Bryce2 dev

src/gmask.cpp

@@ -5,6 +5,7 @@
 #include <opencv2/imgproc.hpp>
 #include <opencv2/highgui.hpp>
 #include <opencv2/video/background_segm.hpp>
+#include "opencvutils.h"
 
 using namespace std;
 using namespace cv;
@@ -45,16 +46,7 @@ void GMask::setMask(std::shared_ptr<QImage> mask){
 void GMask::createMask(std::shared_ptr<QImage> currFrame, int i){
 
     // Detect edges
-    Mat mat;
-    switch (currFrame->format())
-    {
-    case QImage::Format_RGB32:
-        mat = Mat(currFrame->height(), currFrame->width(), CV_8UC4, (void*)currFrame->constBits(), currFrame->bytesPerLine());
-        cvtColor(mat, mat, COLOR_RGBA2RGB);
-        break;
-    default:
-        break;
-    }
+    Mat mat = qimage_to_mat_ref(*currFrame);
 
     // First, blur
     Mat blurMat(mat.size(), CV_8UC3);
@@ -124,4 +116,6 @@ void GMask::createMask(std::shared_ptr<QImage> currFrame, int i){
     mask.convertTo(mask,CV_8UC1,1);
     QImage imgIn = QImage(mask.data, mask.cols, mask.rows, static_cast<int>(mask.step), QImage::Format_Grayscale8);
     m_mask = make_shared<QImage>(imgIn);
+
+    imgIn.save("./imgIn.jpg");
 }

src/main.cpp

@@ -9,6 +9,13 @@
 #include "stylizer.h"
 
 #include "iohandler.h"
+
+//#include "gedge.h"
+//#include "optical-flow/simpleflow.h"
+//#include "opencvutils.h"
+//#include "advector.h"
+//#include "gpos.h"
+
 #include "opencvutils.h"
 #include "advector.h"
 #include "gpos.h"
@@ -69,6 +76,12 @@ int main(int argc, char *argv[])
 
     ioHandler.loadInputData(inputFrames, keyframes);
 
+    GMask g_mask = GMask(inputFrames[10]);
+
+//    std::shared_ptr<QImage> currFrame(new QImage("./data/minitest/video/000.jpg"));
+
+//    GEdge guide(currFrame);
+
     Stylizer style(inputFrames, keyframes, ioHandler);
 //    style.generateGuides();
     style.run();

styletransfer/style.py

@@ -0,0 +1,29 @@
+import tensorflow as tf
+import tensorflow_hub as hub
+import matplotlib.pyplot as plt
+import numpy as np
+import sys
+
+content_image_path = sys.arg[1]
+style_image_path = sys.arg[2]
+output_image_path = sys.arg[3]
+
+# Load content and style images (see example in the attached colab).
+content_image = plt.imread(content_image_path)
+style_image = plt.imread(style_image_path)
+# Convert to float32 numpy array, add batch dimension, and normalize to range [0, 1]. Example using numpy:
+content_image = content_image.astype(np.float32)[np.newaxis, ...] / 255.
+style_image = style_image.astype(np.float32)[np.newaxis, ...] / 255.
+# Optionally resize the images. It is recommended that the style image is about
+# 256 pixels (this size was used when training the style transfer network).
+# The content image can be any size.
+style_image = tf.image.resize(style_image, (256, 256))
+
+# Load image stylization module.
+hub_module = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
+
+# Stylize image.
+outputs = hub_module(tf.constant(content_image), tf.constant(style_image))
+stylized_image = outputs[0]
+
+plt.imsave(output_image_path,np.array(stylized_image[0]))