Issue
class DataGenerator(Sequence):
def __getitem__(self, index):
indexes = self.indexes[index * self.batch_size:(index + 1) * self.batch_size]
list_IDs_temp = [self.list_IDs[k] for k in indexes]
X,y= self.__data_generation(list_IDs_temp)
return X, y
def on_epoch_end(self):
self.indexes = np.arange(len(self.list_IDs))
if self.shuffle == True:
np.random.shuffle(self.indexes)
def __data_generation(self, list_IDs_temp):
X = np.empty((self.batch_size, self.dim[0], self.dim[1], self.n_channels))
y = np.empty((self.batch_size), dtype=int)
for i, ID in enumerate(list_IDs_temp):
X[i,] = self.load_dicom_xray(self.image_path[ID])
y[i] = self.labels[ID]
return X, keras.utils.to_categorical(y, num_classes=self.n_classes)
def load_dicom_xray(self, path):
data = pydicom.read_file(path).pixel_array
if data.mean() == 0:
return data
data = data - np.min(data)
data = data / np.max(data)
data = (data * 255).astype(np.uint8)
return data
training_gen = DataGenerator(index,train_df['Sınıf'],dim=(512,512),n_channels=2,n_classes=10,batch_size=64,shuffle=True,image_path=paths)
Hi, this my code and I'm getting the this error "ValueError: could not broadcast input array from shape (512,512) into shape (512,512,2)". I don't understand why I am getting such an error. I looked on the internet how to fix it, but I couldn't solve it somehow.
Can someone help me here?
/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb
<ipython-input-13-41198dad25ab> in __data_generation(self, list_IDs_temp)
39
40 for i, ID in enumerate(list_IDs_temp):
---> 41 X[i,] = self.load_dicom_xray(self.image_path[ID])
ValueError: could not broadcast input array from shape (512,512) into shape (512,512,2)
Solution
Pydicom.read_file(path).pixel_array
returns an array of size image heigt x image width
, it does not include any channels. An array with the same shape is returned by self.load_dicom_xray
. When trying to put this array into array X
, which shape includes channels, this obviously gives an error because the shapes don't match.
Therefore, the shape of X
should not include the channels, making its definition become the following:
X = np.empty((self.batch_size, self.dim[0], self.dim[1]))
Answered By - The_spider
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