I'm trying to add channel because of below error
ValueError: could not broadcast input array from shape (48,48) into shape (48,48,1)
Code:
img = cv2.imread(f,0)
resized = cv2.resize(img, (48,48), interpolation = cv2.INTER_AREA)
print(resized.shape)
(48, 48)
But I need a channel image like (48,48,1).
How can I solve this?
y = np.expand_dims(x, axis=-1) # Equivalent to x[:,:,np.newaxis]
As the function says, it will add an extra dimension as the new Last Channel
Edit
axis will be -1 instead of 1
You can do this by using split and merge operations:
First, split your 2-channel image into two arrays using split. Then, create the array which gives you the third channel, separately. Finally, merge the three arrays to get one 3-channel Mat.
This is an example:
c1,c2 = cv2.split(img)
merged = cv2.merge((c1,c2,arr))
Where img is your 2-channel image, arr is the array containing the channel to add, and the merged image contains the three channels merged.
Modifying Aditya's answer like this:
y = np.expand_dims(x, axis=1)
axis = 1 will insert new dimension at the beginning, so you could simply change the value of the axis to be = 3. It worked for me.
Another workaround might be creating a placeholder and populating it.
ph = np.ones((resized.shape[0], resized.shape[1], 1), dtype='uint8')
ph[:,:,0] = resized
very easy! on your interactive shell, just do
>>> y = image.resize(48, 48, 1)
>>> y.shape
>>> (48, 48, 1)
Related
I am working on an image processing/building problem. I have a smaller image that I want to place into a larger one. As normal the image is represented as a 3d array. This works fine with the following code (both element_pixels and image_pixels are 3d ndarrays with depth 3 representing RGB, element_pixels is equal to or smaller than image_pixels in the other dimensions):
element_pixels = element.get_pixels()
image_pixels[element.position[0]:element.position[0]+element.height, element.position[1]:element.position[1]+element.width, :] = element_pixels
However I want to treat black pixels in the element as transparent. The simplest way to do this seems to be to mask the element so I don't modify image_pixels where element_pixel is black. I tried the following, but I am tying myself in knots:
element_pixels = element.get_pixels()
b = np.all(element_pixels == [0, 0, 0], axis=-1)
black_pixels_mask = np.dstack([b,b,b])
image_pixels[element.position[0]:element.position[0]+element.height, element.position[1]:element.position[1]+element.width, :][black_pixels_mask] = element_pixels
This looks to be correctly generating a mask but I can't figure out how to use it. I get the following error:
image_pixels[element.position[0]:element.position[0]+element.height, element.position[1]:element.position[1]+element.width, :][black_pixels_mask] = element_pixels
TypeError: NumPy boolean array indexing assignment requires a 0 or 1-dimensional input, input has 3 dimensions
The masking kind-of works (i.e. runs without exceptions) if I replace the final = element_pixels with a constant, but I'm struggling to extrapolate this to a solution.
Extra detail of sizes
element_pixels.shape=(40, 40,3)
image_pixels.shape=(100, 100,3)
image_pixels[element.position[0]:element.position[0]+element.height, element.position[1]:element.position[1]+element.width, :].shape = (40,40,3)
A MRE in 2d
This captures what I'm trying to do without the complexity of the extra dimension.
import numpy as np
bg = np.ones((10,10))*0.5
img = np.concatenate([np.zeros((5,1)),np.ones((5,1))], axis=1)
mask = img == 0
# copy the *non-zero* pixel values of img to a particular location in bg
bg[5:10,5:7][mask] = img # this throws exception
print(bg)
I discovered after some experimentation that the (perhaps obvious in hindsight) answer is the you have to apply the mask to both sides.
So taking my MRE:
import numpy as np
bg = np.ones((10,10))*0.5
img = np.concatenate([np.zeros((5,1)),np.ones((5,1))], axis=1)
mask = img > 0
bg[5:10,5:7][mask] = img[mask]
print(bg)
Or going back to my original code, the only line that changes is:
image_pixels[element.position[0]:element.position[0]+element.height, element.position[1]:element.position[1]+element.width, :][~black_pixels_mask] = element_pixels[~black_pixels_mask]
Well you can use a 2d mask on a 3d array. So something like this will replace all black pixels of img with those of background.
img = np.random.randint(0, 2, (10, 10, 3))
background = np.random.randint(0, 2, (10, 10, 3))
mask = np.all(img == [0,0,0], axis=2)
img[mask] = background[img]
I'm not sure I understand what is in image_pixels but I think you can do something similar.
I want to make a simple Program which outputs a video as an Webcam, but the Cam wants a RGBA Numpy Array but I only have RGB from the video. How can I convert the 3 dimensional array to 4 dimensions?
You're actually not converting a 3-dimensional array to a 4-dimensional array. You're changing the size of one of the dimensions from three to four.
Lets say you have a NxMx3 image. You then need to:
temp = np.zeros((N, M, 4))
temp[:,:,0:3] = image
temp[:,:,3] = whatever default alpha you choose to use.
Generalize as you see fit.
Assuming your existing array is shaped (xsize, ysize, 3) and you want to create alpha as a 4th entry all filled with 1, you should be able to do something like
alpha = np.ones((*rgb.shape[0:2], 1))
rgba = np.concatenate((rgb, alpha), axis=2)
If you wanted a different uniform alpha value you could use np.full with that value instead of np.ones, but normally when converting RGB to RGBA you want fully opaque.
You can np.dstack your original im with np.ones(im.shape[:2])
new_im = np.dstack((im, np.ones(im.shape[:2])))
update: this is equivalent to #hobbs solution np.concatenate(..., axis=2)
Maybe try something like these: (import numpy as np)
arr # shape (n_bands, y_pixels, x_pixels)
swapped = np.moveaxis(arr, 0, 2) # shape (y_pixels, x_pixels, n_bands)
arr4d = np.expand_dims(swapped, 0) # shape (1, y_pixels, x_pixels, n_bands)
I'm trying to use the "compare_ssim" function. I currently have two 2xN matrices of x,y coordinates where the first row is all the x coordinates and the second row is all the y coordinates of each of the two images. How can I calculate the SSIM for these two images (if there is a way to do so)
For example I have:
X = np.array([[1,2,3], [4,5,6]])
Y = np.array([[3,4,5],[5,6,7]])
compare_ssim(X,Y)
But I am getting the error
ValueError: win_size exceeds image extent. If the input is a multichannel (color) image, set multichannel=True.
I'm not sure if I am missing a parameter or if I should convert the matrices in such a way that this function works. Or if there is a way that I am supposed to convert my coordinates to a grayscale matrix? I'm a bit confused on what the matrices for the parameters of the function should look like. I know that they are supposed to be ndarrays but the type(Y) and type(Y) are both numpy.ndarray.
Since you haven't mentioned which framework/library you are using, I am going with the assumption that you are using skimage's compare_ssim.
The error in question is due to the shape of your inputs. You can find more details here.
TL;DR: compare_ssim expects images in (H, W, C) dimensions but your input images have a dimension of (2, 3). So the function is confused which dimension to treat as the channel dimension. When multichannel=True, the last dimension is treated as the channel dimension.
There are 3 key problems with your code,
compare_image expects Images as input. So your X and Y matrices should be of the dimensions (H, W, C) and not (2, 3)
They should of float datatype.
Below I have shown a bit of demo code (note: Since skimage v1.7, compare_ssim has been moved to skimage.metrics.structural_similarity)
import numpy as np
from skimage.metrics import structural_similarity
img1 = np.random.randint(0, 255, size=(200, 200, 3)).astype(np.float32)
img2 = np.random.randint(0, 255, size=(200, 200, 3)).astype(np.float32)
ssim_score = structural_similarity(img1, img2, multichannel=True) #score: 0.0018769083894301646
ssim_score = structural_similarity(img1, img1, multichannel=True) #score: 1.0
I have a black and white image with (224,224) shape, but I want to have (224,224,3), so I need to expand the dim, but not with empty value, so np.expand_dims or np.atleast_3d couldn't help me. How can I do this correctly? Thanks.
What I use:
from PIL import Image
img = Image.open('data/'+link)
rsize = img.resize((224,224))
rsizeArr = np.asarray(rsize)
When we use numpy.dstack(), we don't have to expand the dimension manually, it will take care of that work and stack it along the third axis which is what we want.
In [4]: grayscale = np.random.random_sample((224,224))
# make it RGB by stacking the grayscale image along depth dimension 3 times
In [5]: rgb = np.dstack([grayscale]*3)
In [6]: rgb.shape
Out[6]: (224, 224, 3)
For your specific case, it should be:
rsize_rgb = np.dstack([rsize]*3)
For whatever reason, if you still want to expand the dimension of your grayscale image by 1 and then make it as RGB image, then you can use numpy.concatenate() as in:
In [9]: rgb = np.concatenate([grayscale[..., np.newaxis]]*3, axis=2)
In [10]: rgb.shape
Out[10]: (224, 224, 3)
For your specific case, it would then be:
rsize_rgb = np.concatenate([rsize[..., np.newaxis]]*3, axis=2)
I have a data frame of 2304 columns , as it is a 48*48 image pixels, when I convert it into one channel using this code
x = (df.iloc[:,1:].values).astype('float32')
x = x.reshape(-1,48,48,1)
its perfectly output of shape
(48*48*1)
with generating exact image by this code:
plt.imshow(x[0][:,:,0])
I want to make it into a 3Dimentional like in three channels. I try to merged the df 3 times and do this (48*48*3) it successfully change the df shape but I cannot generate the image again,
If you essentially want to convert a single channel image (which should essentially be a greyscale image) into a 3 channel greyscale image, its the same as concatenating the same image array thrice along the last axis. You can use np.concatenate to achieve the desired result.
import numpy as np
a = np.zeros((2304), dtype = np.uint8) #Just a dummy array representing a single pic
single_channel = a.reshape(48, 48, 1)
result = np.concatenate([single_channel,single_channel,single_channel], axis = -1)
print(result.shape) #(48, 48, 3)
At this point you should have an array that can be accepted by any image library. Just throwing a sample code to show how you may proceed to create the image from the array.
import cv2
cv2.imwrite("hi.jpg", result)
As stated earlier, use numpy instead of pandas for image manipulation.
EDIT: If you were unfortunately starting with a dataframe in the first place, you can always convert it to a numpy array with an extra dimension representing each image.
import pandas as pd
import cv2
import numpy as np
a = np.zeros((2304), dtype = np.uint8) #dummy row
dummy_df = pd.DataFrame(np.concatenate([a.reshape(1,-1)]*10)) #dummy df with 10 rows.
print(dummy_df.shape) #(10, 2304)
arr_images = np.array(dummy_df, dtype = np.uint8)
print(arr_images.shape) #(10, 2304)
multiple_single_channel = arr_images.reshape(-1, 48, 48, 1)
print(multiple_single_channel.shape) #(10, 48, 48, 1)
result = np.concatenate([multiple_single_channel] * 3, axis = -1)
print(result.shape) #(10, 48, 48, 3)
for i,img in enumerate(result):
print(i)
cv2.imwrite("{}.jpg".format(i), img)
#do something with image. you PROBABLY don't want to run this for 35k images though.
The bottom line really is that you should not need to use a dataframe, even for multiple images.
1)Dont use pandas
2) you cant transform 1channel image into 3 channels,
3) Dont use float32, images are usually 8bit (np.uint8)
4) use numpy in combination with OpenCV or with Pillow.
5) Dont use matplotlib to generate images. use libraries mentioned in 4.
6) if you have array with shape (x,y,3) there is nothing more simply than generate image with opencv cv2.imshow('image',array)