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Is there a good way to write 2d arrays or tensors to TFRecords in Tensorflow?

I am currently working on a project using audio data. The first step of the project is to use another model to produce features for the audio example that are about [400 x 10_000] for each wav file and each wav file will have a label that I'm trying to predict. I will then build another model on top of this to produce my final result.
I don't want to run preprocessing every time I run the model, so my plan was to have a preprocessing pipeline that runs the feature extraction model and saves it into a new folder and then I can just have the second model use the saved features directly. I was looking at using TFRecords, but the documentation is quite unhelpful.
tf.io.serialize_tensor
tfrecord
This is what I've come up with to test it so far:
serialized_features = tf.io.serialize_tensor(features)
feature_of_bytes = tf.train.Feature(
bytes_list=tf.train.BytesList(value=[serialized_features.numpy()]))
features_for_example = {
'feature0': feature_of_bytes
}
example_proto = tf.train.Example(
features=tf.train.Features(feature=features_for_example))
filename = 'test.tfrecord'
writer = tf.io.TFRecordWriter(filename)
writer.write(example_proto.SerializeToString())
filenames = [filename]
raw_dataset = tf.data.TFRecordDataset(filenames)
for raw_record in raw_dataset.take(1):
example = tf.train.Example()
example.ParseFromString(raw_record.numpy())
print(example)
But I'm getting this error:
tensorflow.python.framework.errors_impl.DataLossError: truncated record at 0' failed with Read less bytes than requested
tl;dr:
Getting the above error with TFRecords. Any recommendations to get this example working or another solution not using TFRecords?

How do I load the images and the csv file for object detection in tensorflow?

Data loader in Image Classification
Small Target Image
CSV File
I am new to object detection and don't know how to use the dataloader for the task. In image clasification, I know that we can load data for image classification from image_dataset_from_directory in tensorflow and I am looking for similar function to load the images and annotation files in csv format.
I have a csv file that contains the bounding box co-ordinates in the form of (x0,y0,width,height) for every image. How do i load the csv annotation file along with the images for object detection?
Images names are 1.jpg,2.jpg,3.jpg and so on. I have added a picture of how I load the images directly from the folder structure for image classification.
I am trying to use the TensorFlow library in python language.

You can pass your csv file to below code to create an annotation files.
def annotation(row):
annotation = {}
area = (row.xmax -row.xmin)*(row.ymax - row.ymin)
annotation["segmentation"] = []
annotation["iscrowd"] = 0
annotation["area"] = area
annotation["image_id"] = row.fileid
annotation["bbox"] = [row.xmin, row.ymin, row.xmax -row.xmin,row.ymax-row.ymin ]
annotation["category_id"] = row.categoryid
annotation["id"] = row.annid
return annotation
After creating annotations You will need to transform those to TFRecord. Then select the object detection model which you want to train. Please refer to this gist for complete code to create annotations and this document for creating Tf records and training the model. Thank You.

How to save/extract dataset from hdf5 and convert into TiFF?

I am trying to import CT scan data into ImageJ/FIJI (There is HDF5 plugin in ImageJ/Fiji, however the synchrotron CT data has so large datasets.. so it was failed to open). The scan data (Image dataset) is saved as dataset into the hdf5 file. So I have to extract image dataset from the hdf5 file, then converted it into the Tiff file.
HdF5 File path is "F:/New_ESRF/SNT_BTO4/SNT_BTO4_S1/SNT_BTO4_S1_1_1pag_db0005_vol.hdf5"
Herein, 'SNT_BTO4_S1_1_1pag_db0005_vol.hdf5' is divided into several datasets, and the image dataset is in here:/entry0000/reconstruction/results/data
At the moment, I accessed to the image dataset using h5py. However, after that, I am in stuck to extract/save the dataset separately from the hdf5 file.
Which code is required to extract the image dataset from the hdf5 file?
After that, I am thinking of using from PIL to Image then convert the image into Tiff file. Can I get any advice on the code for this?
import numpy as np
import h5py
filename = "F:/New_ESRF/SNT_BTO4/SNT_BTO4_S1/SNT_BTO4_S1_1_1pag_db0005_vol.hdf5"
with h5py.File(filename,'r') as hdf:
base_items = list (hdf.items())
print('#Items in the base directory:', base_items)
#entry0000
G1 = hdf.get ('entry0000')
G1_items = list (G1.items())
print('#Items in entry0000', G1_items)
#reconstruction
G11 = G1.get ('/entry0000/reconstruction')
G11_items = list (G11.items())
print('#Items in reconstruction', G11_items)
#results_data
G12 = G11.get ('/entry0000/reconstruction/results')
G12_items = list (G12.items())
print('#Items in results', G12_items)

Extracting image data from an HDF5 file and converting to an image is a "relatively straight forward" 2 step process:
Access the data in the HDF5 file
Convert to an image with cv2 (or PIL)
A simple example is available here: How to extract individual JPEG images from a HDF5 file.
You can apply the same process to your file. Here is some pseudo-code. It's not complete because you don't show the shape of the image dataset (and the shape affects how to read the data). Also, you didn't say how many images are in dataset /entry0000/reconstruction/results/data --- does it have a single image or multiple images. If multiple images, which axis is the image counter?
import h5py
import cv2 ## for image conversion
filename = "F:/New_ESRF/SNT_BTO4/SNT_BTO4_S1/SNT_BTO4_S1_1_1pag_db0005_vol.hdf5"
with h5py.File(filename,'r') as hdf:
# get image dataset
img_ds = hdf['/entry0000/reconstruction/results/data']
print(f'Image Dataset info: Shape={img_ds.shape},Dtype={img_ds.dtype}')
## following depends on dataset shape/schema
## code below assumes images are along axis=0
for i in range(img_ds.shape[0]):
cv2.imwrite(f'test_img_{i:03}.tiff',img_ds[i,:]) # uses slice notation
# alternately load to a numpy array first
img_arr = img_ds[i,:] # slice notation gets [i,:,:,:]
cv2.imwrite(f'test_img_{i:03}.tiff',img_arr)
Note: you don't need to use .get() to get a dataset. You can simply reference the dataset path. Also, when you use a group object, use the relative path from the dataset to the group, not the absolute path. (You should modify your code to reflect these changes.) For example, the following are equivalent
G1 = hdf['entry0000']
## is the same as G1 = hdf.get('entry0000')
G11 = hdf['entry0000/reconstruction']
## is the same as G11 = hdf.get('entry0000/reconstruction')
## OR referencing G1 group object:
G11 = G1['reconstruction']
## is the same as G11 = G1.get('reconstruction')

Extract waymo dataset to jpeg image files

I'm trying to extract waymo tensorrecord (tensorflow) based dataset to pictrue files.
I've tried the following:
import tensorflow as tf
FILENAME = 'D:\\waymo3\\waymo_open_dataset_v_1_2_0_individual_files\\training\\segment-15832924468527961_1564_160_1584_160_with_camera_labels.tfrecord'
dataset = tf.data.TFRecordDataset(FILENAME, compression_type='')
i=0
for data in dataset:
print(dir(data))
with open('C:\\Users\\my_user\\Desktop\\extracted_pic\\'+str(i)+'.jpeg', 'ab') as the_file:
the_file.write(data.numpy())
i+=1
Unfortunately, it creates a folder with unreadable jpeg.
I believe the dataset of waymo is saved in JPEG.
Therefore I can't understand what is my mistake.
As one sees, I've tried to open the folder in windows (10)

save multi directory images in a single file after preprocessing

I am working on DICOM images, I have 5 scans(folders) each scan contain multiple images, after working some preprocessing on the images, I want to save the processed images in a single file using "np.save", I have the code below that save each folder in a separate file:
data_path = 'E:/jupyter/test/LIDC-IDRI/'
patients_data = os.listdir(data_path)
for pd in range(len(patients_data)):
full_path = load_scan(data_path + patients_data[pd])
after_pixel_hu = get_pixels_hu(full_path)
after_resample, spacing = resample(after_pixel_hu, full_path, [1,1,1])
np.save(output_path + "images_of_%s_patient.npy" % (patients_data[pd]), after_resample)
load_scan is a function for loading(reading) DICOM files, what I want to do with this code is to save all processed images in a single file, not in five files, can anyone tell me how to do that, please?

The first thing to notice is that you are using %s with patients_data[pd]. I assume patients_data is a list of the names of the patients, which means you are constructing a different output path for each patient - you are asking numpy to save each of your processed images to a new location.
Secondly, .npy is probably not the file type you want to use for your purposes, as it does not handle appending data. You probably want to pick a different file type, and then np.save() to the same file path each time.
Edit: Regarding file type, a pdf may be your best option, where you can make each of your images a separate page.