1a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov/*
2a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov * cv_image_sharp.cpp - image sharp
3a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov *
4a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov *  Copyright (c) 2016-2017 Intel Corporation
5a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov *
6a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov * Licensed under the Apache License, Version 2.0 (the "License");
7a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov * you may not use this file except in compliance with the License.
8a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov * You may obtain a copy of the License at
9a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov *
10a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov *   http://www.apache.org/licenses/LICENSE-2.0
11a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov *
12a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov * Unless required by applicable law or agreed to in writing, software
13a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov * distributed under the License is distributed on an "AS IS" BASIS,
14a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov * See the License for the specific language governing permissions and
16a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov * limitations under the License.
17a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov *
18a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov * Author: Andrey Parfenov <a1994ndrey@gmail.com>
19a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov * Author: Wind Yuan <feng.yuan@intel.com>
20a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov */
21a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov
22a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov#include "cv_image_sharp.h"
23a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov
24a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenovnamespace XCam {
25a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov
26a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov
27a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey ParfenovCVImageSharp::CVImageSharp ()
28be3ac1e217dd371c77983c812dfb149c258a3f42Andrey Parfenov    : CVBaseClass ()
29a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov{
30a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov
31a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov}
32a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov
33a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenovcv::Mat
34a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey ParfenovCVImageSharp::sharp_image_gray (const cv::Mat &image, float sigmar)
35a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov{
36a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov    cv::Mat temp_image;
37a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov    image.convertTo (temp_image, CV_32FC1);
38a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov    cv::Mat bilateral_image;
39be3ac1e217dd371c77983c812dfb149c258a3f42Andrey Parfenov    cv::bilateralFilter (temp_image, bilateral_image, 5, sigmar, 2);
40a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov
41a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov    cv::Mat sharp_filter = (cv::Mat_<float>(3, 3) << -1, -1, -1, -1, 8, -1, -1, -1, -1);
42a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov    cv::Mat filtered_image;
43a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov    cv::filter2D (bilateral_image, filtered_image, -1, sharp_filter);
44a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov    cv::normalize (filtered_image, filtered_image, 0, 255.0f, cv::NORM_MINMAX);
45a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov    cv::Mat sharpened = temp_image + filtered_image;
46a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov    cv::normalize (sharpened, sharpened, 0, 255.0f, cv::NORM_MINMAX);
47a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov    return sharpened.clone ();
48a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov}
49a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov
50a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenovfloat
51a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey ParfenovCVImageSharp::measure_sharp (const cv::Mat &image)
52a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov{
53a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov    cv::Mat dst;
54a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov    cv::Laplacian (image, dst, -1, 3, 1, 0, cv::BORDER_CONSTANT);
55a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov    dst.convertTo (dst, CV_8UC1);
56be3ac1e217dd371c77983c812dfb149c258a3f42Andrey Parfenov    float sum = cv::sum (dst)[0];
57a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov    sum /= (image.rows * image.cols);
58a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov    return sum;
59a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov}
60a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov
61a1fb1f1c9ab87798c9c05d74e66dacd7748e73a7Andrey Parfenov}
62