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Im trying to use NudeNet and pyautogui to take a screenshot and then determine if it contains explicit content.
from nudenet import NudeClassifier
import pyautogui
from PIL import Image
import numpy as np
# initialize classifier (downloads the checkpoint file automatically the first time)
classifier = NudeClassifier()
# Classify single image
screenshot = pyautogui.screenshot()
screenshotResized = screenshot.resize((256, 256))
# screenshotResizedNumpyArray = np.asarray(screenshotResized)
# screenshotResizedNumpyArrayTransposed = np.transpose(screenshotResizedNumpyArray, (2, 1, 0))
# finalScreenshot = Image.fromarray(screenshotResizedNumpyArrayTransposed) if you uncomment this change screenshot in the following line to finalScreenshot
screenshot.save(r'C:/users/____/images/screenshot.png')
print(classifier.classify('C:/users/____/images/screenshot.png'))
When I run this code I get the following error message:
return self._sess.run(output_names, input_feed, run_options)
onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: input_1 for the following indices
index: 1 Got: 256 Expected: 3
index: 3 Got: 3 Expected: 256
Please fix either the inputs or the model.
So, apparently the numpy array of my resized image is formatted as [256, 256, 3] when it should be [3, 256, 256], correct?
I attempted to resolve this with the following code by converting the image into to a numpy array and swapping the first and third indices
from nudenet import NudeClassifier
import pyautogui
from PIL import Image
import numpy as np
# initialize classifier (downloads the checkpoint file automatically the first time)
classifier = NudeClassifier()
# Classify single image
screenshot = pyautogui.screenshot()
screenshotResized = screenshot.resize((256, 256))
screenshotResizedNumpyArray = np.asarray(screenshotResized)
screenshotResizedNumpyArrayTransposed = np.transpose(screenshotResizedNumpyArray, (2, 1, 0))
finalScreenshot = Image.fromarray(screenshotResizedNumpyArrayTransposed)
finalScreenshot.save(r'C:/users/____/images/screenshot.png')
print(classifier.classify('C:/users/____/images/screenshot.png'))
When I try to swap those indices with the code above I get this error:
raise TypeError(msg) from e
TypeError: Cannot handle this data type: (1, 1, 256), |u1
This tells me that I'm doing something wrong when transposing. Please help.
Related
I am training my model with several images.
When training my model I realized that I could increase my accuracy by replacing the zero elements in my image array with other values and so I replaced them with the median value of my image as shown with the following code.
import cv2
import imutils
import numpy as np
r_val_all = np.zeros((2000,112,112))
for r in range(len(r_val)):
#LOAD IMAGES
r_image_v = cv2.imread(r_val[r])
r_gray_v = cv2.cvtColor(r_image_v, cv2.COLOR_BGR2GRAY)
r_gray_v = imutils.resize(r_gray_v, width=112, height=112)
n = np.median(r_gray_v[r_gray_v > 0])
r_gray_v[r_gray_v == 0] = n
r_val_all[r,:,:] = r_gray_v
The accuracy did improve however it is not quite there yet.
What I actually require is something where the zero elements are replaced with a continuation of the pre-existent array values.
However I was not sure how to tackle such a problem are there any tools that perform the operation I require?
I used the second answer from the link, tell me if this is close to what you want, because it appeared to be what you wanted.
Creating one sample image and center it, so it's somewhat close to your first example image.
import numpy as np
import matplotlib.pyplot as plt
image = np.zeros((100, 100))
center_noise = np.random.normal(loc=10, size=(50, 50))
image[25:75, 25:75] = center_noise
plt.imshow(image, cmap='gray')
Inspired by rr_gray = np.where(rr_gray==0, np.nan, rr_gray) #convert zero elements to nan in your code, I'm replacing the zeros with NaN.
image_centered = np.where(image == 0, np.nan, image)
plt.imshow(image_centered, cmap='gray')
Now I used the function in the second answer of the link, fill.
test = fill(image_centered)
plt.imshow(test, cmap='gray')
This is the result
I'm sorry I can't help you more. I wish I could, I'm just not very well versed in image processing. I looked at your code and couldn't figure out why it's not working, sorry.
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I'm trying to work through a tutorial at http://www.semspirit.com/artificial-intelligence/machine-learning/regression/support-vector-regression/support-vector-regression-in-python/
but there's no csv file included, so I'm using my own data. Here's the code so far:
import numpy as np
import pandas as pd
from matplotlib import cm
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy import stats
# Here's where I import my data; there's no csv file included in the tutorial
import quasar_functions as qf
dataset, datasetname, mags = qf.loaddata('sdss12')
S = np.asarray(dataset[mags])
t = np.asarray(dataset['z'])
t.reshape(-1,1)
# Feature scaling
from sklearn.preprocessing import StandardScaler as scale
sc_S = scale()
sc_t = scale()
S2 = sc_S.fit_transform(S)
t2 = sc_t.fit_transform(t)
The last line throws an error:
ValueError: Expected 2D array, got 1D array instead:
array=[4.17974 2.06468 5.46959 ... 0.41398 0.3672 1.9235 ].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
and yes, I've reshaped my target array t with t.reshape(-1,1) as shown here, here, here and here, but to no avail. Am I reshaping correctly?
Here's all my variables:
I am guessing you have a dataframe, so you need to reassign the variable t = t.reshape(-1,1):
import pandas as pd
dataset = pd.DataFrame(np.random.normal(2,1,(100,4)),columns=['z','x1','x2','x3'])
mags = ['x1','x2','x3']
S = np.asarray(dataset[mags])
t = np.asarray(dataset['z'])
t = t.reshape(-1,1)
from sklearn.preprocessing import StandardScaler as scale
sc_S = scale()
sc_t = scale()
S2 = sc_S.fit_transform(S)
t2 = sc_t.fit_transform(t)
To check it works:
np.mean(t2)
2.4646951146678477e-16
I'm currently processing images that consist of a stack, 18 images per stack. I then deconvolve these images to produce cleaner sharper images. However when doing this I get border artifacts. I have spent some time writing code so as to determine how wide a pad I would need to pad these images, however I am unsure of how to use np.pad so that I may produce padded images. This is my code so far:
xextra = pad_width_x / 2
yextra = pad_width_y / 2
print (xextra)
print (yextra)
Where xextra and yextra are the pad widths I will be using. I understand that I will need to use this line of code to pad the array:
no_borders = np.pad(sparsebeadmix_sheet_cubic_deconvolution, pad_width_x, mode='constant', constant_values=0)
However how will I be able to process my stack of images (18 images) through this and save them as outputs?
I hope this makes sense!
If your stack is a nxny18 array:
import numpy as np
image_stack = np.ones((2, 2, 18))
extra_left, extra_right = 1, 2
extra_top, extra_bottom = 3, 1
np.pad(image_stack, ((extra_top, extra_bottom), (extra_left, extra_right), (0, 0)),
mode='constant', constant_values=3)
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I'm currently teaching myself pandas and python for machine learning. I've done fine with text data thus far, but dealing with image data with limited knowledge of python and pandas is tripping me.
I have read in a .csv file into pandas dataframe, with one of its columns containing url to an image. So this is what shows when I get info from the dataframe.
dataframe = pandas.read_csv("./sample.csv")
dataframe.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total of 5 columns):
name 5000 non-null object
...
image 5000 non-null object
the image column contains url to the image. The problem is, I do not know how to import the image data from this and save it as numpy array for processing.
Any help is appreciated. Thanks in advance!
If you want to download the images from the web and then, for example, rotate your images from your dataframe, and save the results you can use the following code:
import pandas as pd
import matplotlib.pylab as plt
import numpy as np
from PIL import Image
import urllib2 as urllib
import io
df = pd.DataFrame({
"name": ["Butterfly", "Birds"],
"image": ["https://upload.wikimedia.org/wikipedia/commons/0/0c/Two-tailed_pasha_%28Charaxes_jasius_jasius%29_Greece.jpg",
'https://upload.wikimedia.org/wikipedia/commons/c/c5/Bat_cave_in_El_Maviri_Sinaloa_-_Mexico.jpg']})
def rotate_image(image, theta):
"""
3D rotation matrix around the X-axis by angle theta
"""
rotation_matrix = np.c_[
[1,0,0],
[0,np.cos(theta),-np.sin(theta)],
[0,np.sin(theta),np.cos(theta)]
]
return np.einsum("ijk,lk->ijl", image, rotation_matrix)
for i, imageUrl in enumerate(df.image):
print imageUrl
fd = urllib.urlopen(imageUrl)
image_file = io.BytesIO(fd.read())
im = Image.open(image_file)
im_rotated = rotate_image(im, np.pi)
fig = plt.figure()
plt.imshow(im_rotated)
plt.axis('off')
fig.savefig(df.name.ix[i] + ".jpg")
If instead you want to show the pictures you can do:
plt.show()
The resulting pictures are birds and butterfly which can be seen here as well:
As we don't know your csv-file, you have to tune your pd.read_csv() for your case.
Here i'm using requests to download some image in-memory.
These are then decoded with the help of scipy (which you already should have; if not: you can use Pillow too).
The decoded images are then raw numpy-arrays and shown by matplotlib.
Keep in mind, that we are not using temporary-files here and everything is hold in memory. Read also this (answer by jfs).
For people missing some required libs, one should be able to do the same with (code needs to be changed of course):
requests can be replaced with urllib (standard lib)
i'm not showing code, but this SO-question should be a good start
another relevant SO-question talking about in-memory processing with urllib
pandas can be replaced by csv (standard lib)
scipy can be replaced by Pillow (although internal storage might differ then)
matplotlib is just for demo-purposes (not sure if Pillow allows showing images; edit: it seems it can)
I just selected some random images from some german newspage.
Edit: Free images from wikipedia now used!
Code:
import requests # downloading images
import pandas as pd # csv- / data-input
from scipy.misc import imread # image-decoding -> numpy-array
import matplotlib.pyplot as plt # only for demo / plotting
# Fake data -> pandas DataFrame
urls_df = pd.DataFrame({'urls': ['https://upload.wikimedia.org/wikipedia/commons/thumb/c/cb/Rescue_exercise_RCA_2012.jpg/500px-Rescue_exercise_RCA_2012.jpg',
'https://upload.wikimedia.org/wikipedia/commons/thumb/3/31/Clinotarsus_curtipes-Aralam-2016-10-29-001.jpg/300px-Clinotarsus_curtipes-Aralam-2016-10-29-001.jpg',
'https://upload.wikimedia.org/wikipedia/commons/thumb/9/9f/US_Capitol_east_side.JPG/300px-US_Capitol_east_side.JPG']})
# Download & Decode
imgs = []
for i in urls_df.urls: # iterate over column / pandas Series
r = requests.get(i, stream=True) # See link for stream=True!
r.raw.decode_content = True # Content-Encoding
imgs.append(imread(r.raw)) # Decoding to numpy-array
# imgs: list of numpy arrays with varying shapes of form (x, y, 3)
# as we got 3-color channels
# Beware!: downloading png's might result in a shape of (x, y, 4)
# as some alpha-channel might be available
# For more options: https://docs.scipy.org/doc/scipy/reference/generated/scipy.misc.imread.html
# Plot
f, arr = plt.subplots(len(imgs))
for i in range(len(imgs)):
arr[i].imshow(imgs[i])
plt.show()
Output:
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I have been meticulously reading documentation and rereading/running the below code in order to understand exactly what is occurring. There are still gaps in my knowledge though. I wanted to present the code to you, with comments, which signify the gaps in my knowledge that hopefully some of you are willing to fill.
So here are my request friends:
1) Help me fill in gaps in my knowledge
2) Explain what is going on here step by step in a non-technical and simple format.
import numpy
import scipy.misc
import matplotlib.pyplot
lena = scipy.misc.lena()
''' Generates an artificial range within the framework of the original array (Which is an image)
This artificial range will be paired with another one and used to 'climb'
Through the original array and make changes'''
def get_indices(size):
arr = numpy.arange(size)
#This sets every fourth element to False? How?
return arr % 4 == 0
lena1 = lena.copy()
xindices = get_indices(lena.shape[0])
yindices = get_indices(lena.shape[1])
'''I am unsure of HOW the below code is executing. I know something is being
Set to zero, but what? And how can I verify it?'''
lena[xindices, yindices] = 0
#What does the argument 211 do exactly?
matplotlib.pyplot.subplot(211)
matplotlib.pyplot.imshow(lena1)
matplotlib.pyplot.show()
Thanks mates!
Using the Python debugger is always useful to step through your code while it is executing. Write the following in any place you choose:
import pdb; pdb.set_trace()
Execution will be stopped and you can inspect any variable, use any defined functions, and advance line by line.
Here you have a commented version of your code. The comment on the function is transformed into a docstring with a doctest that could be executed.
import numpy
import scipy.misc
import matplotlib.pyplot
# Get classic image processing example image, Lena, at 8-bit grayscale
# bit-depth, 512 x 512 size.
lena = scipy.misc.lena()
# lena is now a Numpy array of integers, between 245 and 25, of 512 rows and
# 512 columns.
def get_indices(size):
"""
Returns each fourth index in a Numpy vector of the passed in size.
Specifically, return a vector of booleans, where all indices are set to
False except those of every fourth element. This vector can be used to
index another Numpy array and select *only* those elements. Example use:
>>> import numpy as np
>>> vector = np.array([0, 1, 2, 3, 4])
>>> get_indices(vector.size)
array([ True, False, False, False, True], ...)
"""
arr = numpy.arange(size)
return arr % 4 == 0
# Keep a copy of the original image
lena1 = lena.copy()
# Use the defined function to get every fourth index, first in the x direction,
# then in the y direction
xindices = get_indices(lena.shape[0])
yindices = get_indices(lena.shape[1])
# Set every pixel that equals true in the vectors further up to 0. This
# selects **each fourth pixel on the diagonal** (from up left to bottom right).
lena[xindices, yindices] = 0
# Create a Matplotlib plot, with 2 subplots, and selects the one on the 1st
# colum, 1st row. The layout for all subplots is determined from all calls to
# subplot, i.e. if you later call `subplot(212)` you will get a vertical layout
# in one column and two rows; but if you call `subplot(221)` you will get a
# horizontal layout in two columns and one row.
matplotlib.pyplot.subplot(211)
# Show the unaltered image on the first subplot
matplotlib.pyplot.imshow(lena1)
# You could plot the modified original image in the second subplot, and compare
# to the unmodified copy by issuing:
#matplotlib.pyplot.subplot(212)
#matplotlib.pyplot.imshow(lena)
matplotlib.pyplot.show()