I create a tensorflow dataset of filenames of many images in a folder. The images are named [index].jpg, where index is some integer used to identify the images. I have a dictionary of string 'index' to labels as tuples. How, using tf.data.Dataset.map, can I map the index to a label tuple?
Here's the map_func I am trying to pass to the map function:
def grabImages(filepath):
index = getIndexFromFilePath(filepath)
img = tf.io.read_file(filepath)
img = translateImage(img)
dictionary = getLabelDictionary()
return index, img
Where dictionary is the index to labels dict, index is the index of the filepath as tf.Tensor and img is a preprocessed image that was at the filepath.
This returns a dataset with the index, as a tensor, mapped to the corresponding image. Is there a way to get the labels of the index using dictionary using something like dictionary[index]? Basically, I want to find the string content of index.
I have tried using .numpy() and .eval() with the current session within the grabImages function, but neither work.
Here is an example of how to get string part of a tensor in the tf.data.Dataset.map function.
Below are the steps I have implemented in the code to achieve this.
You have to decorate the map function with tf.py_function(get_path, [x], [tf.string]). You can find more about tf.py_function here.
You can get your string part by using bytes.decode(file_path.numpy()) in map function.
Code -
%tensorflow_version 2.x
import tensorflow as tf
import numpy as np
def get_path(file_path):
print("file_path: ",bytes.decode(file_path.numpy()),type(bytes.decode(file_path.numpy())))
return file_path
train_dataset = tf.data.Dataset.list_files('/content/bird.jpg')
train_dataset = train_dataset.map(lambda x: tf.py_function(get_path, [x], [tf.string]))
for one_element in train_dataset:
print(one_element)
Output -
file_path: /content/bird.jpg <class 'str'>
(<tf.Tensor: shape=(), dtype=string, numpy=b'/content/bird.jpg'>,)
Hope this answers your question.
Related
Hi everyone I'm facing an issue after that I elaborate images and labels. To create an unique dataset I use the zip function. After the elaboration both images and labels are 18k and it's correct but when I call the zip(image,labels), items become 563.
Here some code to let you to understand:
# Map the load_and_preprocess_image function over the dataset of image paths
images = image_paths.map(load_and_preprocess_image)
# Map the extract_label function over the dataset of image paths
labels = image_paths.map(extract_label)
# Zip the labels and images together to create a dataset of (image, label) pairs
#HERE SOMETHING STRANGE HAPPENS
data = tf.data.Dataset.zip((images,labels))
# Shuffle and batch the data
data = data.shuffle(buffer_size=1000).batch(32)
# Split the data into train and test sets
data = data.shuffle(buffer_size=len(data))
# Convert the dataset into a collection of data
num_train = int(0.8 * len(data))
train_data = image_paths.take(num_train)
val_data = image_paths.skip(num_train)
I cannot see where is the error. Can you help me plese? Thanks
I'd like to have a dataset of 18k images,labels
tf's zip
tf.data.Dataset.zip is not like Python's zip. The tf.data.Dataset.zip's input is tf datasets. You may check the images/label return from your map function is the correct tf.Dataset object.
check tf.ds
make sure your image/label is correct tf.ds.
print("ele: ", images_dataset.element_spec)
print("num: ", images_dataset.cardinality().numpy())
print("ele: ", labels_dataset.element_spec)
print("num: ", labels_dataset.cardinality().numpy())
workaround
In your case, combine the image and label processing in one map function and return both to bypass to use tf.data.Dataset.zip:
# load_and_preprocess_image_and_label
def load_and_preprocess_image_and_label(image_path):
""" load image and label then some operations """
return image, label
# Map the load_and_preprocess_image function over the dataset of image/label paths
train_list = tf.data.Dataset.list_files(str(PATH / 'train/*.jpg'))
data = train_list.map(load_and_preprocess_image_and_label,
num_parallel_calls=tf.data.AUTOTUNE)
I am trying to move my data to GPU by doing this
batch["img"] = [img.cuda() for img in batch["img"]]
batch["label"] = [label.cuda() for label in batch["label"]]
However, i get this error for the labels for OCR
AttributeError: 'str' object has no attribute 'cuda'
I also tried .to('cuda') and similar error.
More details are as follows. This is the pytorch Dataset class
class SynthDataset(Dataset):
def __init__(self, opt):
super(SynthDataset, self).__init__()
self.path = os.path.join(opt['path'], opt['imgdir'])
self.images = os.listdir(self.path)
self.nSamples = len(self.images)
f = lambda x: os.path.join(self.path, x)
self.imagepaths = list(map(f, self.images))
transform_list = [transforms.Grayscale(1),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))]
self.transform = transforms.Compose(transform_list)
self.collate_fn = SynthCollator()
def __len__(self):
return self.nSamples
def __getitem__(self, index):
assert index <= len(self), 'index range error'
imagepath = self.imagepaths[index]
imagefile = os.path.basename(imagepath)
img = Image.open(imagepath)
if self.transform is not None:
img = self.transform(img)
item = {'img': img, 'idx':index}
item['label'] = imagefile.split('_')[0]
return item
As you can see the generator outputs a dictionary with image and labels where the labels is the text contained in the image
Yo !
If I get your code right, your are building your custom dataset to output an image and its label. Your label comes from the first part of your image filename, so it is a string, as stated in the error message. Your object needs to be a Torch tensor to be moved on the GPU with .cuda().
If you want to keep your code as is, you need to transform your label into a vector form. I suspect your labels to be stuff like "cat", "dog", etc The usual way to transform labels into numerical forms are one-hot-encoding, or simply map each label to an integer. There is abundant resources about it on the web. Then you can turn your label into a Tensor object and move it to the GPU.
However, I would highly recommand to change the way you build your tensor dataset. You are calling .cuda() on every image tensor in your list. When forwarding a neural network, you batch your data into a tensor (for instance stacking your images into a new dimension), then call .cuda() on the whole batch tensor. Same thing for the labels. Check the Pytorch documentation as there is probably the exact example you are looking for (a custom dataset with an image and its label).
Good luck !
I've created a dataloader for my object detection task.
However, I cannot place the image/path name to a tensor. Instead I have it indexed, where in the last portion of the dataloader class, I have this:
target = {}
target['boxes'] = boxes
target['labels'] = labels
target['image_id'] = torch.tensor([index])
target['area'] = area
target['iscrowd'] = iscrowd
target['image_name'] = torch.tensor(index)
return image, target
where atm image_id and image_name are the same thing.
When I print out the image_name from the dataloader, I of course get this:
for image, target in valid_data_loader:
print(target[0]['image_name'])
Output:
tensor(0)
tensor(1)
tensor(2)
tensor(3)
tensor(4)
tensor(5)
tensor(6)
tensor(7)
I'm aware that strings can't be saved into torch tensors, so is there any way I can refer back to the original image name rather than the index of the tensor? Or would I just have to use the number that comes out and refer back to the dataset class (not dataloader)?
I ultimately want to save the image name, and attributes such as bounding box info to a separate numpy dataframe.
Ok, so this is a bit ad-hoc and not exactly what I was thinking but here is one method I have used to retrieve the paths/image names. I basically find the id from the dataloader by removing it from the tensor. I then use the tensor_id to find the corresponding id in the original dataframe:
for image, target in valid_data_loader:
tensor_id = target[0]['image_name'].item()
print(valid_df.iloc[tensor_id]['image_id'])
I don't know if this is efficient though but it got what I wanted...
I try to create a Dataset for Tensorflow from a CSV file that I created with pandas.
The csv file looks like this:
feature1 feature2 filepath label
0.25 0.35 test1.jpg A
0.33 0.15 test2.jpg B
I read the dataframe like this
mydf = pd.read_csv("TraingDatafinal.csv",header=0)
Now I have defined a function which should return a dataframe. This is all according to the quickstart guide
def train_input_fn(features, labels, batch_size):
"""An input function for training"""
# Convert the inputs to a Dataset.
dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
# Shuffle, repeat, and batch the examples.
dataset = dataset.shuffle(1000).repeat().batch(batch_size)
dataset = dataset.map(mappingfunction)
# Return the dataset
return dataset
I call this function like this;
mydataset = train_input_fn(mydf.drop(["label"],axis=1),mydf["label"],200)
This works, if I remove the mapping but I get a questionmark when I print the shape. Why? The dimensions seem to be clearly defined.
This is where the real struggle begins. I want to create a mapping function, that replaces the filepath with an array of the image.
I tried to achieve that by writing this mappingfunction
def mappingfunction(feature,label):
print(feature['Filename'])
image = tf.read_file(feature['Filename'])
image = tf.image.decode_image(image)
return image,label
This will only return the image and the label. I don't know how I would realize it to return all the features but the filepath.
But even this simplified verison won't work. I get an "expected binary or unicode string" error. Can you help me?
The mapping function should return all features and the label. For example:
def mappingfunction(feature,label):
print(feature['Filename'])
image = tf.read_file(feature['Filename'])
image = tf.image.decode_image(image)
features['image'] = image
return features, label
This will add an image key to the features dictionary.
I am trying to train an image classifier in scikit-learn. I have a bunch of input images and I am using Pillow to process them. My question is about what shape to give the Pillow data to scikit-learn.
This is my code now:
training = glob.glob('./img/training/*/*.bmp')
data = []
classes = []
for imagefile in training:
edges = Image.open(imagefile).filter(ImageFilter.FIND_EDGES).convert("L")
in_data = np.asarray(edges, dtype=np.uint8)
data.append(in_data[0])
if 'class1' in imagefile:
classes.append('class1')
else:
classes.append('class2')
clf = svm.SVC(gamma=0.001, C=100.)
clf.fit(data, classes)
This runs without errors, but I have put the code together fairly crudely and I am not sure it is correct.
In particular, I'm not sure whether I should be using in_data[0]. I just did this because using in_data gives me an error: ValueError: Found array with dim 3. Estimator expected <= 2.
Unless you want the first row of the image matrix ( in_data[0] returns you the first row ) of each image, you probably want to use flattening.
Flattening will take each row of the image matrix and put the rows behind eachother in a 1 dimensional vector.
So it becomes data.append(in_data.flatten())
You could resize your image to a smaller format first, to reduce the number of columns of your data matrix.