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#!/usr/bin/env python
# coding: utf-8
# In[6]:
# import the necessary packages
import numpy as np
import cv2
import streamlit as st
from PIL import Image
def colorizer(img):
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
# load our serialized black and white colorizer model and cluster
# center points from disk
#Note: Please take in account the directories of your local system.
prototxt = r"C:\Users\dhananjayan\projects\Colorizer\models\models_colorization_deploy_v2.prototxt"
model = r"C:\Users\dhananjayan\projects\Colorizer\models\colorization_release_v2.caffemodel"
points = r"C:\Users\dhananjayan\projects\Colorizer\models\pts_in_hull.npy"
net = cv2.dnn.readNetFromCaffe(prototxt, model)
pts = np.load(points)
# add the cluster centers as 1x1 convolutions to the model
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")]
# scale the pixel intensities to the range [0, 1], and then convert the image from the BGR to Lab color space
scaled = img.astype("float32") / 255.0
lab = cv2.cvtColor(scaled, cv2.COLOR_RGB2LAB)
# resize the Lab image to 224x224 (the dimensions the colorization
#network accepts), split channels, extract the 'L' channel, and then perform mean centering
resized = cv2.resize(lab, (224, 224))
L = cv2.split(resized)[0]
L -= 50
# pass the L channel through the network which will *predict* the 'a' and 'b' channel values
net.setInput(cv2.dnn.blobFromImage(L))
ab = net.forward()[0, :, :, :].transpose((1, 2, 0))
# resize the predicted 'ab' volume to the same dimensions as our input image
ab = cv2.resize(ab, (img.shape[1], img.shape[0]))
# grab the 'L' channel from the *original* input image (not the
# resized one) and concatenate the original 'L' channel with the predicted 'ab' channels
L = cv2.split(lab)[0]
colorized = np.concatenate((L[:, :, np.newaxis], ab), axis=2)
# convert the output image from the Lab color space to RGB, then clip any values that fall outside the range [0, 1]
colorized = cv2.cvtColor(colorized, cv2.COLOR_LAB2RGB)
colorized = np.clip(colorized, 0, 1)
# the current colorized image is represented as a floating point
# data type in the range [0, 1] -- let's convert to an unsigned 8-bit integer representation in the range [0, 255]
colorized = (255 * colorized).astype("uint8")
# Return the colorized images
return colorized
##########################################################################################################
st.write("""
# Colorize your Black and white image
"""
)
st.write("This is an app to turn Colorize your B&W images.")
st.write("Created on Thursday, 12 November 2020 (IST) \n @author: Dhananjayan")
file = st.sidebar.file_uploader("Please upload an image file", type=["jpg", "png"])
if file is None:
st.text("You haven't uploaded an image file")
else:
image = Image.open(file)
img = np.array(image)
st.text("Your original image")
st.image(image, use_column_width=True)
st.text("Your colorized image")
color = colorizer(img)
st.image(color, use_column_width=True)
print("done!")
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