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How set white color background when concatenating images via python?

import numpy as np
from imageio import imread, imwrite
im1 = imread('https://api.sofascore.app/api/v1/team/2697/image')[...,:3]
im2 = imread('https://api.sofascore.app/api/v1/team/2692/image')[...,:3]
result = np.hstack((im1,im2))
imwrite('result.jpg', result)
Original images opening directly from the url's (I'm trying to concatenate the two images into one and keep the background white):
As can be seen both have no background, but when joining the two via Python, the defined background becomes this moss green:
I tried modifying the color reception:
im1 = imread('https://api.sofascore.app/api/v1/team/2697/image')[...,:1]
im2 = imread('https://api.sofascore.app/api/v1/team/2692/image')[...,:1]
But the result is a Black & White with the background still looking like it was converted from the previous green, even though the PNG's don't have such a background color.
How should I proceed to solve my need?

There is a 4th channel in your images - transparency. You are discarding that channel with [...,:1]. This is a mistake.
If you retain the alpha channel this will work fine:
import numpy as np
from imageio import imread, imwrite
im1 = imread('https://api.sofascore.app/api/v1/team/2697/image')
im2 = imread('https://api.sofascore.app/api/v1/team/2692/image')
result = np.hstack((im1,im2))
imwrite('result.png', result)
However, if you try to make a jpg, you will have a problem:
>>> imwrite('test.jpg', result)
OSError: JPEG does not support alpha channel.
This is correct, as JPGs do not do transparency. If you would like to use transparency and also have your output be a JPG, I suggest a priest.
You can replace the transparent pixels by using np.where and looking for places that the alpha channel is 0:
result = np.hstack((im1,im2))
result[np.where(result[...,3] == 0)] = [255, 255, 255, 255]
imwrite('result.png', result)

If you want to improve image quality, here is a solution. #Brondy
# External libraries used for
# Image IO
from PIL import Image
# Morphological filtering
from skimage.morphology import opening
from skimage.morphology import disk
# Data handling
import numpy as np
# Connected component filtering
import cv2
black = 0
white = 255
threshold = 160
# Open input image in grayscale mode and get its pixels.
img = Image.open("image.jpg").convert("LA")
pixels = np.array(img)[:,:,0]
# Remove pixels above threshold
pixels[pixels > threshold] = white
pixels[pixels < threshold] = black
# Morphological opening
blobSize = 1 # Select the maximum radius of the blobs you would like to remove
structureElement = disk(blobSize) # you can define different shapes, here we take a disk shape
# We need to invert the image such that black is background and white foreground to perform the opening
pixels = np.invert(opening(np.invert(pixels), structureElement))
# Create and save new image.
newImg = Image.fromarray(pixels).convert('RGB')
newImg.save("newImage1.PNG")
# Find the connected components (black objects in your image)
# Because the function searches for white connected components on a black background, we need to invert the image
nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(np.invert(pixels), connectivity=8)
# For every connected component in your image, you can obtain the number of pixels from the stats variable in the last
# column. We remove the first entry from sizes, because this is the entry of the background connected component
sizes = stats[1:,-1]
nb_components -= 1
# Define the minimum size (number of pixels) a component should consist of
minimum_size = 100
# Create a new image
newPixels = np.ones(pixels.shape)*255
# Iterate over all components in the image, only keep the components larger than minimum size
for i in range(1, nb_components):
if sizes[i] > minimum_size:
newPixels[output == i+1] = 0
# Create and save new image.
newImg = Image.fromarray(newPixels).convert('RGB')
newImg.save("new_img.PNG")

If you want to change the background of a Image, pixellib is the best solution because it seemed the most reasonable and easy library to use.
import pixellib
from pixellib.tune_bg import alter_bg
change_bg = alter_bg()
change_bg.load_pascalvoc_model("deeplabv3_xception_tf_dim_ordering_tf_kernels.h5")
change_bg.color_bg("sample.png", colors=(255,255,255), output_image_name="colored_bg.png")
This code requires pixellib to be higher or the same as 0.6.1

Python add noise to image breaks PNG

I'm trying to create a image system in Python 3 to be used in a web app. The idea is to load an image from disk and add some random noise to it. When I try this, I get what looks like a totally random image, not resembling the original:
import cv2
import numpy as np
from skimage.util import random_noise
from random import randint
from pathlib import Path
from PIL import Image
import io
image_files = [
{
'name': 'test1',
'file': 'test1.png'
},
{
'name': 'test2',
'file': 'test2.png'
}
]
def gen_image():
rand_image = randint(0, len(image_files)-1)
image_file = image_files[rand_image]['file']
image_name = image_files[rand_image]['name']
image_path = str(Path().absolute())+'/img/'+image_file
img = cv2.imread(image_path)
noise_img = random_noise(img, mode='s&p', amount=0.1)
img = Image.fromarray(noise_img, 'RGB')
fp = io.BytesIO()
img.save(fp, format="PNG")
content = fp.getvalue()
return content
gen_image()
I have also tried using pypng:
import png
# Added the following to gen_image()
content = png.from_array(noise_img, mode='L;1')
content.save('image.png')
How can I load a png (With alpha transparency) from disk, add some noise to it, and return it so that it can be displayed by web server code (flask, aiohttp, etc).
As indicated in the answer by makayla, this makes it better: noise_img = (noise_img*255).astype(np.uint8) but the colors are still wrong and there's no transparency.
Here's the updated function for that:
def gen_image():
rand_image = randint(0, len(image_files)-1)
image_file = image_files[rand_image]['file']
image_name = image_files[rand_image]['name']
image_path = str(Path().absolute())+'/img/'+image_file
img = cv2.imread(image_path)
cv2.imshow('dst_rt', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
# Problem exists somewhere below this line.
img = random_noise(img, mode='s&p', amount=0.1)
img = (img*255).astype(np.uint8)
img = Image.fromarray(img, 'RGB')
fp = io.BytesIO()
img.save(fp, format="png")
content = fp.getvalue()
return content
This will popup a pre-noise image and return the noised image. RGB (And alpha) problem exists in returned image.
I think the problem is it needs to be RGBA but when I change to that, I get ValueError: buffer is not large enough

Given all the new information I am updating my answer with a few more tips for debugging the issue.
I found a site here which creates sample transparent images. I created a 64x64 cyan (R=0, G=255, B=255) image with a transparency layer of 0.5. I used this to test your code.
I read in the image two ways to compare: im1 = cv2.imread(fileName) and im2 = cv2.imread(fileName,cv2.IMREAD_UNCHANGED). np.shape(im1) returned (64,64,3) and np.shape(im2) returned (64,64,4). This is why that flag is required--the default imread settings in opencv will read in a transparent image as a normal RGB image.
However opencv reads in as BGR instead of RGB, and since you cannot save out with opencv, you'll need to convert it to the correct order otherwise the image will have reversed color. For example, my cyan image, when viewed with the reversed color appears like this:
You can change this using openCV's color conversion function like this im = cv2.cvtColor(im, cv2.COLOR_BGRA2RGBA) (Here is a list of all the color conversion codes). Again, double check the size of your image if you need to, it should still have four channels since you converted it to RGBA.
You can now add your noise to your image. Just so you know, this is also going to add noise to your alpha channel as well, randomly making some pixels more transparent and others less transparent. The random_noise function from skimage converts your image to float and returns it as float. This means the image values, normally integers ranging from 0 to 255, are converted to decimal values from 0 to 1. Your line img = Image.fromarray(noise_img, 'RGB') does not know what to do with the floating point noise_img. That's why the image is all messed up when you save it, as well as when I tried to show it.
So I took my cyan image, added noise, and then converted the floats back to 8 bits.
noise_img = random_noise(im, mode='s&p', amount=0.1)
noise_img = (noise_img*255).astype(np.uint8)
img = Image.fromarray(noise_img, 'RGBA')
It now looks like this (screenshot) using img.show():
I used the PIL library to save out my image instead of openCV so it's as close to your code as possible.
fp = 'saved_im.png'
img.save(fp, format="png")
I loaded the image into powerpoint to double-check that it preserved the transparency when I saved it using this method. Here is a screenshot of the saved image overlaid on a red circle in powerpoint:

How can I apply all the possible colour space in CV2 (274 available colour space..?) to a single image and output 274 variations of the image?

Here is my code, help me with problem on title.
import cv2
import imutils
image = cv2.imread("/home/taral/Desktop/blister_main/blister.jpg")
flags = [i for i in dir(cv2) if i.startswith('COLOR_')] # color space lists provided by OpenCV2 documentation
count = 1
for flag in flags:
# holder = ('cv2.{}'.format(flag))
mode = 'cv2.' + str(flag)
converted = cv2.cvtColor(image, eval(mode))
name = '/home/taral/Desktop/blister_main/color_space'+str(count)+'.jpg'
path = cv2.imwrite(name, converted)
count += 1
How can I apply all the possible colour space in CV2 (274 available colour space..?) to a single image and output 274 variations of the image?

Your image read by cv2 so its channels will arrange like Blue-Green-Red, so you can only use cv2.cvtColor with BGR related mode (E.g: cv2.COLOR_BGR2LUV, cv2.COLOR_BGR2Lab).
The error will appear in gray mode (E.g: cv2.COLOR_GRAY2BGR,cv2.COLOR_GRAY2BGR555), because gray image only have 1 channel, and the output will wrong at other mode with 3 channels such as RGB, HSV, etc.

Resizing JPG using PIL.resize gives a completely black image

I'm using PIL to resize a JPG. I'm expecting the same image, resized as output, but instead I get a correctly sized black box. The new image file is completely devoid of any information, just an empty file. Here is an excerpt for my script:
basewidth = 300
img = Image.open(path_to_image)
wpercent = (basewidth/float(img.size[0]))
hsize = int((float(img.size[1])*float(wpercent)))
img = img.resize((basewidth,hsize))
img.save(dir + "/the_image.jpg")
I've tried resizing with Image.LANCZOS as the second argument, (defaults to Image.NEAREST with 1 argument), but it didn't make a difference. I'm running Python3 on Ubunutu 16.04. Any ideas on why the image file is empty?

I also encountered the same issue when trying to resize an image with transparent background. The "resize" works after I add a white background to the image.
Code to add a white background then resize the image:
from PIL import Image
im = Image.open("path/to/img")
if im.mode == 'RGBA':
alpha = im.split()[3]
bgmask = alpha.point(lambda x: 255-x)
im = im.convert('RGB')
im.paste((255,255,255), None, bgmask)
im = im.resize((new_width, new_height), Image.ANTIALIAS)
ref:
Other's code for making thumbnail
Python: Image resizing: keep proportion - add white background

The simplest way to get to the bottom of this is to post your image! Failing that, we can check the various aspects of your image.
So, import Numpy and PIL, open your image and convert it to a Numpy ndarray, you can then inspect its characteristics:
import numpy as np
from PIL import Image
# Open image
img = Image.open('unhappy.jpg')
# Convert to Numpy Array
n = np.array(img)
Now you can print and inspect the following things:
n.shape # we are expecting something like (1580, 1725, 3)
n.dtype # we expect dtype('uint8')
n.max() # if there's white in the image, we expect 255
n.min() # if there's black in the image, we expect 0
n.mean() # we expect some value between 50-200 for most images

Improve image quality using python

i have some problem with my task. i need get text from image using python+tesseract. But quality of image it's not highter - it`s screenshoot.
I'm using OpenCV lib and have two variant`s:
COLOR_BGR2GRAY
ADAPTIVE_THRESH_GAUSSIAN_C | THRESH_BINARY
and this variant`s working incorrect.
When i binarize image
def binarize_image(img_path, threshold=195):
"""Binarize an image."""
image_file = Image.open(img_path)
image = image_file.convert('L') # convert image to monochrome
image = np.array(image)
image = binarize_array(image, threshold)
im = Image.fromarray(image)
im.save(img_path)
# imsave(target_path, image)
def binarize_array(numpy_array, threshold=250):
"""Binarize a numpy array."""
for i in range(len(numpy_array)):
for j in range(len(numpy_array[0])):
if numpy_array[i][j] > threshold:
numpy_array[i][j] = 255
else:
numpy_array[i][j] = 0
return numpy_array
tesseract doesn`t usualy get text.
How i can resolve it`s problem ?
screenshot example
UPD: solved my problem, i need to add some pixels between two letters. on screenshot letter is white and background is black. How i can do it using numpy ?