Question about distance, boundary, segmentation and watershed algorithm.

[0ozzy0]

Hello, at first, i like to thank you about the code that you have shared. My question is, how can we obtain 1) the probability of belonging to a field; 2) the probability
of belonging to a boundary; and 3) the distance to the closest
boundary with the corresponding code and have you used watershed algorithm as the resunet article suggests.

`dist = ZeroPadding2D(padding=1)(x1)
dist = Conv2D(32, 3)(dist)
dist = self.normalization(dist, self.layer_norm)
dist = Activation('relu')(dist)
dist = ZeroPadding2D(padding=1)(dist)
dist = Conv2D(32, 3)(dist)
dist = self.normalization(dist, self.layer_norm)
dist = Activation('relu')(dist)
dist = Conv2D(self.num_classes, 1, activation='softmax', name = 'distance')(dist)

    bound = Concatenate(axis=-1)([x, dist])
    bound = ZeroPadding2D(padding=1)(bound)
    bound = Conv2D(32, 3)(bound)
    bound = self.normalization(bound, self.layer_norm)
    bound = Activation('relu')(bound)
    bound = Conv2D(self.num_classes, 1, activation='sigmoid', name = 'boundary')(bound)

    seg = Concatenate(axis=-1)([x,bound,dist])
    seg = ZeroPadding2D(padding=1)(seg)
    seg = Conv2D(32, 3)(seg)
    seg = self.normalization(seg, self.layer_norm)
    seg = Activation('relu')(seg)
    seg = ZeroPadding2D(padding=1)(seg)
    seg = Conv2D(32, 3)(seg)
    seg = self.normalization(seg, self.layer_norm)
    seg = Activation('relu')(seg)
    seg = Conv2D(self.num_classes, 1, activation='softmax', name = 'segmentation')(seg)

    model = Model(inputs = input, outputs={'seg': seg, 'bound': bound, 'dist': dist})`