colorize black and white photos automatically

#Colorize black and white photos automatically how to#
#Colorize black and white photos automatically manual#

Treat each of the points as 1×1 convolutions and add them to the model.

Load centers for ab channel quantization used for rebalancing.

OpenCV can read Caffe models via the function.Afterwards ,load the cluster center points directly from the command line argument path to the points file. Now ,we load our Caffe model directly from the command line argument values. Let’s go ahead and load our model and cluster centers into memory: # import the necessary packages import numpy as np import argparse import cv2 # construct the argument parser and parse the arguments ap = argparse.ArgumentParser() ap.add_argument(“-i”, “ - image”, type=str, required=True, help=”path to input black and white image”) ap.add_argument(“-p”, “ - prototxt”, type=str, required=True, help=”path to Caffe prototxt file”) ap.add_argument(“-m”, “ - model”, type=str, required=True, help=”path to Caffe pre-trained model”) ap.add_argument(“-c”, “ - points”, type=str, required=True, help=”path to cluster center points”) args = vars(ap.parse_args()) With the above four flags and corresponding arguments, the script will be able to run with different inputs without changing any code.

- points : The path to a NumPy cluster center points file.

- prototxt : Our path to the Caffe prototxt file.

- image : The path to our input black/white image.

This script requires that these four arguments be passed to the script directly from the terminal: Let’s go ahead and use argparse to parse command line arguments.

Our colorizer script only requires three imports: NumPy, OpenCV, and argparse. Colorizing black and white images with OpenCV Combine the input L channel with the predicted ab channels.įor more detailed information, please refer to original paper by Zhang et al.Use the L channel as the input to the network and train the network to predict the ab channels.Convert all training images from the RGB color space to the Lab color space.The entire (simplified) process can be summarized as: Given the input L channel and the predicted ab channels we can then form our final output image. Since the L channel encodes only the intensity, we can use the L channel as our grayscale input to the network.įrom there the network must learn to predict the a and b channels. The L channel encodes lightness intensity onlyįor more information, you can refer to This Wikipedia article.But unlike the RGB color space, Lab encodes color information differently: Similar to the RGB color space, the Lab color space has three channels. In this tutorial, we will be using LAB color space, but you can try using YCbCr space also. Hence, we have two options : either use YCbCr color space or LAB color space as both Y and L channel encodes the information regarding illumination. Now as we are getting intensity as input and we have to guess colors, we can’t use RGB color space as it doesn’t have information regarding illumination. decided to attack the problem of image colorization by using Convolutional Neural Networks to “hallucinate” what an input grayscale image would look like when colorized.

#Colorize black and white photos automatically manual#

Previous approaches to black and white image colorization relied on manual human annotation and often produced desaturated results that were not “believable” as true colorizations. The technique we’ll be covering here today is from Zhang et al.’s 2016 ECCV paper, Colorful Image Colorization. We will utilize a Convolutional Neural Network capable of colorizing black and white images with results that can even “fool” humans! Lets’ Get Started The novel approach we are going to use here today instead relies on deep learning.

Relied on significant human interaction and annotation.

Previous methods for image colorization either: Image colorization is the process of taking an input grayscale (black and white) image and then producing an output colorized image that represents the semantic colors and tones of the input (for example, an ocean on a clear sunny day must be plausibly “blue” - it can’t be colored “hot pink” by the model).

#Colorize black and white photos automatically how to#

So lets see how to colorize black and white images : Overview But nonetheless, I thought of writing this blog about my project.

After few hours of searching, I finally found a method to colorize these images, and I immediately started working on this project and after an exhausting 6 hours, I finally got a working model, albeit the results are not as perfect as I had imagined.