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feat!: 拆分 maskRange 与 colorScales；增加 colorWithClose 开关数色时闭运算

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zzyyyl
2024-08-16 18:29:10 +08:00
parent b8441f3bd5
commit 856cc6494d
11 changed files with 304 additions and 218 deletions
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83
src/MaaCore/Vision/Matcher.cpp
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@@ -32,7 +32,11 @@ Matcher::ResultOpt Matcher::analyze() const
if (m_log_tracing && max_val > 0.5 && max_val > threshold - 0.2) { // 得分太低的肯定不对，没必要打印
Log.trace("match_templ |", templ_name, "score:", max_val, "rect:", rect, "roi:", m_roi);
}
#ifdef ASST_DEBUG
else {
Log.debug("match_templ |", templ_name, "score:", max_val, "rect:", rect, "roi:", m_roi);
}
#endif
if (max_val < threshold) {
continue;
}
@@ -95,40 +99,44 @@ std::vector<Matcher::RawResult> Matcher::preproc_and_match(const cv::Mat& image,
}
cv::Mat matched;
cv::Mat image_for_match;
cv::Mat templ_for_match;
cv::Mat image_for_count;
cv::Mat templ_for_count;
cv::cvtColor(image, image_for_match, cv::COLOR_BGR2RGB);
cv::cvtColor(templ, templ_for_match, cv::COLOR_BGR2RGB);
cv::Mat image_match, image_count, image_gray;
cv::Mat templ_match, templ_count, templ_gray;
cv::cvtColor(image, image_match, cv::COLOR_BGR2RGB);
cv::cvtColor(templ, templ_match, cv::COLOR_BGR2RGB);
cv::cvtColor(image, image_gray, cv::COLOR_BGR2GRAY);
cv::cvtColor(templ, templ_gray, cv::COLOR_BGR2GRAY);
if (method == MatchMethod::HSVCount) {
cv::cvtColor(image, image_for_count, cv::COLOR_BGR2HSV);
cv::cvtColor(templ, templ_for_count, cv::COLOR_BGR2HSV);
cv::cvtColor(image, image_count, cv::COLOR_BGR2HSV);
cv::cvtColor(templ, templ_count, cv::COLOR_BGR2HSV);
}
else if (method == MatchMethod::RGBCount) {
image_for_count = image_for_match;
templ_for_count = templ_for_match;
image_count = image_match;
templ_count = templ_match;
}
// 目前所有的匹配都是用 TM_CCOEFF_NORMED
int match_algorithm = cv::TM_CCOEFF_NORMED;
auto calc_mask = [&](const cv::Mat& templ_for_mask, bool with_close)->std::optional<cv::Mat> {
cv::Mat templ_for_gray_mask;
cv::cvtColor(templ_for_mask, templ_for_gray_mask, cv::COLOR_BGR2GRAY);
auto calc_mask = [&templ_name](
const MatchTaskInfo::Ranges mask_ranges,
const cv::Mat& templ,
const cv::Mat& templ_gray,
bool with_close)
-> std::optional<cv::Mat> {
// Union all masks, not intersection
cv::Mat mask = cv::Mat::zeros(templ_for_gray_mask.size(), CV_8UC1);
for (const auto& range : params.mask_range) {
cv::Mat mask = cv::Mat::zeros(templ_gray.size(), CV_8UC1);
for (const auto& range : mask_ranges) {
cv::Mat current_mask;
if (range.first.size() == 1 && range.second.size() == 1) {
cv::inRange(templ_for_gray_mask, range.first[0], range.second[0], current_mask);
if (std::holds_alternative<MatchTaskInfo::GrayRange>(range)) {
const auto& gray_range = std::get<MatchTaskInfo::GrayRange>(range);
cv::inRange(templ_gray, gray_range.first, gray_range.second, current_mask);
}
else if (range.first.size() == 3 && range.second.size() == 3) {
cv::inRange(templ_for_mask, range.first, range.second, current_mask);
else if (std::holds_alternative<MatchTaskInfo::ColorRange>(range)) {
const auto& color_range = std::get<MatchTaskInfo::ColorRange>(range);
cv::inRange(templ, color_range.first, color_range.second, current_mask);
}
else {
Log.error("Invalid mask range");
Log.error("The task with template", templ_name, "holds invalid mask range");
return std::nullopt;
}
cv::bitwise_or(mask, current_mask, mask);
@@ -141,35 +149,31 @@ std::vector<Matcher::RawResult> Matcher::preproc_and_match(const cv::Mat& image,
return mask;
};
if (params.mask_range.empty() || method == MatchMethod::RGBCount || method == MatchMethod::HSVCount) {
// workaround: 数色时的模板匹配忽略 maskRange
// TODO: 区分 maskRange 和 colorRange
cv::matchTemplate(image_for_match, templ_for_match, matched, match_algorithm);
if (params.mask_ranges.empty()) {
cv::matchTemplate(image_match, templ_match, matched, match_algorithm);
}
else {
// match 时使用的 mask_range 当作 RGB 的
auto mask_opt = calc_mask(
params.mask_with_src ? image_for_match : templ_for_match,
params.mask_with_close);
params.mask_ranges,
params.mask_src ? image_match : templ_match,
params.mask_src ? image_gray : templ_gray,
params.mask_close);
if (!mask_opt) {
return {};
}
cv::matchTemplate(image_for_match, templ_for_match, matched, match_algorithm, mask_opt.value());
cv::matchTemplate(image_match, templ_match, matched, match_algorithm, mask_opt.value());
}
if (method == MatchMethod::RGBCount || method == MatchMethod::HSVCount) {
auto templ_active_opt = calc_mask(templ_for_count, false);
auto image_active_opt = calc_mask(image_for_count, false);
auto templ_active_opt = calc_mask(params.color_scales, templ_count, templ_gray, params.color_close);
auto image_active_opt = calc_mask(params.color_scales, image_count, image_gray, params.color_close);
if (!image_active_opt || !templ_active_opt) [[unlikely]] {
return {};
}
cv::Mat templ_active = std::move(templ_active_opt).value();
cv::Mat image_active = std::move(image_active_opt).value();
// 闭运算填充小空洞，避免数色的得分过低 TODO: 或许可以做成可选的
cv::Mat kernel = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3));
cv::morphologyEx(templ_active, templ_active, cv::MORPH_CLOSE, kernel);
cv::morphologyEx(image_active, image_active, cv::MORPH_CLOSE, kernel);
cv::threshold(templ_active, templ_active, 1, 1, cv::THRESH_BINARY);
cv::threshold(image_active, image_active, 1, 1, cv::THRESH_BINARY);
// 把 CCORR 当 count 用，计算 image_active 在 templ_active 形状内的像素数量
@@ -181,10 +185,9 @@ std::vector<Matcher::RawResult> Matcher::preproc_and_match(const cv::Mat& image,
// TODO: 这里 TP+FP 是 image_active 的 count，可以消掉一个 matchtemplate
cv::matchTemplate(image_active, templ_inactive, fp, cv::TM_CCORR);
fp.convertTo(fp, CV_32S);
cv::Mat count_result; // 数色结果为 f1_score
cv::divide(2 * tp, tp + fp + tp_fn, count_result, 1, CV_32F);
// 返回的是数色和模板匹配的点积
cv::multiply(matched, count_result, matched);
cv::Mat count_result;
cv::divide(2 * tp, tp + fp + tp_fn, count_result, 1, CV_32F); // 数色结果为 f1_score
cv::multiply(matched, count_result, matched); // 最终结果是数色和模板匹配的点积
}
results.emplace_back(RawResult { .matched = matched, .templ = templ, .templ_name = templ_name });
}