I'm trying to do some image segmentation for ocr, my mask image is a 3 classes image, like this
and my original image is a gray image like this
but when I try to fit the model I get this error
could not broadcast input array from shape (128,128,3) into shape (128,128)
here is the code I'm using to create the datasets
img_size = (128, 128)
batch_size = 32
input_img_paths = sorted(
[ os.path.join(input_dir, fname)
for fname in os.listdir(input_dir)
if fname.endswith(".jpg") ] )
target_img_paths = sorted(
[ os.path.join(target_dir, fname)
for fname in os.listdir(target_dir)
if fname.endswith(".jpg") and not fname.startswith(".") ])
class OxfordPets(keras.utils.Sequence):
"""Helper to iterate over the data (as Numpy arrays)."""
def __init__(self, batch_size, img_size, input_img_paths, target_img_paths):
self.batch_size = batch_size
self.img_size = img_size
self.input_img_paths = input_img_paths
self.target_img_paths = target_img_paths
def __len__(self):
return len(self.target_img_paths) // self.batch_size
def __getitem__(self, idx):
"""Returns tuple (input, target) correspond to batch #idx."""
i = idx * self.batch_size
batch_input_img_paths = self.input_img_paths[i : i + self.batch_size]
batch_target_img_paths = self.target_img_paths[i : i + self.batch_size]
x = np.zeros((batch_size,) + self.img_size, dtype="float32")
for j, path in enumerate(batch_input_img_paths):
img = load_img(path, target_size=self.img_size)
x[j] = img
y = np.zeros((batch_size,) + self.img_size, dtype="float32")
for j, path in enumerate(batch_target_img_paths):
img = load_img(path, target_size=self.img_size, color_mode="rgb")
y[j] = img
return x, y
val_samples = 150
random.Random(1337).shuffle(input_img_paths)
random.Random(1337).shuffle(target_img_paths)
train_input_img_paths = input_img_paths[:-val_samples]
train_target_img_paths = target_img_paths[:-val_samples]
val_input_img_paths = input_img_paths[-val_samples:]
val_target_img_paths = target_img_paths[-val_samples:]
# Instantiate data Sequences for each split
train_gen = OxfordPets(
batch_size, img_size, train_input_img_paths, train_target_img_paths
)
val_gen = OxfordPets(batch_size, img_size, val_input_img_paths, val_target_img_paths)
but when i try to fit whit this
model_history = model.fit(train_gen, epochs=30,
steps_per_epoch=50,
validation_steps=25,
validation_data=val_gen)
I get the error, I am trying to adapt this solution https://keras.io/examples/vision/oxford_pets_image_segmentation/?fbclid=IwAR2wFYju-N0X7FUaWkhvOVaAAaVqLdOryBwg7xDC0Rji9LQ5F2jYOkeNnns from keras
into the example of the tensorflow page https://www.tensorflow.org/tutorials/images/segmentation
and I have the impression that the problem has something to do whit the fact that the original image is on gray scale, how can I solve this error? any advice would be great!
