B4J Code Snippet Automatic Colorization of Black and White images using PyBridge in B4J

The libraries OpenCV, DNN, and Caffe were used in the Python code.
You need to download 3 files.

1. colorization_release_v2.caffemodel: the pre-trained model.
2. colorization_deploy_v2.prototxt:
3. pts_in_hull.npy.

Edit: from here
Place them in any folder ( E:/PyColorize in this example)
Make two folder one for the BW images(test_samples) and one for the output colored images (output_images) in the same folder.
Both the BW and colored images will be shown by Python GUI as the image is colored.

The code:

'Ctrl + click to export as zip: ide://run?File=%B4X%\Zipper.jar&Args=Project.zip
#CustomBuildAction: after packager, %WINDIR%\System32\robocopy.exe, Python temp\build\bin\python /E /XD __pycache__ Doc pip setuptools tests

'Ctrl + click to open Python shell: ide://run?File=%PROJECT%\Objects\Python\WinPython+Command+Prompt.exe
Sub Class_Globals
    Private Root As B4XView
    Private xui As XUI
    Public Py As PyBridge
End Sub

Public Sub Initialize
   
End Sub

'This event will be called once, before the page becomes visible.
Private Sub B4XPage_Created (Root1 As B4XView)
    Root = Root1
    Root.LoadLayout("MainPage")
    Py.Initialize(Me, "Py")
    Dim opt As PyOptions = Py.CreateOptions(File.Combine(File.DirApp, "Python/python/python.exe"))
    Py.Start(opt)
    Wait For Py_Connected (Success As Boolean)
    If Success = False Then
        LogError("Failed to start Python process.")
        Return
    End If
   
    Py.RunNoArgsCode(ColorizePy)
End Sub

Private Sub B4XPage_Background
    Py.KillProcess
End Sub

Private Sub Py_Disconnected
    Log("PyBridge disconnected")
End Sub

Private Sub ColorizePy() As String
    Dim Code As String =$"
import numpy as np
import matplotlib.pyplot as plt
import cv2

# Name of testing image
image = 'scene.png'

# Path of our caffemodel, prototxt, and numpy files
prototxt = "E:/ColorizePy/colorization_deploy_v2.prototxt"
caffe_model = "E:/ColorizePy/colorization_release_v2.caffemodel"
pts_npy = "E:/ColorizePy/pts_in_hull.npy"

test_image =  "E:/ColorizePy/test_samples/"+image

# Loading our model
net = cv2.dnn.readNetFromCaffe(prototxt, caffe_model)
pts = np.load(pts_npy)
 
layer1 = net.getLayerId("class8_ab")
print(layer1)
layer2 = net.getLayerId("conv8_313_rh")
print(layer2)
pts = pts.transpose().reshape(2, 313, 1, 1)
net.getLayer(layer1).blobs = [pts.astype("float32")]
net.getLayer(layer2).blobs = [np.full([1, 313], 2.606, dtype="float32")]

# Converting the image into RGB and plotting it
# Read image from the path
test_image = cv2.imread(test_image)
# Convert image into gray scale
test_image = cv2.cvtColor(test_image, cv2.COLOR_BGR2GRAY)
# Convert image from gray scale to RGB format
test_image = cv2.cvtColor(test_image, cv2.COLOR_GRAY2RGB)
# Check image using matplotlib
plt.imshow(test_image)
plt.show()

# Converting the RGB image into LAB format
# Normalizing the image
normalized = test_image.astype("float32") / 255.0
# Converting the image into LAB
lab_image = cv2.cvtColor(normalized, cv2.COLOR_RGB2LAB)
# Resizing the image
resized = cv2.resize(lab_image, (224, 224))
# Extracting the value of L for LAB image
L = cv2.split(resized)[0]
L -= 50   # OR we can write L = L - 50

# Predicting a and b values
# Setting input
net.setInput(cv2.dnn.blobFromImage(L))
# Finding the values of 'a' and 'b'
ab = net.forward()[0, :, :, :].transpose((1, 2, 0))
# Resizing
ab = cv2.resize(ab, (test_image.shape[1], test_image.shape[0]))

# Combining L, a, and b channels
L = cv2.split(lab_image)[0]
# Combining L,a,b
LAB_colored = np.concatenate((L[:, :, np.newaxis], ab), axis=2)
# Checking the LAB image
plt.imshow(LAB_colored)
#plt.title('LAB image')
#plt.show()

## Converting LAB image to RGB
RGB_colored = cv2.cvtColor(LAB_colored,cv2.COLOR_LAB2RGB)
# Limits the values in array
RGB_colored = np.clip(RGB_colored, 0, 1)
# Changing the pixel intensity back to [0,255],as we did scaling during pre-processing and converted the pixel intensity to [0,1]
RGB_colored = (255 * RGB_colored).astype("uint8")
# Checking the image
plt.imshow(RGB_colored)
plt.title('Colored Image')
plt.show()

# Saving the colored image
# Converting RGB to BGR
RGB_BGR = cv2.cvtColor(RGB_colored, cv2.COLOR_RGB2BGR)
# Saving the image in desired path
cv2.imwrite("E:/ColorizePy/output_images/"+image, RGB_BGR)
"$
    Return Code
End Sub