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# Import libraries
import cv2
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import segmentation_models as sm
def get_mask(image):
model_path = "Segmentation/model_checkpoint.h5"
CLASSES = ['sofa']
BACKBONE = 'resnet50'
# define network parameters
n_classes = 1 if len(CLASSES) == 1 else (len(CLASSES) + 1) # case for binary and multiclass segmentation
activation = 'sigmoid' if n_classes == 1 else 'softmax'
preprocess_input = sm.get_preprocessing(BACKBONE)
sm.set_framework('tf.keras')
LR=0.0001
#create model architecture
model = sm.Unet(BACKBONE, classes=n_classes, activation=activation)
# define optomizer
optim = keras.optimizers.Adam(LR)
# Segmentation models losses can be combined together by '+' and scaled by integer or float factor
dice_loss = sm.losses.DiceLoss()
focal_loss = sm.losses.BinaryFocalLoss() if n_classes == 1 else sm.losses.CategoricalFocalLoss()
total_loss = dice_loss + (1 * focal_loss)
# actulally total_loss can be imported directly from library, above example just show you how to manipulate with losses
# total_loss = sm.losses.binary_focal_dice_loss # or sm.losses.categorical_focal_dice_loss
metrics = [sm.metrics.IOUScore(threshold=0.5), sm.metrics.FScore(threshold=0.5)]
# compile keras model with defined optimozer, loss and metrics
model.compile(optim, total_loss, metrics)
#load model
model.load_weights(model_path)
test_img = np.array(image)#cv2.imread(path, cv2.IMREAD_COLOR)
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(test_img).round()
mask = Image.fromarray(prediction[...,0].squeeze()*255).convert("L")
mask.save("masks/sofa.jpg")
return np.array(mask)
def replace_sofa(image,mask,styled_sofa):
# print(mask.shape)
# mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
# print(mask.shape)
image = np.array(image)
#image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
styled_sofa = cv2.cvtColor(styled_sofa, cv2.COLOR_BGR2RGB)
_, 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 new_image
# image = cv2.imread('input/sofa.jpg')
# mask = cv2.imread('masks/sofa.jpg')
# styled_sofa = cv2.imread('output/sofa_stylized_style.jpg')
# #get_mask(image)
# plt.imshow(replace_sofa(image,mask,styled_sofa))
# plt.show()
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