Applying gaussian blur to images in a loop - numpy

Question

I have a simple ndarray with shape as:
import matplotlib.pyplot as plt
%matplotlib inline
plt.imshow(trainImg[0]) #can display a sample image
print(trainImg.shape) : (4750, 128, 128, 3) #shape of the dataset
I intend to apply Gaussian blur to all the images. The for loop I went with:
trainImg_New = np.empty((4750, 128, 128,3))
for idx, img in enumerate(trainImg):
trainImg_New[idx] = cv2.GaussianBlur(img, (5, 5), 0)
I tried to display a sample blurred image as:
plt.imshow(trainImg_New[0]) #view a sample blurred image
but I get an error:
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
It just displays a blank image.

Answer 1

TL;DR:
The error is most likely caused by trainImg_New is float datatype and its value is larger than 1. So, as #Frightera mentioned, try using np.uint8 to convert images' datatype.
I tested the snippets as below:
import numpy as np
import matplotlib.pyplot as plt
import cv2
trainImg_New = np.random.rand(4750, 128, 128,3) # all value is in range [0, 1]
save = np.empty((4750, 128, 128,3))
for idx, img in enumerate(trainImg_New):
save[idx] = cv2.GaussianBlur(img, (5, 5), 0)
plt.imshow(np.float32(save[0]+255)) # Reported error as question
plt.imshow(np.float32(save[0]+10)) # Reported error as question
plt.imshow(np.uint8(save[0]+10)) # Good to go
First of all, cv2.GaussianBlur will not change the range of the arrays' value and the original image arrays's value is legitimate. So I believe the only reason is the datatype of the trainImg_New[0] is not match its range.
So I tested the snippets above, we can see when the datatype of trainImg_New[0] matter the available range of the arrays' value.

Answer 2

I suggest you use tfa.image.gaussian_filter2d from the tensorflow_addons package. I think you'll be able to pass all your images at once.
import tensorflow as tf
from skimage import data
import tensorflow_addons as tfa
import matplotlib.pyplot as plt
image = data.astronaut()
plt.imshow(image)
plt.show()
blurred = tfa.image.gaussian_filter2d(image,
filter_shape=(25, 25),
sigma=3.)
plt.imshow(blurred)
plt.show()