I'm trying to use the imageai python library, and more particularly this function:
detector.detectObjectsFromImage()
The doc says it should be used with a Numpy array of file stream of any image.
https://imageai.readthedocs.io/en/latest/detection/index.html
When I pass it a Numpy array, like this:
detections = detector.detectObjectsFromImage(input_image=anumpyarray,input_type = "array")
I get the error:
detections =
detector.detectObjectsFromImage(input_image=anumpyarray,input_type =
"array") File
"/usr/local/lib/python3.6/site-packages/imageai/Detection/init.py",
line 517, in detectObjectsFromImage raise ValueError("Ensure you
specified correct input image, input type, output type and/or output
image path ") ValueError: Ensure you specified correct input image,
input type, output type and/or output image path
Is it because a Numpy array and a Numpy array of a stream of an image are different things?
I know it's old, but for anyone who needs help:
Try to set 2 additional params:
minimum_percentage_probability=0, output_type='array'
For more info, go into imageai\Detection\__init__.py -> detectObjectsFromImage
Related
I try to implement this code, using colab:
x = np.load('data_sample.npy',allow_pickle=True)
stacked_x = np.concatenate([x,x,x],1)
stacked_x.shape
but my sample was an image in jpg format, so I wasn't able to convert these images to .npy to use it.
actually, I tried this code:
import numpy as np
array = np.asarray('image.JPG')
print(array.tobytes())
print(array)
x = np.load(array,allow_pickle=True)
stacked_x = np.concatenate([x,x,x],1)
stacked_x.shape
print(x)
but I got:
and when I use this code :
x = np.load(array.tobytes(),allow_pickle=True)
I got:
so, any suggestion to solve this, precisely to convert .jpg to .npy?
Look at what your code does:
In [57]: np.asarray('image.JPG')
Out[57]: array('image.JPG', dtype='<U9')
In [58]: np.asarray('image.JPG').tobytes()
Out[58]: b'i\x00\x00\x00m\x00\x00\x00a\x00\x00\x00g\x00\x00\x00e\x00\x00\x00.\x00\x00\x00J\x00\x00\x00P\x00\x00\x00G\x00\x00\x00'
That's just playing with the string. It's not doing anything with a file named "image.JPG". You need to use some sort of image processing package to first load the file, converting it from the compressed format to a 3d array.
np.load is used to load a file that was created by np.save('file.npy'). It doesn't make sense to give that array variable as the file name. It won't load a jpg file.
np.load('file.npy',allow_pickle=True)
I've been trying to use a hyperspectral image dataset that was in .mat files. I found that using the scipy library with its loadmat function I can load the hyperspectral images and selecting some bands to see them as an RGB.
def RGBread(image):
images = loadmat(image).get('new_image')
return abs(images[:,:,(12,6,4)])
def SIread(image):
images = loadmat(image).get('new_image')
return abs(images[:,:,:])
After trying to implement the pix2pix architecture I found an unexpected error. When passing the list of the names of the dataset files by a function that is responsible for load the data(which are still .mat files), Tensor Flow does not have a direct method for this reading or coding, so I get these data with my RGBread and SIread method and then I turned them into tensors.
def load_image(filename, augment=True):
inimg = tf.cast( tf.convert_to_tensor(RGBread(ImagePATH+'/'+filename)
,dtype=tf.float32),tf.float32)[...,:3]
tgimg = tf.cast( tf.convert_to_tensor(SIread(ImagePATH+'/'+filename)
,dtype=tf.float32),tf.float32)[...,:12]
inimg, tgimg = resize(inimg, tgimg,IMG_HEIGH,IMG_WIDTH)
if augment:
inimg, tgimg = random_jitter(inimg, tgimg)
return inimg, tgimg
When loading an image with the load_image method, using the name and path of a single .mat file (a hyperspectral image) of my dataset as argument of my function the method worked perfectly.
plt.imshow(load_train_image(tr_urls[1])[0])
The problem started when I created my dataSet tensor, because my RGBread function does not receive a tensor as a parameter since loadmat('.mat') expects a string. Having the following error.
train_dataset = tf.data.Dataset.from_tensor_slices(tr_urls)
train_dataset = train_dataset.map(load_train_image,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
TypeError: expected str, bytes or os.PathLike object, not Tensor
After reading a lot about reading .mat files I found a user who recommended passing the data to TFrecord format. I've been trying to do it but I couldn't. Someone could help me?
Rasterio may be useful here.
https://rasterio.readthedocs.io/en/latest/
It can read hyperspectral .tif which can be passed to tf.data using a tf.keras data-generator. It may be a bit slow and perhaps should be done before training rather than at runtime.
An alternative is to ask whether you need the geotiff metadata. If not, you can preprocess and save as numpy arrays for tfrecords.
I'm trying to use Python to parse mxnet params into plain text. The code looks like the below. But the parsing result is not plain string, but some encoded text looks like this, "... \xaa>\x0f\xed\x8e>\xaf!\x8f>g ..." Could anybody give me some tips on it? Thanks a lot!
...
param_file = 'resnet-50-0000.params'
with open(param_file, 'rb') as f:
net_params = f.read()
...
The parameters are binary files. If you want to read them as plain text you need to decode them first as a dictionary of parameter_name->NDArray, that you can convert them to numpy. From numpy you can convert it to a list and then process it as a list (of lists) of scalar.
import mxnet as mx
params = mx.nd.load('resnet-50-0000.params')
for k, param in params.items():
print(k)
print(param.asnumpy().tolist())
I got a TFRecord data file filename = train-00000-of-00001 which contains images of unknown size and maybe other information as well. I know that I can use dataset = tf.data.TFRecordDataset(filename) to open the dataset.
How can I extract the images from this file to save it as a numpy-array?
I also don't know if there is any other information saved in the TFRecord file such as labels or resolution. How can I get these information? How can I save them as a numpy-array?
I normally only use numpy-arrays and am not familiar with TFRecord data files.
1.) How can I extract the images from this file to save it as a numpy-array?
What you are looking for is this:
record_iterator = tf.python_io.tf_record_iterator(path=filename)
for string_record in record_iterator:
example = tf.train.Example()
example.ParseFromString(string_record)
print(example)
# Exit after 1 iteration as this is purely demonstrative.
break
2.) How can I get these information?
Here is the official documentation. I strongly suggest that you read the documentation because it goes step by step in how to extract the values that you are looking for.
Essentially, you have to convert example to a dictionary. So if I wanted to find out what kind of information is in a tfrecord file, I would do something like this (in context with the code stated in the first question): dict(example.features.feature).keys()
3.) How can I save them as a numpy-array?
I would build upon the for loop mentioned above. So for every loop, it extracts the values that you are interested in and appends them to numpy arrays. If you want, you could create a pandas dataframe from those arrays and save it as a csv file.
But...
You seem to have multiple tfrecord files...tf.data.TFRecordDataset(filename) returns a dataset that is used to train models.
So in the event for multiple tfrecords, you would need a double for loop. The outer loop will go through each file. For that particular file, the inner loop will go through all of the tf.examples.
EDIT:
Converting to np.array()
import tensorflow as tf
from PIL import Image
import io
for string_record in record_iterator:
example = tf.train.Example()
example.ParseFromString(string_record)
print(example)
# Get the values in a dictionary
example_bytes = dict(example.features.feature)['image_raw'].bytes_list.value[0]
image_array = np.array(Image.open(io.BytesIO(example_bytes)))
print(image_array)
break
Sources for the code above:
Base code
Converting bytes to PIL.JpegImagePlugin.JpegImageFile
Converting from PIL.JpegImagePlugin.JpegImageFile to np.array
Official Documentation for PIL
EDIT 2:
import tensorflow as tf
from PIL import Image
import io
import numpy as np
# Load image
cat_in_snow = tf.keras.utils.get_file(path, 'https://storage.googleapis.com/download.tensorflow.org/example_images/320px-Felis_catus-cat_on_snow.jpg')
#------------------------------------------------------Convert to tfrecords
def _bytes_feature(value):
"""Returns a bytes_list from a string / byte."""
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def image_example(image_string):
feature = {
'image_raw': _bytes_feature(image_string),
}
return tf.train.Example(features=tf.train.Features(feature=feature))
with tf.python_io.TFRecordWriter('images.tfrecords') as writer:
image_string = open(cat_in_snow, 'rb').read()
tf_example = image_example(image_string)
writer.write(tf_example.SerializeToString())
#------------------------------------------------------
#------------------------------------------------------Begin Operation
record_iterator = tf.python_io.tf_record_iterator(path to tfrecord file)
for string_record in record_iterator:
example = tf.train.Example()
example.ParseFromString(string_record)
print(example)
# OPTION 1: convert bytes to arrays using PIL and IO
example_bytes = dict(example.features.feature)['image_raw'].bytes_list.value[0]
PIL_array = np.array(Image.open(io.BytesIO(example_bytes)))
# OPTION 2: convert bytes to arrays using Tensorflow
with tf.Session() as sess:
TF_array = sess.run(tf.image.decode_jpeg(example_bytes, channels=3))
break
#------------------------------------------------------
#------------------------------------------------------Compare results
(PIL_array.flatten() != TF_array.flatten()).sum()
PIL_array == TF_array
PIL_img = Image.fromarray(PIL_array, 'RGB')
PIL_img.save('PIL_IMAGE.jpg')
TF_img = Image.fromarray(TF_array, 'RGB')
TF_img.save('TF_IMAGE.jpg')
#------------------------------------------------------
Remember that tfrecords is just simply a way of storing information for tensorflow models to read in an efficient manner.
I use PIL and IO to essentially convert the bytes to an image. IO takes the bytes and converts them to a file like object that PIL.Image can then read
Yes, there is a pure tensorflow way to do it: tf.image.decode_jpeg
Yes, there is a difference between the two approaches when you compare the two arrays
Which one should you pick? Tensorflow is not the way to go if you are worried about accuracy as stated in Tensorflow's github : "The TensorFlow-chosen default for jpeg decoding is IFAST, sacrificing image quality for speed". Credit for this information belongs to this post
Well, it seems like a couple of similar questions were asked here in stack overflow, but none of them seem like answered correctly or properly, nor they described the exact examples.
I have a problem with saving array or list into hdf5 ...
I have a several files contains list of (n, 35) dimensions, where n may be different in each file. Each of them can be saved in hdf5 with code below.
hdf = hf.create_dataset(fname, data=d)
However, if I want to merge them to make in 3d the error occurs as below.
Object dtype dtype('O') has no native HDF5 equivalent
I have no idea why it turns to dtype object, since what I have done is only this
all_data = list()
for fname in file_list:
d = np.load(fname)
all_data.append(d)
hdf = hf.create_dataset('all_data', data=all_data)
How can I save such data?
I tried a couple of tests, and it seems like all_data turns to dtype with 'object' when I change them with
all_data = np.array(all_data)
Which looks it has the similar problem with saving hdf5.
Again, how can I save such data in hdf5?
I was running into a similar issue with h5py, and changing the type of the NumPy array using array.astype worked for me (I believe this changes the type from dtype('O') to the data type you specify). Please see the code snippet below:
import numpy as np
print(X.dtype)
--> dtype('O')
print(X.astype(np.float64).dtype)
--> dtype('float64')
When I ran h5.create_dataset with this data type conversion, I was able to successfully create a h5 dataset. Hope this helps!
ONE ADDITIONAL UPDATE: I believe the NumPy object type 'O' is created when the NumPy array itself has mixed element types (e.g. np.int8 and np.float32).
dtype('O') stands for object. In my case I had a list of lists where the lengths were different and got the same error. If you convert it to a numpy array numpy warns Creating an ndarray from ragged nested sequences. h5 files can't handle this type of data for more info see this post
This error comes when I use:
with h5py.File(peakfilename, 'w') as pfile: # saves the data
pfile['peakY'] = np.array(X)
pfile['peakX'] = np.array(Y)
However when I used dtype when saving the arrays... the problem went away... I guess h5py is not able to create datasets from undefined data types.
with h5py.File(peakfilename, 'w') as pfile: # saves the data
pfile['peakY'] = np.array(X, dtype=np.float32)
pfile['peakX'] = np.array(Y, dtype=np.float32)