app.py

import numpy as np 
import cv2
import tensorflow as tf
from tensorflow import keras
from PIL import Image, ImageOps
net = cv2.dnn.readNetFromCaffe('colorization_deploy_v2.prototxt','drive/MyDrive/Lab2market2023/colorization_release_v2.caffemodel')
pts = np.load('pts_in_hull.npy')

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 infer(original_image):
  #image = cv2.imread('bw.jpg')
  image = keras.preprocessing.image.img_to_array(original_image)
  scaled = image.astype("float32")/255.0
  lab = cv2.cvtColor(scaled,cv2.COLOR_BGR2LAB)
  #cv2.imshow("image",lab)

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


  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")

  cv2_imshow(image)
  cv2_imshow(colorized)
  color_coverted = cv2.cvtColor(colorized, cv2.COLOR_BGR2RGB)
  colorized = Image.fromarray(color_coverted)
  return colorized
  cv2.waitKey(0)
    import gradio as gr
examples=['bw.jpg','blw.jpg','boy.jpg']
iface = gr.Interface(
    fn=infer,
    title="Colourization",
    description = "OpenCV implementation of Colorful Image Colorization paper presented in ECCV, 2016.  🌆🎆",
    inputs=[gr.inputs.Image(label="image", type="pil")],
    outputs="image",
    examples=examples,
    cache_examples=True,
     article = "Authors: <a href=\"https://github.com/Uviveknarayan\">Vivek Narayan</a>, <a href=\"https://github.com/chiranjan-7\">Chiranjan</a>,<a href=\"https://github.com/GangaSrujan\">Srujan</a>,<a href=\"https://github.com/RohanPawar3399\">Rohan Pawar</a>,<a href=\"https://github.com/pavankarthik77\">Pavan Karthik</a>").launch(enable_queue=True)