
---|
252 | Imgproc.morphologyEx(test,test,Imgproc.MORPH_OPEN, kernelOpen);
|
---|
253 | imageViewer(" 06 Open", test);
|
---|
254 |
|
---|
255 | Mat kernelDialateAgain = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size((sizeX/5),(sizeY/100)));
|
---|
256 | Imgproc.dilate(test, test, kernelDialateAgain);
|
---|
257 | imageViewer("07 Dialate", test);
|
---|
258 |
|
---|
259 |
|
---|
260 | Mat kernelCloseAgain = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size((sizeX/10),(sizeY/2)));
|
---|
261 | Imgproc.morphologyEx(test,test,Imgproc.MORPH_CLOSE, kernelCloseAgain);
|
---|
262 | imageViewer(" 08 Close Again (Final)", test);
|
---|
263 | }
|
---|
264 | //Successful hardcode for morhpology
|
---|
265 | if (CODE_VERSION == 2) {
|
---|
266 |
|
---|
267 | //MAKE SURE BLACK & WHITE
|
---|
268 | Mat test = original.clone();
|
---|
269 | imageViewer("00 Binarized Original", test);
|
---|
270 |
|
---|
271 | Mat kernelErode = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(100,1));
|
---|
272 | Imgproc.erode(test,test,kernelErode);
|
---|
273 | imageViewer("01 Erode", test);
|
---|
274 |
|
---|
275 | Mat kernelDialate = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(110,10));
|
---|
276 | Imgproc.dilate(test, test, kernelDialate);
|
---|
277 | imageViewer("02 Dialate", test);
|
---|
278 |
|
---|
279 | Mat kernelErodeAgain = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,20));
|
---|
280 | Imgproc.erode(test,test,kernelErodeAgain);
|
---|
281 | imageViewer(" 03 Erode Again", test);
|
---|
282 |
|
---|
283 | Mat kernelClose = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(35,20));
|
---|
284 | Imgproc.morphologyEx(test,test,Imgproc.MORPH_CLOSE, kernelClose);
|
---|
285 | imageViewer("04 Close", test);
|
---|
286 |
|
---|
287 | // Imgproc.threshold(test,test, 127, 255, Imgproc.THRESH_BINARY);
|
---|
288 | // imageViewer("05 Binarized", test);
|
---|
289 |
|
---|
290 | Mat kernelOpen = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(4,4));
|
---|
291 | Imgproc.morphologyEx(test,test,Imgproc.MORPH_OPEN, kernelOpen);
|
---|
292 | imageViewer(" 06 Open", test);
|
---|
293 |
|
---|
294 | // Mat kernelDialateAgain = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(1,10));
|
---|
295 | // Imgproc.dilate(test, test, kernelDialateAgain);
|
---|
296 | // imageViewer("07 Dialate", test);
|
---|
297 |
|
---|
298 | //FIGURE OUT FLOOD FILL!!
|
---|
299 | Imgproc.floodFill(test,test, new Point(1,1), new Scalar(2));
|
---|
300 |
|
---|
301 |
|
---|
302 | Mat kernelCloseAgain = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,50));
|
---|
303 | Imgproc.morphologyEx(test,test,Imgproc.MORPH_CLOSE, kernelCloseAgain);
|
---|
304 | imageViewer(" 08 Close Again (Final)", test);
|
---|
305 |
|
---|
306 | }
|
---|
307 | //Tutorial/Demo Code
|
---|
308 | if (CODE_VERSION == 3) {
|
---|
309 | Mat horizontal = original.clone();
|
---|
310 | Mat vertical = original.clone();
|
---|
311 | // Specify size on horizontal axis
|
---|
312 | int horizontal_size = horizontal.cols() / 50;
|
---|
313 | // Create structure element for extracting horizontal lines through morphology operations
|
---|
314 | Mat horizontalStructure = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(horizontal_size, 2));
|
---|
315 | // Apply morphology operations
|
---|
316 | Imgproc.erode(horizontal, horizontal, horizontalStructure);
|
---|
317 | Imgproc.dilate(horizontal, horizontal, horizontalStructure);
|
---|
318 | // Show extracted horizontal lines
|
---|
319 | imageViewer("horizontal", horizontal);
|
---|
320 | // Specify size on vertical axis
|
---|
321 | int vertical_size = vertical.rows() / 30;
|
---|
322 | // Create structure element for extracting vertical lines through morphology operations
|
---|
323 | Mat verticalStructure = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(1, vertical_size));
|
---|
324 | // Apply morphology operations
|
---|
325 | Imgproc.erode(vertical, vertical, verticalStructure);
|
---|
326 | Imgproc.dilate(vertical, vertical, verticalStructure);
|
---|
327 | // Show extracted vertical lines
|
---|
328 | imageViewer("vertical", vertical);
|
---|
329 | // Inverse vertical image
|
---|
330 | Core.bitwise_not(vertical, vertical);
|
---|
331 | imageViewer("vertical_bit", vertical);
|
---|
332 | // Extract edges and smooth image according to the logic
|
---|
333 | // 1. extract edges
|
---|
334 | // 2. dilate(edges)
|
---|
335 | // 3. src.copyTo(smooth)
|
---|
336 | // 4. blur smooth img
|
---|
337 | // 5. smooth.copyTo(src, edges)
|
---|
338 | // Step 1
|
---|
339 | Mat edges = new Mat();
|
---|
340 | Imgproc.adaptiveThreshold(vertical, edges, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY, 3, -2);
|
---|
341 | imageViewer("edges", edges);
|
---|
342 | // Step 2
|
---|
343 | Mat kernel = Mat.ones(2, 2, CvType.CV_8UC1);
|
---|
344 | Imgproc.dilate(edges, edges, kernel);
|
---|
345 | imageViewer("dilate", edges);
|
---|
346 | // Step 3
|
---|
347 | Mat smooth = new Mat();
|
---|
348 | vertical.copyTo(smooth);
|
---|
349 | // Step 4
|
---|
350 | Imgproc.blur(smooth, smooth, new Size(2, 2));
|
---|
351 | // Step 5
|
---|
352 | smooth.copyTo(vertical, edges);
|
---|
353 | // Show final result
|
---|
354 | imageViewer("smooth - final", vertical);
|
---|
355 | System.exit(0);
|
---|
356 | }
|
---|
357 | //Better morphology attempt - static
|
---|
358 | if(CODE_VERSION ==4) {
|
---|
359 |
|
---|
360 | //Display Original
|
---|
361 | imageViewer("original", original1);
|
---|
362 |
|
---|
363 | Mat test = original.clone();
|
---|
364 | Mat pre = original.clone();
|
---|
365 | Mat dst = new Mat();
|
---|
366 |
|
---|
367 | imageViewer("00 Inverse Binarized Original", test);
|
---|
368 |
|
---|
369 | //remove large items of no interest pre proc
|
---|
370 | //denoize
|
---|
371 | //heal
|
---|
372 | //fnd large images, write to a seperate mat.
|
---|
373 | //draw these onto orignal image(binerized) in red
|
---|
374 | //turn all red pixels in image to black
|
---|
375 |
|
---|
376 |
|
---|
377 | //denoize
|
---|
378 | Mat denoize = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(3,3));
|
---|
379 | Imgproc.morphologyEx(pre,pre, Imgproc.MORPH_OPEN, denoize);
|
---|
380 | imageViewer("Denoize - PRE", pre);
|
---|
381 |
|
---|
382 | //close up gaps
|
---|
383 | Mat gapCloser = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(5,5));
|
---|
384 | Imgproc.morphologyEx(pre,pre,Imgproc.MORPH_CLOSE, gapCloser);
|
---|
385 | imageViewer("gap closer - PRE", pre);
|
---|
386 |
|
---|
387 | Mat kernelHighlightLarge = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10, 10));
|
---|
388 | Imgproc.erode(pre,pre, kernelHighlightLarge);
|
---|
389 | imageViewer("Highlight Large - PRE", pre);
|
---|
390 |
|
---|
391 | //change white pixels to red
|
---|
392 | ArrayList<MatOfPoint> contoursPre = new ArrayList<MatOfPoint>();
|
---|
393 | Mat hierarchyPre = new Mat();
|
---|
394 |
|
---|
395 | //PARAMETERS: input image, output array of arrays, output array, contour retrieval mode, contour approximation method.
|
---|
396 | //(contours) output array of arrays: Detected contours. Each contour is stored as a vector of points
|
---|
397 | //(hierarchy) output array: Optional output vector, containing information about the image topology.
|
---|
398 | //https://docs.opencv.org/3.3.1/d3/dc0/group__imgproc__shape.html#ga17ed9f5d79ae97bd4c7cf18403e1689a
|
---|
399 |
|
---|
400 | Imgproc.findContours(pre, contoursPre, hierarchyPre, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
|
---|
401 |
|
---|
402 | //Draw contours and record areas
|
---|
403 |
|
---|
404 | Mat drawingPre = Mat.zeros(test.size(), CvType.CV_8UC3);
|
---|
405 | Mat mask = new Mat(drawingPre.size(), CV_8UC3,new Scalar(0));
|
---|
406 | Scalar colorPre = new Scalar(255, 255, 255);
|
---|
407 | Imgproc.drawContours(mask, contoursPre, -1, new Scalar(255, 255, 255), FILLED);
|
---|
408 | Imgproc.fillPoly(drawingPre, contoursPre,colorPre);
|
---|
409 | imageViewer("DRAWINGPRE", drawingPre);
|
---|
410 |
|
---|
411 |
|
---|
412 | //FIIIIIIIIIIIIIIIIIIIIIIIIIIXXXXXXXXXXXX
|
---|
413 | //Remove from main Mat
|
---|
414 | Core.bitwise_not(mask,mask);
|
---|
415 | imageViewer("MASK", mask);
|
---|
416 | imageViewer("TEST", test);
|
---|
417 |
|
---|
418 | drawingPre.copyTo(test, mask);
|
---|
419 | imageViewer("COMBINE", test);
|
---|
420 |
|
---|
421 | //start staff line detection
|
---|
422 |
|
---|
423 | Mat kernelErode = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,1));
|
---|
424 | Imgproc.erode(test,test,kernelErode);
|
---|
425 | imageViewer("01 Erode plus pre", test);
|
---|
426 |
|
---|
427 | Mat kernelDilate = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(20,3));
|
---|
428 | Imgproc.dilate(test,test,kernelDilate);
|
---|
429 | imageViewer("02 Dilate", test);
|
---|
430 |
|
---|
431 | Mat kernelOpening = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(4,4));
|
---|
432 | Imgproc.morphologyEx(test, test, Imgproc.MORPH_CLOSE, kernelOpening);
|
---|
433 | imageViewer("03 Open", test);
|
---|
434 |
|
---|
435 | Mat kernelErode02 = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(8,8));
|
---|
436 | Imgproc.erode(test,test,kernelErode02);
|
---|
437 | imageViewer("04 Erode (Final)", test);
|
---|
438 |
|
---|
439 |
|
---|
440 | //DETECT OUTLINE AND FIND AREA OF THESE LINES.
|
---|
441 | ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
|
---|
442 | Mat hierarchy = new Mat();
|
---|
443 |
|
---|
444 | //PARAMETERS: input image, output array of arrays, output array, contour retrieval mode, contour approximation method.
|
---|
445 | //(contours) output array of arrays: Detected contours. Each contour is stored as a vector of points
|
---|
446 | //(hierarchy) output array: Optional output vector, containing information about the image topology.
|
---|
447 | //https://docs.opencv.org/3.3.1/d3/dc0/group__imgproc__shape.html#ga17ed9f5d79ae97bd4c7cf18403e1689a
|
---|
448 |
|
---|
449 | Imgproc.findContours(test, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
|
---|
450 |
|
---|
451 | //Draw contours and record areas
|
---|
452 | Mat drawing = Mat.zeros(test.size(), CvType.CV_8UC3);
|
---|
453 | Mat drawing2 = Mat.zeros(test.size(), CvType.CV_8UC3);
|
---|
454 | int areaCounter = 0;
|
---|
455 | for (int i = 0; i < contours.size(); i++) {
|
---|
456 | double area = Imgproc.contourArea(contours.get(i));
|
---|
457 | if(area > THRESHOLD_AREA_SIZE ) {
|
---|
458 | areaCounter++;
|
---|
459 | Scalar color = new Scalar(0, 0, 255);
|
---|
460 | Imgproc.drawContours(drawing, contours, i, color, 1);
|
---|
461 | System.out.println("AREA: " + area);
|
---|
462 | }
|
---|
463 | }
|
---|
464 |
|
---|
465 | //Classifier Calculation
|
---|
466 | if(areaCounter >= THRESHOLD_AREA_COUNT){
|
---|
467 | System.out.println("THIS IS SHEET MUSIC");
|
---|
468 | System.out.println(areaCounter);
|
---|
469 | }
|
---|
470 |
|
---|
471 |
|
---|
472 | //Show in a window
|
---|
473 | imageViewer("Contours", drawing);
|
---|
474 | }
|
---|
475 | //Better morphology attempt - dynamic
|
---|
476 | if(CODE_VERSION ==5) {
|
---|
477 | int hori = original.width();
|
---|
478 | int vert = original.height();
|
---|
479 | //Find ratio between 100 and width and 100 and height
|
---|
480 | int sizeX100 = 10 * (hori/68);
|
---|
481 | int sizeY100 = 10 * (vert/46);
|
---|
482 | int sizeX10 = (hori/68);
|
---|
483 | int sizeY10 = (vert/46);
|
---|
484 | int sizeX1 = (hori/68)/10;
|
---|
485 | int sizeY1 = (vert/46)/10;
|
---|
486 |
|
---|
487 | //SizeX should always be a 68th * 10. Based off the defualt tester image "coo.*"
|
---|
488 | //SizeT should always be a 46th * 10
|
---|
489 |
|
---|
490 | System.out.println(hori + " " + vert + " " + sizeX1 + " " + sizeY1);
|
---|
491 | //Display Original
|
---|
492 | imageViewer("original", original1);
|
---|
493 |
|
---|
494 | Mat test = original.clone();
|
---|
495 | imageViewer("00 Inverse Binarized Original", test);
|
---|
496 |
|
---|
497 | //Remove very large and wide black spaces (8th of the page)
|
---|
498 | //Mat kernelRemoveLarge = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(hori/8, vert/8));
|
---|
499 | //Imgproc.erode(test,test, kernelRemoveLarge);
|
---|
500 | //imageViewer("Remove Large", test);
|
---|
501 |
|
---|
502 | //Eliminate things that are not long and thin
|
---|
503 | Mat kernelErode = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(sizeX10,sizeY1)); //new Size(10,1));
|
---|
504 | Imgproc.erode(test,test,kernelErode);
|
---|
505 | imageViewer("01 Erode", test);
|
---|
506 |
|
---|
507 | Mat kernelDilate = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(sizeX10*2,sizeY1*3)); //new Size(20,3));
|
---|
508 | Imgproc.dilate(test,test,kernelDilate);
|
---|
509 | imageViewer("02 Dilate", test);
|
---|
510 |
|
---|
511 | Mat kernelClose = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(sizeX1*4,sizeY1*4)); //new Size(4,4));
|
---|
512 | Imgproc.morphologyEx(test, test, Imgproc.MORPH_CLOSE, kernelClose);
|
---|
513 | imageViewer("03 Open", test);
|
---|
514 |
|
---|
515 | Mat kernelErode02 = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(sizeX1*8,sizeX1*8)); //new Size(8,8));
|
---|
516 | Imgproc.erode(test,test,kernelErode02);
|
---|
517 | imageViewer("04 Erode (Final)", test);
|
---|
518 |
|
---|
519 |
|
---|
520 | //DETECT OUTLINE AND FIND AREA OF THESE LINES.
|
---|
521 | ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
|
---|
522 | Mat hierarchy = new Mat();
|
---|
523 |
|
---|
524 | //PARAMETERS: input image, output array of arrays, output array, contour retrieval mode, contour approximation method.
|
---|
525 | //(contours) output array of arrays: Detected contours. Each contour is stored as a vector of points
|
---|
526 | //(hierarchy) output array: Optional output vector, containing information about the image topology.
|
---|
527 | //https://docs.opencv.org/3.3.1/d3/dc0/group__imgproc__shape.html#ga17ed9f5d79ae97bd4c7cf18403e1689a
|
---|
528 |
|
---|
529 | Imgproc.findContours(test, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
|
---|
530 |
|
---|
531 | //Draw contours and record areas
|
---|
532 | Mat drawing = Mat.zeros(test.size(), CvType.CV_8UC3);
|
---|
533 | int areaCounter = 0;
|
---|
534 | for (int i = 0; i < contours.size(); i++) {
|
---|
535 | double area = Imgproc.contourArea(contours.get(i));
|
---|
536 | if(area > THRESHOLD_AREA_SIZE ) {
|
---|
537 | areaCounter++;
|
---|
538 | Scalar color = new Scalar(0, 0, 255);
|
---|
539 | Imgproc.drawContours(drawing, contours, i, color, 1);
|
---|
540 | System.out.println("AREA: " + area);
|
---|
541 | }
|
---|
542 | }
|
---|
543 |
|
---|
544 | //Classifier Calculation
|
---|
545 | if(areaCounter >= THRESHOLD_AREA_COUNT){
|
---|
546 | System.out.println("THIS IS SHEET MUSIC");
|
---|
547 | System.out.println(areaCounter);
|
---|
548 | }
|
---|
549 |
|
---|
550 |
|
---|
551 | //Show in a window
|
---|
552 | imageViewer("Contours", drawing);
|
---|
553 | }
|
---|
554 | //MASK UNDERSTANDING
|
---|
555 | if(CODE_VERSION ==6) {
|
---|
556 | String path ="/Scratch/cpb16/is-sheet-music-encore/hires-download-images/MU/NotSheetMusic/aeu.ark+=13960=t0dv28v9r-1.png";
|
---|
557 | Mat mask = new Mat();
|
---|
558 | Mat dst = new Mat();
|
---|
559 | //Get source image and binerize
|
---|
560 | Mat src = Imgcodecs.imread(path, Imgcodecs.IMREAD_GRAYSCALE);
|
---|
561 | Imgproc.adaptiveThreshold(original1, src,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, THRESHOLD_C);
|
---|
562 | imageViewer("src", src);
|
---|
563 |
|
---|
564 | //Find unwanted material, then invert it so mask removes not keeps.
|
---|
565 | Mat kernelErode = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(100,1));
|
---|
566 | Imgproc.erode(src,mask,kernelErode);
|
---|
567 | Mat kernelDilate = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(110,10));
|
---|
568 | Imgproc.dilate(mask, mask, kernelDilate);
|
---|
569 | Core.bitwise_not(mask,mask);
|
---|
570 | imageViewer("mask", mask);
|
---|
571 |
|
---|
572 | //Copy source image to new Mat, with mask in use
|
---|
573 | src.copyTo(dst, mask);
|
---|
574 | imageViewer("dst", dst);
|
---|
575 |
|
---|
576 |
|
---|
577 |
|
---|
578 |
|
---|
579 | }
|
---|
580 | //Mask implementation
|
---|
581 | if(CODE_VERSION ==7) {
|
---|
582 |
|
---|
583 | //Display Original
|
---|
584 | imageViewer("original", original1);
|
---|
585 |
|
---|
586 | Mat src = original.clone();
|
---|
587 | Mat test = original.clone();
|
---|
588 | Mat mask = new Mat();
|
---|
589 | Mat dst = new Mat();
|
---|
590 |
|
---|
591 | imageViewer("00 Inverse Binarized Original", src);
|
---|
592 |
|
---|
593 | //denoize
|
---|
594 | Mat denoize = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(3,3));
|
---|
595 | Imgproc.morphologyEx(src,mask, Imgproc.MORPH_OPEN, denoize);
|
---|
596 | imageViewer("01 Denoize - mask", mask);
|
---|
597 |
|
---|
598 | //close up gaps
|
---|
599 | Mat gapCloser = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(5,5));
|
---|
600 | Imgproc.morphologyEx(mask,mask,Imgproc.MORPH_CLOSE, gapCloser);
|
---|
601 | imageViewer("02 gap closer - mask", mask);
|
---|
602 |
|
---|
603 | //Isolate large items
|
---|
604 | Mat isolateLarge = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(8, 8));
|
---|
605 | Imgproc.morphologyEx(mask,mask,Imgproc.MORPH_OPEN, isolateLarge);
|
---|
606 | imageViewer("03 Isolate Large - mask", mask);
|
---|
607 | Core.bitwise_not(mask,mask);
|
---|
608 |
|
---|
609 | //Remove unwanted large items from image
|
---|
610 | src.copyTo(dst, mask);
|
---|
611 | imageViewer("04 Large Items Removed", dst);
|
---|
612 |
|
---|
613 | //start staff line detection
|
---|
614 |
|
---|
615 | Mat kernelErode = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,1));
|
---|
616 | Imgproc.erode(dst,test,kernelErode);
|
---|
617 | imageViewer("11 Erode plus pre", test);
|
---|
618 |
|
---|
619 | Mat kernelDilate = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(20,3));
|
---|
620 | Imgproc.dilate(test,test,kernelDilate);
|
---|
621 | imageViewer("12 Dilate", test);
|
---|
622 |
|
---|
623 | Mat kernelOpening = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(4,4));
|
---|
624 | Imgproc.morphologyEx(test, test, Imgproc.MORPH_CLOSE, kernelOpening);
|
---|
625 | imageViewer("13 Open", test);
|
---|
626 |
|
---|
627 | Mat kernelErode02 = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(8,8));
|
---|
628 | Imgproc.erode(test,test,kernelErode02);
|
---|
629 | imageViewer("14 Erode (Final)", test);
|
---|
630 |
|
---|
631 |
|
---|
632 | //DETECT OUTLINE AND FIND AREA OF THESE LINES.
|
---|
633 | ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
|
---|
634 | Mat hierarchy = new Mat();
|
---|
635 |
|
---|
636 | //PARAMETERS: input image, output array of arrays, output array, contour retrieval mode, contour approximation method.
|
---|
637 | //(contours) output array of arrays: Detected contours. Each contour is stored as a vector of points
|
---|
638 | //(hierarchy) output array: Optional output vector, containing information about the image topology.
|
---|
639 | //https://docs.opencv.org/3.3.1/d3/dc0/group__imgproc__shape.html#ga17ed9f5d79ae97bd4c7cf18403e1689a
|
---|
640 |
|
---|
641 | Imgproc.findContours(test, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
|
---|
642 |
|
---|
643 | //Draw contours and record areas
|
---|
644 | Mat drawing = Mat.zeros(test.size(), CvType.CV_8UC3);
|
---|
645 | int areaCounter = 0;
|
---|
646 |
|
---|
647 |
|
---|
648 | Imgproc.drawContours(drawing, contours, -1, new Scalar(0, 255, 0), FILLED);
|
---|
649 | // for (int i = 0; i < contours.size(); i++) {
|
---|
650 | // Scalar color = new Scalar(0, i, i);
|
---|
651 | // double area = Imgproc.contourArea(contours.get(i));
|
---|
652 | // Imgproc.drawContours(drawing, contours, i, color, FILLED);
|
---|
653 | // System.out.println("AREA: " + area);
|
---|
654 | //
|
---|
655 | // }
|
---|
656 | imageViewer("Contours found", drawing);
|
---|
657 |
|
---|
658 | //Classifier Calculation
|
---|
659 | if(areaCounter >= THRESHOLD_AREA_COUNT){
|
---|
660 | System.out.println("THIS IS SHEET MUSIC");
|
---|
661 | System.out.println(areaCounter);
|
---|
662 | }
|
---|
663 |
|
---|
664 |
|
---|
665 | }
|
---|
666 | //Mask implementation - HIGH RES NUMBER MOD
|
---|
667 | if(CODE_VERSION ==8) {
|
---|
668 |
|
---|
669 | //Display Original
|
---|
670 | imageViewer("original", original1);
|
---|
671 |
|
---|
672 | Mat src = original.clone();
|
---|
673 | Mat test = original.clone();
|
---|
674 | Mat mask = new Mat();
|
---|
675 | Mat dst = new Mat();
|
---|
676 |
|
---|
677 | imageViewer("00 Inverse Binarized Original", src);
|
---|
678 |
|
---|
679 | //Close then Open
|
---|
680 |
|
---|
681 | // Mat firstKernel = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,10));
|
---|
682 | // Imgproc.morphologyEx(src, mask, Imgproc.MORPH_CLOSE, firstKernel);
|
---|
683 | // imageViewer("01 Closed - mask", mask);
|
---|
684 | //
|
---|
685 | // Mat secondKernel = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,10));
|
---|
686 | // Imgproc.morphologyEx(src,mask, Imgproc.MORPH_OPEN, secondKernel);
|
---|
687 | // imageViewer("02 Open - mask", mask);
|
---|
688 |
|
---|
689 |
|
---|
690 | //denoize
|
---|
691 | Mat denoize = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(5,5));
|
---|
692 | Imgproc.morphologyEx(src,mask, Imgproc.MORPH_OPEN, denoize);
|
---|
693 | imageViewer("01 Denoize - mask", mask);
|
---|
694 |
|
---|
695 | //close up gaps
|
---|
696 | Mat gapCloser = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(5,5));
|
---|
697 | Imgproc.morphologyEx(mask,mask,Imgproc.MORPH_CLOSE, gapCloser);
|
---|
698 | imageViewer("02 gap closer - mask", mask);
|
---|
699 |
|
---|
700 | //Isolate large items
|
---|
701 | Mat isolateLarge = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(8, 8));
|
---|
702 | Imgproc.morphologyEx(mask,mask,Imgproc.MORPH_OPEN, isolateLarge);
|
---|
703 | imageViewer("03 Isolate Large - mask", mask);
|
---|
704 | Core.bitwise_not(mask,mask);
|
---|
705 |
|
---|
706 | //Remove unwanted large items from image
|
---|
707 | src.copyTo(dst, mask);
|
---|
708 | imageViewer("04 Large Items Removed", dst);
|
---|
709 |
|
---|
710 | //start staff line detection
|
---|
711 |
|
---|
712 | Mat kernelErode = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(15,2)); //10,2
|
---|
713 | Imgproc.erode(dst,test,kernelErode);
|
---|
714 | imageViewer("11 Erode plus pre", test);
|
---|
715 |
|
---|
716 | Mat kernelDilate = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,4)); //20,3
|
---|
717 | Imgproc.dilate(test,test,kernelDilate);
|
---|
718 | imageViewer("12 Dilate", test);
|
---|
719 |
|
---|
720 | Mat kernelOpening = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,4)); //4,4
|
---|
721 | Imgproc.morphologyEx(test, test, Imgproc.MORPH_CLOSE, kernelOpening);
|
---|
722 | imageViewer("13 Open", test);
|
---|
723 |
|
---|
724 | Mat kernelErode02 = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,4)); //10,1
|
---|
725 | Imgproc.erode(test,test,kernelErode02);
|
---|
726 | imageViewer("14 Erode (Final)", test);
|
---|
727 |
|
---|
728 |
|
---|
729 | //DETECT OUTLINE AND FIND AREA OF THESE LINES.
|
---|
730 | ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
|
---|
731 | Mat hierarchy = new Mat();
|
---|
732 |
|
---|
733 | //PARAMETERS: input image, output array of arrays, output array, contour retrieval mode, contour approximation method.
|
---|
734 | //(contours) output array of arrays: Detected contours. Each contour is stored as a vector of points
|
---|
735 | //(hierarchy) output array: Optional output vector, containing information about the image topology.
|
---|
736 | //https://docs.opencv.org/3.3.1/d3/dc0/group__imgproc__shape.html#ga17ed9f5d79ae97bd4c7cf18403e1689a
|
---|
737 |
|
---|
738 | Imgproc.findContours(test, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
|
---|
739 |
|
---|
740 | //Draw contours and record areas
|
---|
741 | Mat drawing = Mat.zeros(test.size(), CvType.CV_8UC3);
|
---|
742 | int areaCounter = 0;
|
---|
743 |
|
---|
744 | //Have created a preprocess to remove large objects.
|
---|
745 | //Need to now finalized Classifier, re try area detection.
|
---|
746 | //Paths to take - rectangle boxes around detected contours over threshold (area or perimeter)
|
---|
747 | //Just use area and periemter to determine if sheet music
|
---|
748 | //Discuss with david before weekend perhaps?
|
---|
749 |
|
---|
750 | Imgproc.drawContours(drawing, contours, -1, new Scalar(0, 255, 0), 1); //USES LINE_8
|
---|
751 | // for (int i = 0; i < contours.size(); i++) {
|
---|
752 | // Scalar color = new Scalar(0, i, i);
|
---|
753 | // double area = Imgproc.contourArea(contours.get(i));
|
---|
754 | // Imgproc.drawContours(drawing, contours, i, color, FILLED);
|
---|
755 | // System.out.println("AREA: " + area);
|
---|
756 | //
|
---|
757 | // }
|
---|
758 | imageViewer("Contours found", drawing);
|
---|
759 |
|
---|
760 | //Classifier Calculation
|
---|
761 | if(areaCounter >= THRESHOLD_AREA_COUNT){
|
---|
762 | System.out.println("THIS IS SHEET MUSIC");
|
---|
763 | System.out.println(areaCounter);
|
---|
764 | }
|
---|
765 |
|
---|
766 |
|
---|
767 | }
|
---|
768 |
|
---|
769 | //USE stuc element, to rule out large wide and long pieces of black and white.
|
---|
770 |
|
---|
771 |
|
---|
772 | //****************MORPHOLOGY****************************************************************************************
|
---|
773 |
|
---|
774 | //BufferedImage toBeClassifiedImg = toBufferedImage(edgesDetectedRGB);
|
---|
775 |
|
---|
776 | //Display Results
|
---|
777 | //HighGui.imshow("Source", original);
|
---|
778 | //HighGui.imshow("Just Edges", justEdges); //TESTING
|
---|
779 |
|
---|
780 |
|
---|
781 | imshow("LINESFOUND", edgesDetectedRGB);
|
---|
782 | HighGui.resizeWindow("LINESFOUND", 1000,1000);
|
---|
783 |
|
---|
784 | //HighGui.imshow("CLUSTERS FOUND", clustersFoundRGB);
|
---|
785 | //HighGui.imshow("Detected Lines (in red) - negative", edgesDetectedRGBProb);
|
---|
786 |
|
---|
787 | //COUNT OF LINES CLASSIFICATION
|
---|
788 | //System.out.println("LINE CLUSTER RESULT: " + ClassifierLineClusterOLD(toBeClassifiedImg).get(0) + '\t' + "LinesFound: " + ClassifierLineClusterOLD(toBeClassifiedImg).get(1) + '\t' + "ClustersFound: " + ClassifierLineClusterOLD(toBeClassifiedImg).get(2));
|
---|
789 | //System.out.println("NEW CLUSTER RESULTS: " + ClassifierLineClusterPt(pointArrayList,clustersFoundRGB).get(0) + '\t' + "LinesFound: " + horizontalLineCount + '\t' + "ClustersFound: " + ClassifierLineClusterPt(pointArrayList,clustersFoundRGB).get(1));
|
---|
790 | //System.out.println(ClassifierLineClusterPt(pointArrayList, clustersFoundRGB));
|
---|
791 |
|
---|
792 | //System.out.println("TEST: " + LineCountOrCluster(horizontalLineCount, pointArrayList, clustersFoundRGB));
|
---|
793 |
|
---|
794 | // Wait and Exit
|
---|
795 | HighGui.waitKey();
|
---|
796 | System.exit(0);
|
---|
797 | }
|
---|
798 | catch(Exception e){
|
---|
799 | System.err.println(e);
|
---|
800 | }
|
---|
801 | }
|
---|
802 | }
|
---|