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Commit 56bcdfd7 authored by berak's avatar berak Committed by Alexander Alekhin
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Merge pull request #10777 from berak:dnn_colorize_cpp 

* dnn: add colorization.cpp

* Update arguments of dnn/colorization.py
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samples/dnn/colorization.cpp 0 → 100644
View file @ 56bcdfd7
//
// This program is based on https://github.com/richzhang/colorization/blob/master/colorization/colorize.py
// download the caffemodel from: http://eecs.berkeley.edu/~rich.zhang/projects/2016_colorization/files/demo_v2/colorization_release_v2.caffemodel
// and the prototxt from: https://github.com/richzhang/colorization/blob/master/colorization/models/colorization_deploy_v2.prototxt
//
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
using namespace cv;
using namespace cv::dnn;
#include <iostream>
using namespace std;
// the 313 ab cluster centers from pts_in_hull.npy (already transposed)
float hull_pts[] = {
-90., -90., -90., -90., -90., -80., -80., -80., -80., -80., -80., -80., -80., -70., -70., -70., -70., -70., -70., -70., -70.,
-70., -70., -60., -60., -60., -60., -60., -60., -60., -60., -60., -60., -60., -60., -50., -50., -50., -50., -50., -50., -50., -50.,
-50., -50., -50., -50., -50., -50., -40., -40., -40., -40., -40., -40., -40., -40., -40., -40., -40., -40., -40., -40., -40., -30.,
-30., -30., -30., -30., -30., -30., -30., -30., -30., -30., -30., -30., -30., -30., -30., -20., -20., -20., -20., -20., -20., -20.,
-20., -20., -20., -20., -20., -20., -20., -20., -20., -10., -10., -10., -10., -10., -10., -10., -10., -10., -10., -10., -10., -10.,
-10., -10., -10., -10., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 10., 10., 10., 10., 10., 10., 10.,
10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 20., 20., 20., 20., 20., 20., 20., 20., 20., 20., 20., 20., 20., 20., 20.,
20., 20., 20., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 40., 40., 40., 40.,
40., 40., 40., 40., 40., 40., 40., 40., 40., 40., 40., 40., 40., 40., 40., 40., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
50., 50., 50., 50., 50., 50., 50., 50., 50., 60., 60., 60., 60., 60., 60., 60., 60., 60., 60., 60., 60., 60., 60., 60., 60., 60.,
60., 60., 60., 70., 70., 70., 70., 70., 70., 70., 70., 70., 70., 70., 70., 70., 70., 70., 70., 70., 70., 70., 70., 80., 80., 80.,
80., 80., 80., 80., 80., 80., 80., 80., 80., 80., 80., 80., 80., 80., 80., 80., 90., 90., 90., 90., 90., 90., 90., 90., 90., 90.,
90., 90., 90., 90., 90., 90., 90., 90., 90., 100., 100., 100., 100., 100., 100., 100., 100., 100., 100., 50., 60., 70., 80., 90.,
20., 30., 40., 50., 60., 70., 80., 90., 0., 10., 20., 30., 40., 50., 60., 70., 80., 90., -20., -10., 0., 10., 20., 30., 40., 50.,
60., 70., 80., 90., -30., -20., -10., 0., 10., 20., 30., 40., 50., 60., 70., 80., 90., 100., -40., -30., -20., -10., 0., 10., 20.,
30., 40., 50., 60., 70., 80., 90., 100., -50., -40., -30., -20., -10., 0., 10., 20., 30., 40., 50., 60., 70., 80., 90., 100., -50.,
-40., -30., -20., -10., 0., 10., 20., 30., 40., 50., 60., 70., 80., 90., 100., -60., -50., -40., -30., -20., -10., 0., 10., 20.,
30., 40., 50., 60., 70., 80., 90., 100., -70., -60., -50., -40., -30., -20., -10., 0., 10., 20., 30., 40., 50., 60., 70., 80., 90.,
100., -80., -70., -60., -50., -40., -30., -20., -10., 0., 10., 20., 30., 40., 50., 60., 70., 80., 90., -80., -70., -60., -50.,
-40., -30., -20., -10., 0., 10., 20., 30., 40., 50., 60., 70., 80., 90., -90., -80., -70., -60., -50., -40., -30., -20., -10.,
0., 10., 20., 30., 40., 50., 60., 70., 80., 90., -100., -90., -80., -70., -60., -50., -40., -30., -20., -10., 0., 10., 20., 30.,
40., 50., 60., 70., 80., 90., -100., -90., -80., -70., -60., -50., -40., -30., -20., -10., 0., 10., 20., 30., 40., 50., 60., 70.,
80., -110., -100., -90., -80., -70., -60., -50., -40., -30., -20., -10., 0., 10., 20., 30., 40., 50., 60., 70., 80., -110., -100.,
-90., -80., -70., -60., -50., -40., -30., -20., -10., 0., 10., 20., 30., 40., 50., 60., 70., 80., -110., -100., -90., -80., -70.,
-60., -50., -40., -30., -20., -10., 0., 10., 20., 30., 40., 50., 60., 70., -110., -100., -90., -80., -70., -60., -50., -40., -30.,
-20., -10., 0., 10., 20., 30., 40., 50., 60., 70., -90., -80., -70., -60., -50., -40., -30., -20., -10., 0.
};
int main(int argc, char **argv)
{
CommandLineParser parser(argc, argv,
"{ help | false | print this help message }"
"{ proto | colorization_deploy_v2.prototxt | model configuration }"
"{ model | colorization_release_v2.caffemodel | model weights }"
"{ image | space_shuttle.jpg | path to image file }"
"{ opencl | false | enable OpenCL }"
);
String modelTxt = parser.get<string>("proto");
String modelBin = parser.get<string>("model");
String imageFile = parser.get<String>("image");
if (parser.get<bool>("help") || modelTxt.empty() || modelBin.empty() || imageFile.empty())
{
cout << "A sample app to demonstrate recoloring grayscale images with dnn." << endl;
parser.printMessage();
return 0;
}
// fixed input size for the pretrained network
int W_in = 224;
int H_in = 224;
Net net = dnn::readNetFromCaffe(modelTxt, modelBin);
// setup additional layers:
int sz[] = {2, 313, 1, 1};
Mat pts_in_hull(4, sz, CV_32F, hull_pts);
Ptr<dnn::Layer> class8_ab = net.getLayer("class8_ab");
class8_ab->blobs.push_back(pts_in_hull);
Ptr<dnn::Layer> conv8_313_rh = net.getLayer("conv8_313_rh");
conv8_313_rh->blobs.push_back(Mat(1, 313, CV_32F, 2.606f));
if (parser.get<bool>("opencl"))
{
net.setPreferableTarget(DNN_TARGET_OPENCL);
}
Mat img = imread(imageFile);
if (img.empty())
{
std::cerr << "Can't read image from the file: " << imageFile << std::endl;
exit(-1);
}
// extract L channel and subtract mean
Mat lab, L, input;
img.convertTo(img, CV_32F, 1.0/255);
cvtColor(img, lab, COLOR_BGR2Lab);
extractChannel(lab, L, 0);
resize(L, input, Size(W_in, H_in));
input -= 50;
// run the L channel through the network
Mat inputBlob = blobFromImage(input);
net.setInput(inputBlob);
Mat result = net.forward("class8_ab");
// retrieve the calculated a,b channels from the network output
Size siz(result.size[2], result.size[3]);
Mat a = Mat(siz, CV_32F, result.ptr(0,0));
Mat b = Mat(siz, CV_32F, result.ptr(0,1));
resize(a, a, img.size());
resize(b, b, img.size());
// merge, and convert back to bgr
Mat color, chn[] = {L, a, b};
merge(chn, 3, lab);
cvtColor(lab, color, COLOR_Lab2BGR);
namedWindow("color", WINDOW_NORMAL);
namedWindow("original", WINDOW_NORMAL);
imshow("color", color);
imshow("original", img);
waitKey();
return 0;
}
samples/dnn/colorization.py
View file @ 56bcdfd7
# Script is based on https://github.com/richzhang/colorization/blob/master/colorize.py
# To download the caffemodel and the prototxt, see: https://github.com/richzhang/colorization/tree/master/models
# To download pts_in_hull.npy, see: https://github.com/richzhang/colorization/blob/master/resources/pts_in_hull.npy
# Script is based on https://github.com/richzhang/colorization/blob/master/colorization/colorize.py
# To download the caffemodel and the prototxt, see: https://github.com/richzhang/colorization/tree/master/colorization/models
# To download pts_in_hull.npy, see: https://github.com/richzhang/colorization/blob/master/colorization/resources/pts_in_hull.npy
import numpy as np
import argparse
import cv2 as cv
......@@ -8,9 +8,9 @@ import cv2 as cv
def parse_args():
parser = argparse.ArgumentParser(description='iColor: deep interactive colorization')
parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
parser.add_argument('--prototxt', help='Path to colorization_deploy_v2.prototxt', default='./models/colorization_release_v2.prototxt')
parser.add_argument('--caffemodel', help='Path to colorization_release_v2.caffemodel', default='./models/colorization_release_v2.caffemodel')
parser.add_argument('--kernel', help='Path to pts_in_hull.npy', default='./resources/pts_in_hull.npy')
parser.add_argument('--prototxt', help='Path to colorization_deploy_v2.prototxt', required=True)
parser.add_argument('--caffemodel', help='Path to colorization_release_v2.caffemodel', required=True)
parser.add_argument('--kernel', help='Path to pts_in_hull.npy', required=True)
args = parser.parse_args()
return args
......
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