mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
aa6931bee9
* add onnx_ort_runtime demo * rm in requirements * support batch eval * fixed MattingResults bug * move assignment for DetectionResult * integrated x2paddle * add model convert readme * update readme * re-lint * add processor api * Add MattingResult Free * change valid_cpu_backends order * add ppocr benchmark * mv bs from 64 to 32 * fixed quantize.md * fixed quantize bugs * Add Monitor for benchmark * update mem monitor * Set trt_max_batch_size default 1 * fixed ocr benchmark bug * support yolov5 in serving * Fixed yolov5 serving * Fixed postprocess * update yolov5 to 7.0 * add poros runtime demos * update readme * Support poros abi=1 * rm useless note * deal with comments * support pp_trt for ppseg * fixed symlink problem * Add is_mini_pad and stride for yolov5 * Add yolo series for paddle format * fixed bugs * fixed bug * support yolov5seg * fixed bug * refactor yolov5seg * fixed bug * mv Mask int32 to uint8 * add yolov5seg example * rm log info * fixed code style * add yolov5seg example in python * fixed dtype bug * update note * deal with comments * get sorted index * add yolov5seg test case * Add GPL-3.0 License * add round func * deal with comments * deal with commens Co-authored-by: Jason <jiangjiajun@baidu.com>
287 lines
12 KiB
C++
287 lines
12 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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#include <algorithm>
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#include "fastdeploy/vision/visualize/visualize.h"
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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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cv::Mat VisDetection(const cv::Mat& im, const DetectionResult& result,
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float score_threshold, int line_size, float font_size) {
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if (result.contain_masks) {
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FDASSERT(result.boxes.size() == result.masks.size(),
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"The size of masks must be equal to the size of boxes, but now "
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"%zu != %zu.",
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result.boxes.size(), result.masks.size());
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}
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int max_label_id =
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*std::max_element(result.label_ids.begin(), result.label_ids.end());
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std::vector<int> color_map = GenerateColorMap(max_label_id);
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int h = im.rows;
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int w = im.cols;
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auto vis_im = im.clone();
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for (size_t i = 0; i < result.boxes.size(); ++i) {
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if (result.scores[i] < score_threshold) {
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continue;
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}
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int x1 = static_cast<int>(round(result.boxes[i][0]));
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int y1 = static_cast<int>(round(result.boxes[i][1]));
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int x2 = static_cast<int>(round(result.boxes[i][2]));
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int y2 = static_cast<int>(round(result.boxes[i][3]));
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int box_h = y2 - y1;
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int box_w = x2 - x1;
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int c0 = color_map[3 * result.label_ids[i] + 0];
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int c1 = color_map[3 * result.label_ids[i] + 1];
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int c2 = color_map[3 * result.label_ids[i] + 2];
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cv::Scalar rect_color = cv::Scalar(c0, c1, c2);
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std::string id = std::to_string(result.label_ids[i]);
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std::string score = std::to_string(result.scores[i]);
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if (score.size() > 4) {
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score = score.substr(0, 4);
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}
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std::string text = id + ", " + score;
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int font = cv::FONT_HERSHEY_SIMPLEX;
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cv::Size text_size = cv::getTextSize(text, font, font_size, 1, nullptr);
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cv::Point origin;
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origin.x = x1;
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origin.y = y1;
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cv::Rect rect(x1, y1, box_w, box_h);
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cv::rectangle(vis_im, rect, rect_color, line_size);
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cv::putText(vis_im, text, origin, font, font_size,
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cv::Scalar(255, 255, 255), 1);
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if (result.contain_masks) {
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int mask_h = static_cast<int>(result.masks[i].shape[0]);
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int mask_w = static_cast<int>(result.masks[i].shape[1]);
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// non-const pointer for cv:Mat constructor
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uint8_t* mask_raw_data = const_cast<uint8_t*>(
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static_cast<const uint8_t*>(result.masks[i].Data()));
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// only reference to mask data (zero copy)
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cv::Mat mask(mask_h, mask_w, CV_8UC1, mask_raw_data);
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if ((mask_h != box_h) || (mask_w != box_w)) {
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cv::resize(mask, mask, cv::Size(box_w, box_h));
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}
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// use a bright color for instance mask
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int mc0 = 255 - c0 >= 127 ? 255 - c0 : 127;
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int mc1 = 255 - c1 >= 127 ? 255 - c1 : 127;
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int mc2 = 255 - c2 >= 127 ? 255 - c2 : 127;
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uint8_t* mask_data = reinterpret_cast<uint8_t*>(mask.data);
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// inplace blending (zero copy)
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uchar* vis_im_data = static_cast<uchar*>(vis_im.data);
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for (size_t i = y1; i < y2; ++i) {
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for (size_t j = x1; j < x2; ++j) {
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if (mask_data[(i - y1) * mask_w + (j - x1)] != 0) {
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vis_im_data[i * w * 3 + j * 3 + 0] = cv::saturate_cast<uchar>(
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static_cast<float>(mc0) * 0.5f +
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static_cast<float>(vis_im_data[i * w * 3 + j * 3 + 0]) * 0.5f);
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vis_im_data[i * w * 3 + j * 3 + 1] = cv::saturate_cast<uchar>(
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static_cast<float>(mc1) * 0.5f +
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static_cast<float>(vis_im_data[i * w * 3 + j * 3 + 1]) * 0.5f);
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vis_im_data[i * w * 3 + j * 3 + 2] = cv::saturate_cast<uchar>(
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static_cast<float>(mc2) * 0.5f +
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static_cast<float>(vis_im_data[i * w * 3 + j * 3 + 2]) * 0.5f);
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}
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}
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}
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}
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}
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return vis_im;
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}
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// Visualize DetectionResult with custom labels.
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cv::Mat VisDetection(const cv::Mat& im, const DetectionResult& result,
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const std::vector<std::string>& labels,
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float score_threshold, int line_size, float font_size) {
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if (result.contain_masks) {
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FDASSERT(result.boxes.size() == result.masks.size(),
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"The size of masks must be equal to the size of boxes, but now "
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"%zu != %zu.",
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result.boxes.size(), result.masks.size());
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}
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int max_label_id =
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*std::max_element(result.label_ids.begin(), result.label_ids.end());
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std::vector<int> color_map = GenerateColorMap(max_label_id);
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int h = im.rows;
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int w = im.cols;
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auto vis_im = im.clone();
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for (size_t i = 0; i < result.boxes.size(); ++i) {
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if (result.scores[i] < score_threshold) {
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continue;
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}
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int x1 = static_cast<int>(result.boxes[i][0]);
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int y1 = static_cast<int>(result.boxes[i][1]);
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int x2 = static_cast<int>(result.boxes[i][2]);
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int y2 = static_cast<int>(result.boxes[i][3]);
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int box_h = y2 - y1;
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int box_w = x2 - x1;
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int c0 = color_map[3 * result.label_ids[i] + 0];
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int c1 = color_map[3 * result.label_ids[i] + 1];
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int c2 = color_map[3 * result.label_ids[i] + 2];
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cv::Scalar rect_color = cv::Scalar(c0, c1, c2);
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std::string id = std::to_string(result.label_ids[i]);
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std::string score = std::to_string(result.scores[i]);
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if (score.size() > 4) {
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score = score.substr(0, 4);
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}
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std::string text = id + "," + score;
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if (labels.size() > result.label_ids[i]) {
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text = labels[result.label_ids[i]] + "," + text;
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} else {
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FDWARNING << "The label_id: " << result.label_ids[i]
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<< " in DetectionResult should be less than length of labels:"
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<< labels.size() << "." << std::endl;
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}
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if (text.size() > 16) {
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text = text.substr(0, 16);
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}
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int font = cv::FONT_HERSHEY_SIMPLEX;
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cv::Size text_size = cv::getTextSize(text, font, font_size, 1, nullptr);
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cv::Point origin;
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origin.x = x1;
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origin.y = y1;
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cv::Rect rect(x1, y1, box_w, box_h);
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cv::rectangle(vis_im, rect, rect_color, line_size);
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cv::putText(vis_im, text, origin, font, font_size,
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cv::Scalar(255, 255, 255), 1);
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if (result.contain_masks) {
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int mask_h = static_cast<int>(result.masks[i].shape[0]);
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int mask_w = static_cast<int>(result.masks[i].shape[1]);
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// non-const pointer for cv:Mat constructor
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int32_t* mask_raw_data = const_cast<int32_t*>(
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static_cast<const int32_t*>(result.masks[i].Data()));
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// only reference to mask data (zero copy)
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cv::Mat mask(mask_h, mask_w, CV_32SC1, mask_raw_data);
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if ((mask_h != box_h) || (mask_w != box_w)) {
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cv::resize(mask, mask, cv::Size(box_w, box_h));
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}
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// use a bright color for instance mask
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int mc0 = 255 - c0 >= 127 ? 255 - c0 : 127;
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int mc1 = 255 - c1 >= 127 ? 255 - c1 : 127;
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int mc2 = 255 - c2 >= 127 ? 255 - c2 : 127;
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int32_t* mask_data = reinterpret_cast<int32_t*>(mask.data);
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// inplace blending (zero copy)
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uchar* vis_im_data = static_cast<uchar*>(vis_im.data);
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for (size_t i = y1; i < y2; ++i) {
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for (size_t j = x1; j < x2; ++j) {
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if (mask_data[(i - y1) * mask_w + (j - x1)] != 0) {
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vis_im_data[i * w * 3 + j * 3 + 0] = cv::saturate_cast<uchar>(
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static_cast<float>(mc0) * 0.5f +
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static_cast<float>(vis_im_data[i * w * 3 + j * 3 + 0]) * 0.5f);
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vis_im_data[i * w * 3 + j * 3 + 1] = cv::saturate_cast<uchar>(
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static_cast<float>(mc1) * 0.5f +
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static_cast<float>(vis_im_data[i * w * 3 + j * 3 + 1]) * 0.5f);
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vis_im_data[i * w * 3 + j * 3 + 2] = cv::saturate_cast<uchar>(
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static_cast<float>(mc2) * 0.5f +
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static_cast<float>(vis_im_data[i * w * 3 + j * 3 + 2]) * 0.5f);
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}
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}
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}
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}
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}
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return vis_im;
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}
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// Default only support visualize num_classes <= 1000
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// If need to visualize num_classes > 1000
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// Please call Visualize::GetColorMap(num_classes) first
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cv::Mat Visualize::VisDetection(const cv::Mat& im,
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const DetectionResult& result,
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float score_threshold, int line_size,
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float font_size) {
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FDWARNING << "DEPRECATED: fastdeploy::vision::Visualize::VisDetection is "
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"deprecated, please use fastdeploy::vision:VisDetection "
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"function instead."
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<< std::endl;
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if (result.contain_masks) {
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FDASSERT(result.boxes.size() == result.masks.size(),
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"The size of masks must be equal the size of boxes!");
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}
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auto color_map = GetColorMap();
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int h = im.rows;
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int w = im.cols;
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auto vis_im = im.clone();
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for (size_t i = 0; i < result.boxes.size(); ++i) {
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if (result.scores[i] < score_threshold) {
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continue;
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}
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int x1 = static_cast<int>(result.boxes[i][0]);
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int y1 = static_cast<int>(result.boxes[i][1]);
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int x2 = static_cast<int>(result.boxes[i][2]);
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int y2 = static_cast<int>(result.boxes[i][3]);
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int box_h = y2 - y1;
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int box_w = x2 - x1;
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int c0 = color_map[3 * result.label_ids[i] + 0];
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int c1 = color_map[3 * result.label_ids[i] + 1];
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int c2 = color_map[3 * result.label_ids[i] + 2];
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cv::Scalar rect_color = cv::Scalar(c0, c1, c2);
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std::string id = std::to_string(result.label_ids[i]);
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std::string score = std::to_string(result.scores[i]);
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if (score.size() > 4) {
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score = score.substr(0, 4);
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}
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std::string text = id + "," + score;
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int font = cv::FONT_HERSHEY_SIMPLEX;
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cv::Size text_size = cv::getTextSize(text, font, font_size, 1, nullptr);
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cv::Point origin;
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origin.x = x1;
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origin.y = y1;
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cv::Rect rect(x1, y1, box_w, box_h);
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cv::rectangle(vis_im, rect, rect_color, line_size);
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cv::putText(vis_im, text, origin, font, font_size,
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cv::Scalar(255, 255, 255), 1);
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if (result.contain_masks) {
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int mask_h = static_cast<int>(result.masks[i].shape[0]);
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int mask_w = static_cast<int>(result.masks[i].shape[1]);
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// non-const pointer for cv:Mat constructor
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int32_t* mask_raw_data = const_cast<int32_t*>(
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static_cast<const int32_t*>(result.masks[i].Data()));
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// only reference to mask data (zero copy)
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cv::Mat mask(mask_h, mask_w, CV_32SC1, mask_raw_data);
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if ((mask_h != box_h) || (mask_w != box_w)) {
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cv::resize(mask, mask, cv::Size(box_w, box_h));
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}
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// use a bright color for instance mask
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int mc0 = 255 - c0 >= 127 ? 255 - c0 : 127;
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int mc1 = 255 - c1 >= 127 ? 255 - c1 : 127;
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int mc2 = 255 - c2 >= 127 ? 255 - c2 : 127;
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int32_t* mask_data = reinterpret_cast<int32_t*>(mask.data);
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// inplace blending (zero copy)
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uchar* vis_im_data = static_cast<uchar*>(vis_im.data);
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for (size_t i = y1; i < y2; ++i) {
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for (size_t j = x1; j < x2; ++j) {
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if (mask_data[(i - y1) * mask_w + (j - x1)] != 0) {
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vis_im_data[i * w * 3 + j * 3 + 0] = cv::saturate_cast<uchar>(
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static_cast<float>(mc0) * 0.5f +
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static_cast<float>(vis_im_data[i * w * 3 + j * 3 + 0]) * 0.5f);
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vis_im_data[i * w * 3 + j * 3 + 1] = cv::saturate_cast<uchar>(
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static_cast<float>(mc1) * 0.5f +
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static_cast<float>(vis_im_data[i * w * 3 + j * 3 + 1]) * 0.5f);
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vis_im_data[i * w * 3 + j * 3 + 2] = cv::saturate_cast<uchar>(
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static_cast<float>(mc2) * 0.5f +
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static_cast<float>(vis_im_data[i * w * 3 + j * 3 + 2]) * 0.5f);
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}
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}
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}
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}
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}
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return vis_im;
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}
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} // namespace vision
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} // namespace fastdeploy
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