/*
 * Copyright (c) 2011-2016, Peter Abeles. All Rights Reserved.
 *
 * This file is part of BoofCV (http://boofcv.org).
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

import boofcv.alg.filter.binary.BinaryImageOps;
import boofcv.alg.filter.binary.Contour;
import boofcv.alg.filter.binary.GThresholdImageOps;
import boofcv.alg.filter.binary.ThresholdImageOps;
import boofcv.gui.ListDisplayPanel;
import boofcv.gui.binary.VisualizeBinaryData;
import boofcv.gui.image.ShowImages;
import boofcv.io.UtilIO;
import boofcv.io.image.ConvertBufferedImage;
import boofcv.io.image.UtilImageIO;
import boofcv.struct.ConnectRule;
import boofcv.struct.image.GrayF32;
import boofcv.struct.image.GrayS32;
import boofcv.struct.image.GrayU8;
import jhplot.*;
import java.awt.image.BufferedImage;
import java.util.List;

/**
 * Demonstrates how to create binary images by thresholding, applying binary morphological operations, and
 * then extracting detected features by finding their contours.
 *
 * @see boofcv.examples.segmentation.ExampleThresholding
 *
 * @author Peter Abeles
 */
public class identify_edge_detection  {

	public static void main( String args[] ) {
		// load and convert the image into a usable format


               String http="http://datamelt.org/examples/data/";
               Web.get(http+"cells.png");
	       BufferedImage image = UtilImageIO.loadImage("cells.png");

		// convert into a usable format
		GrayF32 input = ConvertBufferedImage.convertFromSingle(image, null, GrayF32.class);
		GrayU8 binary = new GrayU8(input.width,input.height);
		GrayS32 label = new GrayS32(input.width,input.height);

		// Select a global threshold using Otsu's method.
		double threshold = GThresholdImageOps.computeOtsu(input, 0, 255);

		// Apply the threshold to create a binary image
		ThresholdImageOps.threshold(input,binary,(float)threshold,true);

		// remove small blobs through erosion and dilation
		// The null in the input indicates that it should internally declare the work image it needs
		// this is less efficient, but easier to code.
		GrayU8 filtered = BinaryImageOps.erode8(binary, 1, null);
		filtered = BinaryImageOps.dilate8(filtered, 1, null);

		// Detect blobs inside the image using an 8-connect rule
		List contours = BinaryImageOps.contour(filtered, ConnectRule.EIGHT, label);

		// colors of contours
		int colorExternal = 0xFFFFFF;
		int colorInternal = 0xFF2020;

		// display the results
		BufferedImage visualBinary = VisualizeBinaryData.renderBinary(binary, false, null);
		BufferedImage visualFiltered = VisualizeBinaryData.renderBinary(filtered, false, null);
		BufferedImage visualLabel = VisualizeBinaryData.renderLabeledBG(label, contours.size(), null);
		BufferedImage visualContour = VisualizeBinaryData.renderContours(contours, colorExternal, colorInternal,
				input.width, input.height, null);

		ListDisplayPanel panel = new ListDisplayPanel();
		panel.addImage(visualBinary, "Binary Original");
		panel.addImage(visualFiltered, "Binary Filtered");
		panel.addImage(visualLabel, "Labeled Blobs");
		panel.addImage(visualContour, "Contours");
		ShowImages.showWindow(panel,"Binary Operations",true);
	}

}