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SegmentationModel.py
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SegmentationModel.py
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import os
import numpy as np
import matplotlib.pyplot as plt
from keras_segmentation.models.fcn import fcn_32,fcn_8, fcn_8_vgg, fcn_32_vgg
from keras_segmentation.models.segnet import segnet, vgg_segnet
from keras_segmentation.data_utils.data_loader import \
verify_segmentation_dataset
import tensorflow as tf
from skimage.io import imread
import cv2
from keras.preprocessing import image
from keras_segmentation.predict import predict_multiple
# from keras_segmentation.models.model_utils import transfer_weights
from keras_segmentation.pretrained import pspnet_50_ADE_20K
from keras_segmentation.models.pspnet import pspnet_50
from tqdm import tqdm
from keras_segmentation.models.unet import resnet50_unet
trainImage = os.listdir("""food-11/training""")
valImage = os.listdir("""food-11/validation""")
trainMask = os.listdir("""food-11/segmentation/train_masks_INCEPTIONV3""")
valMask = os.listdir("""food-11/segmentation/val_masks_INCEPTIONV3""")
nb_train_samples = len(trainImage)
def loadImage(img_path):
# img = image.load_img(img_path) #, target_size=(299, 299, 1))
img = cv2.imread(img_path)
img = cv2.resize(img, (299, 299))
img = image.img_to_array(img)
return img
def labels():
Bread = 4227136 #[64, 128, 64] , HEX:4227136
Dairy = 12583040 #[192, 0, 128] HEX:C00080
Dessert = 32960 #[0, 128, 192] HEX:0080C0
Egg = 8388608 #[128, 0, 0] HEX: 800000
Fried = 4194432 #[64, 0, 128] HEX:400080
Meat = 4194496 #[64, 0, 192] HEX:4000C0
Noodles = 12615744 #[192, 128, 64] HEX:C08040
Rice = 12632192 #[192, 192, 128] HEX:C0C080
Seafood = 4210816 #[64, 64, 128] HEX:404080
Soup = 32832 #[0, 128, 64] HEX:008040
Vegetable = 8388800 #[128, 0, 192] HEX:8000C0
COLOR_DICT = np.array([Bread, Dairy, Dessert, Egg, Fried, Meat, Noodles, Rice, Seafood, Soup, Vegetable])
return COLOR_DICT
def modifyValData(image_height, image_width, channels):
for n, id in (enumerate(valImage)):
image_1 = cv2.imread('food-11/validation/' + valImage[n])
image_1 = cv2.resize(image_1, (image_height, image_width), interpolation=cv2.INTER_AREA) #INTER_NEAREST
# height, width, channels = image_1.shape
plt.imsave('food-11/segmentation/val_images/' + valImage[n].replace('.jpg','.png'), image_1)
for m, mask_file in (enumerate(valMask)):
name = (valMask[m]).replace('png','jpg')
if (name==id):
id = id.replace('jpg', 'png')
mask_1 = cv2.imread('food-11/segmentation/val_masks_INCEPTIONV3/'+ id) #'food-11/training_segm_gt/'
mask_1 = cv2.resize(mask_1, (image_height, image_width), interpolation=cv2.INTER_AREA) #INTER_NEAREST or INTER_AREA
# height, width, channels = mask_1.shape
mask_1[mask_1 > 0] = 1
# colored = np.where(mask_ > 0, colors[label], mask_)
plt.imsave('food-11/segmentation/val_masks/' + valMask[m], mask_1)
def modifyTrainData(image_height, image_width,channels):
for n, id in (enumerate(trainImage)):
image_1 = cv2.imread('food-11/training/' + trainImage[n])
image_1 = cv2.resize(image_1, (image_height, image_width), interpolation=cv2.INTER_AREA)
plt.imsave('food-11/segmentation/train_images/' + trainImage[n].replace('.jpg','.png'), image_1)
for m, mask_file in (enumerate(trainMask)):
name = (trainMask[m]).replace('png','jpg')
if (name==id):
id = id.replace('jpg', 'png')
mask_1 = cv2.imread('food-11/segmentation/train_masks_INCEPTIONV3/'+ id) #'food-11/training_segm_gt/'
mask_1 = cv2.resize(mask_1, (image_height, image_width), interpolation=cv2.INTER_AREA) #INTER_NEAREST or INTER_AREA
mask_1[mask_1 > 0] = 1
plt.imsave('food-11/segmentation/train_masks/' + trainMask[m], mask_1)
def check():
for m, mask_file in (enumerate(trainMask)):
# mask_ = imread('food-11/training_segm_gt/' +trainMask[m])
mask_ = imread('food-11/segmentation/train_masks_INCEPTIONV3/' + trainMask[m])
if np.argmax(mask_)> 9:
print (trainMask[m])
print (np.unique(mask_))
def dice_coef(y_true, y_pred, smooth=1):
intersection = np.sum(y_true * y_pred, axis=-1)
union = np.sum(y_true, axis=-1) + np.sum(y_pred, axis=-1)
dice = np.mean((2. * intersection + smooth)/(union + smooth), axis=0)
return dice
def test_verify():
verify_segmentation_dataset(trainImage, trainMask, 50)
def metrics():
testMaskManual = os.listdir("""food-11/segmentation/val_masks_INCEPTIONV3""") #ground truth
testMask = os.listdir("""food-11/validationSegmentationOutputs-segnet""") #mask created from segmentation model
for n, id in (enumerate(testMask)):
image_test2 = cv2.imread('food-11/validationSegmentationOutputs-segnet/' + testMask[n])
image_test2 = cv2.resize(image_test2, (299, 299), interpolation=cv2.INTER_AREA)
plt.imsave('food-11/segmentation/evaluationMetric-segnet/' + testMask[n].replace('.jpg','.png'), image_test2)
for t in range(len(testMaskManual)):
img_path = 'food-11/segmentation/val_masks_INCEPTIONV3/' + str(testMaskManual[t])
newt = testMaskManual[t].replace('.png', '.png')
for s in range(len(testMask)):
mask_path = 'food-11/segmentation/validationSegmentationOutputs-segnet/' + str(testMask[s])
if (testMask[s] == newt): # and s == 3):
image_ = loadImage(img_path)
gray_image = cv2.cvtColor(image_,cv2.COLOR_BGR2GRAY)
gray_image = gray_image / 255
gray_image[gray_image > 0] = 3
gray_image[gray_image == 0] = 0
gray_image[gray_image == 3] = 1
mask_ = loadImage(mask_path)
gray_mask = cv2.cvtColor(mask_, cv2.COLOR_BGR2GRAY)
gray_mask = gray_mask / 255
gray_mask[gray_mask > 0] = 5
gray_mask[gray_mask == 0] = 0
gray_mask[gray_mask == 5] = 1
DICE = dice_coef(gray_image, gray_mask)
def transfer_weights(m1, m2, verbose=True):
assert len(m1.layers) == len(
m2.layers), "Both models should have same number of layers"
nSet = 0
nNotSet = 0
if verbose:
print("Copying weights ")
bar = tqdm(zip(m1.layers, m2.layers))
else:
bar = zip(m1.layers, m2.layers)
for l, ll in bar:
if not any([w.shape != ww.shape for w, ww in zip(list(l.weights),
list(ll.weights))]):
if len(list(l.weights)) > 0:
ll.set_weights(l.get_weights())
nSet += 1
else:
nNotSet += 1
if verbose:
print("Copied weights of %d layers and skipped %d layers" %
(nSet, nNotSet))
def test_model():
h = 299
w = 299
n_c = 11
modifyTrainData(h,w,3)
modifyValData(h, w, 3)
with tf.device('/gpu:3'):
model = fcn_32(n_classes=n_c, input_height=h, input_width=w)
# pretrained_model = pspnet_50_ADE_20K()
# model = pspnet_50(n_classes=n_c, input_height=h, input_width=w)
# transfer_weights(pretrained_model, model)
model.train(
train_images="food-11/segmentation/train_images/",
train_annotations="food-11/segmentation/train_masks/",validate=True, val_images="food-11/segmentation/val_images/",val_annotations="food-11/segmentation/val_masks/",
epochs=50, optimizer_name='adadelta', verify_dataset=True, checkpoints_path="food-11/segmentationCheckpoints-fcn_32/fcn_32"
)
predict_multiple(
checkpoints_path="food-11/segmentationCheckpoints-fcn_32/fcn_32",
inp_dir="food-11/evaluation/",
out_dir="food-11/segmentation/testPredict-fc32/"
)
print ("DONE")
if __name__ == "__main__":
test_model()
# metrics()