watershed.py

import cv2
import os
import numpy as np
from example_plot import ver_duas_imagens, ver_tres_imagens, ver_quatro_imagens

import itertools
from matplotlib import pyplot as plt
import numpy as np

def ver_img(img):
    cv2.imshow('1.png', img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


def segmentar_watersherd(path, nome_img):

    img = cv2.imread(path)

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)

    # noise removal
    kernel = np.ones((3, 3), np.uint8)

    opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)

    # sure background area
    sure_bg = cv2.dilate(opening, kernel, iterations=3)

    # Finding sure foreground area
    dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)

    ret, sure_fg = cv2.threshold(dist_transform, 0.7 * dist_transform.max(), 255, 0)
    # Finding unknown region

    sure_fg = np.uint8(sure_fg)

    unknown = cv2.subtract(sure_bg, sure_fg)

    # Marker labelling
    ret, markers = cv2.connectedComponents(sure_fg)

    # ver_duas_imagens('normal', 'segmentada', opening, markers)

    # Add one to all labels so that sure background is not 0, but 1
    markers = markers + 1
    # Now, mark the region of unknown with zero
    markers[unknown == 255] = 0

    markers = cv2.watershed(img, markers)

    # img[markers == -1] = [255,0,0]

    cv2.imwrite(nome_img, markers)
    #return  markers
    #ver_quatro_imagens('sure_bg', 'sure_fb', 'unknow', 'markers', sure_bg, sure_fg, unknown, markers)
    #ver_duas_imagens('normal', 'segmentada', img, markers)


def plot_confusion_matrix(cm, classes,normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """

    """"""
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.show()