![]() Line 15-17, we are transposing our NumPy file and reshaping the cluster centers stored in them as a 1×1 matrix, then adding it to our model. In line 11 & line 13 we are fetching the layer IDs of the two outputs (“class8_ab”, “conv8_313_rh”) from the last layer of the network. The “.getLayerId()” takes one parameter.Įxample : net.getLayerId(“name of the layer”) Next step is to get the layer id from the caffee model by using the function “.getLayerId()”. In line 10, we loaded the “.npy” file using NumPy. caffe_model – path to “.caffemodel” file.In line 9, we are loading our Caffe model. Merging of the specified path with the variable “image”, we can get access to the test sample image. Now, we defined the path where the test image is located and merged it with the variable “image”. These paths will be used to access the model from the specified location. Next, we will provide the path where the files “.cafffemodel”, “.prototxt”, “.npy” and the testing image is located. Pts_npy = "./b&w_to_color/model/pts_in_hull.npy" prototxt = "./b&w_to_color/model/colorization_deploy_v2.prototxt"Ĭaffe_model = "./b&w_to_color/model/colorization_release_v2.caffemodel" The reason for saving the name of the test sample in a separate variable is to use the same name to save the colored image of the test sample. The name of the test sample(black and white image) is stored in a variable named “image”. NumPy - pip install numpy image = 'test_sample.jpg' To install these libraries in your Python application you can use the commands below. In case you don’t have these libraries installed, you can install them using the windows command prompt. Automatic Colorization of Black and White images code import numpy as npįirst, we need to import the libraries that we will be using in this code. First, we need to import the libraries that we will be using. Now, let’s begin the step by step explanation for the conversion of black & white image into a colored image. Download the Caffe model, Prototxt, and NumPy file. You can download these files from the link below. It consists of 313 cluster kernels, i.e (0-312). pts_in_hull.npy : It is a NumPy file that stores the cluster center points in NumPy format.colorization_deploy_v2.prototxt : It consists of different parameters that define the network and it also helps in deploying the Caffe model.colorization_release_v2.caffemodel : It is a pre-trained model stored in the Caffe framework’s format that can be used to predict new unseen data. ![]() To proceed with further explanation on the coloring of black & white images using Python, we need to download 3 files. This project takes a black and white image as its input and returns an automatically colored image as the output. In this tutorial, we will learn how to convert an old black & white image into a colored image automatically by using Python and it’s libraries OpenCV, DNN, and Caffe.
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