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feat.集成企鹅物流识别库，删除特征点检测相关接口

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MistEO
2021-10-04 15:54:55 +08:00
parent ab38b383be
commit f0a12ead8e
40 changed files with 9 additions and 431 deletions
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98
src/MeoAssistance/InfrastAbstractTask.cpp
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@@ -124,11 +124,7 @@ bool asst::InfrastAbstractTask::append_task_to_back_to_infrast_home()
return true;
}
std::vector<TextArea> asst::InfrastAbstractTask::detect_operators_name(
const cv::Mat& image,
std::unordered_map<std::string,
std::string>& feature_cond,
std::unordered_set<std::string>& feature_whatever)
std::vector<TextArea> asst::InfrastAbstractTask::detect_operators_name(const cv::Mat& image)
{
DebugTraceFunction;
@@ -191,98 +187,6 @@ std::vector<TextArea> asst::InfrastAbstractTask::detect_operators_name(
std::make_move_iterator(lower_part_names.begin()),
std::make_move_iterator(lower_part_names.end()));
// 如果ocr结果中已经有某个干员了，就没必要再尝试对他特征检测了，直接删了
for (const TextArea& textarea : all_opers_textarea) {
auto cond_iter = std::find_if(feature_cond.begin(), feature_cond.end(),
[&textarea](const auto& pair) -> bool {
return textarea.text == pair.second;
});
if (cond_iter != feature_cond.end()) {
feature_cond.erase(cond_iter);
}
auto whatever_iter = std::find_if(feature_whatever.begin(), feature_whatever.end(),
[&textarea](const std::string& str) -> bool {
return textarea.text == str;
});
if (whatever_iter != feature_whatever.end()) {
feature_whatever.erase(whatever_iter);
}
}
// 用特征检测再筛选一遍OCR识别漏了的——有关键字的
for (const TextArea& textarea : all_text_area) {
auto find_iter = std::find_if(all_opers_textarea.cbegin(), all_opers_textarea.cend(),
[&textarea](const auto& rhs) -> bool {
return textarea.text == rhs.text; });
if (find_iter != all_opers_textarea.cend()) {
continue;
}
for (auto iter = feature_cond.begin(); iter != feature_cond.end(); ++iter) {
auto& [key, value] = *iter;
// 识别到了key，但是没识别到value，这种情况就需要进行特征检测进一步确认了
if (textarea.text.find(key) != std::string::npos
&& textarea.text.find(value) == std::string::npos) {
// 把key所在的矩形放大一点送去做特征检测，不需要把整张图片都送去检测
Rect magnified_area = textarea.rect.center_zoom(2.0, image.cols, image.rows);
// key是关键字而已，真正要识别的是value
auto&& ret = OcrAbstractTask::m_identify_ptr->feature_match(
image(make_rect<cv::Rect>(magnified_area)), value);
if (ret) {
// 匹配上了下次就不用再匹配这个了，直接删了
all_opers_textarea.emplace_back(value, textarea.rect);
iter = feature_cond.erase(iter);
--iter;
// 也从whatever里面删了
auto whatever_iter = std::find_if(feature_whatever.begin(), feature_whatever.end(),
[&textarea](const std::string& str) -> bool {
return textarea.text == str;
});
if (whatever_iter != feature_whatever.end()) {
feature_whatever.erase(whatever_iter);
}
// 顺便再涂黑了，避免后面被whatever特征检测的误识别
// 注意这里是浅拷贝，原图image也会被涂黑
//cv::rectangle(draw_image, cv_rect, cv::Scalar(0, 0, 0), -1);
}
}
}
}
// 用特征检测再筛选一遍OCR识别漏了的——无论如何都进行识别的
for (auto iter = feature_whatever.begin(); iter != feature_whatever.end(); ++iter) {
// 上半部分长条形的图片
auto&& upper_ret = OcrAbstractTask::m_identify_ptr->feature_match(upper_part_name_image, *iter);
if (upper_ret) {
TextArea temp = std::move(upper_ret.value());
#ifdef LOG_TRACE // 也顺便涂黑一下，方便看谁没被识别出来
// 注意这里是浅拷贝，原图image也会被涂黑
cv::rectangle(upper_part_name_image, make_rect<cv::Rect>(temp.rect), cv::Scalar(0, 0, 0), -1);
#endif
// 因为图片是裁剪过的，所以对应原图的坐标要加上裁剪的参数
temp.rect.y += cropped_upper_y;
all_opers_textarea.emplace_back(std::move(temp));
iter = feature_whatever.erase(iter);
--iter;
continue;
}
// 下半部分长条形的图片
auto&& lower_ret = OcrAbstractTask::m_identify_ptr->feature_match(lower_part_name_image, *iter);
if (lower_ret) {
TextArea temp = std::move(lower_ret.value());
#ifdef LOG_TRACE // 也顺便涂黑一下，方便看谁没被识别出来
// 注意这里是浅拷贝，原图image也会被涂黑
cv::rectangle(lower_part_name_image, make_rect<cv::Rect>(temp.rect), cv::Scalar(0, 0, 0), -1);
#endif
// 因为图片是裁剪过的，所以对应原图的坐标要加上裁剪的参数
temp.rect.y += cropped_lower_y;
all_opers_textarea.emplace_back(std::move(temp));
iter = feature_whatever.erase(iter);
--iter;
continue;
}
}
return all_opers_textarea;
}