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Merge branch 'master' of https://github.com/LiamSho/MaaAssistantArknights

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This commit is contained in:
Liam Sho
2022-05-09 00:55:23 +08:00
parent a2d6617a0f 164728eb9c
commit 4e6e3cdd78
8 changed files with 110 additions and 72 deletions
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2
.github/workflows/maa-doc-azure-deploy.yaml vendored
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@@ -6,12 +6,12 @@ on:
- master
paths:
- "docs/*"
workflow_dispatch:
jobs:
build_and_deploy:
runs-on: ubuntu-latest
name: Build and Deploy Job
if: github.repository_owner == 'MaaAssistantArknights'
steps:
- uses: actions/checkout@v2
with:

2
3rdparty/include/penguin-stats-recognize/depot.hpp vendored
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@@ -158,4 +158,4 @@ private:
};
} // namespace penguin
#endif // PENGUIN_DEPOT_HPP_
#endif // PENGUIN_DEPOT_HPP_

9
3rdparty/include/penguin-stats-recognize/recognize.hpp vendored
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@@ -15,6 +15,7 @@ using dict = nlohmann::ordered_json;
namespace penguin
{
enum class StatusFlags
{
NORMAL = 0,
@@ -842,9 +843,11 @@ private:
}
void _get_quantity()
{
cv::Rect quantityrect = cv::Rect(0, static_cast<int>(round(height * _ITEM_QTY_Y_PROP)),
static_cast<int>(round(width * _ITEM_QTY_WIDTH_PROP)),
static_cast<int>(round(height * _ITEM_QTY_HEIGHT_PROP)));
cv::Rect quantityrect =
cv::Rect(0,
static_cast<int>(round(height * _ITEM_QTY_Y_PROP)),
static_cast<int>(round(width * _ITEM_QTY_WIDTH_PROP)),
static_cast<int>(round(height * _ITEM_QTY_HEIGHT_PROP)));
cv::Mat quantityimg = _img(quantityrect);
_quantity.set_img(quantityimg);
_quantity.analyze();

8
3rdparty/include/penguin-stats-recognize/recognizer.hpp vendored
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@@ -18,7 +18,13 @@
#include "depot.hpp"
#include "result.hpp"
static const std::string version = "4.2.3";
#ifdef PENGUIN_RECOGNIZER_RELEASE_VERSION
#define PENGUIN_RECOGNIZER_VERSION_STRING PENGUIN_RECOGNIZER_RELEASE_VERSION
#else
#define PENGUIN_RECOGNIZER_VERSION_STRING "v0.0.0"
#endif
static const std::string version = PENGUIN_RECOGNIZER_VERSION_STRING;
static const std::string opencv_version = CV_VERSION;
cv::Mat decode(std::string JSarrayBuffer)

151
3rdparty/include/penguin-stats-recognize/result.hpp vendored
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@@ -13,6 +13,12 @@
#include "core.hpp"
#include "recognize.hpp"
#if defined(__GLIBCXX__) || defined(__GLIBCPP__)
#define Widget_container std::deque
#else
#define Widget_container std::vector
#endif
using dict = nlohmann::ordered_json;
// extern void show_img(cv::Mat src);
@@ -25,7 +31,7 @@ const double DROP_AREA_X_PROP = 0.21;
const double DROP_AREA_Y_PROP = 0.2;
const double DROP_AREA_HEIGHT_PROP = 0.8;
const double ITEM_DIAMETER_PROP = 0.524;
const double W_H_PROP = 7;
const double W_H_PROP = 6.5;
enum DroptypeFlags
{
@@ -195,7 +201,7 @@ public:
}
private:
size_t _stars = 0;
int _stars = 0;
void _get_is_3stars()
{
auto& self = *this;
@@ -226,7 +232,7 @@ private:
star_rect.width = sp.back().end - star_rect.x;
_img = star_img = star_img(star_rect);
self._relate(star_rect.tl());
_stars = sp.size();
_stars = static_cast<int>(sp.size());
}
};
@@ -323,7 +329,7 @@ private:
const int _CANDIDATES_COUNT = 5;
bool _existance = false;
std::string _difficulty = "NORMAL"; // for temporary workaround
std::vector<Widget_Character> _stage_chrs;
Widget_container<Widget_Character> _stage_chrs;
const std::string _stage_code() const
{
return _stage_code(_candidate_index);
@@ -407,8 +413,8 @@ private:
}
void _get_candidates()
{
auto comp = [](std::vector<Widget_Character> a,
std::vector<Widget_Character> b) {
auto comp = [](Widget_container<Widget_Character> a,
Widget_container<Widget_Character> b) {
int dist_a = 0, dist_b = 0;
for (const auto& chr : a)
{
@@ -421,8 +427,8 @@ private:
return dist_a > dist_b;
};
std::priority_queue<
std::vector<Widget_Character>,
std::vector<std::vector<Widget_Character>>,
Widget_container<Widget_Character>,
std::vector<Widget_container<Widget_Character>>,
decltype(comp)>
q(comp);
auto last_pop = _stage_chrs;
@@ -606,7 +612,7 @@ private:
{
continue;
}
int dist = static_cast<int>(abs(kh - hsv[H]));
int dist = static_cast<int>(round(abs(kh - hsv[H])));
_candidates.emplace_back(vtype, dist);
}
std::sort(_candidates.begin(), _candidates.end(),
@@ -745,11 +751,23 @@ public:
const int items_count() const { return _items_count; }
Widget_Droptype() = default;
Widget_Droptype(const cv::Mat& img, const std::string& label, Widget* const parent_widget = nullptr)
: WidgetWithCandidate(img, label, parent_widget) {}
: WidgetWithCandidate(label, parent_widget)
{
set_img(img);
}
void set_img(const cv::Mat& img)
{
cv::Mat img_bin = img;
cv::cvtColor(img_bin, img_bin, cv::COLOR_BGR2GRAY);
cv::threshold(img_bin, img_bin, 127, 255, cv::THRESH_BINARY);
int bottom = separate(img_bin, DirectionFlags::BOTTOM, 1)[0].end;
Widget::set_img(img(cv::Rect(0, 0, img.cols, bottom)));
}
Widget_Droptype& analyze()
{
if (!_img.empty())
{
_get_items_count();
_get_candidates();
}
if (const auto type = droptype();
@@ -784,7 +802,7 @@ public:
}
private:
int _items_count = static_cast<int>(round(width / (height * W_H_PROP)));
int _items_count = 0;
Widget_DroptypeLine _line {this};
Widget_DroptypeText _text {this};
void _get_candidates()
@@ -803,6 +821,10 @@ private:
_candidates = _text.candidates();
}
}
void _get_items_count()
{
_items_count = static_cast<int>(round(width / (height * W_H_PROP)));
}
};
class Widget_DropArea : public Widget
@@ -821,8 +843,7 @@ public:
: Widget("dropArea", parent_widget) {}
Widget_DropArea(const cv::Mat& img, Widget* const parent_widget = nullptr)
: Widget(img, "dropArea", parent_widget) {}
Widget_DropArea& analyze(const std::string& stage,
const std::string& difficulty)
Widget_DropArea& analyze(const std::string& stage)
{
if (!_img.empty())
{
@@ -843,7 +864,7 @@ public:
widget_label = "droptypes";
push_exception(ERROR, ExcSubtypeFlags::EXC_ILLEGAL);
}
_get_drops(stage, difficulty);
_get_drops(stage);
}
else
{
@@ -882,7 +903,7 @@ public:
private:
dict _drops_data;
std::vector<Drop> _drop_list;
std::vector<Widget_Droptype> _droptype_list;
Widget_container<Widget_Droptype> _droptype_list;
auto _get_separate()
{
cv::Mat img_bin = _img;
@@ -960,8 +981,8 @@ private:
}
void _next_droptype_candidate()
{
auto comp = [](std::vector<Widget_Droptype> a,
std::vector<Widget_Droptype> b) {
auto comp = [](Widget_container<Widget_Droptype> a,
Widget_container<Widget_Droptype> b) {
int dist_a = 0, dist_b = 0;
for (const auto& type : a)
{
@@ -974,8 +995,8 @@ private:
return dist_a > dist_b;
};
std::priority_queue<
std::vector<Widget_Droptype>,
std::vector<std::vector<Widget_Droptype>>,
Widget_container<Widget_Droptype>,
std::vector<Widget_container<Widget_Droptype>>,
decltype(comp)>
q(comp);
auto last_pop = _droptype_list;
@@ -989,7 +1010,7 @@ private:
_droptype_list = q.top();
}
void _get_drops(const std::string& stage, [[maybe_unused]] const std::string& difficulty)
void _get_drops(const std::string& stage)
{
if (_status == StatusFlags::HAS_ERROR || _status == StatusFlags::ERROR)
{
@@ -1009,10 +1030,10 @@ private:
{
continue;
}
else if (std::string label =
"drops." + std::to_string(_drop_list.size());
type == DroptypeFlags::FURNITURE)
else if (type == DroptypeFlags::FURNITURE)
{
std::string label =
"drops." + std::to_string(_drop_list.size());
_drop_list.emplace_back(
Drop(Widget_Item(FURNI_1, label, this), type));
}
@@ -1028,13 +1049,13 @@ private:
int length = (droptype.width) / items_count;
for (int i = 0; i < items_count; i++)
{
std::string cur_label =
std::string label =
"drops." + std::to_string(_drop_list.size());
auto range =
cv::Range(droptype.x - x + length * i,
droptype.x - x + length * (i + 1));
auto dropimg = _img(cv::Range(0, droptype.y - y), range);
Widget_Item drop {dropimg, item_diameter, cur_label, this};
Widget_Item drop {dropimg, item_diameter, label, this};
drop.analyze(templs);
_drop_list.emplace_back(drop, type);
_drops_data.push_back({{"dropType", Droptype2Str[type]},
@@ -1210,10 +1231,10 @@ private:
}
const auto& bv = _baseline_v;
auto drop_area_img = _img(
cv::Range(static_cast<int>(bv.y + bv.height * DROP_AREA_Y_PROP), bv.y + bv.height),
cv::Range(static_cast<int>(bv.x + bv.height * DROP_AREA_X_PROP), width));
cv::Range(bv.y + static_cast<int>(bv.height * DROP_AREA_Y_PROP), bv.y + bv.height),
cv::Range(bv.x + static_cast<int>(bv.height * DROP_AREA_X_PROP), width));
_drop_area.set_img(drop_area_img);
_drop_area.analyze(_stage.stage_code(), "NORMAL");
_drop_area.analyze(_stage.stage_code());
}
};
@@ -1279,11 +1300,10 @@ private:
{
return;
}
cv::Mat img_bin = _img(cv::Rect(
0,
static_cast<int>(0.2 * height),
static_cast<int>(0.2 * width),
static_cast<int>(0.4 * height)));
cv::Mat img_bin = _img(cv::Rect(0,
static_cast<int>(0.2 * height),
static_cast<int>(0.2 * width),
static_cast<int>(0.4 * height)));
cv::cvtColor(img_bin, img_bin, cv::COLOR_BGR2GRAY);
cv::threshold(img_bin, img_bin, 120, 255, cv::THRESH_BINARY);
@@ -1303,8 +1323,9 @@ private:
abs(img_temp.cols - last_height) <= 1 &&
abs(first_height - last_height) <= 1)
{
baseline_v_rect = cv::Rect(range.start, sp2.front().start + static_cast<int>(0.2 * height),
img_temp.cols, sp2.back().end - sp2.front().start);
baseline_v_rect =
cv::Rect(range.start, sp2.front().start + static_cast<int>(0.2 * height),
img_temp.cols, sp2.back().end - sp2.front().start);
break;
}
}
@@ -1330,11 +1351,10 @@ private:
: x(x_), y(y_), width(width_), height(height_), area(area_) {}
};
cv::Mat img_bin = _img(cv::Rect(
0,
static_cast<int>(0.2 * height),
static_cast<int>(0.2 * width),
static_cast<int>(0.4 * height)));
cv::Mat img_bin = _img(cv::Rect(0,
static_cast<int>(0.2 * height),
static_cast<int>(0.2 * width),
static_cast<int>(0.4 * height)));
cv::cvtColor(img_bin, img_bin, cv::COLOR_BGR2GRAY);
cv::threshold(img_bin, img_bin, 120, 255, cv::THRESH_BINARY);
cv::Mat _;
@@ -1375,10 +1395,11 @@ private:
{
if (ccomps[0].x == ccomps[1].x)
{
baseline_v_rect = cv::Rect(ccomps[0].x,
ccomps[0].y + static_cast<int>(0.2 * height),
ccomps[0].width,
ccomps[1].y - ccomps[0].y + ccomps[1].height);
baseline_v_rect =
cv::Rect(ccomps[0].x,
ccomps[0].y + static_cast<int>(0.2 * height),
ccomps[0].width,
ccomps[1].y - ccomps[0].y + ccomps[1].height);
}
}
else if (const auto ccsize = ccomps.size();
@@ -1387,10 +1408,11 @@ private:
for (int i = 0; i < ccsize - 1; ++i)
{
if (ccomps[i].x == ccomps[i + 1].x)
baseline_v_rect = cv::Rect(ccomps[i].x,
ccomps[i].y + static_cast<int>(0.2 * height),
ccomps[i].width,
ccomps[i + 1].y - ccomps[i].y + ccomps[i + 1].height);
baseline_v_rect =
cv::Rect(ccomps[i].x,
ccomps[i].y + static_cast<int>(0.2 * height),
ccomps[i].width,
ccomps[i + 1].y - ccomps[i].y + ccomps[i + 1].height);
}
}
@@ -1414,8 +1436,9 @@ private:
}
const auto& bv = _baseline_v;
int left_margin = bv.x + bv.width;
auto result_img = _img(cv::Range(bv.y, bv.y + bv.height / 2),
cv::Range(left_margin, static_cast<int>(left_margin + 1.5 * bv.height)));
auto result_img =
_img(cv::Range(bv.y, bv.y + bv.height / 2),
cv::Range(left_margin, left_margin + static_cast<int>(1.5 * bv.height)));
cv::Mat img_bin;
cv::cvtColor(result_img, img_bin, cv::COLOR_BGR2GRAY);
cv::threshold(img_bin, img_bin, 200, 255, cv::THRESH_BINARY);
@@ -1432,8 +1455,9 @@ private:
}
const auto& bv = _baseline_v;
int left_margin = bv.x + bv.width;
auto star_img = _img(cv::Range(bv.y + bv.height / 2, bv.y + bv.height),
cv::Range(left_margin, static_cast<int>(left_margin + 1.2 * bv.height)));
auto star_img =
_img(cv::Range(bv.y + bv.height / 2, bv.y + bv.height),
cv::Range(left_margin, left_margin + static_cast<int>(1.2 * bv.height)));
cv::Mat img_bin;
cv::cvtColor(star_img, img_bin, cv::COLOR_BGR2GRAY);
cv::threshold(img_bin, img_bin, 127, 255, cv::THRESH_BINARY);
@@ -1450,9 +1474,10 @@ private:
}
const auto& bv = _baseline_v;
int left_margin = bv.x + bv.width;
auto stage_img = _img(cv::Range(0, bv.y),
cv::Range(static_cast<int>(left_margin + 0.43 * bv.height),
static_cast<int>(left_margin + 1.5 * bv.height)));
auto stage_img =
_img(cv::Range(0, bv.y),
cv::Range(left_margin + static_cast<int>(0.43 * bv.height),
left_margin + static_cast<int>(1.5 * bv.height)));
cv::Mat img_bin;
cv::cvtColor(stage_img, img_bin, cv::COLOR_BGR2GRAY);
cv::threshold(img_bin, img_bin, 200, 255, cv::THRESH_BINARY);
@@ -1469,8 +1494,9 @@ private:
}
const auto& bv = _baseline_v;
int left_margin = bv.x + bv.width;
auto diff_img = _img(cv::Range(0, bv.y),
cv::Range(left_margin, static_cast<int>(left_margin + 0.43 * bv.height)));
auto diff_img =
_img(cv::Range(0, bv.y),
cv::Range(left_margin, left_margin + static_cast<int>(0.43 * bv.height)));
cv::Mat img_bin;
cv::cvtColor(diff_img, img_bin, cv::COLOR_BGR2GRAY);
cv::threshold(img_bin, img_bin, 64, 255, cv::THRESH_BINARY);
@@ -1486,19 +1512,22 @@ private:
return;
}
const auto& bv = _baseline_v;
cv::Mat img_bin = _img(cv::Range(bv.y + bv.height, height),
cv::Range(bv.x + bv.width, static_cast<int>(0.2 * width)));
cv::Mat img_bin =
_img(cv::Range(bv.y + bv.height, height),
cv::Range(bv.x + bv.width, static_cast<int>(0.2 * width)));
cv::cvtColor(img_bin, img_bin, cv::COLOR_BGR2GRAY);
cv::threshold(img_bin, img_bin, 127, 255, cv::THRESH_BINARY);
auto sp = separate(img_bin, DirectionFlags::TOP, 2);
int top_margin = bv.y + bv.height + sp[1].start;
auto drop_area_img = _img(
cv::Range(top_margin + static_cast<int>(bv.height * DROP_AREA_Y_PROP), top_margin + bv.height),
cv::Range(top_margin + static_cast<int>(bv.height * DROP_AREA_Y_PROP),
top_margin + bv.height),
cv::Range(bv.x + bv.width, width));
_drop_area.set_img(drop_area_img);
_drop_area.analyze(_stage.stage_code(), _difficutly.difficulty());
_drop_area.analyze(_stage.stage_code());
}
};
} // namespace penguin
#endif // PENGUIN_RESULT_HPP_

2
docs/Linux编译教程.md
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@@ -29,7 +29,7 @@ cmake ../ -DPADDLE_LIB=/your_path/paddle_inference/ -DOpenCV_DIR=/your_path_to_o
### zlib
Ubuntu 下:
Ubuntu 下:
```bash
sudo apt update && sudo apt install zlib1g-dev

6
resource/tasks.json
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@@ -10,7 +10,7 @@
"algorithm": "OcrDetect",
"action": "clickSelf",
"text": [
"活动已开放"
"已开放"
],
"roi": [
40,
@@ -69,7 +69,7 @@
"algorithm": "OcrDetect",
"action": "clickSelf",
"text": [
"活动已开放"
"已开放"
],
"roi": [
40,
@@ -128,7 +128,7 @@
"algorithm": "OcrDetect",
"action": "clickSelf",
"text": [
"活动已开放"
"已开放"
],
"roi": [
40,

2
src/MeoAssistant/Version.h
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@@ -2,5 +2,5 @@
namespace asst
{
constexpr static const char* Version = "v3.3.1";
constexpr static const char* Version = "v3.3.2";
}