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Python collage of four image together

I am trying to combine 4 images, image 1 on top left, image 2 on top right, image 3 on bottom left and image 4 on bottom right. However, my images are different sizes and not sure how to resize the images to same size. I am pretty new to Python and this is my first time using PIL.
I have this so far (after opening the images)
img1 = img1.resize(img2.size)
img1 = img1.resize(img3.size)
img1 = img1.resize(img4.size)

This shall suffice your basic requirement.
This shall suffice your basic requirement.
Steps:
Images are read and stored to list of arrays using io.imread(img) in
a list comprehension.
We resize images to custom height and width.You can change IMAGE_WIDTH,IMAGE_HEIGHT as per your need with respect
to the input image size.
You just have to pass the location of n
images (n=4 for your case) to the function.
If you are
passing more than 2 images (for your case 4), it will work create 2
rows of images. In the top row, images in the first half of the list
are stacked and the remaining ones are placed in bottom row using
hconcat().
The two rows are stacked vertically using vconcat().
Finally, we convert the result to RGB image using
image.convert("RGB") and is saved using image.save().
The code:
import cv2
from PIL import Image
from skimage import io
IMAGE_WIDTH = 1920
IMAGE_HEIGHT = 1080
def create_collage(images):
images = [io.imread(img) for img in images]
images = [cv2.resize(image, (IMAGE_WIDTH, IMAGE_HEIGHT)) for image in images]
if len(images) > 2:
half = len(images) // 2
h1 = cv2.hconcat(images[:half])
h2 = cv2.hconcat(images[half:])
concat_images = cv2.vconcat([h1, h2])
else:
concat_images = cv2.hconcat(images)
image = Image.fromarray(concat_images)
# Image path
image_name = "result.jpg"
image = image.convert("RGB")
image.save(f"{image_name}")
return image_name
images=["image1.png","image2.png","image3.png","image4.png"]
#image1 on top left, image2 on top right, image3 on bottom left,image4 on bottom right
create_collage(images)
To create advanced college make you can look into this:
https://codereview.stackexchange.com/questions/275727/python-3-script-to-make-photo-collages

Scaling pixels between -1 and 1 using cv2.Normalize()

def preprocess(self):
# Import image
pic1 = self.path
raw_image = cv2.imread(pic1)
#cv2.imshow('Raw image',raw_image)
#cv2.waitKey(0)
# Resize image
dim = (320,180)
resized = cv2.resize(raw_image, dim)
#cv2.imshow('Resized Image',resized)
#cv2.waitKey(0)
# Scale image
scaled = cv2.normalize(resized, None, alpha=-1, beta=1, norm_type=cv2.NORM_MINMAX,dtype=cv2.CV_32F)
#cv2.imshow('Scaled Image',scaled)
#cv2.waitKey(0)
return scaled
I'm trying to scale the pixel values of "raw_image" to within the range -1 to 1 as part of a pre-process for identifying an object using machine learning. Essentially, a camera takes a picture, resizes and scales the image to the same size as the images within a dataset used for training and validating. Then that image is inferred by the model generated using model.fit() to detect what the object in the image actually is.
The question here is: " Is this scaling function correct for putting the pixel values in the range of -1 to 1?" It appears SUPER dark when I use cv2.imshow and I'm afraid the model isn't recognizing it properly.

Resizing non uniform images with precise face location

I work at a studio that does school photos and we are trying to make a script to eliminate the job of cropping each photo to a template. The photos we work with are fairly uniform but they vary in resolution and head position a bit. I took up the mantle of trying to write the script with my fairly limited Python knowledge and through a lot of trial and error and online resources I think I have got most of the way there.
At the moment I am trying to figure out the best way to have the image crop from the NumPy array with the head where I want and I just cant find a good flexible solution. The head needs to be positioned slightly differently for pose 1 and pose 2 so its needs to be easy to change on the fly (Probably going to implement some sort of simple GUI to input stuff like that, but for now I can just change the code).
I also need to be able to change the output resolution of the photo so they are all uniform (2000x2500). Anyone have any ideas?
At the moment this is my current code, it just saves the detected face square:
import cv2
import os.path
import glob
# Cascade path
cascPath = 'haarcascade_frontalface_default.xml'
# Create the haar cascade
faceCascade = cv2.CascadeClassifier(cascPath)
#Check for output folder and create if its not there
if not os.path.exists('output'):
os.makedirs('output')
# Read Images
images = glob.glob('*.jpg')
for c, i in enumerate(images):
image = cv2.imread(i, 1)
# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Find face(s) using cascade
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1, # size of groups
minNeighbors=5, # How many groups around are detected as face for it to be valid
minSize=(500, 500) # Min size in pixels for face
)
# Outputs number of faces found in image
print('Found {0} faces!'.format(len(faces)))
# Places a rectangle on face
for (x, y, w, h) in faces:
imgCrop = image[y:y+h,x:x+w]
if len(faces) > 0:
#Saves Images to output folder with OG name
cv2.imwrite('output/'+ i, imgCrop)
I can crop using it like this:
# Crop Padding
left = 300
right = 300
top = 400
bottom = 1000
for (x, y, w, h) in faces:
imgCrop = image[y-top:y+h+bottom, x-left:x+w+right]
but that outputs pretty random resolutions and changes based on the image resolution

TL;DR
To set a new resolution with the dimension, you can use cv2.resize. There may be a pixel loss so you can use the interpolation method.
The newly resized image may be in BGR format, so you may need to convert to RGB format.
cv2.resize(src=crop, dsize=(2000, 2500), interpolation=cv2.INTER_LANCZOS4)
crop = cv2.cvtColor(crop, cv2.COLOR_BGR2RGB) # Make sure the cropped image is in RGB format
cv2.imwrite("image-1.png", crop)
Suggestion:
One approach is using python's face-recognition library.
The approach is using two sample images for training.
Predict the next image based on training images.
For instance, The followings are the training images:
We want to predict the faces in the below image:
When we get the facial encodings of the training images and apply to the next image:
import face_recognition
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw
# Load a sample picture and learn how to recognize it.
first_image = face_recognition.load_image_file("images/ex.jpg")
first_face_encoding = face_recognition.face_encodings(first_image)[0]
# Load a second sample picture and learn how to recognize it.
second_image = face_recognition.load_image_file("images/index.jpg")
sec_face_encoding = face_recognition.face_encodings(second_image)[0]
# Create arrays of known face encodings and their names
known_face_encodings = [
first_face_encoding,
sec_face_encoding
]
print('Learned encoding for', len(known_face_encodings), 'images.')
# Load an image with an unknown face
unknown_image = face_recognition.load_image_file("images/babes.jpg")
# Find all the faces and face encodings in the unknown image
face_locations = face_recognition.face_locations(unknown_image)
face_encodings = face_recognition.face_encodings(unknown_image, face_locations)
# Convert the image to a PIL-format image so that we can draw on top of it with the Pillow library
# See http://pillow.readthedocs.io/ for more about PIL/Pillow
pil_image = Image.fromarray(unknown_image)
# Create a Pillow ImageDraw Draw instance to draw with
draw = ImageDraw.Draw(pil_image)
# Loop through each face found in the unknown image
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255), width=5)
# Remove the drawing library from memory as per the Pillow docs
del draw
# Display the resulting image
plt.imshow(pil_image)
plt.show()
The output will be:
The above is my suggestion. When you create a new resolution with the current image, there will be a pixel loss. Therefore you need to use an interpolation method.
For instance: after finding the face locations, select the coordinates in the original image.
# Add after draw.rectangle function.
crop = unknown_image[top:bottom, left:right]
Set new resolution with the size 2000 x 2500 and interpolation with CV2.INTERN_LANCZOS4.
Possible Question: Why CV2.INTERN_LANCZOS4?
Of course, you can select whatever you like, but in this post CV2.INTERN_LANCZOS4 was suggested.
cv2.resize(src=crop, dsize=(2000, 2500), interpolation=cv2.INTER_LANCZOS4)
Save the image
crop = cv2.cvtColor(crop, cv2.COLOR_BGR2RGB) # Make sure the cropped image is in RGB format
cv2.imwrite("image-1.png", crop)
Outputs are around 4.3 MB Therefore I can't display in here.
From the final result, we clearly see and identify faces. The library precisely finds the faces in the image.
Here what you can do:
Either you can use the training images of your own-set, or you can use the example above.
Apply the face-recognition function for each image, using the trained face-locations and save the results in the directory.

here is how I got it to crop how I wanted, this is added right below the "output number of faces" function
#Get the face postion and output values into variables, might not be needed but I did it
for (x, y, w, h) in faces:
xdis = x
ydis = y
w = w
h = h
#Get scale value by dividing wanted head hight by detected head hight
ws = 600/w
hs = 600/h
#scale image to get head to right size, uses bilinear interpolation by default
scale = cv2.resize(image,(0,0),fx=hs,fy=ws)
#calculate head postion for given values
sxdis = int(xdis*ws) #applying scale to x distance and turning it into a integer
sydis = int(ydis*hs) #applying scale to y distance and turning it into a integer
sycent = sydis+300 #adding half head hight to get center
ystart = sycent-700 #subtract where you want the head center to be in pixels, this is for the vertical
yend = ystart+2500 #Add whatever you want vertical resolution to be
xcent = sxdis+300 #adding half head hight to get center
xstart = xcent-1000 #subtract where you want the head center to be in pixels, this is for the horizontal
xend = xstart+2000 #add whatever you want the horizontal resolution to be
#Crop the image
cropped = scale[ystart:yend, xstart:xend]
Its a mess but it works exactly how I wanted it to work.
ended up going with openCV instead of switching to python-Recognition because of speed but I might switch over if I can get multithreading to work in python-recognition.

Edit image pixel by pixel - Python

I have two images, one overlay and one background.
I want to create a new image, by editing overlay image and manipulating it to show only the pixels which have blue colour in the background image.
I dont want to add the background, it is only for selecting the pixels.
Rest part should be transparent.
Any hints or ideas please? PS: I edited result image with paint so its not perfect.
Image 1 is background image.
Image 2 is overlay image.
Image 3 is the check I want to perform. (to find out which pixels have blue in background and making remaining pixels transparent)
Image 4 is the result image after editing.

I renamed your images according to my way of thinking, so I took this as image.png:
and this as mask.png:
Then I did what I think you want as follows. I wrote it quite verbosely so you can see all the steps along the way:
#!/usr/local/bin/python3
from PIL import Image
import numpy as np
# Open input images
image = Image.open("image.png")
mask = Image.open("mask.png")
# Get dimensions
h,w=image.size
# Resize mask to match image, taking care not to introduce new colours (Image.NEAREST)
mask = mask.resize((h,w), Image.NEAREST)
mask.save('mask_resized.png')
# Convert both images to numpy equivalents
npimage = np.array(image)
npmask = np.array(mask)
# Make image transparent where mask is not blue
# Blue pixels in mask seem to show up as RGB(163 204 255)
npimage[:,:,3] = np.where((npmask[:,:,0]<170) & (npmask[:,:,1]<210) & (npmask[:,:,2]>250),255,0).astype(np.uint8)
# Identify grey pixels in image, i.e. R=G=B, and make transparent also
RequalsG=np.where(npimage[:,:,0]==npimage[:,:,1],1,0)
RequalsB=np.where(npimage[:,:,0]==npimage[:,:,2],1,0)
grey=(RequalsG*RequalsB).astype(np.uint8)
npimage[:,:,3] *= 1-grey
# Convert numpy image to PIL image and save
PILrgba=Image.fromarray(npimage)
PILrgba.save("result.png")
And this is the result:
Notes:
a) Your image already has an (unused) alpha channel present.
b) Any lines starting:
npimage[:,:,3] = ...
are just modifying the 4th channel, i.e. the alpha/transparency channel of the image

Stitch images regardless of orientation OpenCV Numpy

I have run into an issue with a stitching program I made. The way I am slicing the image makes it so the only way it works is if the first image if to the left and above the one it would be stitched to.
def stitchMatches(self,image1,image2,homography):
#gather x and y axis of images that will be stitched
height1, width1 = image1.shape[0], image1.shape[1]
height2, width2 = image2.shape[0], image2.shape[1]
#create blank image that will be large enough to hold stitch image
blank_image = np.zeros(((width1 + width2),(height1 + height2),3),np.uint8)
#stitch image two into the resulting image while using blank_image
#to create a large enough frame for images
result = cv2.warpPerspective((image1),homography,blank_image.shape[0:2])
#numpy notation for slicing a matrix together
#allows you to see the image
result[0:image2.shape[0], 0:image2.shape[1]] = image2
This code runs when the left most image is represented by image1.
When I reverse the order of the images however I only have on image received as it the final line in my code "result[0...=image2" is unable to slive an image in an orientation that is not oriented with the first image in the upper left most corner between tho two images being stitched.
Here is a full example with homography
This is the homgraphy between the two images and Their result:
This is the correct result with imaege1 on the left
This is the incorrect result with image1 on the right
I know the issue is with th final slicing line I am just at a loss to get it to work, Any help is appreciated.