image-recognition

Hi I have a bunch of images. Let's assume all of them of the same size. The images have a black background and some quasi round green spots which represent fluorescence. I have to calculate the amount (in percentage) of fluorescence of each image. I.e. the area of green spots. Any idea how to do this, for example in Java? A few thoughts: You could do an edge detection and then perform a hough circle transform . This should work well if you already know the radius of the circles. When comparing colors you could use a color space that is better suited for fuzzy comparison. For example the HSV

I've programmed a solution in Python which worked great, but required several libraries to install and a lot of burocratic setup to work. I've decided to build it with a GUI in C# on Visual Studio Community 2017 but in the first successful function the result was way slower than in Python. Which IMO it should actually be faster. The code essentially is just doing a needle in a haystack image search, by getting all images from a folder and testing each needle (total 60 images) in a haystack, in python I return the string, but in C# I'm only printing. My code in Python is the following: def

I have a problem with java tensorflow API. I have run the training using the python tensorflow API, generating the files output_graph.pb and output_labels.txt. Now for some reason I want to use those files as input to the LabelImage module in java tensorflow API. I thought everything would have worked fine since that module wants exactly one .pb and one .txt. Nevertheless, when I run the module, I get this error: 2017-04-26 10:12:56.711402: W tensorflow/core/framework/op_def_util.cc:332] Op BatchNormWithGlobalNormalization is deprecated. It will cease to work in GraphDef version 9. Use tf.nn

Could you help me find the right algorithm for image resizing? I have an image of a number. The maximum size is 200x200, I need to get an image with size 15x15 or even less. The image is monochrome (black and white) and the result should be the same. That's the info about my task. I've already tried one algorithm, here it is // xscale, yscale - decrease/increase rate for (int f = 0; f<=49; f++) { for (int g = 0; g<=49; g++)//49+1 - final size { xpos = (int)f * xscale; ypos = (int)g * yscale; picture3[f][g]=picture4[xpos][ypos]; } } But it won't work with the decrease of an image, which is my

Thanks for help in advance. I have lots of R&D and search but I can't find any solution for detect blur image or not. I have used this https://github.com/BloodAxe/OpenCV-Tutorial and for blur detection used Laplacian formula but can't get blur detection in image -(void) checkForBurryImage:(UIImage *) image { cv::Mat matImage = [image toMat]; cv::Mat matImageGrey; cv::cvtColor(matImage, matImageGrey, CV_BGRA2GRAY); cv::Mat dst2 =[image toMat]; cv::Mat laplacianImage; dst2.convertTo(laplacianImage, CV_8UC1); cv::Laplacian(matImageGrey, laplacianImage, CV_8U); cv::Mat laplacianImage8bit;

I have a very strange problem. I'm using this code to detect an image in another one (java opencv): UPDATED CODE: public void startRecognition() { //load images, I want to find img_object in img_scene Mat img_scene = Highgui.imread("D:/opencvws/ImageRecognition/src/main/resources/ascene.jpg"); Mat img_object = Highgui.imread("D:/opencvws/ImageRecognition/src/main/resources/aobj1.jpg"); run++; System.out.println("RUN NO: " + run); //init detector FeatureDetector detector = FeatureDetector.create(FeatureDetector.SURF); //keypoint detection for both images (keyponts_scene for img_scene, keypoint