Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
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In binary segmentation (camvid).ipynb Example. I'm try to use my custom dataset to find patches. But I didn't getting my masks, when I load data using, provided function. #533
# helper function for data visualization
def visualize(**images):
"""PLot images in one row."""
n = len(images)
plt.figure(figsize=(16, 5))
for i, (name, image) in enumerate(images.items()):
plt.subplot(1, n, i + 1)
plt.xticks([])
plt.yticks([])
plt.title(' '.join(name.split('_')).title())
plt.imshow(image)
plt.show()
# helper function for data visualization
def denormalize(x):
"""Scale image to range 0..1 for correct plot"""
x_max = np.percentile(x, 98)
x_min = np.percentile(x, 2)
x = (x - x_min) / (x_max - x_min)
x = x.clip(0, 1)
return x
# classes for data loading and preprocessing
class Dataset:
"""CamVid Dataset. Read images, apply augmentation and preprocessing transformations.
Args:
images_dir (str): path to images folder
masks_dir (str): path to segmentation masks folder
class_values (list): values of classes to extract from segmentation mask
augmentation (albumentations.Compose): data transfromation pipeline
(e.g. flip, scale, etc.)
preprocessing (albumentations.Compose): data preprocessing
(e.g. noralization, shape manipulation, etc.)
"""
CLASSES = ['patch']
def __init__(
self,
images_dir,
masks_dir,
classes=None,
augmentation=None,
preprocessing=None,
):
self.ids = os.listdir(images_dir)
self.images_fps = [os.path.join(images_dir, image_id) for image_id in self.ids]
self.masks_fps = [os.path.join(masks_dir, image_id) for image_id in self.ids]
# convert str names to class values on masks
self.class_values = [self.CLASSES.index(cls.lower()) for cls in classes]
self.augmentation = augmentation
self.preprocessing = preprocessing
def __getitem__(self, i):
# read data
image = cv2.imread(self.images_fps[i])
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
mask = cv2.imread(self.masks_fps[i], 0)
# extract certain classes from mask (e.g. cars)
masks = [(mask == v) for v in self.class_values]
mask = np.stack(masks, axis=-1).astype('float')
# add background if mask is not binary
if mask.shape[-1] != 1:
background = 1 - mask.sum(axis=-1, keepdims=True)
mask = np.concatenate((mask, background), axis=-1)
# apply augmentations
if self.augmentation:
sample = self.augmentation(image=image, mask=mask)
image, mask = sample['image'], sample['mask']
# apply preprocessing
if self.preprocessing:
sample = self.preprocessing(image=image, mask=mask)
image, mask = sample['image'], sample['mask']
return image, mask
def __len__(self):
return len(self.ids)
class Dataloder(keras.utils.Sequence):
"""Load data from dataset and form batches
Args:
dataset: instance of Dataset class for image loading and preprocessing.
batch_size: Integet number of images in batch.
shuffle: Boolean, if `True` shuffle image indexes each epoch.
"""
def __init__(self, dataset, batch_size=1, shuffle=False):
self.dataset = dataset
self.batch_size = batch_size
self.shuffle = shuffle
self.indexes = np.arange(len(dataset))
self.on_epoch_end()
def __getitem__(self, i):
# collect batch data
start = i * self.batch_size
stop = (i + 1) * self.batch_size
data = []
for j in range(start, stop):
data.append(self.dataset[j])
# transpose list of lists
batch = [np.stack(samples, axis=0) for samples in zip(*data)]
return batch
def __len__(self):
"""Denotes the number of batches per epoch"""
return len(self.indexes) // self.batch_size
def on_epoch_end(self):
"""Callback function to shuffle indexes each epoch"""
if self.shuffle:
self.indexes = np.random.permutation(self.indexes)
# Lets look at data we have
dataset = Dataset(x_train_dir, y_train_dir, classes=['patch'])
print(mask)
In binary segmentation (camvid).ipynb Example. Link: https://github.com/qubvel/segmentation_models/blob/master/examples/binary%20segmentation%20(camvid).ipynb I'm try to use my custom dataset to find patches. But I didn't getting my masks, when I load data using, provided function
Code:
output: [0.]
insted of: