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ccc277247a
* fix(wechat_qrcode): Initialize nBytes after the count value is determined * fix(wechat_qrcode): Incorrect count data repair * chore: format expr * fix(wechat_qrcode): Avoid null pointer exception * fix(wechat_qrcode): return when bytes_ is empty * test(wechat_qrcode): add test case --------- Co-authored-by: GZTime <Time.GZ@outlook.com>
471 lines
23 KiB
C++
471 lines
23 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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//
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// Tencent is pleased to support the open source community by making WeChat QRCode available.
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// Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
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#include "test_precomp.hpp"
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#include "opencv2/objdetect.hpp"
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namespace opencv_test {
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namespace {
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std::string qrcode_images_name[] = {
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"version_1_down.jpg", /*"version_1_left.jpg", "version_1_right.jpg", "version_1_up.jpg",*/
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"version_1_top.jpg",
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/*"version_2_down.jpg",*/ "version_2_left.jpg", /*"version_2_right.jpg",*/
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"version_2_up.jpg",
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"version_2_top.jpg",
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"version_3_down.jpg",
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"version_3_left.jpg",
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/*"version_3_right.jpg",*/ "version_3_up.jpg",
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"version_3_top.jpg",
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"version_4_down.jpg",
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"version_4_left.jpg",
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/*"version_4_right.jpg",*/ "version_4_up.jpg",
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"version_4_top.jpg",
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"version_5_down.jpg",
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"version_5_left.jpg",
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/*"version_5_right.jpg",*/ "version_5_up.jpg",
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"version_5_top.jpg",
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"russian.jpg",
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"kanji.jpg", /*"link_github_ocv.jpg",*/
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"link_ocv.jpg",
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"link_wiki_cv.jpg"};
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std::string qrcode_images_close[] = {/*"close_1.png",*/ "close_2.png", "close_3.png", "close_4.png",
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"close_5.png"};
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std::string qrcode_images_monitor[] = {"monitor_1.png", "monitor_2.png", "monitor_3.png",
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"monitor_4.png", "monitor_5.png"};
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std::string qrcode_images_curved[] = {"curved_1.jpg", /*"curved_2.jpg", "curved_3.jpg",
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"curved_4.jpg",*/
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"curved_5.jpg", "curved_6.jpg",
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/*"curved_7.jpg", "curved_8.jpg"*/};
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std::string qrcode_images_multiple[] = {/*"2_qrcodes.png",*/ "3_close_qrcodes.png", /*"3_qrcodes.png",
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"4_qrcodes.png", "5_qrcodes.png", "6_qrcodes.png",*/
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"7_qrcodes.png"/*, "8_close_qrcodes.png"*/};
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typedef testing::TestWithParam<std::string> Objdetect_QRCode;
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TEST_P(Objdetect_QRCode, regression) {
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const std::string name_current_image = GetParam();
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const std::string root = "qrcode/";
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std::string image_path = findDataFile(root + name_current_image);
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Mat src = imread(image_path, IMREAD_GRAYSCALE);
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ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
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vector<Mat> points;
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// can not find the model file
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// so we temporarily comment it out
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// auto detector = wechat_qrcode::WeChatQRCode(
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// findDataFile("detect.prototxt", false), findDataFile("detect.caffemodel", false),
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// findDataFile("sr.prototxt", false), findDataFile("sr.caffemodel", false));
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auto detector = wechat_qrcode::WeChatQRCode();
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auto decoded_info = detector.detectAndDecode(src, points);
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const std::string dataset_config = findDataFile(root + "dataset_config.json");
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FileStorage file_config(dataset_config, FileStorage::READ);
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ASSERT_TRUE(file_config.isOpened()) << "Can't read validation data: " << dataset_config;
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{
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FileNode images_list = file_config["test_images"];
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size_t images_count = static_cast<size_t>(images_list.size());
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ASSERT_GT(images_count, 0u)
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<< "Can't find validation data entries in 'test_images': " << dataset_config;
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for (size_t index = 0; index < images_count; index++) {
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FileNode config = images_list[(int)index];
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std::string name_test_image = config["image_name"];
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if (name_test_image == name_current_image) {
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std::string original_info = config["info"];
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string decoded_str;
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if (decoded_info.size()) {
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decoded_str = decoded_info[0];
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}
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EXPECT_EQ(decoded_str, original_info);
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return; // done
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}
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}
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std::cerr << "Not found results for '" << name_current_image
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<< "' image in config file:" << dataset_config << std::endl
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<< "Re-run tests with enabled UPDATE_QRCODE_TEST_DATA macro to update test data."
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<< std::endl;
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}
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}
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typedef testing::TestWithParam<std::string> Objdetect_QRCode_Close;
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TEST_P(Objdetect_QRCode_Close, regression) {
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const std::string name_current_image = GetParam();
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const std::string root = "qrcode/close/";
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std::string image_path = findDataFile(root + name_current_image);
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Mat src = imread(image_path, IMREAD_GRAYSCALE);
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ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
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vector<Mat> points;
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// can not find the model file
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// so we temporarily comment it out
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// auto detector = wechat_qrcode::WeChatQRCode(
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// findDataFile("detect.prototxt", false), findDataFile("detect.caffemodel", false),
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// findDataFile("sr.prototxt", false), findDataFile("sr.caffemodel", false));
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auto detector = wechat_qrcode::WeChatQRCode();
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auto decoded_info = detector.detectAndDecode(src, points);
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const std::string dataset_config = findDataFile(root + "dataset_config.json");
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FileStorage file_config(dataset_config, FileStorage::READ);
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ASSERT_TRUE(file_config.isOpened()) << "Can't read validation data: " << dataset_config;
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{
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FileNode images_list = file_config["close_images"];
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size_t images_count = static_cast<size_t>(images_list.size());
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ASSERT_GT(images_count, 0u)
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<< "Can't find validation data entries in 'close_images': " << dataset_config;
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for (size_t index = 0; index < images_count; index++) {
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FileNode config = images_list[(int)index];
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std::string name_test_image = config["image_name"];
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if (name_test_image == name_current_image) {
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std::string original_info = config["info"];
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string decoded_str;
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if (decoded_info.size()) {
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decoded_str = decoded_info[0];
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}
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EXPECT_EQ(decoded_str, original_info);
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return; // done
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}
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}
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std::cerr << "Not found results for '" << name_current_image
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<< "' image in config file:" << dataset_config << std::endl
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<< "Re-run tests with enabled UPDATE_QRCODE_TEST_DATA macro to update test data."
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<< std::endl;
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}
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}
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typedef testing::TestWithParam<std::string> Objdetect_QRCode_Monitor;
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TEST_P(Objdetect_QRCode_Monitor, regression) {
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const std::string name_current_image = GetParam();
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const std::string root = "qrcode/monitor/";
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std::string image_path = findDataFile(root + name_current_image);
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Mat src = imread(image_path, IMREAD_GRAYSCALE);
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ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
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vector<Mat> points;
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// can not find the model file
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// so we temporarily comment it out
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// auto detector = wechat_qrcode::WeChatQRCode(
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// findDataFile("detect.prototxt", false), findDataFile("detect.caffemodel", false),
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// findDataFile("sr.prototxt", false), findDataFile("sr.caffemodel", false));
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auto detector = wechat_qrcode::WeChatQRCode();
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auto decoded_info = detector.detectAndDecode(src, points);
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const std::string dataset_config = findDataFile(root + "dataset_config.json");
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FileStorage file_config(dataset_config, FileStorage::READ);
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ASSERT_TRUE(file_config.isOpened()) << "Can't read validation data: " << dataset_config;
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{
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FileNode images_list = file_config["monitor_images"];
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size_t images_count = static_cast<size_t>(images_list.size());
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ASSERT_GT(images_count, 0u)
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<< "Can't find validation data entries in 'monitor_images': " << dataset_config;
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for (size_t index = 0; index < images_count; index++) {
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FileNode config = images_list[(int)index];
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std::string name_test_image = config["image_name"];
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if (name_test_image == name_current_image) {
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std::string original_info = config["info"];
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string decoded_str;
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if (decoded_info.size()) {
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decoded_str = decoded_info[0];
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}
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EXPECT_EQ(decoded_str, original_info);
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return; // done
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}
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}
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std::cerr << "Not found results for '" << name_current_image
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<< "' image in config file:" << dataset_config << std::endl
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<< "Re-run tests with enabled UPDATE_QRCODE_TEST_DATA macro to update test data."
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<< std::endl;
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}
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}
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typedef testing::TestWithParam<std::string> Objdetect_QRCode_Curved;
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TEST_P(Objdetect_QRCode_Curved, regression) {
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const std::string name_current_image = GetParam();
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const std::string root = "qrcode/curved/";
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std::string image_path = findDataFile(root + name_current_image);
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Mat src = imread(image_path, IMREAD_GRAYSCALE);
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ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
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vector<Mat> points;
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// can not find the model file
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// so we temporarily comment it out
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// auto detector = wechat_qrcode::WeChatQRCode(
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// findDataFile("detect.prototxt", false), findDataFile("detect.caffemodel", false),
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// findDataFile("sr.prototxt", false), findDataFile("sr.caffemodel", false));
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auto detector = wechat_qrcode::WeChatQRCode();
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auto decoded_info = detector.detectAndDecode(src, points);
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const std::string dataset_config = findDataFile(root + "dataset_config.json");
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FileStorage file_config(dataset_config, FileStorage::READ);
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ASSERT_TRUE(file_config.isOpened()) << "Can't read validation data: " << dataset_config;
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{
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FileNode images_list = file_config["test_images"];
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size_t images_count = static_cast<size_t>(images_list.size());
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ASSERT_GT(images_count, 0u)
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<< "Can't find validation data entries in 'test_images': " << dataset_config;
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for (size_t index = 0; index < images_count; index++) {
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FileNode config = images_list[(int)index];
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std::string name_test_image = config["image_name"];
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if (name_test_image == name_current_image) {
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std::string original_info = config["info"];
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string decoded_str;
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if (decoded_info.size()) {
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decoded_str = decoded_info[0];
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}
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EXPECT_EQ(decoded_str, original_info);
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return; // done
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}
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}
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std::cerr << "Not found results for '" << name_current_image
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<< "' image in config file:" << dataset_config << std::endl
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<< "Re-run tests with enabled UPDATE_QRCODE_TEST_DATA macro to update test data."
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<< std::endl;
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}
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}
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typedef testing::TestWithParam<std::string> Objdetect_QRCode_Multi;
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TEST_P(Objdetect_QRCode_Multi, regression) {
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const std::string name_current_image = GetParam();
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const std::string root = "qrcode/multiple/";
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string path_detect_prototxt, path_detect_caffemodel, path_sr_prototxt, path_sr_caffemodel;
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string model_version = "_2021-01";
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path_detect_prototxt = findDataFile("dnn/wechat"+model_version+"/detect.prototxt", false);
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path_detect_caffemodel = findDataFile("dnn/wechat"+model_version+"/detect.caffemodel", false);
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path_sr_prototxt = findDataFile("dnn/wechat"+model_version+"/sr.prototxt", false);
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path_sr_caffemodel = findDataFile("dnn/wechat"+model_version+"/sr.caffemodel", false);
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std::string image_path = findDataFile(root + name_current_image);
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Mat src = imread(image_path);
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ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
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vector<Mat> points;
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auto detector = wechat_qrcode::WeChatQRCode(path_detect_prototxt, path_detect_caffemodel, path_sr_prototxt,
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path_sr_caffemodel);
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vector<string> decoded_info = detector.detectAndDecode(src, points);
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const std::string dataset_config = findDataFile(root + "dataset_config.json");
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FileStorage file_config(dataset_config, FileStorage::READ);
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ASSERT_TRUE(file_config.isOpened()) << "Can't read validation data: " << dataset_config;
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{
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FileNode images_list = file_config["multiple_images"];
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size_t images_count = static_cast<size_t>(images_list.size());
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ASSERT_GT(images_count, 0u)
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<< "Can't find validation data entries in 'test_images': " << dataset_config;
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for (size_t index = 0; index < images_count; index++) {
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FileNode config = images_list[(int)index];
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std::string name_test_image = config["image_name"];
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if (name_test_image == name_current_image) {
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size_t count_eq_info = 0;
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for (int i = 0; i < int(decoded_info.size()); i++) {
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for (int j = 0; j < int(config["info"].size()); j++) {
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std::string original_info = config["info"][j];
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if (original_info == decoded_info[i]) {
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count_eq_info++;
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break;
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}
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}
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}
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EXPECT_EQ(config["info"].size(), count_eq_info);
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return; // done
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}
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}
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std::cerr << "Not found results for '" << name_current_image
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<< "' image in config file:" << dataset_config << std::endl
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<< "Re-run tests with enabled UPDATE_QRCODE_TEST_DATA macro to update test data."
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<< std::endl;
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}
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}
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TEST(Objdetect_QRCode_points_position, rotate45) {
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string path_detect_prototxt, path_detect_caffemodel, path_sr_prototxt, path_sr_caffemodel;
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string model_version = "_2021-01";
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path_detect_prototxt = findDataFile("dnn/wechat"+model_version+"/detect.prototxt", false);
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path_detect_caffemodel = findDataFile("dnn/wechat"+model_version+"/detect.caffemodel", false);
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path_sr_prototxt = findDataFile("dnn/wechat"+model_version+"/sr.prototxt", false);
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path_sr_caffemodel = findDataFile("dnn/wechat"+model_version+"/sr.caffemodel", false);
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auto detector = wechat_qrcode::WeChatQRCode(path_detect_prototxt, path_detect_caffemodel, path_sr_prototxt,
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path_sr_caffemodel);
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const cv::String expect_msg = "OpenCV";
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QRCodeEncoder::Params params;
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params.version = 5; // 37x37
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Ptr<QRCodeEncoder> qrcode_enc = cv::QRCodeEncoder::create(params);
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Mat qrImage;
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qrcode_enc->encode(expect_msg, qrImage);
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Mat image(800, 800, CV_8UC1);
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const int pixInBlob = 4;
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Size qrSize = Size((21+(params.version-1)*4)*pixInBlob,(21+(params.version-1)*4)*pixInBlob);
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Rect2f rec(static_cast<float>((image.cols - qrSize.width)/2),
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static_cast<float>((image.rows - qrSize.height)/2),
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static_cast<float>(qrSize.width),
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static_cast<float>(qrSize.height));
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vector<float> goldCorners = {rec.x, rec.y,
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rec.x+rec.width, rec.y,
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rec.x+rec.width, rec.y+rec.height,
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rec.x, rec.y+rec.height};
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Mat roiImage = image(rec);
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cv::resize(qrImage, roiImage, qrSize, 1., 1., INTER_NEAREST);
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vector<Mat> points1;
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auto decoded_info1 = detector.detectAndDecode(image, points1);
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ASSERT_EQ(1ull, decoded_info1.size());
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ASSERT_EQ(expect_msg, decoded_info1[0]);
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EXPECT_NEAR(0, cvtest::norm(Mat(goldCorners), points1[0].reshape(1, 8), NORM_INF), 8.);
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const double angle = 45;
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Point2f pc(image.cols/2.f, image.rows/2.f);
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Mat rot = getRotationMatrix2D(pc, angle, 1.);
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warpAffine(image, image, rot, image.size());
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vector<float> rotateGoldCorners;
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for (int i = 0; i < static_cast<int>(goldCorners.size()); i+= 2) {
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rotateGoldCorners.push_back(static_cast<float>(rot.at<double>(0, 0) * goldCorners[i] +
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rot.at<double>(0, 1) * goldCorners[i+1] + rot.at<double>(0, 2)));
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rotateGoldCorners.push_back(static_cast<float>(rot.at<double>(1, 0) * goldCorners[i] +
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rot.at<double>(1, 1) * goldCorners[i+1] + rot.at<double>(1, 2)));
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}
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vector<Mat> points2;
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auto decoded_info2 = detector.detectAndDecode(image, points2);
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ASSERT_EQ(1ull, decoded_info2.size());
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ASSERT_EQ(expect_msg, decoded_info2[0]);
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EXPECT_NEAR(0, cvtest::norm(Mat(rotateGoldCorners), points2[0].reshape(1, 8), NORM_INF), 11.);
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}
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INSTANTIATE_TEST_CASE_P(/**/, Objdetect_QRCode, testing::ValuesIn(qrcode_images_name));
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INSTANTIATE_TEST_CASE_P(/**/, Objdetect_QRCode_Close, testing::ValuesIn(qrcode_images_close));
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INSTANTIATE_TEST_CASE_P(/**/, Objdetect_QRCode_Monitor, testing::ValuesIn(qrcode_images_monitor));
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INSTANTIATE_TEST_CASE_P(/**/, Objdetect_QRCode_Curved, testing::ValuesIn(qrcode_images_curved));
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INSTANTIATE_TEST_CASE_P(/**/, Objdetect_QRCode_Multi, testing::ValuesIn(qrcode_images_multiple));
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TEST(Objdetect_QRCode_Big, regression) {
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string path_detect_prototxt, path_detect_caffemodel, path_sr_prototxt, path_sr_caffemodel;
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string model_version = "_2021-01";
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path_detect_prototxt = findDataFile("dnn/wechat"+model_version+"/detect.prototxt", false);
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path_detect_caffemodel = findDataFile("dnn/wechat"+model_version+"/detect.caffemodel", false);
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path_sr_prototxt = findDataFile("dnn/wechat"+model_version+"/sr.prototxt", false);
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path_sr_caffemodel = findDataFile("dnn/wechat"+model_version+"/sr.caffemodel", false);
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auto detector = wechat_qrcode::WeChatQRCode(path_detect_prototxt, path_detect_caffemodel, path_sr_prototxt,
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path_sr_caffemodel);
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const cv::String expect_msg = "OpenCV";
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QRCodeEncoder::Params params;
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params.version = 4; // 33x33
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Ptr<QRCodeEncoder> qrcode_enc = cv::QRCodeEncoder::create(params);
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Mat qrImage;
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qrcode_enc->encode(expect_msg, qrImage);
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Mat largeImage(4032, 3024, CV_8UC1);
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const int pixInBlob = 4;
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Size qrSize = Size((21+(params.version-1)*4)*pixInBlob,(21+(params.version-1)*4)*pixInBlob);
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Mat roiImage = largeImage(Rect((largeImage.cols - qrSize.width)/2, (largeImage.rows - qrSize.height)/2,
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qrSize.width, qrSize.height));
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cv::resize(qrImage, roiImage, qrSize, 1., 1., INTER_NEAREST);
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vector<Mat> points;
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detector.setScaleFactor(0.25f);
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auto decoded_info = detector.detectAndDecode(largeImage, points);
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ASSERT_EQ(1ull, decoded_info.size());
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ASSERT_EQ(expect_msg, decoded_info[0]);
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}
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TEST(Objdetect_QRCode_Tiny, regression) {
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string path_detect_prototxt, path_detect_caffemodel, path_sr_prototxt, path_sr_caffemodel;
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string model_version = "_2021-01";
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path_detect_prototxt = findDataFile("dnn/wechat"+model_version+"/detect.prototxt", false);
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path_detect_caffemodel = findDataFile("dnn/wechat"+model_version+"/detect.caffemodel", false);
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path_sr_prototxt = findDataFile("dnn/wechat"+model_version+"/sr.prototxt", false);
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path_sr_caffemodel = findDataFile("dnn/wechat"+model_version+"/sr.caffemodel", false);
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auto detector = wechat_qrcode::WeChatQRCode(path_detect_prototxt, path_detect_caffemodel, path_sr_prototxt,
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path_sr_caffemodel);
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const cv::String expect_msg = "OpenCV";
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QRCodeEncoder::Params params;
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params.version = 4; // 33x33
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Ptr<QRCodeEncoder> qrcode_enc = cv::QRCodeEncoder::create(params);
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Mat qrImage;
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qrcode_enc->encode(expect_msg, qrImage);
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Mat tinyImage(80, 80, CV_8UC1);
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const int pixInBlob = 2;
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Size qrSize = Size((21+(params.version-1)*4)*pixInBlob,(21+(params.version-1)*4)*pixInBlob);
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Mat roiImage = tinyImage(Rect((tinyImage.cols - qrSize.width)/2, (tinyImage.rows - qrSize.height)/2,
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qrSize.width, qrSize.height));
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cv::resize(qrImage, roiImage, qrSize, 1., 1., INTER_NEAREST);
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vector<Mat> points;
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auto decoded_info = detector.detectAndDecode(tinyImage, points);
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ASSERT_EQ(1ull, decoded_info.size());
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ASSERT_EQ(expect_msg, decoded_info[0]);
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}
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typedef testing::TestWithParam<std::string> Objdetect_QRCode_Easy_Multi;
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TEST_P(Objdetect_QRCode_Easy_Multi, regression) {
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string path_detect_prototxt, path_detect_caffemodel, path_sr_prototxt, path_sr_caffemodel;
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string model_path = GetParam();
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if (!model_path.empty()) {
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path_detect_prototxt = findDataFile(model_path + "/detect.prototxt", false);
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path_detect_caffemodel = findDataFile(model_path + "/detect.caffemodel", false);
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path_sr_prototxt = findDataFile(model_path + "/sr.prototxt", false);
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path_sr_caffemodel = findDataFile(model_path + "/sr.caffemodel", false);
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}
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|
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auto detector = wechat_qrcode::WeChatQRCode(path_detect_prototxt, path_detect_caffemodel, path_sr_prototxt,
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path_sr_caffemodel);
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|
|
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const cv::String expect_msg1 = "OpenCV1", expect_msg2 = "OpenCV2";
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QRCodeEncoder::Params params;
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params.version = 4; // 33x33
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Ptr<QRCodeEncoder> qrcode_enc = cv::QRCodeEncoder::create(params);
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Mat qrImage1, qrImage2;
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qrcode_enc->encode(expect_msg1, qrImage1);
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qrcode_enc->encode(expect_msg2, qrImage2);
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const int pixInBlob = 2;
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const int offset = 14;
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const int qr_size = (params.version - 1) * 4 + 21;
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Mat tinyImage = Mat::zeros(qr_size*pixInBlob+offset, (qr_size*pixInBlob+offset)*2, CV_8UC1);
|
|
Size qrSize = Size(qrImage1.cols, qrImage1.rows);
|
|
|
|
Mat roiImage = tinyImage(Rect((tinyImage.cols/2 - qrSize.width)/2, (tinyImage.rows - qrSize.height)/2,
|
|
qrSize.width, qrSize.height));
|
|
cv::resize(qrImage1, roiImage, qrSize, 1., 1., INTER_NEAREST);
|
|
|
|
roiImage = tinyImage(Rect((tinyImage.cols/2 - qrSize.width)/2+tinyImage.cols/2, (tinyImage.rows - qrSize.height)/2,
|
|
qrSize.width, qrSize.height));
|
|
cv::resize(qrImage2, roiImage, qrSize, 1., 1., INTER_NEAREST);
|
|
|
|
vector<Mat> points;
|
|
auto decoded_info = detector.detectAndDecode(tinyImage, points);
|
|
ASSERT_EQ(2ull, decoded_info.size());
|
|
ASSERT_TRUE((expect_msg1 == decoded_info[0] && expect_msg2 == decoded_info[1]) ||
|
|
(expect_msg1 == decoded_info[1] && expect_msg2 == decoded_info[0]));
|
|
}
|
|
|
|
std::string qrcode_model_path[] = {"", "dnn/wechat_2021-01"};
|
|
INSTANTIATE_TEST_CASE_P(/**/, Objdetect_QRCode_Easy_Multi, testing::ValuesIn(qrcode_model_path));
|
|
|
|
TEST(Objdetect_QRCode_bug, issue_3478) {
|
|
auto detector = wechat_qrcode::WeChatQRCode();
|
|
std::string image_path = findDataFile("qrcode/issue_3478.png");
|
|
Mat src = imread(image_path, IMREAD_GRAYSCALE);
|
|
ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
|
|
std::vector<std::string> outs = detector.detectAndDecode(src);
|
|
ASSERT_EQ(1, (int) outs.size());
|
|
ASSERT_EQ(16, (int) outs[0].size());
|
|
ASSERT_EQ("KFCVW50 ", outs[0]);
|
|
}
|
|
|
|
} // namespace
|
|
} // namespace opencv_test
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