I have this code:
import cv2
im = cv2.imread("0.jpg")
print(len(im.tobytes()))
fp = open("0.jpg", 'rb')
imb = fp.read()
print(len(imb))
They are different! Now a function take 'imb' format as input. But I just have 'im'. I must use the cv2.imwrite to the disk and then use fp.read()?
Is there a faster way?
I use the mxnet image imdecode. imdecode take 'imb' as input. But im is what I get. How to pass the 'im' to the mx.img.imdecode?
https://github.com/apache/incubator-mxnet/issues/13545
I'm not familiar with mxnet framework. But according to the code you gave, I guess you want image data in binary format.
Take a look at OpenCV cv.imencode() API.
import cv2
im = cv2.imread("0.jpg")
img_encode = cv2.imencode('.jpg', im)[1]
I think the variable img_encode is might what you want.
Or try to use cv2.imwrite in more intuitive way like Memory Filesystem in "pyfilesystem" module.[link]
Related
I'm trying to use Tensorflow to Machine Learning to analyze an image and return the probability if is positive or negative based on a model created (extension .h5). I couldn't found a documentation exactly for that, or repository, so even a link to read will be awesome.
Link for the application: https://share.streamlit.io/felipelx/hackathon/IDC_Detector.py
Libraries that I'm trying to use.
import numpy as np
import streamlit as st
import tensorflow as tf
from keras.models import load_model
The function to load the model.
#st.cache(allow_output_mutation=True)
def loadIDCModel():
model_idc = load_model('models/IDC_model.h5', compile=False)
model_idc.summary()
return model_idc
The function to work the image, and what I'm trying to see: model.predict - I can see but is not updating the %, independent of the image the value is always the same.
if uploaded_file is not None:
# transform image to numpy array
file_bytes = tf.keras.preprocessing.image.load_img(uploaded_file, target_size=(96,96), grayscale = False, interpolation = 'nearest', color_mode = 'rgb', keep_aspect_ratio = False)
c.image(file_bytes, channels="RGB")
Genrate_pred = st.button("Generate Prediction")
if Genrate_pred:
model = loadMetModel()
input_arr = tf.keras.preprocessing.image.img_to_array(file_bytes)
input_arr = np.array([input_arr])
probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])
prediction = probability_model.predict(input_arr)
dict_pred = {0: 'Benigno/Normal', 1: 'Maligno'}
result = dict_pred[np.argmax(prediction)]
value = 0
if result == 'Benigno/Normal':
value = str(((prediction[0][0])*100).round(2)) + '%'
else:
value = str(((prediction[0][1])*100).round(2)) + '%'
c.metric('Predição', result, delta=value, delta_color='normal')
Thank you in advance to any help.
The first thing I'm noticing is that your function for loading the model is named loadIDCModel, but then the function you call for loading the model is loadMetModel. When I check your source code, though, it looks like you've already addressed this issue. I'd recommend updating your question to reflect this.
Playing around with your application, I think the issue is your model itself. I tried various images — images containing carcinomas, and even a picture of a cat — and each gave me a probability around 73%. The lowest score I got was 72.74%, and the highest was 73.11% (this one was the cat). It seems that the output percentage is varying slightly, hinting that rather than something being wrong in the code, your model itself is likely at fault. You might need to retrain your model, as it seems to have learned to always return a value of approximately 0.73.
Hi I've trying to get a TFX Pipeline going just as an exercise really. I'm using ImportExampleGen to load TFRecords from disk. Each Example in the TFRecord contains a jpg in the form of a byte string, height, width, depth, steering and throttle labels.
I'm trying to use StatisticsGen but I'm receiving this warning;
WARNING:root:Feature "image_raw" has bytes value "None" which cannot be decoded as a UTF-8 string. and crashing my Colab Notebook. As far as I can tell all the byte-string images in the TFRecord are not corrupt.
I cannot find concrete examples on StatisticsGen and handling image data. According to the docs Tensorflow Data Validation can deal with image data.
In addition to computing a default set of data statistics, TFDV can also compute statistics for semantic domains (e.g., images, text). To enable computation of semantic domain statistics, pass a tfdv.StatsOptions object with enable_semantic_domain_stats set to True to tfdv.generate_statistics_from_tfrecord.
But I'm not sure how this fits in with StatisticsGen.
Here is the code that instantiates an ImportExampleGen then the StatisticsGen
from tfx.utils.dsl_utils import tfrecord_input
from tfx.components.example_gen.import_example_gen.component import ImportExampleGen
from tfx.proto import example_gen_pb2
examples = tfrecord_input(_tf_record_dir)
# https://www.tensorflow.org/tfx/guide/examplegen#custom_inputoutput_split
# has a good explanation of splitting the data the 'output_config' param
# Input train split is _tf_record_dir/*'
# Output 2 splits: train:eval=8:2.
train_ratio = 8
eval_ratio = 10-train_ratio
output = example_gen_pb2.Output(
split_config=example_gen_pb2.SplitConfig(splits=[
example_gen_pb2.SplitConfig.Split(name='train',
hash_buckets=train_ratio),
example_gen_pb2.SplitConfig.Split(name='eval',
hash_buckets=eval_ratio)
]))
example_gen = ImportExampleGen(input=examples,
output_config=output)
context.run(example_gen)
statistics_gen = StatisticsGen(
examples=example_gen.outputs['examples'])
context.run(statistics_gen)
Thanks in advance.
From git issue response
Thanks Evan Rosen
Hi Folks,
The warnings you are seeing indicate that StatisticsGen is trying to treat your raw image features like a categorical string feature. The image bytes are being decoded just fine. The issue is that when the stats (including top K examples) are being written, the output proto is expecting a UTF-8 valid string, but instead gets the raw image bytes. Nothing is wrong with your setups from what I can tell, but this is just an unintended side-effect of a well-intentioned warning in the event that you have a categorical string feature which can't be serialized. We'll look into finding a better default that handles image data more elegantly.
In the meantime, to tell StatisticsGen that this feature is really an opaque blob, you can pass in a user-modified schema as described in the StatsGen docs. To generate this schema, you can run StatisticsGen and SchemaGen once (on a sample of data) and then modify the inferred schema to annotate that image features. Here is a modified version of the colab from #tall-josh:
Open In Colab
The additional steps are a bit verbose, but having a curated schema is often a good practice for other reasons. Here is the cell that I added to the notebook:
from google.protobuf import text_format
from tensorflow.python.lib.io import file_io
from tensorflow_metadata.proto.v0 import schema_pb2
# Load autogenerated schema (using stats from small batch)
schema = tfx.utils.io_utils.SchemaReader().read(
tfx.utils.io_utils.get_only_uri_in_dir(
tfx.types.artifact_utils.get_single_uri(schema_gen.outputs['schema'].get())))
# Modify schema to indicate which string features are images.
# Ideally you would persist a golden version of this schema somewhere rather
# than regenerating it on every run.
for feature in schema.feature:
if feature.name == 'image/raw':
feature.image_domain.SetInParent()
# Write modified schema to local file
user_schema_dir ='/tmp/user-schema/'
tfx.utils.io_utils.write_pbtxt_file(
os.path.join(user_schema_dir, 'schema.pbtxt'), schema)
# Create ImportNode to make modified schema available to other components
user_schema_importer = tfx.components.ImporterNode(
instance_name='import_user_schema',
source_uri=user_schema_dir,
artifact_type=tfx.types.standard_artifacts.Schema)
# Run the user schema ImportNode
context.run(user_schema_importer)
Hopefully you find this workaround is useful. In the meantime, we'll take a look at a better default experience for image-valued features.
Groked this and found the solution to be dramatically simpler than i thought...
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
import logging
...
logger = logging.getLogger()
logger.setLevel(logging.CRITICAL)
...
context = InteractiveContext(pipeline_name='my_pipe')
...
c = StatisticsGen(...)
...
context.run(c)
I have a ndarray representing an image with different channels like this:
image = (8,100,100) where 8=channels, 100x100 the actual image per channel
I am interested in extracting the RGB components of that image:
imageRGB = np.take(image, [4,2,1], axis = 0)
in this way I have an array of (3,100,100) with the RGB components.
However, I need to visualize it so I need an array of (100,100,3), I think it's quite straightforward to do it but I all the methods I try do not work.
numpy einsum is a good tool to be used.
Official document: https://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html
import numpy as np
imageRGB = np.random.randint(0,5,size=(3,100,101))
# set the last dim to 101 just to make stuff more clear
imageRGB.shape
# (3,100,101)
imageRGB_reshape = np.einsum('kij->ijk',imageRGB)
imageRGB_reshape.shape
# (100,101,3)
In my opinion it's the most clear way to write and read.
Wow thank you! I have never thought to use Einstein summation, actually it works very well.
Just for curiosity is it possible to build it manually?
For example:
R = image[4,:,:]
G = image[2,:,:]
B = image[1,:,:]
imageRGB = ???
I want to compare two images for similarity. Since my purpose is to match a given image against a massive collection of images, I want to run the comparisons on GPU.
I came across tf.image.ssim and tf.image.psnr functions but I am unable to find and working examples only. The solutions in PyTorch is also appreciated. Since I don't have a good understanding of CUDA and C language, I am hesitant to try kernels in PyCuda.
Will it be helpful in terms of processing if I read the entire image collection and store as Tensorflow Records for future processing?
Any guidance or solution, greatly appreciated. Thank you.
Edit:- I am matching images of same size only. I don't want to do mere histogram match. I want to do SSIM or PSNR implementation for image similarity. So, I am assuming it would be similar in color, content etc
Check out the example on the tensorflow doc page (link):
im1 = tf.decode_png('path/to/im1.png')
im2 = tf.decode_png('path/to/im2.png')
print(tf.image.ssim(im1, im2, max_val=255))
This should work on latest version of tensorflow. If you use older versions tf.image.ssim will return a tensor (print will not give you a value), but you can call .run() to evaluate it.
There is no implementation of PSNR or SSIM in PyTorch. You can either implement them yourself or use a third-party package, like piqa which I have developed.
Assuming you already have torch and torchvision installed, you can get it with
pip install piqa
Then for the image comparison
import torch
from torchvision import transforms
from PIL import Image
im1 = Image.open('path/to/im1.png')
im2 = Image.open('path/to/im2.png')
transform = transforms.ToTensor()
x = transform(im1).unsqueeze(0).cuda() # .cuda() for GPU
y = transform(im2).unsqueeze(0).cuda()
from piqa import PSNR, SSIM
psnr = PSNR()
ssim = SSIM().cuda()
print('PSNR:', psnr(x, y))
print('SSIM:', ssim(x, y))
I am trying to create a 78TB HDF5 dataset by filling it in a 2d block-partition manner. This is very slow when the block I'm writing spans rows that haven't ever been written to, because HDF5 is going in and allocating the diskspace and filling in the missing entries with zero.
Instead, I would like h5py to allocate the disk space for my dataset as soon as its created, and never fill it. This is possible with the C api according to Table 16 in the HDF5 Dataset documentation, but how can I do this with h5py, preferably with the high level interface?
I believe that you want to set the fill time to "never", with the H5Pset_fill_time() routine, but I don't know the h5py way to do that.
As Quincey suggested. You can use the low-level H5py API to create the dataset with the FILL_TIME_NEVER property then convert it back to a high-level Dataset object:
# create the rows dataset using the low-level api so I can force it to not do zero-filling, then convert to a high level object
spaceid = h5py.h5s.create_simple((numRows, numCols))
plist = h5py.h5p.create(h5py.h5p.DATASET_CREATE)
plist.set_fill_time(h5py.h5d.FILL_TIME_NEVER)
plist.set_chunk((rowchunk, colchunk))
datasetid = h5py.h5d.create(fout.id, "rows", h5py.h5t.NATIVE_DOUBLE, spaceid, plist)
rows = h5py.Dataset(datasetid)
Try specifying a chunk shape that matches your write pattern. For example if you are writing in blocks of 1024x1024, it would look like this:
import h5py
import numpy as np
f = h5py.File('mybigdset.h5', 'w')
dset = f.create_dataset('dset', (78*1024*1024, 1024*1024), dtype='f4', chunks=(1024,1024))
arr = np.random.rand(1024,1024)
dset[0:1024, 0:1024] = arr
f.close()
Thankfully, this didn't use 78TB of disk, the file size was just 4MB.