Semantic Image Segmentation with colored masks

Question

So i have a set of pictures with their masks in color for example color blue is for chair, red for lamps, etc.

As I am new to all of this i have tried doing doing this with the unet model, i have processed the images with keras and like this.

def data_generator(img_path,mask_path,batch_size):
    c=0
    n = os.listdir(img_path)
    m = os.listdir(mask_path)
    random.shuffle(n)
    while(True):
        img = np.zeros((batch_size,256,256,3)).astype("float")
        mask = np.zeros((batch_size,256,256,1)).astype("float")

        for i in range(c,c+batch_size):
            train_img = cv2.imread(img_path+"/"+n[i])/255.
            train_img = cv2.resize(train_img,(256,256))
            img[i-c] = train_img

            train_mask = cv2.imread(mask_path+"/"+m[i],cv2.IMREAD_GRAYSCALE)/255.
            train_mask = cv2.resize(train_mask,(256,256))
            train_mask = train_mask.reshape(256,256,1)

            mask[i-c]=train_mask

        c+=batch_size
        if(c+batch_size>=len(os.listdir(img_path))):
            c=0
            random.shuffle(n)

        yield img,mask

Now looking closer I think this way wont work with my masks, i tried processing the masks as rgb color but my model wont train like that.

model.

def unet(pretrained_weights = None,input_size = (256,256,3)):
    inputs = Input(input_size)
    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
    conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

    conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
    conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
    drop5 = Dropout(0.5)(conv5)

    up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
    merge6 = concatenate([drop4,up6], axis = 3)
    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)

    up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
    merge7 = concatenate([conv3,up7], axis = 3)
    conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
    conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)

    up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
    merge8 = concatenate([conv2,up8], axis = 3)
    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)

    up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
    merge9 = concatenate([conv1,up9], axis = 3)
    conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
    conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
    conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
    conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)

    model = Model(input = inputs, output = conv10)

    model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])

    #model.summary()

    if(pretrained_weights):
        model.load_weights(pretrained_weights)

    return model

So my question is how do I train a model with colored image masks.

Edit, example of the data i have.

given image to train the model

mask of it

and the percentage of every mask like this. {"water": 4.2, "building": 33.5, "road": 0.0}

Answer 1

In a Semantic segmentation problem, each pixel belongs to any of the target output classes/labels. Therefore, your output layer, conv10, should have the total number of classes (n_classes) as the value of no._of_kernels and softmax as the activation function like follows:

conv10 = Conv2D(**n_classes**, 1, activation = 'softmax')(conv9)

In this case, the loss should also be changed to categorical_crossentropy while compiling u-net model.

model.compile(optimizer = Adam(lr = 1e-4), loss = 'categorical_crossentropy', metrics = ['accuracy'])

Additionally, you should not normalize your true label/mask image rather can encode as follows:

train_mask = np.zeros((height, width, n_classes))
for c in range(n_classes):
    train_mask[:, :, c] = (img == c).astype(int)

[I have assumed that you have more than two true output classes/labels as you mentioned yours mask contains different colors for water, road, building, ...etc; If you have only two classes then your model configuration is fine except train_mask processing.]