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Merge remote-tracking branch 'upstream/dygraph' into dy3

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deploy/android_demo/app/src/main/cpp/ocr_clipper.hpp deleted 100644 → 0
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/*******************************************************************************
* *
* Author : Angus Johnson *
* Version : 6.4.2 *
* Date : 27 February 2017 *
* Website : http://www.angusj.com *
* Copyright : Angus Johnson 2010-2017 *
* *
* License: *
* Use, modification & distribution is subject to Boost Software License Ver 1. *
* http://www.boost.org/LICENSE_1_0.txt *
* *
* Attributions: *
* The code in this library is an extension of Bala Vatti's clipping algorithm: *
* "A generic solution to polygon clipping" *
* Communications of the ACM, Vol 35, Issue 7 (July 1992) pp 56-63. *
* http://portal.acm.org/citation.cfm?id=129906 *
* *
* Computer graphics and geometric modeling: implementation and algorithms *
* By Max K. Agoston *
* Springer; 1 edition (January 4, 2005) *
* http://books.google.com/books?q=vatti+clipping+agoston *
* *
* See also: *
* "Polygon Offsetting by Computing Winding Numbers" *
* Paper no. DETC2005-85513 pp. 565-575 *
* ASME 2005 International Design Engineering Technical Conferences *
* and Computers and Information in Engineering Conference (IDETC/CIE2005) *
* September 24-28, 2005 , Long Beach, California, USA *
* http://www.me.berkeley.edu/~mcmains/pubs/DAC05OffsetPolygon.pdf *
* *
*******************************************************************************/
#ifndef clipper_hpp
#define clipper_hpp
#define CLIPPER_VERSION "6.4.2"
//use_int32: When enabled 32bit ints are used instead of 64bit ints. This
//improve performance but coordinate values are limited to the range +/- 46340
//#define use_int32
//use_xyz: adds a Z member to IntPoint. Adds a minor cost to perfomance.
//#define use_xyz
//use_lines: Enables line clipping. Adds a very minor cost to performance.
#define use_lines
//use_deprecated: Enables temporary support for the obsolete functions
//#define use_deprecated
#include <vector>
#include <list>
#include <set>
#include <stdexcept>
#include <cstring>
#include <cstdlib>
#include <ostream>
#include <functional>
#include <queue>
namespace ClipperLib {
enum ClipType {
ctIntersection, ctUnion, ctDifference, ctXor
};
enum PolyType {
ptSubject, ptClip
};
//By far the most widely used winding rules for polygon filling are
//EvenOdd & NonZero (GDI, GDI+, XLib, OpenGL, Cairo, AGG, Quartz, SVG, Gr32)
//Others rules include Positive, Negative and ABS_GTR_EQ_TWO (only in OpenGL)
//see http://glprogramming.com/red/chapter11.html
enum PolyFillType {
pftEvenOdd, pftNonZero, pftPositive, pftNegative
};
#ifdef use_int32
typedef int cInt;
static cInt const loRange = 0x7FFF;
static cInt const hiRange = 0x7FFF;
#else
typedef signed long long cInt;
static cInt const loRange = 0x3FFFFFFF;
static cInt const hiRange = 0x3FFFFFFFFFFFFFFFLL;
typedef signed long long long64; //used by Int128 class
typedef unsigned long long ulong64;
#endif
struct IntPoint {
cInt X;
cInt Y;
#ifdef use_xyz
cInt Z;
IntPoint(cInt x = 0, cInt y = 0, cInt z = 0): X(x), Y(y), Z(z) {};
#else
IntPoint(cInt x = 0, cInt y = 0) : X(x), Y(y) {};
#endif
friend inline bool operator==(const IntPoint &a, const IntPoint &b) {
return a.X == b.X && a.Y == b.Y;
}
friend inline bool operator!=(const IntPoint &a, const IntPoint &b) {
return a.X != b.X || a.Y != b.Y;
}
};
//------------------------------------------------------------------------------
typedef std::vector <IntPoint> Path;
typedef std::vector <Path> Paths;
inline Path &operator<<(Path &poly, const IntPoint &p) {
poly.push_back(p);
return poly;
}
inline Paths &operator<<(Paths &polys, const Path &p) {
polys.push_back(p);
return polys;
}
std::ostream &operator<<(std::ostream &s, const IntPoint &p);
std::ostream &operator<<(std::ostream &s, const Path &p);
std::ostream &operator<<(std::ostream &s, const Paths &p);
struct DoublePoint {
double X;
double Y;
DoublePoint(double x = 0, double y = 0) : X(x), Y(y) {}
DoublePoint(IntPoint ip) : X((double) ip.X), Y((double) ip.Y) {}
};
//------------------------------------------------------------------------------
#ifdef use_xyz
typedef void (*ZFillCallback)(IntPoint& e1bot, IntPoint& e1top, IntPoint& e2bot, IntPoint& e2top, IntPoint& pt);
#endif
enum InitOptions {
ioReverseSolution = 1, ioStrictlySimple = 2, ioPreserveCollinear = 4
};
enum JoinType {
jtSquare, jtRound, jtMiter
};
enum EndType {
etClosedPolygon, etClosedLine, etOpenButt, etOpenSquare, etOpenRound
};
class PolyNode;
typedef std::vector<PolyNode *> PolyNodes;
class PolyNode {
public:
PolyNode();
virtual ~PolyNode() {};
Path Contour;
PolyNodes Childs;
PolyNode *Parent;
PolyNode *GetNext() const;
bool IsHole() const;
bool IsOpen() const;
int ChildCount() const;
private:
//PolyNode& operator =(PolyNode& other);
unsigned Index; //node index in Parent.Childs
bool m_IsOpen;
JoinType m_jointype;
EndType m_endtype;
PolyNode *GetNextSiblingUp() const;
void AddChild(PolyNode &child);
friend class Clipper; //to access Index
friend class ClipperOffset;
};
class PolyTree : public PolyNode {
public:
~PolyTree() { Clear(); };
PolyNode *GetFirst() const;
void Clear();
int Total() const;
private:
//PolyTree& operator =(PolyTree& other);
PolyNodes AllNodes;
friend class Clipper; //to access AllNodes
};
bool Orientation(const Path &poly);
double Area(const Path &poly);
int PointInPolygon(const IntPoint &pt, const Path &path);
void SimplifyPolygon(const Path &in_poly, Paths &out_polys, PolyFillType fillType = pftEvenOdd);
void SimplifyPolygons(const Paths &in_polys, Paths &out_polys, PolyFillType fillType = pftEvenOdd);
void SimplifyPolygons(Paths &polys, PolyFillType fillType = pftEvenOdd);
void CleanPolygon(const Path &in_poly, Path &out_poly, double distance = 1.415);
void CleanPolygon(Path &poly, double distance = 1.415);
void CleanPolygons(const Paths &in_polys, Paths &out_polys, double distance = 1.415);
void CleanPolygons(Paths &polys, double distance = 1.415);
void MinkowskiSum(const Path &pattern, const Path &path, Paths &solution, bool pathIsClosed);
void MinkowskiSum(const Path &pattern, const Paths &paths, Paths &solution, bool pathIsClosed);
void MinkowskiDiff(const Path &poly1, const Path &poly2, Paths &solution);
void PolyTreeToPaths(const PolyTree &polytree, Paths &paths);
void ClosedPathsFromPolyTree(const PolyTree &polytree, Paths &paths);
void OpenPathsFromPolyTree(PolyTree &polytree, Paths &paths);
void ReversePath(Path &p);
void ReversePaths(Paths &p);
struct IntRect {
cInt left;
cInt top;
cInt right;
cInt bottom;
};
//enums that are used internally ...
enum EdgeSide {
esLeft = 1, esRight = 2
};
//forward declarations (for stuff used internally) ...
struct TEdge;
struct IntersectNode;
struct LocalMinimum;
struct OutPt;
struct OutRec;
struct Join;
typedef std::vector<OutRec *> PolyOutList;
typedef std::vector<TEdge *> EdgeList;
typedef std::vector<Join *> JoinList;
typedef std::vector<IntersectNode *> IntersectList;
//------------------------------------------------------------------------------
//ClipperBase is the ancestor to the Clipper class. It should not be
//instantiated directly. This class simply abstracts the conversion of sets of
//polygon coordinates into edge objects that are stored in a LocalMinima list.
class ClipperBase {
public:
ClipperBase();
virtual ~ClipperBase();
virtual bool AddPath(const Path &pg, PolyType PolyTyp, bool Closed);
bool AddPaths(const Paths &ppg, PolyType PolyTyp, bool Closed);
virtual void Clear();
IntRect GetBounds();
bool PreserveCollinear() { return m_PreserveCollinear; };
void PreserveCollinear(bool value) { m_PreserveCollinear = value; };
protected:
void DisposeLocalMinimaList();
TEdge *AddBoundsToLML(TEdge *e, bool IsClosed);
virtual void Reset();
TEdge *ProcessBound(TEdge *E, bool IsClockwise);
void InsertScanbeam(const cInt Y);
bool PopScanbeam(cInt &Y);
bool LocalMinimaPending();
bool PopLocalMinima(cInt Y, const LocalMinimum *&locMin);
OutRec *CreateOutRec();
void DisposeAllOutRecs();
void DisposeOutRec(PolyOutList::size_type index);
void SwapPositionsInAEL(TEdge *edge1, TEdge *edge2);
void DeleteFromAEL(TEdge *e);
void UpdateEdgeIntoAEL(TEdge *&e);
typedef std::vector <LocalMinimum> MinimaList;
MinimaList::iterator m_CurrentLM;
MinimaList m_MinimaList;
bool m_UseFullRange;
EdgeList m_edges;
bool m_PreserveCollinear;
bool m_HasOpenPaths;
PolyOutList m_PolyOuts;
TEdge *m_ActiveEdges;
typedef std::priority_queue <cInt> ScanbeamList;
ScanbeamList m_Scanbeam;
};
//------------------------------------------------------------------------------
class Clipper : public virtual ClipperBase {
public:
Clipper(int initOptions = 0);
bool Execute(ClipType clipType,
Paths &solution,
PolyFillType fillType = pftEvenOdd);
bool Execute(ClipType clipType,
Paths &solution,
PolyFillType subjFillType,
PolyFillType clipFillType);
bool Execute(ClipType clipType,
PolyTree &polytree,
PolyFillType fillType = pftEvenOdd);
bool Execute(ClipType clipType,
PolyTree &polytree,
PolyFillType subjFillType,
PolyFillType clipFillType);
bool ReverseSolution() { return m_ReverseOutput; };
void ReverseSolution(bool value) { m_ReverseOutput = value; };
bool StrictlySimple() { return m_StrictSimple; };
void StrictlySimple(bool value) { m_StrictSimple = value; };
//set the callback function for z value filling on intersections (otherwise Z is 0)
#ifdef use_xyz
void ZFillFunction(ZFillCallback zFillFunc);
#endif
protected:
virtual bool ExecuteInternal();
private:
JoinList m_Joins;
JoinList m_GhostJoins;
IntersectList m_IntersectList;
ClipType m_ClipType;
typedef std::list <cInt> MaximaList;
MaximaList m_Maxima;
TEdge *m_SortedEdges;
bool m_ExecuteLocked;
PolyFillType m_ClipFillType;
PolyFillType m_SubjFillType;
bool m_ReverseOutput;
bool m_UsingPolyTree;
bool m_StrictSimple;
#ifdef use_xyz
ZFillCallback m_ZFill; //custom callback
#endif
void SetWindingCount(TEdge &edge);
bool IsEvenOddFillType(const TEdge &edge) const;
bool IsEvenOddAltFillType(const TEdge &edge) const;
void InsertLocalMinimaIntoAEL(const cInt botY);
void InsertEdgeIntoAEL(TEdge *edge, TEdge *startEdge);
void AddEdgeToSEL(TEdge *edge);
bool PopEdgeFromSEL(TEdge *&edge);
void CopyAELToSEL();
void DeleteFromSEL(TEdge *e);
void SwapPositionsInSEL(TEdge *edge1, TEdge *edge2);
bool IsContributing(const TEdge &edge) const;
bool IsTopHorz(const cInt XPos);
void DoMaxima(TEdge *e);
void ProcessHorizontals();
void ProcessHorizontal(TEdge *horzEdge);
void AddLocalMaxPoly(TEdge *e1, TEdge *e2, const IntPoint &pt);
OutPt *AddLocalMinPoly(TEdge *e1, TEdge *e2, const IntPoint &pt);
OutRec *GetOutRec(int idx);
void AppendPolygon(TEdge *e1, TEdge *e2);
void IntersectEdges(TEdge *e1, TEdge *e2, IntPoint &pt);
OutPt *AddOutPt(TEdge *e, const IntPoint &pt);
OutPt *GetLastOutPt(TEdge *e);
bool ProcessIntersections(const cInt topY);
void BuildIntersectList(const cInt topY);
void ProcessIntersectList();
void ProcessEdgesAtTopOfScanbeam(const cInt topY);
void BuildResult(Paths &polys);
void BuildResult2(PolyTree &polytree);
void SetHoleState(TEdge *e, OutRec *outrec);
void DisposeIntersectNodes();
bool FixupIntersectionOrder();
void FixupOutPolygon(OutRec &outrec);
void FixupOutPolyline(OutRec &outrec);
bool IsHole(TEdge *e);
bool FindOwnerFromSplitRecs(OutRec &outRec, OutRec *&currOrfl);
void FixHoleLinkage(OutRec &outrec);
void AddJoin(OutPt *op1, OutPt *op2, const IntPoint offPt);
void ClearJoins();
void ClearGhostJoins();
void AddGhostJoin(OutPt *op, const IntPoint offPt);
bool JoinPoints(Join *j, OutRec *outRec1, OutRec *outRec2);
void JoinCommonEdges();
void DoSimplePolygons();
void FixupFirstLefts1(OutRec *OldOutRec, OutRec *NewOutRec);
void FixupFirstLefts2(OutRec *InnerOutRec, OutRec *OuterOutRec);
void FixupFirstLefts3(OutRec *OldOutRec, OutRec *NewOutRec);
#ifdef use_xyz
void SetZ(IntPoint& pt, TEdge& e1, TEdge& e2);
#endif
};
//------------------------------------------------------------------------------
class ClipperOffset {
public:
ClipperOffset(double miterLimit = 2.0, double roundPrecision = 0.25);
~ClipperOffset();
void AddPath(const Path &path, JoinType joinType, EndType endType);
void AddPaths(const Paths &paths, JoinType joinType, EndType endType);
void Execute(Paths &solution, double delta);
void Execute(PolyTree &solution, double delta);
void Clear();
double MiterLimit;
double ArcTolerance;
private:
Paths m_destPolys;
Path m_srcPoly;
Path m_destPoly;
std::vector <DoublePoint> m_normals;
double m_delta, m_sinA, m_sin, m_cos;
double m_miterLim, m_StepsPerRad;
IntPoint m_lowest;
PolyNode m_polyNodes;
void FixOrientations();
void DoOffset(double delta);
void OffsetPoint(int j, int &k, JoinType jointype);
void DoSquare(int j, int k);
void DoMiter(int j, int k, double r);
void DoRound(int j, int k);
};
//------------------------------------------------------------------------------
class clipperException : public std::exception {
public:
clipperException(const char *description) : m_descr(description) {}
virtual ~clipperException() throw() {}
virtual const char *what() const throw() { return m_descr.c_str(); }
private:
std::string m_descr;
};
//------------------------------------------------------------------------------
} //ClipperLib namespace
#endif //clipper_hpp
deploy/android_demo/app/src/main/cpp/ocr_crnn_process.cpp deleted 100644 → 0
View file @ 47752ddf
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "ocr_crnn_process.h"
#include <iostream>
#include <vector>
#include <iostream>
#include <cstring>
#include <fstream>
#include <cmath>
const std::string CHARACTER_TYPE = "ch";
const int MAX_DICT_LENGTH = 6624;
const std::vector<int> REC_IMAGE_SHAPE = {3, 32, 320};
static cv::Mat crnn_resize_norm_img(cv::Mat img, float wh_ratio) {
int imgC = REC_IMAGE_SHAPE[0];
int imgW = REC_IMAGE_SHAPE[2];
int imgH = REC_IMAGE_SHAPE[1];
if (CHARACTER_TYPE == "ch")
imgW = int(32 * wh_ratio);
float ratio = float(img.cols) / float(img.rows);
int resize_w = 0;
if (ceilf(imgH * ratio) > imgW)
resize_w = imgW;
else
resize_w = int(ceilf(imgH * ratio));
cv::Mat resize_img;
cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f, cv::INTER_CUBIC);
resize_img.convertTo(resize_img, CV_32FC3, 1 / 255.f);
for (int h = 0; h < resize_img.rows; h++) {
for (int w = 0; w < resize_img.cols; w++) {
resize_img.at<cv::Vec3f>(h, w)[0] = (resize_img.at<cv::Vec3f>(h, w)[0] - 0.5) * 2;
resize_img.at<cv::Vec3f>(h, w)[1] = (resize_img.at<cv::Vec3f>(h, w)[1] - 0.5) * 2;
resize_img.at<cv::Vec3f>(h, w)[2] = (resize_img.at<cv::Vec3f>(h, w)[2] - 0.5) * 2;
}
}
cv::Mat dist;
cv::copyMakeBorder(resize_img, dist, 0, 0, 0, int(imgW - resize_w), cv::BORDER_CONSTANT,
{0, 0, 0});
return dist;
}
cv::Mat crnn_resize_img(const cv::Mat &img, float wh_ratio) {
int imgC = REC_IMAGE_SHAPE[0];
int imgW = REC_IMAGE_SHAPE[2];
int imgH = REC_IMAGE_SHAPE[1];
if (CHARACTER_TYPE == "ch") {
imgW = int(32 * wh_ratio);
}
float ratio = float(img.cols) / float(img.rows);
int resize_w = 0;
if (ceilf(imgH * ratio) > imgW)
resize_w = imgW;
else
resize_w = int(ceilf(imgH * ratio));
cv::Mat resize_img;
cv::resize(img, resize_img, cv::Size(resize_w, imgH));
return resize_img;
}
cv::Mat get_rotate_crop_image(const cv::Mat &srcimage, const std::vector<std::vector<int>> &box) {
std::vector<std::vector<int>> points = box;
int x_collect[4] = {box[0][0], box[1][0], box[2][0], box[3][0]};
int y_collect[4] = {box[0][1], box[1][1], box[2][1], box[3][1]};
int left = int(*std::min_element(x_collect, x_collect + 4));
int right = int(*std::max_element(x_collect, x_collect + 4));
int top = int(*std::min_element(y_collect, y_collect + 4));
int bottom = int(*std::max_element(y_collect, y_collect + 4));
cv::Mat img_crop;
srcimage(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop);
for (int i = 0; i < points.size(); i++) {
points[i][0] -= left;
points[i][1] -= top;
}
int img_crop_width = int(sqrt(pow(points[0][0] - points[1][0], 2) +
pow(points[0][1] - points[1][1], 2)));
int img_crop_height = int(sqrt(pow(points[0][0] - points[3][0], 2) +
pow(points[0][1] - points[3][1], 2)));
cv::Point2f pts_std[4];
pts_std[0] = cv::Point2f(0., 0.);
pts_std[1] = cv::Point2f(img_crop_width, 0.);
pts_std[2] = cv::Point2f(img_crop_width, img_crop_height);
pts_std[3] = cv::Point2f(0.f, img_crop_height);
cv::Point2f pointsf[4];
pointsf[0] = cv::Point2f(points[0][0], points[0][1]);
pointsf[1] = cv::Point2f(points[1][0], points[1][1]);
pointsf[2] = cv::Point2f(points[2][0], points[2][1]);
pointsf[3] = cv::Point2f(points[3][0], points[3][1]);
cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std);
cv::Mat dst_img;
cv::warpPerspective(img_crop, dst_img, M, cv::Size(img_crop_width, img_crop_height),
cv::BORDER_REPLICATE);
if (float(dst_img.rows) >= float(dst_img.cols) * 1.5) {
/*
cv::Mat srcCopy = cv::Mat(dst_img.rows, dst_img.cols, dst_img.depth());
cv::transpose(dst_img, srcCopy);
cv::flip(srcCopy, srcCopy, 0);
return srcCopy;
*/
cv::transpose(dst_img, dst_img);
cv::flip(dst_img, dst_img, 0);
return dst_img;
} else {
return dst_img;
}
}
deploy/android_demo/app/src/main/cpp/ocr_crnn_process.h deleted 100644 → 0
View file @ 47752ddf
//
// Created by fujiayi on 2020/7/3.
//
#pragma once
#include <vector>
#include <opencv2/opencv.hpp>
#include "common.h"
extern const std::vector<int> REC_IMAGE_SHAPE;
cv::Mat get_rotate_crop_image(const cv::Mat &srcimage, const std::vector<std::vector<int>> &box);
cv::Mat crnn_resize_img(const cv::Mat &img, float wh_ratio);
template<class ForwardIterator>
inline size_t argmax(ForwardIterator first, ForwardIterator last) {
return std::distance(first, std::max_element(first, last));
}
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/ocr_db_post_process.cpp deleted 100644 → 0
View file @ 47752ddf
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <iostream>
#include <vector>
#include <math.h>
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include "ocr_clipper.hpp"
static void getcontourarea(float **box, float unclip_ratio, float &distance) {
int pts_num = 4;
float area = 0.0f;
float dist = 0.0f;
for (int i = 0; i < pts_num; i++) {
area += box[i][0] * box[(i + 1) % pts_num][1] - box[i][1] * box[(i + 1) % pts_num][0];
dist += sqrtf(
(box[i][0] - box[(i + 1) % pts_num][0]) * (box[i][0] - box[(i + 1) % pts_num][0]) +
(box[i][1] - box[(i + 1) % pts_num][1]) * (box[i][1] - box[(i + 1) % pts_num][1]));
}
area = fabs(float(area / 2.0));
distance = area * unclip_ratio / dist;
}
static cv::RotatedRect unclip(float **box) {
float unclip_ratio = 2.0;
float distance = 1.0;
getcontourarea(box, unclip_ratio, distance);
ClipperLib::ClipperOffset offset;
ClipperLib::Path p;
p << ClipperLib::IntPoint(int(box[0][0]), int(box[0][1]))
<< ClipperLib::IntPoint(int(box[1][0]), int(box[1][1])) <<
ClipperLib::IntPoint(int(box[2][0]), int(box[2][1]))
<< ClipperLib::IntPoint(int(box[3][0]), int(box[3][1]));
offset.AddPath(p, ClipperLib::jtRound, ClipperLib::etClosedPolygon);
ClipperLib::Paths soln;
offset.Execute(soln, distance);
std::vector<cv::Point2f> points;
for (int j = 0; j < soln.size(); j++) {
for (int i = 0; i < soln[soln.size() - 1].size(); i++) {
points.emplace_back(soln[j][i].X, soln[j][i].Y);
}
}
cv::RotatedRect res = cv::minAreaRect(points);
return res;
}
static float **Mat2Vec(cv::Mat mat) {
auto **array = new float *[mat.rows];
for (int i = 0; i < mat.rows; ++i){
array[i] = new float[mat.cols];
}
for (int i = 0; i < mat.rows; ++i) {
for (int j = 0; j < mat.cols; ++j) {
array[i][j] = mat.at<float>(i, j);
}
}
return array;
}
static void quickSort(float **s, int l, int r) {
if (l < r) {
int i = l, j = r;
float x = s[l][0];
float *xp = s[l];
while (i < j) {
while (i < j && s[j][0] >= x){
j--;
}
if (i < j){
std::swap(s[i++], s[j]);
}
while (i < j && s[i][0] < x){
i++;
}
if (i < j){
std::swap(s[j--], s[i]);
}
}
s[i] = xp;
quickSort(s, l, i - 1);
quickSort(s, i + 1, r);
}
}
static void quickSort_vector(std::vector<std::vector<int>> &box, int l, int r, int axis) {
if (l < r) {
int i = l, j = r;
int x = box[l][axis];
std::vector<int> xp(box[l]);
while (i < j) {
while (i < j && box[j][axis] >= x){
j--;
}
if (i < j){
std::swap(box[i++], box[j]);
}
while (i < j && box[i][axis] < x){
i++;
}
if (i < j){
std::swap(box[j--], box[i]);
}
}
box[i] = xp;
quickSort_vector(box, l, i - 1, axis);
quickSort_vector(box, i + 1, r, axis);
}
}
static std::vector<std::vector<int>> order_points_clockwise(std::vector<std::vector<int>> pts) {
std::vector<std::vector<int>> box = pts;
quickSort_vector(box, 0, int(box.size() - 1), 0);
std::vector<std::vector<int>> leftmost = {box[0], box[1]};
std::vector<std::vector<int>> rightmost = {box[2], box[3]};
if (leftmost[0][1] > leftmost[1][1]){
std::swap(leftmost[0], leftmost[1]);
}
if (rightmost[0][1] > rightmost[1][1]){
std::swap(rightmost[0], rightmost[1]);
}
std::vector<std::vector<int>> rect = {leftmost[0], rightmost[0], rightmost[1], leftmost[1]};
return rect;
}
static float **get_mini_boxes(cv::RotatedRect box, float &ssid) {
ssid = box.size.width >= box.size.height ? box.size.height : box.size.width;
cv::Mat points;
cv::boxPoints(box, points);
// sorted box points
auto array = Mat2Vec(points);
quickSort(array, 0, 3);
float *idx1 = array[0], *idx2 = array[1], *idx3 = array[2], *idx4 = array[3];
if (array[3][1] <= array[2][1]) {
idx2 = array[3];
idx3 = array[2];
} else {
idx2 = array[2];
idx3 = array[3];
}
if (array[1][1] <= array[0][1]) {
idx1 = array[1];
idx4 = array[0];
} else {
idx1 = array[0];
idx4 = array[1];
}
array[0] = idx1;
array[1] = idx2;
array[2] = idx3;
array[3] = idx4;
return array;
}
template <class T> T clamp(T x, T min, T max) {
if (x > max){
return max;
}
if (x < min){
return min;
}
return x;
}
static float clampf(float x, float min, float max) {
if (x > max)
return max;
if (x < min)
return min;
return x;
}
float box_score_fast(float **box_array, cv::Mat pred) {
auto array = box_array;
int width = pred.cols;
int height = pred.rows;
float box_x[4] = {array[0][0], array[1][0], array[2][0], array[3][0]};
float box_y[4] = {array[0][1], array[1][1], array[2][1], array[3][1]};
int xmin = clamp(int(std::floorf(*(std::min_element(box_x, box_x + 4)))), 0, width - 1);
int xmax = clamp(int(std::ceilf(*(std::max_element(box_x, box_x + 4)))), 0, width - 1);
int ymin = clamp(int(std::floorf(*(std::min_element(box_y, box_y + 4)))), 0, height - 1);
int ymax = clamp(int(std::ceilf(*(std::max_element(box_y, box_y + 4)))), 0, height - 1);
cv::Mat mask;
mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);
cv::Point root_point[4];
root_point[0] = cv::Point(int(array[0][0]) - xmin, int(array[0][1]) - ymin);
root_point[1] = cv::Point(int(array[1][0]) - xmin, int(array[1][1]) - ymin);
root_point[2] = cv::Point(int(array[2][0]) - xmin, int(array[2][1]) - ymin);
root_point[3] = cv::Point(int(array[3][0]) - xmin, int(array[3][1]) - ymin);
const cv::Point *ppt[1] = {root_point};
int npt[] = {4};
cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));
cv::Mat croppedImg;
pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1)).copyTo(croppedImg);
auto score = cv::mean(croppedImg, mask)[0];
return score;
}
std::vector<std::vector<std::vector<int>>>
boxes_from_bitmap(const cv::Mat& pred, const cv::Mat& bitmap) {
const int min_size = 3;
const int max_candidates = 1000;
const float box_thresh = 0.5;
int width = bitmap.cols;
int height = bitmap.rows;
std::vector<std::vector<cv::Point>> contours;
std::vector<cv::Vec4i> hierarchy;
cv::findContours(bitmap, contours, hierarchy, cv::RETR_LIST, cv::CHAIN_APPROX_SIMPLE);
int num_contours = contours.size() >= max_candidates ? max_candidates : contours.size();
std::vector<std::vector<std::vector<int>>> boxes;
for (int _i = 0; _i < num_contours; _i++) {
float ssid;
cv::RotatedRect box = cv::minAreaRect(contours[_i]);
auto array = get_mini_boxes(box, ssid);
auto box_for_unclip = array;
//end get_mini_box
if (ssid < min_size) {
continue;
}
float score;
score = box_score_fast(array, pred);
//end box_score_fast
if (score < box_thresh){
continue;
}
// start for unclip
cv::RotatedRect points = unclip(box_for_unclip);
// end for unclip
cv::RotatedRect clipbox = points;
auto cliparray = get_mini_boxes(clipbox, ssid);
if (ssid < min_size + 2) continue;
int dest_width = pred.cols;
int dest_height = pred.rows;
std::vector<std::vector<int>> intcliparray;
for (int num_pt = 0; num_pt < 4; num_pt++) {
std::vector<int> a{
int(clampf(roundf(cliparray[num_pt][0] / float(width) * float(dest_width)), 0,
float(dest_width))),
int(clampf(roundf(cliparray[num_pt][1] / float(height) * float(dest_height)), 0,
float(dest_height)))};
intcliparray.emplace_back(std::move(a));
}
boxes.emplace_back(std::move(intcliparray));
}//end for
return boxes;
}
int _max(int a, int b) {
return a >= b ? a : b;
}
int _min(int a, int b) {
return a >= b ? b : a;
}
std::vector<std::vector<std::vector<int>>>
filter_tag_det_res(const std::vector<std::vector<std::vector<int>>>& o_boxes,
float ratio_h, float ratio_w,const cv::Mat& srcimg) {
int oriimg_h = srcimg.rows;
int oriimg_w = srcimg.cols;
std::vector<std::vector<std::vector<int>>> boxes{o_boxes};
std::vector<std::vector<std::vector<int>>> root_points;
for (int n = 0; n < boxes.size(); n++) {
boxes[n] = order_points_clockwise(boxes[n]);
for (int m = 0; m < boxes[0].size(); m++) {
boxes[n][m][0] /= ratio_w;
boxes[n][m][1] /= ratio_h;
boxes[n][m][0] = int(_min(_max(boxes[n][m][0], 0), oriimg_w - 1));
boxes[n][m][1] = int(_min(_max(boxes[n][m][1], 0), oriimg_h - 1));
}
}
for (int n = 0; n < boxes.size(); n++) {
int rect_width, rect_height;
rect_width = int(sqrt(
pow(boxes[n][0][0] - boxes[n][1][0], 2) + pow(boxes[n][0][1] - boxes[n][1][1], 2)));
rect_height = int(sqrt(
pow(boxes[n][0][0] - boxes[n][3][0], 2) + pow(boxes[n][0][1] - boxes[n][3][1], 2)));
if (rect_width <= 10 || rect_height <= 10)
continue;
root_points.push_back(boxes[n]);
}
return root_points;
}
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deploy/android_demo/app/src/main/cpp/ocr_db_post_process.h deleted 100644 → 0
View file @ 47752ddf
//
// Created by fujiayi on 2020/7/2.
//
#pragma once
#include <vector>
#include <opencv2/opencv.hpp>
std::vector<std::vector<std::vector<int>>>
boxes_from_bitmap(const cv::Mat &pred, const cv::Mat &bitmap);
std::vector<std::vector<std::vector<int>>>
filter_tag_det_res(
const std::vector<std::vector<std::vector<int>>> &o_boxes,
float ratio_h,
float ratio_w,
const cv::Mat &srcimg
);
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/ocr_ppredictor.cpp deleted 100644 → 0
View file @ 47752ddf
//
// Created by fujiayi on 2020/7/1.
//
#include "ocr_ppredictor.h"
#include "preprocess.h"
#include "common.h"
#include "ocr_db_post_process.h"
#include "ocr_crnn_process.h"
namespace ppredictor {
OCR_PPredictor::OCR_PPredictor(const OCR_Config &config) : _config(config) {
}
int
OCR_PPredictor::init(const std::string &det_model_content, const std::string &rec_model_content) {
_det_predictor = std::unique_ptr<PPredictor>(
new PPredictor{_config.thread_num, NET_OCR, _config.mode});
_det_predictor->init_nb(det_model_content);
_rec_predictor = std::unique_ptr<PPredictor>(
new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
_rec_predictor->init_nb(rec_model_content);
return RETURN_OK;
}
int OCR_PPredictor::init_from_file(const std::string &det_model_path, const std::string &rec_model_path){
_det_predictor = std::unique_ptr<PPredictor>(
new PPredictor{_config.thread_num, NET_OCR, _config.mode});
_det_predictor->init_from_file(det_model_path);
_rec_predictor = std::unique_ptr<PPredictor>(
new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
_rec_predictor->init_from_file(rec_model_path);
return RETURN_OK;
}
/**
* for debug use, show result of First Step
* @param filter_boxes
* @param boxes
* @param srcimg
*/
static void visual_img(const std::vector<std::vector<std::vector<int>>> &filter_boxes,
const std::vector<std::vector<std::vector<int>>> &boxes,
const cv::Mat &srcimg) {
// visualization
cv::Point rook_points[filter_boxes.size()][4];
for (int n = 0; n < filter_boxes.size(); n++) {
for (int m = 0; m < filter_boxes[0].size(); m++) {
rook_points[n][m] = cv::Point(int(filter_boxes[n][m][0]), int(filter_boxes[n][m][1]));
}
}
cv::Mat img_vis;
srcimg.copyTo(img_vis);
for (int n = 0; n < boxes.size(); n++) {
const cv::Point *ppt[1] = {rook_points[n]};
int npt[] = {4};
cv::polylines(img_vis, ppt, npt, 1, 1, CV_RGB(0, 255, 0), 2, 8, 0);
}
// 调试用，自行替换需要修改的路径
cv::imwrite("/sdcard/1/vis.png", img_vis);
}
std::vector<OCRPredictResult>
OCR_PPredictor::infer_ocr(const std::vector<int64_t> &dims, const float *input_data, int input_len,
int net_flag, cv::Mat &origin) {
PredictorInput input = _det_predictor->get_first_input();
input.set_dims(dims);
input.set_data(input_data, input_len);
std::vector<PredictorOutput> results = _det_predictor->infer();
PredictorOutput &res = results.at(0);
std::vector<std::vector<std::vector<int>>> filtered_box
= calc_filtered_boxes(res.get_float_data(), res.get_size(), (int) dims[2], (int) dims[3],
origin);
LOGI("Filter_box size %ld", filtered_box.size());
return infer_rec(filtered_box, origin);
}
std::vector<OCRPredictResult>
OCR_PPredictor::infer_rec(const std::vector<std::vector<std::vector<int>>> &boxes,
const cv::Mat &origin_img) {
std::vector<float> mean = {0.5f, 0.5f, 0.5f};
std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
std::vector<int64_t> dims = {1, 3, 0, 0};
std::vector<OCRPredictResult> ocr_results;
PredictorInput input = _rec_predictor->get_first_input();
for (auto bp = boxes.crbegin(); bp != boxes.crend(); ++bp) {
const std::vector<std::vector<int>> &box = *bp;
cv::Mat crop_img = get_rotate_crop_image(origin_img, box);
float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
cv::Mat input_image = crnn_resize_img(crop_img, wh_ratio);
input_image.convertTo(input_image, CV_32FC3, 1 / 255.0f);
const float *dimg = reinterpret_cast<const float *>(input_image.data);
int input_size = input_image.rows * input_image.cols;
dims[2] = input_image.rows;
dims[3] = input_image.cols;
input.set_dims(dims);
neon_mean_scale(dimg, input.get_mutable_float_data(), input_size, mean, scale);
std::vector<PredictorOutput> results = _rec_predictor->infer();
OCRPredictResult res;
res.word_index = postprocess_rec_word_index(results.at(0));
if (res.word_index.empty()) {
continue;
}
res.score = postprocess_rec_score(results.at(1));
res.points = box;
ocr_results.emplace_back(std::move(res));
}
LOGI("ocr_results finished %lu", ocr_results.size());
return ocr_results;
}
std::vector<std::vector<std::vector<int>>>
OCR_PPredictor::calc_filtered_boxes(const float *pred, int pred_size, int output_height,
int output_width, const cv::Mat &origin) {
const double threshold = 0.3;
const double maxvalue = 1;
cv::Mat pred_map = cv::Mat::zeros(output_height, output_width, CV_32F);
memcpy(pred_map.data, pred, pred_size * sizeof(float));
cv::Mat cbuf_map;
pred_map.convertTo(cbuf_map, CV_8UC1);
cv::Mat bit_map;
cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
std::vector<std::vector<std::vector<int>>> boxes = boxes_from_bitmap(pred_map, bit_map);
float ratio_h = output_height * 1.0f / origin.rows;
float ratio_w = output_width * 1.0f / origin.cols;
std::vector<std::vector<std::vector<int>>> filter_boxes = filter_tag_det_res(boxes, ratio_h,
ratio_w, origin);
return filter_boxes;
}
std::vector<int> OCR_PPredictor::postprocess_rec_word_index(const PredictorOutput &res) {
const int *rec_idx = res.get_int_data();
const std::vector<std::vector<uint64_t>> rec_idx_lod = res.get_lod();
std::vector<int> pred_idx;
for (int n = int(rec_idx_lod[0][0]); n < int(rec_idx_lod[0][1] * 2); n += 2) {
pred_idx.emplace_back(rec_idx[n]);
}
return pred_idx;
}
float OCR_PPredictor::postprocess_rec_score(const PredictorOutput &res) {
const float *predict_batch = res.get_float_data();
const std::vector<int64_t> predict_shape = res.get_shape();
const std::vector<std::vector<uint64_t>> predict_lod = res.get_lod();
int blank = predict_shape[1];
float score = 0.f;
int count = 0;
for (int n = predict_lod[0][0]; n < predict_lod[0][1] - 1; n++) {
int argmax_idx = argmax(predict_batch + n * predict_shape[1],
predict_batch + (n + 1) * predict_shape[1]);
float max_value = predict_batch[n * predict_shape[1] + argmax_idx];
if (blank - 1 - argmax_idx > 1e-5) {
score += max_value;
count += 1;
}
}
if (count == 0) {
LOGE("calc score count 0");
} else {
score /= count;
}
LOGI("calc score: %f", score);
return score;
}
NET_TYPE OCR_PPredictor::get_net_flag() const {
return NET_OCR;
}
}
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/ocr_ppredictor.h deleted 100644 → 0
View file @ 47752ddf
//
// Created by fujiayi on 2020/7/1.
//
#pragma once
#include <string>
#include <opencv2/opencv.hpp>
#include <paddle_api.h>
#include "ppredictor.h"
namespace ppredictor {
/**
* Config
*/
struct OCR_Config {
int thread_num = 4; // Thread num
paddle::lite_api::PowerMode mode = paddle::lite_api::LITE_POWER_HIGH; // PaddleLite Mode
};
/**
* PolyGone Result
*/
struct OCRPredictResult {
std::vector<int> word_index;
std::vector<std::vector<int>> points;
float score;
};
/**
* OCR there are 2 models
* 1. First model（det），select polygones to show where are the texts
* 2. crop from the origin images, use these polygones to infer
*/
class OCR_PPredictor : public PPredictor_Interface {
public:
OCR_PPredictor(const OCR_Config &config);
virtual ~OCR_PPredictor() {
}
/**
* 初始化二个模型的Predictor
* @param det_model_content
* @param rec_model_content
* @return
*/
int init(const std::string &det_model_content, const std::string &rec_model_content);
int init_from_file(const std::string &det_model_path, const std::string &rec_model_path);
/**
* Return OCR result
* @param dims
* @param input_data
* @param input_len
* @param net_flag
* @param origin
* @return
*/
virtual std::vector<OCRPredictResult>
infer_ocr(const std::vector<int64_t> &dims, const float *input_data, int input_len,
int net_flag, cv::Mat &origin);
virtual NET_TYPE get_net_flag() const;
private:
/**
* calcul Polygone from the result image of first model
* @param pred
* @param output_height
* @param output_width
* @param origin
* @return
*/
std::vector<std::vector<std::vector<int>>>
calc_filtered_boxes(const float *pred, int pred_size, int output_height, int output_width,
const cv::Mat &origin);
/**
* infer for second model
*
* @param boxes
* @param origin
* @return
*/
std::vector<OCRPredictResult>
infer_rec(const std::vector<std::vector<std::vector<int>>> &boxes, const cv::Mat &origin);
/**
* Postprocess or sencod model to extract text
* @param res
* @return
*/
std::vector<int> postprocess_rec_word_index(const PredictorOutput &res);
/**
* calculate confidence of second model text result
* @param res
* @return
*/
float postprocess_rec_score(const PredictorOutput &res);
std::unique_ptr<PPredictor> _det_predictor;
std::unique_ptr<PPredictor> _rec_predictor;
OCR_Config _config;
};
}
deploy/android_demo/app/src/main/cpp/ppredictor.cpp deleted 100644 → 0
View file @ 47752ddf
#include "ppredictor.h"
#include "common.h"
namespace ppredictor {
PPredictor::PPredictor(int thread_num, int net_flag, paddle::lite_api::PowerMode mode) :
_thread_num(thread_num), _net_flag(net_flag), _mode(mode) {
}
int PPredictor::init_nb(const std::string &model_content) {
paddle::lite_api::MobileConfig config;
config.set_model_from_buffer(model_content);
return _init(config);
}
int PPredictor::init_from_file(const std::string &model_content){
paddle::lite_api::MobileConfig config;
config.set_model_from_file(model_content);
return _init(config);
}
template<typename ConfigT>
int PPredictor::_init(ConfigT &config) {
config.set_threads(_thread_num);
config.set_power_mode(_mode);
_predictor = paddle::lite_api::CreatePaddlePredictor(config);
LOGI("paddle instance created");
return RETURN_OK;
}
PredictorInput PPredictor::get_input(int index) {
PredictorInput input{_predictor->GetInput(index), index, _net_flag};
_is_input_get = true;
return input;
}
std::vector<PredictorInput> PPredictor::get_inputs(int num) {
std::vector<PredictorInput> results;
for (int i = 0; i < num; i++) {
results.emplace_back(get_input(i));
}
return results;
}
PredictorInput PPredictor::get_first_input() {
return get_input(0);
}
std::vector<PredictorOutput> PPredictor::infer() {
LOGI("infer Run start %d", _net_flag);
std::vector<PredictorOutput> results;
if (!_is_input_get) {
return results;
}
_predictor->Run();
LOGI("infer Run end");
for (int i = 0; i < _predictor->GetOutputNames().size(); i++) {
std::unique_ptr<const paddle::lite_api::Tensor> output_tensor = _predictor->GetOutput(i);
LOGI("output tensor[%d] size %ld", i, product(output_tensor->shape()));
PredictorOutput result{std::move(output_tensor), i, _net_flag};
results.emplace_back(std::move(result));
}
return results;
}
NET_TYPE PPredictor::get_net_flag() const {
return (NET_TYPE) _net_flag;
}
}
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/ppredictor.h deleted 100644 → 0
View file @ 47752ddf
#pragma once
#include "paddle_api.h"
#include "predictor_input.h"
#include "predictor_output.h"
namespace ppredictor {
/**
* PaddleLite Preditor Common Interface
*/
class PPredictor_Interface {
public:
virtual ~PPredictor_Interface() {
}
virtual NET_TYPE get_net_flag() const = 0;
};
/**
* Common Predictor
*/
class PPredictor : public PPredictor_Interface {
public:
PPredictor(int thread_num, int net_flag = 0,
paddle::lite_api::PowerMode mode = paddle::lite_api::LITE_POWER_HIGH);
virtual ~PPredictor() {
}
/**
* init paddlitelite opt model，nb format ，or use ini_paddle
* @param model_content
* @return 0
*/
virtual int init_nb(const std::string &model_content);
virtual int init_from_file(const std::string &model_content);
std::vector<PredictorOutput> infer();
std::shared_ptr<paddle::lite_api::PaddlePredictor> get_predictor() {
return _predictor;
}
virtual std::vector<PredictorInput> get_inputs(int num);
virtual PredictorInput get_input(int index);
virtual PredictorInput get_first_input();
virtual NET_TYPE get_net_flag() const;
protected:
template<typename ConfigT>
int _init(ConfigT &config);
private:
int _thread_num;
paddle::lite_api::PowerMode _mode;
std::shared_ptr<paddle::lite_api::PaddlePredictor> _predictor;
bool _is_input_get = false;
int _net_flag;
};
}
deploy/android_demo/app/src/main/cpp/predictor_input.cpp deleted 100644 → 0
View file @ 47752ddf
#include "predictor_input.h"
namespace ppredictor {
void PredictorInput::set_dims(std::vector<int64_t> dims) {
// yolov3
if (_net_flag == 101 && _index == 1) {
_tensor->Resize({1, 2});
_tensor->mutable_data<int>()[0] = (int) dims.at(2);
_tensor->mutable_data<int>()[1] = (int) dims.at(3);
} else {
_tensor->Resize(dims);
}
_is_dims_set = true;
}
float *PredictorInput::get_mutable_float_data() {
if (!_is_dims_set) {
LOGE("PredictorInput::set_dims is not called");
}
return _tensor->mutable_data<float>();
}
void PredictorInput::set_data(const float *input_data, int input_float_len) {
float *input_raw_data = get_mutable_float_data();
memcpy(input_raw_data, input_data, input_float_len * sizeof(float));
}
}
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/predictor_input.h deleted 100644 → 0
View file @ 47752ddf
#pragma once
#include <paddle_api.h>
#include <vector>
#include "common.h"
namespace ppredictor {
class PredictorInput {
public:
PredictorInput(std::unique_ptr<paddle::lite_api::Tensor> &&tensor, int index, int net_flag) :
_tensor(std::move(tensor)), _index(index),_net_flag(net_flag) {
}
void set_dims(std::vector<int64_t> dims);
float *get_mutable_float_data();
void set_data(const float *input_data, int input_float_len);
private:
std::unique_ptr<paddle::lite_api::Tensor> _tensor;
bool _is_dims_set = false;
int _index;
int _net_flag;
};
}
deploy/android_demo/app/src/main/cpp/predictor_output.cpp deleted 100644 → 0
View file @ 47752ddf
#include "predictor_output.h"
namespace ppredictor {
const float* PredictorOutput::get_float_data() const{
return _tensor->data<float>();
}
const int* PredictorOutput::get_int_data() const{
return _tensor->data<int>();
}
const std::vector<std::vector<uint64_t>> PredictorOutput::get_lod() const{
return _tensor->lod();
}
int64_t PredictorOutput::get_size() const{
if (_net_flag == NET_OCR) {
return _tensor->shape().at(2) * _tensor->shape().at(3);
} else {
return product(_tensor->shape());
}
}
const std::vector<int64_t> PredictorOutput::get_shape() const{
return _tensor->shape();
}
}
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/predictor_output.h deleted 100644 → 0
View file @ 47752ddf
#pragma once
#include <paddle_api.h>
#include <vector>
#include "common.h"
namespace ppredictor {
class PredictorOutput {
public:
PredictorOutput(){
}
PredictorOutput(std::unique_ptr<const paddle::lite_api::Tensor> &&tensor, int index, int net_flag) :
_tensor(std::move(tensor)), _index(index), _net_flag(net_flag) {
}
const float* get_float_data() const;
const int* get_int_data() const;
int64_t get_size() const;
const std::vector<std::vector<uint64_t>> get_lod() const;
const std::vector<int64_t> get_shape() const;
std::vector<float> data; // return float, or use data_int
std::vector<int> data_int; // several layers return int ，or use data
std::vector<int64_t> shape; // PaddleLite output shape
std::vector<std::vector<uint64_t>> lod; // PaddleLite output lod
private:
std::unique_ptr<const paddle::lite_api::Tensor> _tensor;
int _index;
int _net_flag;
};
}
deploy/android_demo/app/src/main/cpp/preprocess.cpp deleted 100644 → 0
View file @ 47752ddf
#include "preprocess.h"
#include <android/bitmap.h>
cv::Mat bitmap_to_cv_mat(JNIEnv *env, jobject bitmap) {
AndroidBitmapInfo info;
int result = AndroidBitmap_getInfo(env, bitmap, &info);
if (result != ANDROID_BITMAP_RESULT_SUCCESS) {
LOGE("AndroidBitmap_getInfo failed, result: %d", result);
return cv::Mat{};
}
if (info.format != ANDROID_BITMAP_FORMAT_RGBA_8888) {
LOGE("Bitmap format is not RGBA_8888 !");
return cv::Mat{};
}
unsigned char *srcData = NULL;
AndroidBitmap_lockPixels(env, bitmap, (void **) &srcData);
cv::Mat mat = cv::Mat::zeros(info.height, info.width, CV_8UC4);
memcpy(mat.data, srcData, info.height * info.width * 4);
AndroidBitmap_unlockPixels(env, bitmap);
cv::cvtColor(mat, mat, cv::COLOR_RGBA2BGR);
/**
if (!cv::imwrite("/sdcard/1/copy.jpg", mat)){
LOGE("Write image failed " );
}
*/
return mat;
}
cv::Mat resize_img(const cv::Mat& img, int height, int width){
if (img.rows == height && img.cols == width){
return img;
}
cv::Mat new_img;
cv::resize(img, new_img, cv::Size(height, width));
return new_img;
}
// fill tensor with mean and scale and trans layout: nhwc -> nchw, neon speed up
void neon_mean_scale(const float* din,
float* dout,
int size,
const std::vector<float>& mean,
const std::vector<float>& scale) {
if (mean.size() != 3 || scale.size() != 3) {
LOGE("[ERROR] mean or scale size must equal to 3");
return;
}
float32x4_t vmean0 = vdupq_n_f32(mean[0]);
float32x4_t vmean1 = vdupq_n_f32(mean[1]);
float32x4_t vmean2 = vdupq_n_f32(mean[2]);
float32x4_t vscale0 = vdupq_n_f32(scale[0]);
float32x4_t vscale1 = vdupq_n_f32(scale[1]);
float32x4_t vscale2 = vdupq_n_f32(scale[2]);
float* dout_c0 = dout;
float* dout_c1 = dout + size;
float* dout_c2 = dout + size * 2;
int i = 0;
for (; i < size - 3; i += 4) {
float32x4x3_t vin3 = vld3q_f32(din);
float32x4_t vsub0 = vsubq_f32(vin3.val[0], vmean0);
float32x4_t vsub1 = vsubq_f32(vin3.val[1], vmean1);
float32x4_t vsub2 = vsubq_f32(vin3.val[2], vmean2);
float32x4_t vs0 = vmulq_f32(vsub0, vscale0);
float32x4_t vs1 = vmulq_f32(vsub1, vscale1);
float32x4_t vs2 = vmulq_f32(vsub2, vscale2);
vst1q_f32(dout_c0, vs0);
vst1q_f32(dout_c1, vs1);
vst1q_f32(dout_c2, vs2);
din += 12;
dout_c0 += 4;
dout_c1 += 4;
dout_c2 += 4;
}
for (; i < size; i++) {
*(dout_c0++) = (*(din++) - mean[0]) * scale[0];
*(dout_c1++) = (*(din++) - mean[1]) * scale[1];
*(dout_c2++) = (*(din++) - mean[2]) * scale[2];
}
}
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/preprocess.h deleted 100644 → 0
View file @ 47752ddf
#pragma once
#include <jni.h>
#include <opencv2/opencv.hpp>
#include "common.h"
cv::Mat bitmap_to_cv_mat(JNIEnv *env, jobject bitmap);
cv::Mat resize_img(const cv::Mat& img, int height, int width);
void neon_mean_scale(const float* din,
float* dout,
int size,
const std::vector<float>& mean,
const std::vector<float>& scale);
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/AppCompatPreferenceActivity.java deleted 100644 → 0
View file @ 47752ddf
/*
* Copyright (C) 2014 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.baidu.paddle.lite.demo.ocr;
import android.content.res.Configuration;
import android.os.Bundle;
import android.preference.PreferenceActivity;
import android.view.MenuInflater;
import android.view.View;
import android.view.ViewGroup;
import androidx.annotation.LayoutRes;
import androidx.annotation.Nullable;
import androidx.appcompat.app.ActionBar;
import androidx.appcompat.app.AppCompatDelegate;
import androidx.appcompat.widget.Toolbar;
/**
* A {@link PreferenceActivity} which implements and proxies the necessary calls
* to be used with AppCompat.
* <p>
* This technique can be used with an {@link android.app.Activity} class, not just
* {@link PreferenceActivity}.
*/
public abstract class AppCompatPreferenceActivity extends PreferenceActivity {
private AppCompatDelegate mDelegate;
@Override
protected void onCreate(Bundle savedInstanceState) {
getDelegate().installViewFactory();
getDelegate().onCreate(savedInstanceState);
super.onCreate(savedInstanceState);
}
@Override
protected void onPostCreate(Bundle savedInstanceState) {
super.onPostCreate(savedInstanceState);
getDelegate().onPostCreate(savedInstanceState);
}
public ActionBar getSupportActionBar() {
return getDelegate().getSupportActionBar();
}
public void setSupportActionBar(@Nullable Toolbar toolbar) {
getDelegate().setSupportActionBar(toolbar);
}
@Override
public MenuInflater getMenuInflater() {
return getDelegate().getMenuInflater();
}
@Override
public void setContentView(@LayoutRes int layoutResID) {
getDelegate().setContentView(layoutResID);
}
@Override
public void setContentView(View view) {
getDelegate().setContentView(view);
}
@Override
public void setContentView(View view, ViewGroup.LayoutParams params) {
getDelegate().setContentView(view, params);
}
@Override
public void addContentView(View view, ViewGroup.LayoutParams params) {
getDelegate().addContentView(view, params);
}
@Override
protected void onPostResume() {
super.onPostResume();
getDelegate().onPostResume();
}
@Override
protected void onTitleChanged(CharSequence title, int color) {
super.onTitleChanged(title, color);
getDelegate().setTitle(title);
}
@Override
public void onConfigurationChanged(Configuration newConfig) {
super.onConfigurationChanged(newConfig);
getDelegate().onConfigurationChanged(newConfig);
}
@Override
protected void onStop() {
super.onStop();
getDelegate().onStop();
}
@Override
protected void onDestroy() {
super.onDestroy();
getDelegate().onDestroy();
}
public void invalidateOptionsMenu() {
getDelegate().invalidateOptionsMenu();
}
private AppCompatDelegate getDelegate() {
if (mDelegate == null) {
mDelegate = AppCompatDelegate.create(this, null);
}
return mDelegate;
}
}
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/MainActivity.java deleted 100644 → 0
View file @ 47752ddf
package com.baidu.paddle.lite.demo.ocr;
import android.Manifest;
import android.app.ProgressDialog;
import android.content.ContentResolver;
import android.content.Context;
import android.content.Intent;
import android.content.SharedPreferences;
import android.content.pm.PackageManager;
import android.database.Cursor;
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.media.ExifInterface;
import android.net.Uri;
import android.os.Bundle;
import android.os.Environment;
import android.os.Handler;
import android.os.HandlerThread;
import android.os.Message;
import android.preference.PreferenceManager;
import android.provider.MediaStore;
import android.text.method.ScrollingMovementMethod;
import android.util.Log;
import android.view.Menu;
import android.view.MenuInflater;
import android.view.MenuItem;
import android.widget.ImageView;
import android.widget.TextView;
import android.widget.Toast;
import androidx.annotation.NonNull;
import androidx.appcompat.app.AppCompatActivity;
import androidx.core.app.ActivityCompat;
import androidx.core.content.ContextCompat;
import androidx.core.content.FileProvider;
import java.io.File;
import java.io.IOException;
import java.io.InputStream;
import java.text.SimpleDateFormat;
import java.util.Date;
public class MainActivity extends AppCompatActivity {
private static final String TAG = MainActivity.class.getSimpleName();
public static final int OPEN_GALLERY_REQUEST_CODE = 0;
public static final int TAKE_PHOTO_REQUEST_CODE = 1;
public static final int REQUEST_LOAD_MODEL = 0;
public static final int REQUEST_RUN_MODEL = 1;
public static final int RESPONSE_LOAD_MODEL_SUCCESSED = 0;
public static final int RESPONSE_LOAD_MODEL_FAILED = 1;
public static final int RESPONSE_RUN_MODEL_SUCCESSED = 2;
public static final int RESPONSE_RUN_MODEL_FAILED = 3;
protected ProgressDialog pbLoadModel = null;
protected ProgressDialog pbRunModel = null;
protected Handler receiver = null; // Receive messages from worker thread
protected Handler sender = null; // Send command to worker thread
protected HandlerThread worker = null; // Worker thread to load&run model
// UI components of object detection
protected TextView tvInputSetting;
protected ImageView ivInputImage;
protected TextView tvOutputResult;
protected TextView tvInferenceTime;
// Model settings of object detection
protected String modelPath = "";
protected String labelPath = "";
protected String imagePath = "";
protected int cpuThreadNum = 1;
protected String cpuPowerMode = "";
protected String inputColorFormat = "";
protected long[] inputShape = new long[]{};
protected float[] inputMean = new float[]{};
protected float[] inputStd = new float[]{};
protected float scoreThreshold = 0.1f;
private String currentPhotoPath;
protected Predictor predictor = new Predictor();
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
// Clear all setting items to avoid app crashing due to the incorrect settings
SharedPreferences sharedPreferences = PreferenceManager.getDefaultSharedPreferences(this);
SharedPreferences.Editor editor = sharedPreferences.edit();
editor.clear();
editor.commit();
// Prepare the worker thread for mode loading and inference
receiver = new Handler() {
@Override
public void handleMessage(Message msg) {
switch (msg.what) {
case RESPONSE_LOAD_MODEL_SUCCESSED:
pbLoadModel.dismiss();
onLoadModelSuccessed();
break;
case RESPONSE_LOAD_MODEL_FAILED:
pbLoadModel.dismiss();
Toast.makeText(MainActivity.this, "Load model failed!", Toast.LENGTH_SHORT).show();
onLoadModelFailed();
break;
case RESPONSE_RUN_MODEL_SUCCESSED:
pbRunModel.dismiss();
onRunModelSuccessed();
break;
case RESPONSE_RUN_MODEL_FAILED:
pbRunModel.dismiss();
Toast.makeText(MainActivity.this, "Run model failed!", Toast.LENGTH_SHORT).show();
onRunModelFailed();
break;
default:
break;
}
}
};
worker = new HandlerThread("Predictor Worker");
worker.start();
sender = new Handler(worker.getLooper()) {
public void handleMessage(Message msg) {
switch (msg.what) {
case REQUEST_LOAD_MODEL:
// Load model and reload test image
if (onLoadModel()) {
receiver.sendEmptyMessage(RESPONSE_LOAD_MODEL_SUCCESSED);
} else {
receiver.sendEmptyMessage(RESPONSE_LOAD_MODEL_FAILED);
}
break;
case REQUEST_RUN_MODEL:
// Run model if model is loaded
if (onRunModel()) {
receiver.sendEmptyMessage(RESPONSE_RUN_MODEL_SUCCESSED);
} else {
receiver.sendEmptyMessage(RESPONSE_RUN_MODEL_FAILED);
}
break;
default:
break;
}
}
};
// Setup the UI components
tvInputSetting = findViewById(R.id.tv_input_setting);
ivInputImage = findViewById(R.id.iv_input_image);
tvInferenceTime = findViewById(R.id.tv_inference_time);
tvOutputResult = findViewById(R.id.tv_output_result);
tvInputSetting.setMovementMethod(ScrollingMovementMethod.getInstance());
tvOutputResult.setMovementMethod(ScrollingMovementMethod.getInstance());
}
@Override
protected void onResume() {
super.onResume();
SharedPreferences sharedPreferences = PreferenceManager.getDefaultSharedPreferences(this);
boolean settingsChanged = false;
String model_path = sharedPreferences.getString(getString(R.string.MODEL_PATH_KEY),
getString(R.string.MODEL_PATH_DEFAULT));
String label_path = sharedPreferences.getString(getString(R.string.LABEL_PATH_KEY),
getString(R.string.LABEL_PATH_DEFAULT));
String image_path = sharedPreferences.getString(getString(R.string.IMAGE_PATH_KEY),
getString(R.string.IMAGE_PATH_DEFAULT));
settingsChanged |= !model_path.equalsIgnoreCase(modelPath);
settingsChanged |= !label_path.equalsIgnoreCase(labelPath);
settingsChanged |= !image_path.equalsIgnoreCase(imagePath);
int cpu_thread_num = Integer.parseInt(sharedPreferences.getString(getString(R.string.CPU_THREAD_NUM_KEY),
getString(R.string.CPU_THREAD_NUM_DEFAULT)));
settingsChanged |= cpu_thread_num != cpuThreadNum;
String cpu_power_mode =
sharedPreferences.getString(getString(R.string.CPU_POWER_MODE_KEY),
getString(R.string.CPU_POWER_MODE_DEFAULT));
settingsChanged |= !cpu_power_mode.equalsIgnoreCase(cpuPowerMode);
String input_color_format =
sharedPreferences.getString(getString(R.string.INPUT_COLOR_FORMAT_KEY),
getString(R.string.INPUT_COLOR_FORMAT_DEFAULT));
settingsChanged |= !input_color_format.equalsIgnoreCase(inputColorFormat);
long[] input_shape =
Utils.parseLongsFromString(sharedPreferences.getString(getString(R.string.INPUT_SHAPE_KEY),
getString(R.string.INPUT_SHAPE_DEFAULT)), ",");
float[] input_mean =
Utils.parseFloatsFromString(sharedPreferences.getString(getString(R.string.INPUT_MEAN_KEY),
getString(R.string.INPUT_MEAN_DEFAULT)), ",");
float[] input_std =
Utils.parseFloatsFromString(sharedPreferences.getString(getString(R.string.INPUT_STD_KEY)
, getString(R.string.INPUT_STD_DEFAULT)), ",");
settingsChanged |= input_shape.length != inputShape.length;
settingsChanged |= input_mean.length != inputMean.length;
settingsChanged |= input_std.length != inputStd.length;
if (!settingsChanged) {
for (int i = 0; i < input_shape.length; i++) {
settingsChanged |= input_shape[i] != inputShape[i];
}
for (int i = 0; i < input_mean.length; i++) {
settingsChanged |= input_mean[i] != inputMean[i];
}
for (int i = 0; i < input_std.length; i++) {
settingsChanged |= input_std[i] != inputStd[i];
}
}
float score_threshold =
Float.parseFloat(sharedPreferences.getString(getString(R.string.SCORE_THRESHOLD_KEY),
getString(R.string.SCORE_THRESHOLD_DEFAULT)));
settingsChanged |= scoreThreshold != score_threshold;
if (settingsChanged) {
modelPath = model_path;
labelPath = label_path;
imagePath = image_path;
cpuThreadNum = cpu_thread_num;
cpuPowerMode = cpu_power_mode;
inputColorFormat = input_color_format;
inputShape = input_shape;
inputMean = input_mean;
inputStd = input_std;
scoreThreshold = score_threshold;
// Update UI
tvInputSetting.setText("Model: " + modelPath.substring(modelPath.lastIndexOf("/") + 1) + "\n" + "CPU" +
" Thread Num: " + Integer.toString(cpuThreadNum) + "\n" + "CPU Power Mode: " + cpuPowerMode);
tvInputSetting.scrollTo(0, 0);
// Reload model if configure has been changed
loadModel();
}
}
public void loadModel() {
pbLoadModel = ProgressDialog.show(this, "", "Loading model...", false, false);
sender.sendEmptyMessage(REQUEST_LOAD_MODEL);
}
public void runModel() {
pbRunModel = ProgressDialog.show(this, "", "Running model...", false, false);
sender.sendEmptyMessage(REQUEST_RUN_MODEL);
}
public boolean onLoadModel() {
return predictor.init(MainActivity.this, modelPath, labelPath, cpuThreadNum,
cpuPowerMode,
inputColorFormat,
inputShape, inputMean,
inputStd, scoreThreshold);
}
public boolean onRunModel() {
return predictor.isLoaded() && predictor.runModel();
}
public void onLoadModelSuccessed() {
// Load test image from path and run model
try {
if (imagePath.isEmpty()) {
return;
}
Bitmap image = null;
// Read test image file from custom path if the first character of mode path is '/', otherwise read test
// image file from assets
if (!imagePath.substring(0, 1).equals("/")) {
InputStream imageStream = getAssets().open(imagePath);
image = BitmapFactory.decodeStream(imageStream);
} else {
if (!new File(imagePath).exists()) {
return;
}
image = BitmapFactory.decodeFile(imagePath);
}
if (image != null && predictor.isLoaded()) {
predictor.setInputImage(image);
runModel();
}
} catch (IOException e) {
Toast.makeText(MainActivity.this, "Load image failed!", Toast.LENGTH_SHORT).show();
e.printStackTrace();
}
}
public void onLoadModelFailed() {
}
public void onRunModelSuccessed() {
// Obtain results and update UI
tvInferenceTime.setText("Inference time: " + predictor.inferenceTime() + " ms");
Bitmap outputImage = predictor.outputImage();
if (outputImage != null) {
ivInputImage.setImageBitmap(outputImage);
}
tvOutputResult.setText(predictor.outputResult());
tvOutputResult.scrollTo(0, 0);
}
public void onRunModelFailed() {
}
public void onImageChanged(Bitmap image) {
// Rerun model if users pick test image from gallery or camera
if (image != null && predictor.isLoaded()) {
predictor.setInputImage(image);
runModel();
}
}
public void onSettingsClicked() {
startActivity(new Intent(MainActivity.this, SettingsActivity.class));
}
@Override
public boolean onCreateOptionsMenu(Menu menu) {
MenuInflater inflater = getMenuInflater();
inflater.inflate(R.menu.menu_action_options, menu);
return true;
}
public boolean onPrepareOptionsMenu(Menu menu) {
boolean isLoaded = predictor.isLoaded();
menu.findItem(R.id.open_gallery).setEnabled(isLoaded);
menu.findItem(R.id.take_photo).setEnabled(isLoaded);
return super.onPrepareOptionsMenu(menu);
}
@Override
public boolean onOptionsItemSelected(MenuItem item) {
switch (item.getItemId()) {
case android.R.id.home:
finish();
break;
case R.id.open_gallery:
if (requestAllPermissions()) {
openGallery();
}
break;
case R.id.take_photo:
if (requestAllPermissions()) {
takePhoto();
}
break;
case R.id.settings:
if (requestAllPermissions()) {
// Make sure we have SDCard r&w permissions to load model from SDCard
onSettingsClicked();
}
break;
}
return super.onOptionsItemSelected(item);
}
@Override
public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions,
@NonNull int[] grantResults) {
super.onRequestPermissionsResult(requestCode, permissions, grantResults);
if (grantResults[0] != PackageManager.PERMISSION_GRANTED || grantResults[1] != PackageManager.PERMISSION_GRANTED) {
Toast.makeText(this, "Permission Denied", Toast.LENGTH_SHORT).show();
}
}
private boolean requestAllPermissions() {
if (ContextCompat.checkSelfPermission(this, Manifest.permission.WRITE_EXTERNAL_STORAGE)
!= PackageManager.PERMISSION_GRANTED || ContextCompat.checkSelfPermission(this,
Manifest.permission.CAMERA)
!= PackageManager.PERMISSION_GRANTED) {
ActivityCompat.requestPermissions(this, new String[]{Manifest.permission.WRITE_EXTERNAL_STORAGE,
Manifest.permission.CAMERA},
0);
return false;
}
return true;
}
private void openGallery() {
Intent intent = new Intent(Intent.ACTION_PICK, null);
intent.setDataAndType(MediaStore.Images.Media.EXTERNAL_CONTENT_URI, "image/*");
startActivityForResult(intent, OPEN_GALLERY_REQUEST_CODE);
}
private void takePhoto() {
Intent takePictureIntent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE);
// Ensure that there's a camera activity to handle the intent
if (takePictureIntent.resolveActivity(getPackageManager()) != null) {
// Create the File where the photo should go
File photoFile = null;
try {
photoFile = createImageFile();
} catch (IOException ex) {
Log.e("MainActitity", ex.getMessage(), ex);
Toast.makeText(MainActivity.this,
"Create Camera temp file failed: " + ex.getMessage(), Toast.LENGTH_SHORT).show();
}
// Continue only if the File was successfully created
if (photoFile != null) {
Log.i(TAG, "FILEPATH " + getExternalFilesDir("Pictures").getAbsolutePath());
Uri photoURI = FileProvider.getUriForFile(this,
"com.baidu.paddle.lite.demo.ocr.fileprovider",
photoFile);
currentPhotoPath = photoFile.getAbsolutePath();
takePictureIntent.putExtra(MediaStore.EXTRA_OUTPUT, photoURI);
startActivityForResult(takePictureIntent, TAKE_PHOTO_REQUEST_CODE);
Log.i(TAG, "startActivityForResult finished");
}
}
}
private File createImageFile() throws IOException {
// Create an image file name
String timeStamp = new SimpleDateFormat("yyyyMMdd_HHmmss").format(new Date());
String imageFileName = "JPEG_" + timeStamp + "_";
File storageDir = getExternalFilesDir(Environment.DIRECTORY_PICTURES);
File image = File.createTempFile(
imageFileName, /* prefix */
".bmp", /* suffix */
storageDir /* directory */
);
return image;
}
@Override
protected void onActivityResult(int requestCode, int resultCode, Intent data) {
super.onActivityResult(requestCode, resultCode, data);
if (resultCode == RESULT_OK) {
switch (requestCode) {
case OPEN_GALLERY_REQUEST_CODE:
if (data == null) {
break;
}
try {
ContentResolver resolver = getContentResolver();
Uri uri = data.getData();
Bitmap image = MediaStore.Images.Media.getBitmap(resolver, uri);
String[] proj = {MediaStore.Images.Media.DATA};
Cursor cursor = managedQuery(uri, proj, null, null, null);
cursor.moveToFirst();
onImageChanged(image);
} catch (IOException e) {
Log.e(TAG, e.toString());
}
break;
case TAKE_PHOTO_REQUEST_CODE:
if (currentPhotoPath != null) {
ExifInterface exif = null;
try {
exif = new ExifInterface(currentPhotoPath);
} catch (IOException e) {
e.printStackTrace();
}
int orientation = exif.getAttributeInt(ExifInterface.TAG_ORIENTATION,
ExifInterface.ORIENTATION_UNDEFINED);
Log.i(TAG, "rotation " + orientation);
Bitmap image = BitmapFactory.decodeFile(currentPhotoPath);
image = Utils.rotateBitmap(image, orientation);
onImageChanged(image);
} else {
Log.e(TAG, "currentPhotoPath is null");
}
break;
default:
break;
}
}
}
@Override
protected void onDestroy() {
if (predictor != null) {
predictor.releaseModel();
}
worker.quit();
super.onDestroy();
}
}
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/MiniActivity.java deleted 100644 → 0
View file @ 47752ddf
package com.baidu.paddle.lite.demo.ocr;
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.os.Build;
import android.os.Bundle;
import android.os.Handler;
import android.os.HandlerThread;
import android.os.Message;
import android.util.Log;
import android.view.View;
import android.widget.Button;
import android.widget.ImageView;
import android.widget.TextView;
import android.widget.Toast;
import androidx.appcompat.app.AppCompatActivity;
import java.io.IOException;
import java.io.InputStream;
public class MiniActivity extends AppCompatActivity {
public static final int REQUEST_LOAD_MODEL = 0;
public static final int REQUEST_RUN_MODEL = 1;
public static final int REQUEST_UNLOAD_MODEL = 2;
public static final int RESPONSE_LOAD_MODEL_SUCCESSED = 0;
public static final int RESPONSE_LOAD_MODEL_FAILED = 1;
public static final int RESPONSE_RUN_MODEL_SUCCESSED = 2;
public static final int RESPONSE_RUN_MODEL_FAILED = 3;
private static final String TAG = "MiniActivity";
protected Handler receiver = null; // Receive messages from worker thread
protected Handler sender = null; // Send command to worker thread
protected HandlerThread worker = null; // Worker thread to load&run model
protected volatile Predictor predictor = null;
private String assetModelDirPath = "models/ocr_v1_for_cpu";
private String assetlabelFilePath = "labels/ppocr_keys_v1.txt";
private Button button;
private ImageView imageView; // image result
private TextView textView; // text result
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_mini);
Log.i(TAG, "SHOW in Logcat");
// Prepare the worker thread for mode loading and inference
worker = new HandlerThread("Predictor Worker");
worker.start();
sender = new Handler(worker.getLooper()) {
public void handleMessage(Message msg) {
switch (msg.what) {
case REQUEST_LOAD_MODEL:
// Load model and reload test image
if (!onLoadModel()) {
runOnUiThread(new Runnable() {
@Override
public void run() {
Toast.makeText(MiniActivity.this, "Load model failed!", Toast.LENGTH_SHORT).show();
}
});
}
break;
case REQUEST_RUN_MODEL:
// Run model if model is loaded
final boolean isSuccessed = onRunModel();
runOnUiThread(new Runnable() {
@Override
public void run() {
if (isSuccessed){
onRunModelSuccessed();
}else{
Toast.makeText(MiniActivity.this, "Run model failed!", Toast.LENGTH_SHORT).show();
}
}
});
break;
}
}
};
sender.sendEmptyMessage(REQUEST_LOAD_MODEL); // corresponding to REQUEST_LOAD_MODEL， to call onLoadModel()
imageView = findViewById(R.id.imageView);
textView = findViewById(R.id.sample_text);
button = findViewById(R.id.button);
button.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View v) {
sender.sendEmptyMessage(REQUEST_RUN_MODEL);
}
});
}
@Override
protected void onDestroy() {
onUnloadModel();
if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.JELLY_BEAN_MR2) {
worker.quitSafely();
} else {
worker.quit();
}
super.onDestroy();
}
/**
* call in onCreate, model init
*
* @return
*/
private boolean onLoadModel() {
if (predictor == null) {
predictor = new Predictor();
}
return predictor.init(this, assetModelDirPath, assetlabelFilePath);
}
/**
* init engine
* call in onCreate
*
* @return
*/
private boolean onRunModel() {
try {
String assetImagePath = "images/5.jpg";
InputStream imageStream = getAssets().open(assetImagePath);
Bitmap image = BitmapFactory.decodeStream(imageStream);
// Input is Bitmap
predictor.setInputImage(image);
return predictor.isLoaded() && predictor.runModel();
} catch (IOException e) {
e.printStackTrace();
return false;
}
}
private void onRunModelSuccessed() {
Log.i(TAG, "onRunModelSuccessed");
textView.setText(predictor.outputResult);
imageView.setImageBitmap(predictor.outputImage);
}
private void onUnloadModel() {
if (predictor != null) {
predictor.releaseModel();
}
}
}
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OCRPredictorNative.java deleted 100644 → 0
View file @ 47752ddf
package com.baidu.paddle.lite.demo.ocr;
import android.graphics.Bitmap;
import android.util.Log;
import java.util.ArrayList;
import java.util.concurrent.atomic.AtomicBoolean;
public class OCRPredictorNative {
private static final AtomicBoolean isSOLoaded = new AtomicBoolean();
public static void loadLibrary() throws RuntimeException {
if (!isSOLoaded.get() && isSOLoaded.compareAndSet(false, true)) {
try {
System.loadLibrary("Native");
} catch (Throwable e) {
RuntimeException exception = new RuntimeException(
"Load libNative.so failed, please check it exists in apk file.", e);
throw exception;
}
}
}
private Config config;
private long nativePointer = 0;
public OCRPredictorNative(Config config) {
this.config = config;
loadLibrary();
nativePointer = init(config.detModelFilename, config.recModelFilename,
config.cpuThreadNum, config.cpuPower);
Log.i("OCRPredictorNative", "load success " + nativePointer);
}
public void release() {
if (nativePointer != 0) {
nativePointer = 0;
destory(nativePointer);
}
}
public ArrayList<OcrResultModel> runImage(float[] inputData, int width, int height, int channels, Bitmap originalImage) {
Log.i("OCRPredictorNative", "begin to run image " + inputData.length + " " + width + " " + height);
float[] dims = new float[]{1, channels, height, width};
float[] rawResults = forward(nativePointer, inputData, dims, originalImage);
ArrayList<OcrResultModel> results = postprocess(rawResults);
return results;
}
public static class Config {
public int cpuThreadNum;
public String cpuPower;
public String detModelFilename;
public String recModelFilename;
}
protected native long init(String detModelPath, String recModelPath, int threadNum, String cpuMode);
protected native float[] forward(long pointer, float[] buf, float[] ddims, Bitmap originalImage);
protected native void destory(long pointer);
private ArrayList<OcrResultModel> postprocess(float[] raw) {
ArrayList<OcrResultModel> results = new ArrayList<OcrResultModel>();
int begin = 0;
while (begin < raw.length) {
int point_num = Math.round(raw[begin]);
int word_num = Math.round(raw[begin + 1]);
OcrResultModel model = parse(raw, begin + 2, point_num, word_num);
begin += 2 + 1 + point_num * 2 + word_num;
results.add(model);
}
return results;
}
private OcrResultModel parse(float[] raw, int begin, int pointNum, int wordNum) {
int current = begin;
OcrResultModel model = new OcrResultModel();
model.setConfidence(raw[current]);
current++;
for (int i = 0; i < pointNum; i++) {
model.addPoints(Math.round(raw[current + i * 2]), Math.round(raw[current + i * 2 + 1]));
}
current += (pointNum * 2);
for (int i = 0; i < wordNum; i++) {
int index = Math.round(raw[current + i]);
model.addWordIndex(index);
}
Log.i("OCRPredictorNative", "word finished " + wordNum);
return model;
}
}
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OcrResultModel.java deleted 100644 → 0
View file @ 47752ddf
package com.baidu.paddle.lite.demo.ocr;
import android.graphics.Point;
import java.util.ArrayList;
import java.util.List;
public class OcrResultModel {
private List<Point> points;
private List<Integer> wordIndex;
private String label;
private float confidence;
public OcrResultModel() {
super();
points = new ArrayList<>();
wordIndex = new ArrayList<>();
}
public void addPoints(int x, int y) {
Point point = new Point(x, y);
points.add(point);
}
public void addWordIndex(int index) {
wordIndex.add(index);
}
public List<Point> getPoints() {
return points;
}
public List<Integer> getWordIndex() {
return wordIndex;
}
public String getLabel() {
return label;
}
public void setLabel(String label) {
this.label = label;
}
public float getConfidence() {
return confidence;
}
public void setConfidence(float confidence) {
this.confidence = confidence;
}
}
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