I am making a program in Tensorflow to guess the type of flower it is. I was told that to make the program more accurate, I should resize all the images to the same size. I was getting a permission error, but even after changing all the permissions to give admin, and even running in admin Command Prompt, it still didn't work. It also gave me a success message after I used the "icacls" command to alter the permissions.
import os
from PIL import Image
target_size = (224, 224)
for class_name in os.listdir('C:/Users/myname/OneDrive/Desktop/pyimportant/Automation/Flower Classification V2/V2/ Training Data'):
class_dir = os.path.join('C:/Users/myname/OneDrive/Desktop/pyimportant/Automation/Flower Classification V2/V2/Training Data', class_name)
for filename in os.listdir(class_dir):
image_path = os.path.join(class_dir, filename)
with Image.open(image_path) as img:
img = img.resize(target_size)
img.save(image_path)
If you have any questions about specs or such, I'd be happy to answer!
Also, in case you want, here's the dataset:
https://www.kaggle.com/datasets/utkarshsaxenadn/flower-classification-5-classes-roselilyetc
Turns out I had to restart my computer, and then it was working fine. Sorry for the inconvinience! I don't know why this would happen, but sometimes restarting just helps.
Related
This question already has answers here:
How to run a flask application?
(6 answers)
Closed yesterday.
This post was edited and submitted for review yesterday.
I'm trying to follow this online tutorial for a Flask/Tensorflow/React application, but I'm having some trouble at the end now trying to run the Flask server.
Flask version: 2.2.3
Python version: 3.10.0
I've search online for solutions, but nothing I've tried has worked. Here's the ways I've tried to run the application:
Not sure if this could be helpful in coming to a solution, but incase it is, here's my app.py file:
import os
import numpy as np
from flask import Flask, request
from flask_cors import CORS
from keras.models import load_model
from PIL import Image, ImageOps
app = Flask(__name__) # new
CORS(app) # new
#app.route('/upload', methods=['POST'])
def upload():
# Disable scientific notation for clarity
np.set_printoptions(suppress=True)
# Load the model
model = load_model("keras_Model.h5", compile=False)
# Load the labels
class_names = open("labels.txt", "r").readlines()
# Create the array of the right shape to feed into the keras model
# The 'length' or number of images you can put into the array is
# determined by the first position in the shape tuple, in this case 1
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
# Replace this with the path to your image
image = Image.open("<IMAGE_PATH>").convert("RGB")
# resizing the image to be at least 224x224 and then cropping from the center
size = (224, 224)
image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
# turn the image into a numpy array
image_array = np.asarray(image)
# Normalize the image
normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
# Load the image into the array
data[0] = normalized_image_array
# Predicts the model
prediction = model.predict(data)
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index]
# Print prediction and confidence score
print("Class:", class_name[2:], end="")
print("Confidence Score:", confidence_score)
Does anyone know what I'm doing wrong here, is there maybe something obvious I'm missing that's causing the problem? If there's any other info I can add that may be helpful, please let me know, thanks.
Edit:
I've added the execution section at the end of my code:
if __name__ == '__main__':
app.run(host="127.0.0.1", port=5000)
And now 'python -m flask run' does attempt to run the app, so the original post question is answered. There does seem to be a subsequent problem now though, it constantly returns this error. I installed tensorflow using 'pip3 install tensorflow' and it does install successfully, but then it always returns with the module not found error. Tensorflow doesn't appear in pip freeze package list, am now looking to see how/why this is.
Edit2: My question was flagged as already answered here, though I'm struggling to see how, as that post has absolutely no mention at all on why 'flask run' or any way of trying to run a flask app might not work, or what to do when it doesn't, which is what this question is. That post is simply discussing the 'correct' way to run a flask app, not how to run one at all when it's not running.
If you don't want to use the flask command, you need to add code to run the development server instead.
if __name__ == "__main__":
app.run()
You're not using the flask command, but you forgot to add code to run the development server instead.
if __name__ == '__main__':
app.run()
I am doing custom image segmentation using PixelLib.
import pixellib
from pixellib.instance import custom_segmentation
segment_image = custom_segmentation()
segment_image.inferConfig(num_classes= 1, class_names= ["BG", "road"])
segment_image.load_model("/content/mask_rcnn_model.143-0.199986.h5")
segment_image.segmentImage("/content/FullTrainingData/Warlick.PNG", show_bboxes=False, output_image_name="a0000sample_out" + str(i) +".PNG")
The segmentation looks good on my machine, but running it on another machine with the same h5 file the result is far worse
The result differs each time I reload the model. Any reason this could be happening?
adding these imports
import pixellib
from pixellib.tune_bg import alter_bg
causes it to work. No idea why though
I am trying to complete some homework in a DeepLearning.ai course assignment.
When I try the assignment in Coursera platform everything works fine, however, when I try to do the same imports on my local machine it gives me an error,
ModuleNotFoundError: No module named 'lr_utils'
I have tried resolving the issue by installing lr_utils but to no avail.
There is no mention of this module online, and now I started to wonder if that's a proprietary to deeplearning.ai?
Or can we can resolve this issue in any other way?
You will be able to find the lr_utils.py and all the other .py files (and thus the code inside them) required by the assignments:
Go to the first assignment (ie. Python Basics with numpy) - which you can always access whether you are a paid user or not
And then click on 'Open' button in the Menu bar above. (see the image below)
.
Then you can include the code of the modules directly in your code.
As per the answer above, lr_utils is a part of the deep learning course and is a utility to download the data sets. It should readily work with the paid version of the course but in case you 'lost' access to it, I noticed this github project has the lr_utils.py as well as some data sets
https://github.com/andersy005/deep-learning-specialization-coursera/tree/master/01-Neural-Networks-and-Deep-Learning/week2/Programming-Assignments
Note:
The chinese website links did not work when I looked at them. Maybe the server storing the files expired. I did see that this github project had some datasets though as well as the lr_utils file.
EDIT: The link no longer seems to work. Maybe this one will do?
https://github.com/knazeri/coursera/blob/master/deep-learning/1-neural-networks-and-deep-learning/2-logistic-regression-as-a-neural-network/lr_utils.py
Download the datasets from the answer above.
And use this code (It's better than the above since it closes the files after usage):
def load_dataset():
with h5py.File('datasets/train_catvnoncat.h5', "r") as train_dataset:
train_set_x_orig = np.array(train_dataset["train_set_x"][:])
train_set_y_orig = np.array(train_dataset["train_set_y"][:])
with h5py.File('datasets/test_catvnoncat.h5', "r") as test_dataset:
test_set_x_orig = np.array(test_dataset["test_set_x"][:])
test_set_y_orig = np.array(test_dataset["test_set_y"][:])
classes = np.array(test_dataset["list_classes"][:])
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes
"lr_utils" is not official library or something like that.
Purpose of "lr_utils" is to fetch the dataset that is required for course.
option (didn't work for me): go to this page and there is a python code for downloading dataset and creating "lr_utils"
I had a problem with fetching data from provided url (but at least you can try to run it, maybe it will work)
option (worked for me): in the comments (at the same page 1) there are links for manually downloading dataset and "lr_utils.py", so here they are:
link for dataset download
link for lr_utils.py script download
Remember to extract dataset when you download it and you have to put dataset folder and "lr_utils.py" in the same folder as your python script that is using it (script with this line "import lr_utils").
The way I fixed this problem was by:
clicking File -> Open -> You will see the lr_utils.py file ( it does not matter whether you have paid/free version of the course).
opening the lr_utils.py file in Jupyter Notebooks and clicking File -> Download ( store it in your own folder ), rerun importing the modules. It will work like magic.
I did the same process for the datasets folder.
You can download train and test dataset directly here: https://github.com/berkayalan/Deep-Learning/tree/master/datasets
And you need to add this code to the beginning:
import numpy as np
import h5py
import os
def load_dataset():
train_dataset = h5py.File('datasets/train_catvnoncat.h5', "r")
train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels
test_dataset = h5py.File('datasets/test_catvnoncat.h5', "r")
test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels
classes = np.array(test_dataset["list_classes"][:]) # the list of classes
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes
I faced similar problem and I had followed the following steps:
1. import the following library
import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
2. download the train_catvnoncat.h5 and test_catvnoncat.h5 from any of the below link:
[https://github.com/berkayalan/Neural-Networks-and-Deep-Learning/tree/master/datasets]
or
[https://github.com/JudasDie/deeplearning.ai/tree/master/Improving%20Deep%20Neural%20Networks/Week1/Regularization/datasets]
3. create a folder named datasets and paste these two files in this folder.
[ Note: datasets folder and your source code file should be in same directory]
4. run the following code
def load_dataset():
with h5py.File('datasets1/train_catvnoncat.h5', "r") as train_dataset:
train_set_x_orig = np.array(train_dataset["train_set_x"][:])
train_set_y_orig = np.array(train_dataset["train_set_y"][:])
with h5py.File('datasets1/test_catvnoncat.h5', "r") as test_dataset:
test_set_x_orig = np.array(test_dataset["test_set_x"][:])
test_set_y_orig = np.array(test_dataset["test_set_y"][:])
classes = np.array(test_dataset["list_classes"][:])
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes
5. Load the data:
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()
check datasets
print(len(train_set_x_orig))
print(len(test_set_x_orig))
your data set is ready, you may check the len of the train_set_x_orig, train_set_y variable. For mine, it was 209 and 50
I could download the dataset directly from coursera page.
Once you open the Coursera notebook you go to File -> Open and the following window will be display:
enter image description here
Here the notebooks and datasets are displayed, you can go to the datasets folder and download the required data for the assignment. The package lr_utils.py is also available for downloading.
below is your code, just save your file named "lr_utils.py" and now you can use it.
import numpy as np
import h5py
def load_dataset():
train_dataset = h5py.File('datasets/train_catvnoncat.h5', "r")
train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels
test_dataset = h5py.File('datasets/test_catvnoncat.h5', "r")
test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels
classes = np.array(test_dataset["list_classes"][:]) # the list of classes
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes
if your code file can not find you newly created lr_utils.py file just write this code:
import sys
sys.path.append("full path of the directory where you saved Ir_utils.py file")
Here is the way to get dataset from as #ThinkBonobo:
https://github.com/andersy005/deep-learning-specialization-coursera/tree/master/01-Neural-Networks-and-Deep-Learning/week2/Programming-Assignments/datasets
write a lr_utils.py file, as above answer #StationaryTraveller, put it into any of sys.path() directory.
def load_dataset():
with h5py.File('datasets/train_catvnoncat.h5', "r") as train_dataset:
....
!!! BUT make sure that you delete 'datasets/', cuz now the name of your data file is train_catvnoncat.h5
restart kernel and good luck.
I may add to the answers that you can save the file with lr_utils script on the disc and import that as a module using importlib util function in the following way.
The below code came from the general thread about import functions from external files into the current user session:
How to import a module given the full path?
### Source load_dataset() function from a file
# Specify a name (I think it can be whatever) and path to the lr_utils.py script locally on your PC:
util_script = importlib.util.spec_from_file_location("utils function", "D:/analytics/Deep_Learning_AI/functions/lr_utils.py")
# Make a module
load_utils = importlib.util.module_from_spec(util_script)
# Execute it on the fly
util_script.loader.exec_module(load_utils)
# Load your function
load_utils.load_dataset()
# Then you can use your load_dataset() coming from above specified 'module' called load_utils
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_utils.load_dataset()
# This could be a general way of calling different user specified modules so I did the same for the rest of the neural network function and put them into separate file to keep my script clean.
# Just remember that Python treat it like a module so you need to prefix the function name with a 'module' name eg.:
# d = nnet_utils.model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 1000, learning_rate = 0.005, print_cost = True)
nnet_script = importlib.util.spec_from_file_location("utils function", "D:/analytics/Deep_Learning_AI/functions/lr_nnet.py")
nnet_utils = importlib.util.module_from_spec(nnet_script)
nnet_script.loader.exec_module(nnet_utils)
That was the most convenient way for me to source functions/methods from different files in Python so far.
I am coming from the R background where you can call just one line function source() to bring external scripts contents into your current session.
The above answers didn't help, some links had expired.
So, lr_utils is not a pip library but a file in the same notebook as the CourseEra website.
You can click on "Open", and it'll open the explorer where you can download everything that you would want to run in another environment.
(I used this on a browser.)
This is how i solved mine, i copied the lir_utils file and paste it in my notebook thereafter i downloaded the dataset by zipping the file and extracting it. With the following code. Note: Run the code on coursera notebook and select only the zipped file in the directory to download.
!pip install zipfile36
zf = zipfile.ZipFile('datasets/train_catvnoncat_h5.zip', mode='w')
try:
zf.write('datasets/train_catvnoncat.h5')
zf.write('datasets/test_catvnoncat.h5')
finally:
zf.close()
I have a form that saves an image, and everything is working fine, but I want to be able to crop the image. However, when I used Pillow to do this, I get a strange error that doesn't really give me much to go on.
Attribute error at /userprofile/addGame/
_committed
Looking further into the traceback, the following sections were highlighted:
I'm not really sure what this error means. I think it has something to do with the form.save(committed=False), and not being able to edit files after that point, but I'm not positive. I can edit the user after that line using form.user = request.user, so I think it's the fact that I'm trying to change the image getting uploaded that is part of the issue.
This is an old post but I recently experienced a similar problem.
The information in the question is somewhat incomplete but I had the same error.
Assuming the Error being
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/PIL/Image.py", line 627, in __getattr__
raise AttributeError(name)
AttributeError: _committed
So the root problem lies with using PIL (Pillow). The function :
image = Image.open(self.cleaned_data['image'])
We wrongly assume that the object image would continue to have the
instance of image throughout the function(clean_image in my case).
So in my code structure,
def clean_image(self):
if self.cleaned_data['image']:
try:
image = Image.open(self.cleaned_data['image'])
image.verify()
except IOError:
raise forms.ValidationError(_('The file is not an image'))
return image
The error mentioned above is thrown after we execute this code. It is so because
After several of the other PIL(pillow) functions we use, we need to
open the file again. Check this out in pillow documentation
As in the code above(last line),
return image
doesn't really return anything.
To fix it
Simply add ,
image = self.cleaned_data['image']
before the return image line.
Code looks like,
except IOError:
raise forms.ValidationError(_('The file is not an image'))
image = self.cleaned_data['image']
return image
I had faced the same issue trying to save a model with an ImageFileField. My requirement was to save an image from a zip object.
Using pillow gave me the AttributeError: _committed error. Following is what worked for me:
from django.core.files.images import ImageFile
zf = zipfile.ZipFile(zip_file, mode='r')
image_list = zf.infolist()
Model.objects.create(img=ImageFile(zipObj.open(image_list[0])),
file_name=image_list[0].filename.split("/")[-1].strip(),
...)
I wrapped opencv today with simplecv python interface. After going through the official SimpleCV Cookbook I was able to successfully Load, Save, and Manipulate images. Thus, I know the library is being loaded properly.
However, under the Using a Camera, Kinect, or Virtual Camera heading I was unsuccessful in running some commands. In particular, mycam = Camera() worked, but img = mycam.getImage() produced the following error:
In [35]: img = mycam.getImage().save()
OpenCV Error: Bad argument (Array should be CvMat or IplImage) in cvGetSize, file /home/jordan/OpenCV-2.2.0/modules/core/src/array.cpp, line 1237
---------------------------------------------------------------------------
error Traceback (most recent call last)
/home/simplecv/<ipython console> in <module>()
/usr/local/lib/python2.7/dist-packages/SimpleCV-1.1-py2.7.egg/SimpleCV/Camera.pyc in getImage(self)
332
333 frame = cv.RetrieveFrame(self.capture)
--> 334 newimg = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 3)
335 cv.Copy(frame, newimg)
336 return Image(newimg, self)
error: Array should be CvMat or IplImage
I'm running Ubuntu Natty on a HP TX2500 tablet. It has a built in webcam, (CyberLink Youcam?) Has anybody seen this error before? I've been all over the web today looking for a solution, but nothing seems to be doing the trick.
Update 1: I tested cv.QueryFrame(capture) using the code found here in a separate Stack Overflow question and it worked; so I've pretty much nailed this down to a webcam issue.
Update 2: In fact, I get the exact same errors on a machine that doesn't even have a webcam! It's looking like the TX2500 is not compatible...
since the error raised from Camera.py of SimpleCV, you need to debug the getImage() method. If you can edit it:
def getImage(self):
if (not self.threaded):
cv.GrabFrame(self.capture)
frame = cv.RetrieveFrame(self.capture)
import pdb # <-- add this line
pdb.set_trace() # <-- add this line
newimg = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 3)
cv.Copy(frame, newimg)
return Image(newimg, self)
then run your program, it will be paused as pdb.set_trace(), here you can inspect the type of frame, and try to figure out how get the size of frame.
Or you can do the capture in your code, and inspect the frame object:
mycam = Camera()
cv.GrabFrame(mycam.capture)
frame = cv.RetrieveFrame(mycam.capture)
To answer my own question...
I bought a Logitech C210 today and the problem disappeared.
I'm now getting warnings:
Corrupt JPEG data: X extraneous bytes before marker 0xYY.
However, I am able to successfully push a video stream to my web-browser via JpegStreamer(). If I cannot solve this error, I'll open a new thread.
Thus, for now, I'll blame the TX2500.
If anybody finds a fix in the future, please post.
Props to #HYRY for the investigation. Thanks.
I'm geting the camera with OpenCV
from opencv import cv
from opencv import highgui
from opencv import adaptors
def get_image()
cam = highgui.cvCreateCameraCapture(0)
im = highgui.cvQueryFrame(cam)
# Add the line below if you need it (Ubuntu 8.04+)
#im = opencv.cvGetMat(im)
return im
Anthony, one of the SimpleCV developers here.
Also instead of using image.save(), this function writes the file/video to disk, you instead probably want to use image.show(). You can save if you want, but you need to specify a file path like image.save("/tmp/blah.png")
So you want to do:
img = mycam.getImage()
img.show()
As for that model of camera I'm not sure if it works or not. I should note that we also wrapper different camera classes not just OpenCV, this is because OpenCV has a problem with webcams over 640x480, we now can do high resolution cameras.
Also I should mention, which I didn't realize, is that OpenCV less than 2.3 is broken with webcams on Ubuntu 11.04 and up. I didn't realize this as I was running Ubuntu 10.10 before, by the looks of your output you are using python 2.7 which makes me think you are on Ubuntu 11.04 or higher. Anyway, we have a fix for this problem. It is now pushed up into the master, it basically does a check to see if OpenCV is working, if not it will fall back to pygame.
This fix will also be in the 1.2 release of SimpleCV (It's in the master branch now)