png = tf.read_file(filename)
image = tf.image.decode_png(png, channels=3)
image = tf.cast(image, tf.float32)
The images are read and casted to float32. How do I perform a normalization on this? I have performed normalization on grayscale. But need some help with RGB image.
I thought of doing this
def normalized(down):
norm=np.zeros((600,800,3),np.float32)
norm_rgb=np.zeros((600,800,3),np.uint8)
b=rgb[:,:,0]
g=rgb[:,:,1]
r=rgb[:,:,2]
sum=b+g+r
norm[:,:,0]=b/sum*255.0
norm[:,:,1]=g/sum*255.0
norm[:,:,2]=r/sum*255.0
But for this above function to work I need to start a sess on the image and then perform numpy operations.
Could someone help me do this in tensorflow itself?
you can use tf.image.per_image_standardization. It linearly scales image to have zero mean and unit norm.
image = tf.image.per_image_standardization(image)
Related
I tried to import pictures from the camera and I could get 3-dim data from the image.
img = WebcamModule.getImg(True, size=[240,120])
img = image.img_to_array(img)
then, from the code below I tried to expand one more dimention which is batch size. But it doesn't work.
How do I import batch size into the data.
There is a picture of the result below.
img = np.expand_dims(img,axis=0)
Finally, the code below for predicting doesn't work.
val = float(model.predict(img))
I usually do this:
input = input_raw[np.newaxis, ...]
prediction = model.predict(input)
pred = np.squeeze(prediction)
draw(pred)
Check the shape of the image and the image the network wants, check if the model is channel first or channel last, but this always worked for me.
I have a single image that looks like this:
And I need to generate an image dataset that keeps the basic characteristics of this image but adds some noise, such as we see a line at 1:30 time in the image.
Mainly, there's the pink part of the image (vertical lines), blue part (central bluesh hue) and yellow/green part at the edges. I'm looking to "learn" the image in a way that I could control these 3 things and randomly generate:
bluesh central hue's small colors changes and size
vertical pink lines thickness and color
Yellow/Green edges and their size (I could expand them at the expense of blue in the middle or vice virsa
CONSTRAINT: The yellowish circle (which is image of a semi-conductor wafer) cannot change in size or shape. It can move on top of the black square though. structures inside it can change as well, as mentioned in above 3 points.
This might be an easy question for people with experience in computer vision but I, unfortunately, don't have a lot of experience in this domain. So, I'd love to get any ideas on making progress in this direction. Thanks.
Changing the shape of your inner structures while safely keeping all possible characteristics seems non-trivial to me. There are however a number of simple transformation you could do to create an augmented dataset such as:
Mirroring: Horizontally, vertically, diagonally - will keep all of your line characteristics
Rotation: Normally you would also do some rotations, but this will obviously change the orientation of your lines which you want to preserve, so this does not apply in your case
Shearing: Might still apply and work nicely to add some robustness, as long as you don't overdo it and end up bending your features too much
Other than that you might also want to add some noise to your image, or transformed versions of it as listed above, such as Gaussian noise or salt and pepper noise.
You could also play around with the color values, e.g. by slighly shifting the saturation of different hue values in HSV space.
You can combine any of those methods in different combinations, if you try all possible permutations with different amount/type of noise you will get quite a big dataset.
One approach is using keras's ImageDataGenerator
Decide how many samples you want? Assume 5.
total_number = 5
Initialize ImageDataGenerator class. For instance
data_gen = ImageDataGenerator(rescale=1. / 255, shear_range=0.2,
zoom_range=0.2, horizontal_flip=True)
Turn your image to the tensor.
img = load_img("xIzEG.png", grayscale=False) # You can also create gray-images.
arr = img_to_array(img)
tensor_img = arr.reshape((1, ) + arr.shape)
Create a folder you want to store the result, i.e. populated, then Populate
for i, _ in enumerate(data_gen.flow(x=tensor_img,
batch_size=1,
save_to_dir="populated",
save_prefix="generated",
save_format=".png")):
if i > total_number:
break
Now, if you look at your populated folder:
Code
from keras.preprocessing.image import load_img, img_to_array
from keras.preprocessing.image import ImageDataGenerator
# Total Generated number
total_number = 5
data_gen = ImageDataGenerator(rescale=1. / 255, shear_range=0.2,
zoom_range=0.2, horizontal_flip=True)
# Create image to tensor
img = load_img("xIzEG.png", grayscale=False)
arr = img_to_array(img)
tensor_image = arr.reshape((1, ) + arr.shape)
for i, _ in enumerate(data_gen.flow(x=tensor_image,
batch_size=1,
save_to_dir="populated",
save_prefix="generated",
save_format=".png")):
if i > total_number:
break
I just start the module worcloud in Python 3.7, and I'm using the next cxode to generate wordclouds from a dictionary and I'm trying to use differents masks, but this works for some images: in two cases works with images of 831x816 and 1000x808. This has to be with the size of the image? Or is because the images is kind a blurry? Or what is it?
I paste my code:
from PIL import Image
our_mask = np.array(Image.open('twitter.png'))
twitter_cloud = WordCloud(background_color = 'white', mask = our_mask)
twitter_cloud.generate_from_frequencies(frequencies)
twitter_cloud.to_file("twitter_cloud.jpg")
plt.imshow(twitter_cloud)
plt.axis('off')
plt.show()
How can i fix this?
I had a similar problem with a black-and-white image I used. What fixed it for me was when I cropped the image more closely to the black drawing so there was no unnecessary bulk white area on the edges.
Some images should be adjusted for the process. Note only white point values for image is mask_out (other values are mask_in). The problem is that some of images are not suitable for masking. The reason is that the color's np.array somewhat mismatches. To solve this, following can be done:
1.Creating mask object: (Please try with your own image as I couldn't upload:)
import numpy as np;
import pandas as pd;
from PIL import Image;
from wordcloud import WordCloud
mask = np.array(Image.open("filepath/picture.png"))
print(mask)
If the output values for white np.array is 255, then it is okay. But if it is 0 or probably other value, we have to change this to 255.
2.In the case of other values, the code for changing the values:
2-1. Create function for transforming (here our value = 0)
def transform_zeros(val):
if val == 0:
return 255
else:
return val
2-2. Creating the same shaped np.array:
maskable_image = np.ndarray((mask.shape[0],mask.shape[1]), np.int32)
2-3. Transformation:
for i in range(len(mask)):
maskable_image[i] = list(map(transform_zeros, mask[i]))
3.Checking:
print(maskable_image)
Then you can use this array for your mask.
mask = maskable_image
All this is copied and interpreted from this link, so check it if you find my attempted explanation unclear, as I just provided solution but don't understand that much about color arrays of image and its transformation.
I'm Marius, a maths student in the first year.
We have recieved a team-assignment where we have to implement a fourier transformation and we chose to try to encode the transformation of an image to a JPEG image.
to simplify the problem for ourselves, we chose to do it only for pictures that are greyscaled.
This is my code so far:
from PIL import Image
import numpy as np
import sympy as sp
#
#ALLEMAAL INFORMATIE GEEN BEREKENINGEN
img = Image.open('mario.png')
img = img.convert('L') # convert to monochrome picture
img.show() #opens the picture
pixels = list(img.getdata())
print(pixels) #to see if we got the pixel numeric values correct
grootte = list(img.size)
print(len(pixels)) #to check if the amount of pixels is correct.
kolommen, rijen = img.size
print("het aantal kolommen is",kolommen,"het aantal rijen is",rijen)
#tot hier allemaal informatie
pixelMatrix = []
while pixels != []:
pixelMatrix.append(pixels[:kolommen])
pixels = pixels[kolommen:]
print(pixelMatrix)
pixelMatrix = np.array(pixelMatrix)
print(pixelMatrix.shape)
Now the problem forms itself in the last 3 lines. I want to try to convert the matrix of values back into an Image with the matrix 'pixelMatrix' as it's value.
I've tried many things, but this seems to be the most obvious way:
im2 = Image.new('L',(kolommen,rijen))
im2.putdata(pixels)
im2.show()
When I use this, it just gives me a black image of the correct dimensions.
Any ideas on how to get back the original picture, starting from the values in my matrix pixelMatrix?
Post Scriptum: We still have to implement the transformation itself, but that would be useless unless we are sure we can convert a matrix back into a greyscaled image.
I'm trying to read an image in unchanged format, do some operations and convert it back to the colored format
im = cv2.imread(fname,cv2.IMREAD_UNCHANGED) # shape(240,240,4)
....
im2 = cv2.imread(im,cv2.IMREAD_COLOR) # required shape(240,240,3)
But, looks like I can't input the result of first numpy array into the second imread.
So currently I've created a temporary image after the operations and reading that value to get the required im2 value.
im = cv2.imread(fname,cv2.IMREAD_UNCHANGED) # shape(240,240,4)
....
cv2.imwrite('img.png',im)
im2 = cv2.imread('img.png',cv2.IMREAD_COLOR) # required shape(240,240,3)
However I would like to avoid the step of creating temporary image. How would I achieve the same with a better approach
OpenCV has a function for color conversion cvtColor
https://docs.opencv.org/3.1.0/de/d25/imgproc_color_conversions.html
im2 = cv2.cvtColor(im, <conversion code>)
You should figure out conversion code yourself, based on image format you have. Probably, it would be cv2.COLOR_BGRA2BGR