Finding the American flag in a picture?
为了纪念7月4日，我有兴趣找到一种在图片中探测美国国旗的程序性方法。关于在图片中找到可口可乐罐，有一个早先流行的问题描述了许多解决这个问题的好方法，尽管我不确定它们是否适用于旗帜，因为
是否有任何标准的图像处理或识别技术特别适合这项任务？
我的方法概括了这个问题，事实上，在蓝色区域附近寻找一个红色和白色的条纹图案(水平或垂直)。因此，它适用于只有美国国旗有这种图案的场景。
我的方法是用Java开发的，使用Marvin Framework。
算法：
输入：
滤色器：
旗帜：
更有趣的是，在有许多标志的情况下的性能。
输入：
滤色器：
图案匹配：
旗帜：
源代码：
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public class AmericanFlag { public AmericanFlag(){ process("./res/flags/","flag_0", Color.yellow); process("./res/flags/","flag_1", Color.yellow); process("./res/flags/","flag_2", Color.yellow); process("./res/flags/","flag_3", Color.yellow); process("./res/flags/","flag_4", Color.blue); } private void process(String dir, String fileName, Color color){ MarvinImage originalImage = MarvinImageIO.loadImage(dir+fileName+".jpg"); MarvinImage image = originalImage.clone(); colorFilter(image); MarvinImageIO.saveImage(image, dir+fileName+"_color.png"); MarvinImage output = new MarvinImage(image.getWidth(), image.getHeight()); output.clear(0xFFFFFFFF); findStripsH(image, output); findStripsV(image, output); MarvinImageIO.saveImage(output, dir+fileName+"_1.png"); MarvinImage bin = MarvinColorModelConverter.rgbToBinary(output, 127); morphologicalErosion(bin.clone(), bin, MarvinMath.getTrueMatrix(5, 5)); morphologicalDilation(bin.clone(), bin, MarvinMath.getTrueMatrix(15, 15)); MarvinImageIO.saveImage(bin, dir+fileName+"_2.png"); int[] centroid = getCentroid(bin); image.fillRect(centroid[0], centroid[1], 30, 30, Color.yellow); int area = getMass(bin); boolean blueNeighbors = hasBlueNeighbors(image, bin, centroid[0], centroid[1], area); if(blueNeighbors){ int[] seg = getSegment(bin); for(int i=0; i<4; i++){ originalImage.drawRect(seg[0]+i, seg[1]+i, seg[2]-seg[0], seg[3]-seg[1], color); } MarvinImageIO.saveImage(originalImage, dir+fileName+"_final.png"); } } private boolean hasBlueNeighbors(MarvinImage image, MarvinImage bin, int centerX, int centerY, int area){ int totalBlue=0; int r,g,b; int maxDistance = (int)(Math.sqrt(area)*1.2); for(int y=0; y<image.getHeight(); y++){ for(int x=0; x<image.getWidth(); x++){ r = image.getIntComponent0(x, y); g = image.getIntComponent1(x, y); b = image.getIntComponent2(x, y); if( (b == 255 && r == 0 && g == 0) && (MarvinMath.euclideanDistance(x, y, centerX, centerY) < maxDistance) ){ totalBlue++; bin.setBinaryColor(x, y, true); } } } if(totalBlue > area/5){ return true; } return false; } private int[] getCentroid(MarvinImage bin){ long totalX=0, totalY=0, totalPixels=0; for(int y=0; y<bin.getHeight(); y++){ for(int x=0; x<bin.getWidth(); x++){ if(bin.getBinaryColor(x, y)){ totalX += x; totalY += y; totalPixels++; } } } totalPixels = Math.max(1, totalPixels); return new int[]{(int)(totalX/totalPixels), (int)(totalY/totalPixels)}; } private int getMass(MarvinImage bin){ int totalPixels=0; for(int y=0; y<bin.getHeight(); y++){ for(int x=0; x<bin.getWidth(); x++){ if(bin.getBinaryColor(x, y)){ totalPixels++; } } } return totalPixels; } private int[] getSegment(MarvinImage bin){ int x1=-1, x2=-1, y1=-1, y2=-1; for(int y=0; y<bin.getHeight(); y++){ for(int x=0; x<bin.getWidth(); x++){ if(bin.getBinaryColor(x, y)){ if(x1 == -1 || x < x1){ x1 = x; } if(x2 == -1 || x > x2){ x2 = x; } if(y1 == -1 || y < y1){ y1 = y; } if(y2 == -1 || y > y2){ y2 = y; } } } } return new int[]{x1,y1,x2,y2}; } private void findStripsH(MarvinImage imageIn, MarvinImage imageOut){ int strips=0; int totalPixels=0; int r,g,b; int patternStart; boolean cR=true; int patternLength = -1; for(int y=0; y<imageIn.getHeight(); y++){ patternStart = -1; strips = 0; patternLength=-1; for(int x=0; x<imageIn.getWidth(); x++){ r = imageIn.getIntComponent0(x, y); g = imageIn.getIntComponent1(x, y); b = imageIn.getIntComponent2(x, y); if(cR){ if(r == 255 && g == 0 && b == 0){ if(patternStart == -1){ patternStart = x;} totalPixels++; } else{ if(patternLength == -1){ if(totalPixels >=3 && totalPixels <= 100){ patternLength = (int)(totalPixels); } else{ totalPixels=0; patternStart=-1; strips=0; patternLength=-1; } } else{ if(totalPixels >= Math.max(patternLength*0.5,3) && totalPixels <= patternLength * 2){ strips++; totalPixels=1; cR = false; } else{ totalPixels=0; patternStart=-1; strips=0; patternLength=-1; } } } } else{ if(r == 255 && g == 255 && b == 255){ totalPixels++; } else{ if(totalPixels >= Math.max(patternLength*0.5,3) && totalPixels <= patternLength * 2){ strips++; totalPixels=1; cR = true; } else{ totalPixels=0; patternStart=-1; strips=0; patternLength=-1; cR=true; } } } if(strips >= 4){ imageOut.fillRect(patternStart, y, x-patternStart, 2, Color.black); totalPixels=0; patternStart=-1; strips=0; patternLength=-1; cR=true; } } } } private void findStripsV(MarvinImage imageIn, MarvinImage imageOut){ int strips=0; int totalPixels=0; int r,g,b; int patternStart; boolean cR=true; int patternLength = -1; for(int x=0; x<imageIn.getWidth(); x++){ patternStart = -1; strips = 0; patternLength=-1; for(int y=0; y<imageIn.getHeight(); y++){ r = imageIn.getIntComponent0(x, y); g = imageIn.getIntComponent1(x, y); b = imageIn.getIntComponent2(x, y); if(cR){ if(r == 255 && g == 0 && b == 0){ if(patternStart == -1){ patternStart = y;} totalPixels++; } else{ if(patternLength == -1){ if(totalPixels >=3 && totalPixels <= 100){ patternLength = (int)(totalPixels); } else{ totalPixels=0; patternStart=-1; strips=0; patternLength=-1; } } else{ if(totalPixels >= Math.max(patternLength*0.5,3) && totalPixels <= patternLength * 2){ strips++; totalPixels=1; cR = false; } else{ totalPixels=0; patternStart=-1; strips=0; patternLength=-1; } } } // if(maxL != -1 && totalPixels > maxL){ // totalPixels=0; patternStart=-1; strips=0; maxL=-1; // } } else{ if(r == 255 && g == 255 && b == 255){ totalPixels++; } else{ if(totalPixels >= Math.max(patternLength*0.5,3) && totalPixels <= patternLength * 2){ strips++; totalPixels=1; cR = true; } else{ totalPixels=0; patternStart=-1; strips=0; patternLength=-1; cR=true; } } // if(maxL != -1 && totalPixels > maxL){ // totalPixels=0; patternStart=-1; strips=0; maxL=-1; // cR=true; // } } if(strips >= 4){ imageOut.fillRect(x, patternStart, 2, y-patternStart, Color.black); totalPixels=0; patternStart=-1; strips=0; patternLength=-1; cR=true; } } } } private void colorFilter(MarvinImage image){ int r,g,b; boolean isR, isB; for(int y=0; y<image.getHeight(); y++){ for(int x=0; x<image.getWidth(); x++){ r = image.getIntComponent0(x, y); g = image.getIntComponent1(x, y); b = image.getIntComponent2(x, y); isR = (r > 120 && r > g * 1.3 && r > b * 1.3); isB = (b > 30 && b < 150 && b > r * 1.3 && b > g * 1.3); if(isR){ image.setIntColor(x, y, 255,0,0); } else if(isB){ image.setIntColor(x, y, 0,0,255); } else{ image.setIntColor(x, y, 255,255,255); } } } } public static void main(String[] args) { new AmericanFlag(); } } |
其他结果：
您可以通过OpenCV库使用"模板匹配"。
以下是该方法背后的理论：
Template Matching is a method for searching and finding the location
of a template image in a larger image. OpenCV comes with a function
cv2.matchTemplate() for this purpose. It simply slides the template
image over the input image (as in 2D convolution) and compares the
template and patch of input image under the template image.
代码示例和实现说明如下：http://docs.opencv.org/master/d4/dc6/tutorial_py_template_matching.html_gsc.tab=0