Merge pull request #2 from PaddlePaddle/develop

mergepaddleocr

deploy/android_demo/app/src/main/cpp/ocr_crnn_process.h

+//
+// 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

+// 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;
+}
\ No newline at end of file

deploy/android_demo/app/src/main/cpp/ocr_db_post_process.h

+//
+// 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

+//
+// 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

+//
+// 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

+#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

+#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

+#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

+#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

+#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

+#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

+#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

+#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

+/*
+ * 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

+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

+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

+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

+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;
+    }
+}

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Predictor.java

+package com.baidu.paddle.lite.demo.ocr;
+
+import android.content.Context;
+import android.graphics.Bitmap;
+import android.graphics.Canvas;
+import android.graphics.Color;
+import android.graphics.Paint;
+import android.graphics.Path;
+import android.graphics.Point;
+import android.util.Log;
+
+import java.io.File;
+import java.io.InputStream;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.List;
+import java.util.Vector;
+
+import static android.graphics.Color.*;
+
+public class Predictor {
+    private static final String TAG = Predictor.class.getSimpleName();
+    public boolean isLoaded = false;
+    public int warmupIterNum = 1;
+    public int inferIterNum = 1;
+    public int cpuThreadNum = 4;
+    public String cpuPowerMode = "LITE_POWER_HIGH";
+    public String modelPath = "";
+    public String modelName = "";
+    protected OCRPredictorNative paddlePredictor = null;
+    protected float inferenceTime = 0;
+    // Only for object detection
+    protected Vector<String> wordLabels = new Vector<String>();
+    protected String inputColorFormat = "BGR";
+    protected long[] inputShape = new long[]{1, 3, 960};
+    protected float[] inputMean = new float[]{0.485f, 0.456f, 0.406f};
+    protected float[] inputStd = new float[]{1.0f / 0.229f, 1.0f / 0.224f, 1.0f / 0.225f};
+    protected float scoreThreshold = 0.1f;
+    protected Bitmap inputImage = null;
+    protected Bitmap outputImage = null;
+    protected volatile String outputResult = "";
+    protected float preprocessTime = 0;
+    protected float postprocessTime = 0;
+
+
+    public Predictor() {
+    }
+
+    public boolean init(Context appCtx, String modelPath, String labelPath) {
+        isLoaded = loadModel(appCtx, modelPath, cpuThreadNum, cpuPowerMode);
+        if (!isLoaded) {
+            return false;
+        }
+        isLoaded = loadLabel(appCtx, labelPath);
+        return isLoaded;
+    }
+
+
+    public boolean init(Context appCtx, String modelPath, String labelPath, int cpuThreadNum, String cpuPowerMode,
+                        String inputColorFormat,
+                        long[] inputShape, float[] inputMean,
+                        float[] inputStd, float scoreThreshold) {
+        if (inputShape.length != 3) {
+            Log.e(TAG, "Size of input shape should be: 3");
+            return false;
+        }
+        if (inputMean.length != inputShape[1]) {
+            Log.e(TAG, "Size of input mean should be: " + Long.toString(inputShape[1]));
+            return false;
+        }
+        if (inputStd.length != inputShape[1]) {
+            Log.e(TAG, "Size of input std should be: " + Long.toString(inputShape[1]));
+            return false;
+        }
+        if (inputShape[0] != 1) {
+            Log.e(TAG, "Only one batch is supported in the image classification demo, you can use any batch size in " +
+                    "your Apps!");
+            return false;
+        }
+        if (inputShape[1] != 1 && inputShape[1] != 3) {
+            Log.e(TAG, "Only one/three channels are supported in the image classification demo, you can use any " +
+                    "channel size in your Apps!");
+            return false;
+        }
+        if (!inputColorFormat.equalsIgnoreCase("BGR")) {
+            Log.e(TAG, "Only  BGR color format is supported.");
+            return false;
+        }
+        boolean isLoaded = init(appCtx, modelPath, labelPath);
+        if (!isLoaded) {
+            return false;
+        }
+        this.inputColorFormat = inputColorFormat;
+        this.inputShape = inputShape;
+        this.inputMean = inputMean;
+        this.inputStd = inputStd;
+        this.scoreThreshold = scoreThreshold;
+        return true;
+    }
+
+    protected boolean loadModel(Context appCtx, String modelPath, int cpuThreadNum, String cpuPowerMode) {
+        // Release model if exists
+        releaseModel();
+
+        // Load model
+        if (modelPath.isEmpty()) {
+            return false;
+        }
+        String realPath = modelPath;
+        if (!modelPath.substring(0, 1).equals("/")) {
+            // Read model files from custom path if the first character of mode path is '/'
+            // otherwise copy model to cache from assets
+            realPath = appCtx.getCacheDir() + "/" + modelPath;
+            Utils.copyDirectoryFromAssets(appCtx, modelPath, realPath);
+        }
+        if (realPath.isEmpty()) {
+            return false;
+        }
+
+        OCRPredictorNative.Config config = new OCRPredictorNative.Config();
+        config.cpuThreadNum = cpuThreadNum;
+        config.detModelFilename = realPath + File.separator + "ch_det_mv3_db_opt.nb";
+        config.recModelFilename = realPath + File.separator + "ch_rec_mv3_crnn_opt.nb";
+        Log.e("Predictor", "model path" + config.detModelFilename + " ; " + config.recModelFilename);
+        config.cpuPower = cpuPowerMode;
+        paddlePredictor = new OCRPredictorNative(config);
+
+        this.cpuThreadNum = cpuThreadNum;
+        this.cpuPowerMode = cpuPowerMode;
+        this.modelPath = realPath;
+        this.modelName = realPath.substring(realPath.lastIndexOf("/") + 1);
+        return true;
+    }
+
+    public void releaseModel() {
+        if (paddlePredictor != null) {
+            paddlePredictor.release();
+            paddlePredictor = null;
+        }
+        isLoaded = false;
+        cpuThreadNum = 1;
+        cpuPowerMode = "LITE_POWER_HIGH";
+        modelPath = "";
+        modelName = "";
+    }
+
+    protected boolean loadLabel(Context appCtx, String labelPath) {
+        wordLabels.clear();
+        // Load word labels from file
+        try {
+            InputStream assetsInputStream = appCtx.getAssets().open(labelPath);
+            int available = assetsInputStream.available();
+            byte[] lines = new byte[available];
+            assetsInputStream.read(lines);
+            assetsInputStream.close();
+            String words = new String(lines);
+            String[] contents = words.split("\n");
+            for (String content : contents) {
+                wordLabels.add(content);
+            }
+            Log.i(TAG, "Word label size: " + wordLabels.size());
+        } catch (Exception e) {
+            Log.e(TAG, e.getMessage());
+            return false;
+        }
+        return true;
+    }
+
+
+    public boolean runModel() {
+        if (inputImage == null || !isLoaded()) {
+            return false;
+        }
+
+        // Pre-process image, and feed input tensor with pre-processed data
+
+        Bitmap scaleImage = Utils.resizeWithStep(inputImage, Long.valueOf(inputShape[2]).intValue(), 32);
+
+        Date start = new Date();
+        int channels = (int) inputShape[1];
+        int width = scaleImage.getWidth();
+        int height = scaleImage.getHeight();
+        float[] inputData = new float[channels * width * height];
+        if (channels == 3) {
+            int[] channelIdx = null;
+            if (inputColorFormat.equalsIgnoreCase("RGB")) {
+                channelIdx = new int[]{0, 1, 2};
+            } else if (inputColorFormat.equalsIgnoreCase("BGR")) {
+                channelIdx = new int[]{2, 1, 0};
+            } else {
+                Log.i(TAG, "Unknown color format " + inputColorFormat + ", only RGB and BGR color format is " +
+                        "supported!");
+                return false;
+            }
+            int[] channelStride = new int[]{width * height, width * height * 2};
+            int p = scaleImage.getPixel(scaleImage.getWidth() - 1, scaleImage.getHeight() - 1);
+            for (int y = 0; y < height; y++) {
+                for (int x = 0; x < width; x++) {
+                    int color = scaleImage.getPixel(x, y);
+                    float[] rgb = new float[]{(float) red(color) / 255.0f, (float) green(color) / 255.0f,
+                            (float) blue(color) / 255.0f};
+                    inputData[y * width + x] = (rgb[channelIdx[0]] - inputMean[0]) / inputStd[0];
+                    inputData[y * width + x + channelStride[0]] = (rgb[channelIdx[1]] - inputMean[1]) / inputStd[1];
+                    inputData[y * width + x + channelStride[1]] = (rgb[channelIdx[2]] - inputMean[2]) / inputStd[2];
+
+                }
+            }
+        } else if (channels == 1) {
+            for (int y = 0; y < height; y++) {
+                for (int x = 0; x < width; x++) {
+                    int color = inputImage.getPixel(x, y);
+                    float gray = (float) (red(color) + green(color) + blue(color)) / 3.0f / 255.0f;
+                    inputData[y * width + x] = (gray - inputMean[0]) / inputStd[0];
+                }
+            }
+        } else {
+            Log.i(TAG, "Unsupported channel size " + Integer.toString(channels) + ",  only channel 1 and 3 is " +
+                    "supported!");
+            return false;
+        }
+        float[] pixels = inputData;
+        Log.i(TAG, "pixels " + pixels[0] + " " + pixels[1] + " " + pixels[2] + " " + pixels[3]
+                + " " + pixels[pixels.length / 2] + " " + pixels[pixels.length / 2 + 1] + " " + pixels[pixels.length - 2] + " " + pixels[pixels.length - 1]);
+        Date end = new Date();
+        preprocessTime = (float) (end.getTime() - start.getTime());
+
+        // Warm up
+        for (int i = 0; i < warmupIterNum; i++) {
+            paddlePredictor.runImage(inputData, width, height, channels, inputImage);
+        }
+        warmupIterNum = 0; // do not need warm
+        // Run inference
+        start = new Date();
+        ArrayList<OcrResultModel> results = paddlePredictor.runImage(inputData, width, height, channels, inputImage);
+        end = new Date();
+        inferenceTime = (end.getTime() - start.getTime()) / (float) inferIterNum;
+
+        results = postprocess(results);
+        Log.i(TAG, "[stat] Preprocess Time: " + preprocessTime
+                + " ; Inference Time: " + inferenceTime + " ;Box Size " + results.size());
+        drawResults(results);
+
+        return true;
+    }
+
+
+    public boolean isLoaded() {
+        return paddlePredictor != null && isLoaded;
+    }
+
+    public String modelPath() {
+        return modelPath;
+    }
+
+    public String modelName() {
+        return modelName;
+    }
+
+    public int cpuThreadNum() {
+        return cpuThreadNum;
+    }
+
+    public String cpuPowerMode() {
+        return cpuPowerMode;
+    }
+
+    public float inferenceTime() {
+        return inferenceTime;
+    }
+
+    public Bitmap inputImage() {
+        return inputImage;
+    }
+
+    public Bitmap outputImage() {
+        return outputImage;
+    }
+
+    public String outputResult() {
+        return outputResult;
+    }
+
+    public float preprocessTime() {
+        return preprocessTime;
+    }
+
+    public float postprocessTime() {
+        return postprocessTime;
+    }
+
+
+    public void setInputImage(Bitmap image) {
+        if (image == null) {
+            return;
+        }
+        this.inputImage = image.copy(Bitmap.Config.ARGB_8888, true);
+    }
+
+    private ArrayList<OcrResultModel> postprocess(ArrayList<OcrResultModel> results) {
+        for (OcrResultModel r : results) {
+            StringBuffer word = new StringBuffer();
+            for (int index : r.getWordIndex()) {
+                if (index >= 0 && index < wordLabels.size()) {
+                    word.append(wordLabels.get(index));
+                } else {
+                    Log.e(TAG, "Word index is not in label list:" + index);
+                    word.append("×");
+                }
+            }
+            r.setLabel(word.toString());
+        }
+        return results;
+    }
+
+    private void drawResults(ArrayList<OcrResultModel> results) {
+        StringBuffer outputResultSb = new StringBuffer("");
+        for (int i = 0; i < results.size(); i++) {
+            OcrResultModel result = results.get(i);
+            StringBuilder sb = new StringBuilder("");
+            sb.append(result.getLabel());
+            sb.append(" ").append(result.getConfidence());
+            sb.append("; Points: ");
+            for (Point p : result.getPoints()) {
+                sb.append("(").append(p.x).append(",").append(p.y).append(") ");
+            }
+            Log.i(TAG, sb.toString()); // show LOG in Logcat panel
+            outputResultSb.append(i + 1).append(": ").append(result.getLabel()).append("\n");
+        }
+        outputResult = outputResultSb.toString();
+        outputImage = inputImage;
+        Canvas canvas = new Canvas(outputImage);
+        Paint paintFillAlpha = new Paint();
+        paintFillAlpha.setStyle(Paint.Style.FILL);
+        paintFillAlpha.setColor(Color.parseColor("#3B85F5"));
+        paintFillAlpha.setAlpha(50);
+
+        Paint paint = new Paint();
+        paint.setColor(Color.parseColor("#3B85F5"));
+        paint.setStrokeWidth(5);
+        paint.setStyle(Paint.Style.STROKE);
+
+        for (OcrResultModel result : results) {
+            Path path = new Path();
+            List<Point> points = result.getPoints();
+            path.moveTo(points.get(0).x, points.get(0).y);
+            for (int i = points.size() - 1; i >= 0; i--) {
+                Point p = points.get(i);
+                path.lineTo(p.x, p.y);
+            }
+            canvas.drawPath(path, paint);
+            canvas.drawPath(path, paintFillAlpha);
+        }
+    }
+
+}

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/SettingsActivity.java

+package com.baidu.paddle.lite.demo.ocr;
+
+import android.content.SharedPreferences;
+import android.os.Bundle;
+import android.preference.CheckBoxPreference;
+import android.preference.EditTextPreference;
+import android.preference.ListPreference;
+
+import androidx.appcompat.app.ActionBar;
+
+import java.util.ArrayList;
+import java.util.List;
+
+
+public class SettingsActivity extends AppCompatPreferenceActivity implements SharedPreferences.OnSharedPreferenceChangeListener {
+    ListPreference lpChoosePreInstalledModel = null;
+    CheckBoxPreference cbEnableCustomSettings = null;
+    EditTextPreference etModelPath = null;
+    EditTextPreference etLabelPath = null;
+    EditTextPreference etImagePath = null;
+    ListPreference lpCPUThreadNum = null;
+    ListPreference lpCPUPowerMode = null;
+    ListPreference lpInputColorFormat = null;
+    EditTextPreference etInputShape = null;
+    EditTextPreference etInputMean = null;
+    EditTextPreference etInputStd = null;
+    EditTextPreference etScoreThreshold = null;
+
+    List<String> preInstalledModelPaths = null;
+    List<String> preInstalledLabelPaths = null;
+    List<String> preInstalledImagePaths = null;
+    List<String> preInstalledInputShapes = null;
+    List<String> preInstalledCPUThreadNums = null;
+    List<String> preInstalledCPUPowerModes = null;
+    List<String> preInstalledInputColorFormats = null;
+    List<String> preInstalledInputMeans = null;
+    List<String> preInstalledInputStds = null;
+    List<String> preInstalledScoreThresholds = null;
+
+    @Override
+    public void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+        addPreferencesFromResource(R.xml.settings);
+        ActionBar supportActionBar = getSupportActionBar();
+        if (supportActionBar != null) {
+            supportActionBar.setDisplayHomeAsUpEnabled(true);
+        }
+
+        // Initialized pre-installed models
+        preInstalledModelPaths = new ArrayList<String>();
+        preInstalledLabelPaths = new ArrayList<String>();
+        preInstalledImagePaths = new ArrayList<String>();
+        preInstalledInputShapes = new ArrayList<String>();
+        preInstalledCPUThreadNums = new ArrayList<String>();
+        preInstalledCPUPowerModes = new ArrayList<String>();
+        preInstalledInputColorFormats = new ArrayList<String>();
+        preInstalledInputMeans = new ArrayList<String>();
+        preInstalledInputStds = new ArrayList<String>();
+        preInstalledScoreThresholds = new ArrayList<String>();
+        // Add ssd_mobilenet_v1_pascalvoc_for_cpu
+        preInstalledModelPaths.add(getString(R.string.MODEL_PATH_DEFAULT));
+        preInstalledLabelPaths.add(getString(R.string.LABEL_PATH_DEFAULT));
+        preInstalledImagePaths.add(getString(R.string.IMAGE_PATH_DEFAULT));
+        preInstalledCPUThreadNums.add(getString(R.string.CPU_THREAD_NUM_DEFAULT));
+        preInstalledCPUPowerModes.add(getString(R.string.CPU_POWER_MODE_DEFAULT));
+        preInstalledInputColorFormats.add(getString(R.string.INPUT_COLOR_FORMAT_DEFAULT));
+        preInstalledInputShapes.add(getString(R.string.INPUT_SHAPE_DEFAULT));
+        preInstalledInputMeans.add(getString(R.string.INPUT_MEAN_DEFAULT));
+        preInstalledInputStds.add(getString(R.string.INPUT_STD_DEFAULT));
+        preInstalledScoreThresholds.add(getString(R.string.SCORE_THRESHOLD_DEFAULT));
+
+        // Setup UI components
+        lpChoosePreInstalledModel =
+                (ListPreference) findPreference(getString(R.string.CHOOSE_PRE_INSTALLED_MODEL_KEY));
+        String[] preInstalledModelNames = new String[preInstalledModelPaths.size()];
+        for (int i = 0; i < preInstalledModelPaths.size(); i++) {
+            preInstalledModelNames[i] =
+                    preInstalledModelPaths.get(i).substring(preInstalledModelPaths.get(i).lastIndexOf("/") + 1);
+        }
+        lpChoosePreInstalledModel.setEntries(preInstalledModelNames);
+        lpChoosePreInstalledModel.setEntryValues(preInstalledModelPaths.toArray(new String[preInstalledModelPaths.size()]));
+        cbEnableCustomSettings =
+                (CheckBoxPreference) findPreference(getString(R.string.ENABLE_CUSTOM_SETTINGS_KEY));
+        etModelPath = (EditTextPreference) findPreference(getString(R.string.MODEL_PATH_KEY));
+        etModelPath.setTitle("Model Path (SDCard: " + Utils.getSDCardDirectory() + ")");
+        etLabelPath = (EditTextPreference) findPreference(getString(R.string.LABEL_PATH_KEY));
+        etImagePath = (EditTextPreference) findPreference(getString(R.string.IMAGE_PATH_KEY));
+        lpCPUThreadNum =
+                (ListPreference) findPreference(getString(R.string.CPU_THREAD_NUM_KEY));
+        lpCPUPowerMode =
+                (ListPreference) findPreference(getString(R.string.CPU_POWER_MODE_KEY));
+        lpInputColorFormat =
+                (ListPreference) findPreference(getString(R.string.INPUT_COLOR_FORMAT_KEY));
+        etInputShape = (EditTextPreference) findPreference(getString(R.string.INPUT_SHAPE_KEY));
+        etInputMean = (EditTextPreference) findPreference(getString(R.string.INPUT_MEAN_KEY));
+        etInputStd = (EditTextPreference) findPreference(getString(R.string.INPUT_STD_KEY));
+        etScoreThreshold = (EditTextPreference) findPreference(getString(R.string.SCORE_THRESHOLD_KEY));
+    }
+
+    private void reloadPreferenceAndUpdateUI() {
+        SharedPreferences sharedPreferences = getPreferenceScreen().getSharedPreferences();
+        boolean enableCustomSettings =
+                sharedPreferences.getBoolean(getString(R.string.ENABLE_CUSTOM_SETTINGS_KEY), false);
+        String modelPath = sharedPreferences.getString(getString(R.string.CHOOSE_PRE_INSTALLED_MODEL_KEY),
+                getString(R.string.MODEL_PATH_DEFAULT));
+        int modelIdx = lpChoosePreInstalledModel.findIndexOfValue(modelPath);
+        if (modelIdx >= 0 && modelIdx < preInstalledModelPaths.size()) {
+            if (!enableCustomSettings) {
+                SharedPreferences.Editor editor = sharedPreferences.edit();
+                editor.putString(getString(R.string.MODEL_PATH_KEY), preInstalledModelPaths.get(modelIdx));
+                editor.putString(getString(R.string.LABEL_PATH_KEY), preInstalledLabelPaths.get(modelIdx));
+                editor.putString(getString(R.string.IMAGE_PATH_KEY), preInstalledImagePaths.get(modelIdx));
+                editor.putString(getString(R.string.CPU_THREAD_NUM_KEY), preInstalledCPUThreadNums.get(modelIdx));
+                editor.putString(getString(R.string.CPU_POWER_MODE_KEY), preInstalledCPUPowerModes.get(modelIdx));
+                editor.putString(getString(R.string.INPUT_COLOR_FORMAT_KEY),
+                        preInstalledInputColorFormats.get(modelIdx));
+                editor.putString(getString(R.string.INPUT_SHAPE_KEY), preInstalledInputShapes.get(modelIdx));
+                editor.putString(getString(R.string.INPUT_MEAN_KEY), preInstalledInputMeans.get(modelIdx));
+                editor.putString(getString(R.string.INPUT_STD_KEY), preInstalledInputStds.get(modelIdx));
+                editor.putString(getString(R.string.SCORE_THRESHOLD_KEY),
+                        preInstalledScoreThresholds.get(modelIdx));
+                editor.commit();
+            }
+            lpChoosePreInstalledModel.setSummary(modelPath);
+        }
+        cbEnableCustomSettings.setChecked(enableCustomSettings);
+        etModelPath.setEnabled(enableCustomSettings);
+        etLabelPath.setEnabled(enableCustomSettings);
+        etImagePath.setEnabled(enableCustomSettings);
+        lpCPUThreadNum.setEnabled(enableCustomSettings);
+        lpCPUPowerMode.setEnabled(enableCustomSettings);
+        lpInputColorFormat.setEnabled(enableCustomSettings);
+        etInputShape.setEnabled(enableCustomSettings);
+        etInputMean.setEnabled(enableCustomSettings);
+        etInputStd.setEnabled(enableCustomSettings);
+        etScoreThreshold.setEnabled(enableCustomSettings);
+        modelPath = sharedPreferences.getString(getString(R.string.MODEL_PATH_KEY),
+                getString(R.string.MODEL_PATH_DEFAULT));
+        String labelPath = sharedPreferences.getString(getString(R.string.LABEL_PATH_KEY),
+                getString(R.string.LABEL_PATH_DEFAULT));
+        String imagePath = sharedPreferences.getString(getString(R.string.IMAGE_PATH_KEY),
+                getString(R.string.IMAGE_PATH_DEFAULT));
+        String cpuThreadNum = sharedPreferences.getString(getString(R.string.CPU_THREAD_NUM_KEY),
+                getString(R.string.CPU_THREAD_NUM_DEFAULT));
+        String cpuPowerMode = sharedPreferences.getString(getString(R.string.CPU_POWER_MODE_KEY),
+                getString(R.string.CPU_POWER_MODE_DEFAULT));
+        String inputColorFormat = sharedPreferences.getString(getString(R.string.INPUT_COLOR_FORMAT_KEY),
+                getString(R.string.INPUT_COLOR_FORMAT_DEFAULT));
+        String inputShape = sharedPreferences.getString(getString(R.string.INPUT_SHAPE_KEY),
+                getString(R.string.INPUT_SHAPE_DEFAULT));
+        String inputMean = sharedPreferences.getString(getString(R.string.INPUT_MEAN_KEY),
+                getString(R.string.INPUT_MEAN_DEFAULT));
+        String inputStd = sharedPreferences.getString(getString(R.string.INPUT_STD_KEY),
+                getString(R.string.INPUT_STD_DEFAULT));
+        String scoreThreshold = sharedPreferences.getString(getString(R.string.SCORE_THRESHOLD_KEY),
+                getString(R.string.SCORE_THRESHOLD_DEFAULT));
+        etModelPath.setSummary(modelPath);
+        etModelPath.setText(modelPath);
+        etLabelPath.setSummary(labelPath);
+        etLabelPath.setText(labelPath);
+        etImagePath.setSummary(imagePath);
+        etImagePath.setText(imagePath);
+        lpCPUThreadNum.setValue(cpuThreadNum);
+        lpCPUThreadNum.setSummary(cpuThreadNum);
+        lpCPUPowerMode.setValue(cpuPowerMode);
+        lpCPUPowerMode.setSummary(cpuPowerMode);
+        lpInputColorFormat.setValue(inputColorFormat);
+        lpInputColorFormat.setSummary(inputColorFormat);
+        etInputShape.setSummary(inputShape);
+        etInputShape.setText(inputShape);
+        etInputMean.setSummary(inputMean);
+        etInputMean.setText(inputMean);
+        etInputStd.setSummary(inputStd);
+        etInputStd.setText(inputStd);
+        etScoreThreshold.setText(scoreThreshold);
+        etScoreThreshold.setSummary(scoreThreshold);
+    }
+
+    @Override
+    protected void onResume() {
+        super.onResume();
+        getPreferenceScreen().getSharedPreferences().registerOnSharedPreferenceChangeListener(this);
+        reloadPreferenceAndUpdateUI();
+    }
+
+    @Override
+    protected void onPause() {
+        super.onPause();
+        getPreferenceScreen().getSharedPreferences().unregisterOnSharedPreferenceChangeListener(this);
+    }
+
+    @Override
+    public void onSharedPreferenceChanged(SharedPreferences sharedPreferences, String key) {
+        if (key.equals(getString(R.string.CHOOSE_PRE_INSTALLED_MODEL_KEY))) {
+            SharedPreferences.Editor editor = sharedPreferences.edit();
+            editor.putBoolean(getString(R.string.ENABLE_CUSTOM_SETTINGS_KEY), false);
+            editor.commit();
+        }
+        reloadPreferenceAndUpdateUI();
+    }
+}