问题描述
我正在尝试对ocr进行图像分割,我的遮罩图像是3类图像,像这样
我的原始图像是这样的灰色图像
但是当我尝试拟合模型时会出现此错误
无法将形状(128,128,3)的输入数组广播到形状(128,128)
这是我用来创建数据集的代码
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,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,train_input_img_paths,train_target_img_paths
)
val_gen = OxfordPets(batch_size,val_input_img_paths,val_target_img_paths)
但是当我尝试适合这种情况
model_history = model.fit(train_gen,epochs=30,steps_per_epoch=50,validation_steps=25,validation_data=val_gen)
我收到错误,我正在尝试调整此解决方案 https://keras.io/examples/vision/oxford_pets_image_segmentation/?fbclid=IwAR2wFYju-N0X7FUaWkhvOVaAAaVqLdOryBwg7xDC0Rji9LQ5F2jYOkeNnns 来自喀拉拉邦
进入tensorflow页面的示例 https://www.tensorflow.org/tutorials/images/segmentation
并且我觉得问题与原始图像是灰度图像有关,我该如何解决呢?任何建议都会很棒!
解决方法
您的遮罩是RGB,具有3个通道。但是您的图像是灰度的,并且只有一个通道。有关将RGB图像转换为灰度图像的信息,请参见This question
,您应该先将图像转换为RGB。您的图像是灰度的,只有1个通道。它的形状是(128,128,1)。它们适用于opencv:()
之类的东西,对您数据中的每张图片都可以