Segmentation/segmentation.py

# Import libraries

import cv2
from tensorflow import keras
import numpy as np
from PIL import Image
import segmentation_models as sm

sm.set_framework("tf.keras")

# Load segmentation model
BACKBONE = "resnet50"
preprocess_input = sm.get_preprocessing(BACKBONE)
model = keras.models.load_model("Segmentation/model_final.h5", compile=False)


def get_mask(image: Image) -> Image:
    """
    This function generates a mask of the image that highlights all the sofas
    in the image. This uses a pre-trained Unet model with a resnet50 backbone.
    Remark: The model was trained on 640by640 images and it is therefore best
    that the image has the same size.

    Parameters:
            image = original image
    Return:
            mask  = corresponding maks of the image
    """
    test_img = np.array(image)
    test_img = cv2.resize(test_img, (640, 640))
    test_img = cv2.cvtColor(test_img, cv2.COLOR_RGB2BGR)
    test_img = np.expand_dims(test_img, axis=0)

    prediction = model.predict(preprocess_input(np.array(test_img))).round()
    mask = Image.fromarray(prediction[..., 0].squeeze() * 255).convert("L")
    return mask


def replace_sofa(image: Image, mask: Image, styled_sofa: Image) -> Image:
    """
    This function replaces the original sofa in the image by the new styled
    sofa according to the mask.
    Remark: All images should have the same size.
    Input:
        image       = Original image
        mask        = Generated masks highlighting the sofas in the image
        styled_sofa = Styled image
    Return:
        new_image   = Image containing the styled sofa
    """
    image, mask, styled_sofa = np.array(image), np.array(mask), np.array(styled_sofa)

    _, mask = cv2.threshold(mask, 10, 255, cv2.THRESH_BINARY)
    mask_inv = cv2.bitwise_not(mask)
    image_bg = cv2.bitwise_and(image, image, mask=mask_inv)
    sofa_fg = cv2.bitwise_and(styled_sofa, styled_sofa, mask=mask)
    new_image = cv2.add(image_bg, sofa_fg)
    return Image.fromarray(new_image)