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120 lines
4.6 KiB
C++
120 lines
4.6 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#ifdef ENABLE_VISION_VISUALIZE
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#include "fastdeploy/vision/visualize/visualize.h"
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc/imgproc.hpp"
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namespace fastdeploy {
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namespace vision {
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static void RemoveSmallConnectedArea(cv::Mat* alpha_pred,
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float threshold = 0.05f) {
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// 移除小的联通区域和噪点 开闭合形态学处理
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// 假设输入的是透明度alpha, 值域(0.,1.)
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cv::Mat gray, binary;
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(*alpha_pred).convertTo(gray, CV_8UC1, 255.f);
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// 255 * 0.05 ~ 13
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unsigned int binary_threshold = static_cast<unsigned int>(255.f * threshold);
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cv::threshold(gray, binary, binary_threshold, 255, cv::THRESH_BINARY);
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// morphologyEx with OPEN operation to remove noise first.
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auto kernel = cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(3, 3),
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cv::Point(-1, -1));
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cv::morphologyEx(binary, binary, cv::MORPH_OPEN, kernel);
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// Computationally connected domain
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cv::Mat labels = cv::Mat::zeros((*alpha_pred).size(), CV_32S);
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cv::Mat stats, centroids;
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int num_labels =
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cv::connectedComponentsWithStats(binary, labels, stats, centroids, 8, 4);
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if (num_labels <= 1) {
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// no noise, skip.
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return;
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}
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// find max connected area, 0 is background
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int max_connected_id = 1; // 1,2,...
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int max_connected_area = stats.at<int>(max_connected_id, cv::CC_STAT_AREA);
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for (int i = 1; i < num_labels; ++i) {
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int tmp_connected_area = stats.at<int>(i, cv::CC_STAT_AREA);
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if (tmp_connected_area > max_connected_area) {
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max_connected_area = tmp_connected_area;
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max_connected_id = i;
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}
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}
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const int h = (*alpha_pred).rows;
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const int w = (*alpha_pred).cols;
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// remove small connected area.
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for (int i = 0; i < h; ++i) {
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int* label_row_ptr = labels.ptr<int>(i);
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float* alpha_row_ptr = (*alpha_pred).ptr<float>(i);
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for (int j = 0; j < w; ++j) {
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if (label_row_ptr[j] != max_connected_id) alpha_row_ptr[j] = 0.f;
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}
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}
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}
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cv::Mat Visualize::VisMattingAlpha(const cv::Mat& im,
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const MattingResult& result,
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bool remove_small_connected_area) {
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// 只可视化alpha，fgr(前景)本身就是一张图 不需要可视化
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FDASSERT((!im.empty()), "im can't be empty!");
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FDASSERT((im.channels() == 3), "Only support 3 channels mat!");
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auto vis_img = im.clone();
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int out_h = static_cast<int>(result.shape[0]);
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int out_w = static_cast<int>(result.shape[1]);
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int height = im.rows;
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int width = im.cols;
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// alpha to cv::Mat && 避免resize等操作修改外部数据
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std::vector<float> alpha_copy;
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alpha_copy.assign(result.alpha.begin(), result.alpha.end());
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float* alpha_ptr = static_cast<float*>(alpha_copy.data());
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cv::Mat alpha(out_h, out_w, CV_32FC1, alpha_ptr);
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if (remove_small_connected_area) {
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RemoveSmallConnectedArea(&alpha, 0.05f);
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}
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if ((out_h != height) || (out_w != width)) {
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cv::resize(alpha, alpha, cv::Size(width, height));
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}
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if ((vis_img).type() != CV_8UC3) {
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(vis_img).convertTo((vis_img), CV_8UC3);
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}
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uchar* vis_data = static_cast<uchar*>(vis_img.data);
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uchar* im_data = static_cast<uchar*>(im.data);
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float* alpha_data = reinterpret_cast<float*>(alpha.data);
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for (size_t i = 0; i < height; ++i) {
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for (size_t j = 0; j < width; ++j) {
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float alpha_val = alpha_data[i * width + j];
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vis_data[i * width * 3 + j * 3 + 0] = cv::saturate_cast<uchar>(
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static_cast<float>(im_data[i * width * 3 + j * 3 + 0]) * alpha_val +
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(1.f - alpha_val) * 153.f);
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vis_data[i * width * 3 + j * 3 + 1] = cv::saturate_cast<uchar>(
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static_cast<float>(im_data[i * width * 3 + j * 3 + 1]) * alpha_val +
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(1.f - alpha_val) * 255.f);
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vis_data[i * width * 3 + j * 3 + 2] = cv::saturate_cast<uchar>(
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static_cast<float>(im_data[i * width * 3 + j * 3 + 2]) * alpha_val +
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(1.f - alpha_val) * 120.f);
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}
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}
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return vis_img;
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}
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} // namespace vision
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} // namespace fastdeploy
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#endif
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