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Add MODNet(Matting) model support (PaddlePaddle#76)
* Add MODNet(Matting) model support * from pre_commit.main import main
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| // Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
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| #ifdef ENABLE_VISION_VISUALIZE | ||
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| #include "fastdeploy/vision/visualize/visualize.h" | ||
| #include "opencv2/highgui.hpp" | ||
| #include "opencv2/imgproc/imgproc.hpp" | ||
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| namespace fastdeploy { | ||
| namespace vision { | ||
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| static void RemoveSmallConnectedArea(cv::Mat* alpha_pred, | ||
| float threshold = 0.05f) { | ||
| // 移除小的联通区域和噪点 开闭合形态学处理 | ||
| // 假设输入的是透明度alpha, 值域(0.,1.) | ||
| cv::Mat gray, binary; | ||
| (*alpha_pred).convertTo(gray, CV_8UC1, 255.f); | ||
| // 255 * 0.05 ~ 13 | ||
| unsigned int binary_threshold = static_cast<unsigned int>(255.f * threshold); | ||
| cv::threshold(gray, binary, binary_threshold, 255, cv::THRESH_BINARY); | ||
| // morphologyEx with OPEN operation to remove noise first. | ||
| auto kernel = cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(3, 3), | ||
| cv::Point(-1, -1)); | ||
| cv::morphologyEx(binary, binary, cv::MORPH_OPEN, kernel); | ||
| // Computationally connected domain | ||
| cv::Mat labels = cv::Mat::zeros((*alpha_pred).size(), CV_32S); | ||
| cv::Mat stats, centroids; | ||
| int num_labels = | ||
| cv::connectedComponentsWithStats(binary, labels, stats, centroids, 8, 4); | ||
| if (num_labels <= 1) { | ||
| // no noise, skip. | ||
| return; | ||
| } | ||
| // find max connected area, 0 is background | ||
| int max_connected_id = 1; // 1,2,... | ||
| int max_connected_area = stats.at<int>(max_connected_id, cv::CC_STAT_AREA); | ||
| for (int i = 1; i < num_labels; ++i) { | ||
| int tmp_connected_area = stats.at<int>(i, cv::CC_STAT_AREA); | ||
| if (tmp_connected_area > max_connected_area) { | ||
| max_connected_area = tmp_connected_area; | ||
| max_connected_id = i; | ||
| } | ||
| } | ||
| const int h = (*alpha_pred).rows; | ||
| const int w = (*alpha_pred).cols; | ||
| // remove small connected area. | ||
| for (int i = 0; i < h; ++i) { | ||
| int* label_row_ptr = labels.ptr<int>(i); | ||
| float* alpha_row_ptr = (*alpha_pred).ptr<float>(i); | ||
| for (int j = 0; j < w; ++j) { | ||
| if (label_row_ptr[j] != max_connected_id) alpha_row_ptr[j] = 0.f; | ||
| } | ||
| } | ||
| } | ||
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| void Visualize::VisMattingAlpha(const cv::Mat& im, const MattingResult& result, | ||
| cv::Mat* vis_img, | ||
| bool remove_small_connected_area) { | ||
| // 只可视化alpha，fgr(前景)本身就是一张图 不需要可视化 | ||
| FDASSERT((!im.empty()), "im can't be empty!"); | ||
| FDASSERT((im.channels() == 3), "Only support 3 channels mat!"); | ||
| int out_h = static_cast<int>(result.shape[0]); | ||
| int out_w = static_cast<int>(result.shape[1]); | ||
| int height = im.rows; | ||
| int width = im.cols; | ||
| // alpha to cv::Mat && 避免resize等操作修改外部数据 | ||
| std::vector<float> alpha_copy; | ||
| alpha_copy.assign(result.alpha.begin(), result.alpha.end()); | ||
| float* alpha_ptr = static_cast<float*>(alpha_copy.data()); | ||
| cv::Mat alpha(out_h, out_w, CV_32FC1, alpha_ptr); | ||
| if (remove_small_connected_area) { | ||
| RemoveSmallConnectedArea(&alpha, 0.05f); | ||
| } | ||
| if ((out_h != height) || (out_w != width)) { | ||
| cv::resize(alpha, alpha, cv::Size(width, height)); | ||
| } | ||
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| int vis_h = (*vis_img).rows; | ||
| int vis_w = (*vis_img).cols; | ||
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| if ((vis_h != height) || (vis_w != width)) { | ||
| // faster than resize | ||
| (*vis_img) = cv::Mat::zeros(height, width, CV_8UC3); | ||
| } | ||
| if ((*vis_img).type() != CV_8UC3) { | ||
| (*vis_img).convertTo((*vis_img), CV_8UC3); | ||
| } | ||
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| uchar* vis_data = static_cast<uchar*>(vis_img->data); | ||
| uchar* im_data = static_cast<uchar*>(im.data); | ||
| float* alpha_data = reinterpret_cast<float*>(alpha.data); | ||
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| for (size_t i = 0; i < height; ++i) { | ||
| for (size_t j = 0; j < width; ++j) { | ||
| float alpha_val = alpha_data[i * width + j]; | ||
| vis_data[i * width * 3 + j * 3 + 0] = cv::saturate_cast<uchar>( | ||
| static_cast<float>(im_data[i * width * 3 + j * 3 + 0]) * alpha_val + | ||
| (1.f - alpha_val) * 153.f); | ||
| vis_data[i * width * 3 + j * 3 + 1] = cv::saturate_cast<uchar>( | ||
| static_cast<float>(im_data[i * width * 3 + j * 3 + 1]) * alpha_val + | ||
| (1.f - alpha_val) * 255.f); | ||
| vis_data[i * width * 3 + j * 3 + 2] = cv::saturate_cast<uchar>( | ||
| static_cast<float>(im_data[i * width * 3 + j * 3 + 2]) * alpha_val + | ||
| (1.f - alpha_val) * 120.f); | ||
| } | ||
| } | ||
| } | ||
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| } // namespace vision | ||
| } // namespace fastdeploy | ||
| #endif |
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