150 lines
4.2 KiB
C++
150 lines
4.2 KiB
C++
// Copyright (c) 2020 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 "opencv2/core.hpp"
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#include "opencv2/imgcodecs.hpp"
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#include "opencv2/imgproc.hpp"
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#include "paddle_api.h"
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#include "paddle_inference_api.h"
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#include <chrono>
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#include <iomanip>
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#include <iostream>
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#include <ostream>
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#include <vector>
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#include <cstring>
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#include <fstream>
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#include <math.h>
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#include <numeric>
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#include "preprocess_op.h"
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namespace Feature {
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void Permute::Run(const cv::Mat *im, float *data) {
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int rh = im->rows;
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int rw = im->cols;
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int rc = im->channels();
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for (int i = 0; i < rc; ++i) {
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cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i);
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}
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}
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void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
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const std::vector<float> &std, float scale) {
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(*im).convertTo(*im, CV_32FC3, scale);
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for (int h = 0; h < im->rows; h++) {
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for (int w = 0; w < im->cols; w++) {
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im->at<cv::Vec3f>(h, w)[0] =
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(im->at<cv::Vec3f>(h, w)[0] - mean[0]) / std[0];
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im->at<cv::Vec3f>(h, w)[1] =
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(im->at<cv::Vec3f>(h, w)[1] - mean[1]) / std[1];
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im->at<cv::Vec3f>(h, w)[2] =
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(im->at<cv::Vec3f>(h, w)[2] - mean[2]) / std[2];
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}
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}
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}
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void CenterCropImg::Run(cv::Mat &img, const int crop_size) {
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int resize_w = img.cols;
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int resize_h = img.rows;
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int w_start = int((resize_w - crop_size) / 2);
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int h_start = int((resize_h - crop_size) / 2);
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cv::Rect rect(w_start, h_start, crop_size, crop_size);
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img = img(rect);
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}
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void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
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int resize_short_size, int size) {
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int resize_h = 0;
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int resize_w = 0;
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if (size > 0) {
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resize_h = size;
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resize_w = size;
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} else {
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int w = img.cols;
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int h = img.rows;
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float ratio = 1.f;
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if (h < w) {
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ratio = float(resize_short_size) / float(h);
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} else {
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ratio = float(resize_short_size) / float(w);
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}
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resize_h = round(float(h) * ratio);
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resize_w = round(float(w) * ratio);
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}
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cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
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}
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} // namespace Feature
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namespace PaddleClas {
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void Permute::Run(const cv::Mat *im, float *data) {
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int rh = im->rows;
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int rw = im->cols;
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int rc = im->channels();
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for (int i = 0; i < rc; ++i) {
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cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i);
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}
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}
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void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
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const std::vector<float> &scale, const bool is_scale) {
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double e = 1.0;
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if (is_scale) {
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e /= 255.0;
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}
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(*im).convertTo(*im, CV_32FC3, e);
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for (int h = 0; h < im->rows; h++) {
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for (int w = 0; w < im->cols; w++) {
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im->at<cv::Vec3f>(h, w)[0] =
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(im->at<cv::Vec3f>(h, w)[0] - mean[0]) / scale[0];
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im->at<cv::Vec3f>(h, w)[1] =
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(im->at<cv::Vec3f>(h, w)[1] - mean[1]) / scale[1];
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im->at<cv::Vec3f>(h, w)[2] =
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(im->at<cv::Vec3f>(h, w)[2] - mean[2]) / scale[2];
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}
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}
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}
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void CenterCropImg::Run(cv::Mat &img, const int crop_size) {
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int resize_w = img.cols;
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int resize_h = img.rows;
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int w_start = int((resize_w - crop_size) / 2);
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int h_start = int((resize_h - crop_size) / 2);
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cv::Rect rect(w_start, h_start, crop_size, crop_size);
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img = img(rect);
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}
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void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
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int resize_short_size) {
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int w = img.cols;
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int h = img.rows;
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float ratio = 1.f;
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if (h < w) {
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ratio = float(resize_short_size) / float(h);
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} else {
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ratio = float(resize_short_size) / float(w);
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}
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int resize_h = round(float(h) * ratio);
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int resize_w = round(float(w) * ratio);
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cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
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}
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} // namespace PaddleClas
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