app.py

# Import statements
import numpy as np
import argparse
import cv2
import os
import gradio as gr

PROTOTXT = "colorization_deploy_v2.prototxt"
POINTS = "pts_in_hull.npy"
MODEL = "colorization_release_v2.caffemodel"


# Load the Model
print("Load model")
net = cv2.dnn.readNetFromCaffe(PROTOTXT, MODEL)
pts = np.load(POINTS)

# Load centers for ab channel quantization used for rebalancing.
class8 = net.getLayerId("class8_ab")
conv8 = net.getLayerId("conv8_313_rh")
pts = pts.transpose().reshape(2, 313, 1, 1)
net.getLayer(class8).blobs = [pts.astype("float32")]
net.getLayer(conv8).blobs = [np.full([1, 313], 2.606, dtype="float32")]

def colorizedTheImage(image):
	# Load the input image
	scaled = image.astype("float32") / 255.0
	lab = cv2.cvtColor(scaled, cv2.COLOR_BGR2LAB)

	resized = cv2.resize(lab, (224, 224))
	L = cv2.split(resized)[0]
	L -= 50

	print("Colorizing the image")
	net.setInput(cv2.dnn.blobFromImage(L))
	ab = net.forward()[0, :, :, :].transpose((1, 2, 0))

	ab = cv2.resize(ab, (image.shape[1], image.shape[0]))

	L = cv2.split(lab)[0]
	colorized = np.concatenate((L[:, :, np.newaxis], ab), axis=2)

	colorized = cv2.cvtColor(colorized, cv2.COLOR_LAB2BGR)
	colorized = np.clip(colorized, 0, 1)

	colorized = (255 * colorized).astype("uint8")
	return colorized
# image = cv2.resize(image, (0,0), fx=0.5, fy=0.5) 
# colorized = cv2.resize(colorized, (0,0), fx=0.5, fy=0.5) 
# cv2.imshow("Original", image)
# cv2.imshow("Colorized", colorized)
# cv2.waitKey(0)
demo=gr.Interface(fn=colorizedTheImage,
                  inputs=["image"],
                  outputs=["image"],
                  examples=[["einstein.jpg"],["tiger.jpg"]],
                  title="Black&White To Color Image")