tm_yolov5s_timvx.cpp

/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you 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.
 */

/*
 * Copyright (c) 2021, OPEN AI LAB
 * Author: xwwang@openailab.com
 * Author: stevenwudi@fiture.com
 * 
 * original model: https://github.com/ultralytics/yolov5
 */

#include <cmath>
#include <vector>
#include <string>
#include <algorithm>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#include "common.h"
#include "tengine/c_api.h"
#include "tengine_operations.h"

#define YOLOV5_VERSION6 0

struct Object
{
    cv::Rect_<float> rect;
    int label;
    float prob;
};

static inline float sigmoid(float x)
{
    return static_cast<float>(1.f / (1.f + exp(-x)));
}

static inline float intersection_area(const Object& a, const Object& b)
{
    cv::Rect_<float> inter = a.rect & b.rect;
    return inter.area();
}

static void qsort_descent_inplace(std::vector<Object>& faceobjects, int left, int right)
{
    int i = left;
    int j = right;
    float p = faceobjects[(left + right) / 2].prob;

    while (i <= j)
    {
        while (faceobjects[i].prob > p)
            i++;

        while (faceobjects[j].prob < p)
            j--;

        if (i <= j)
        {
            // swap
            std::swap(faceobjects[i], faceobjects[j]);

            i++;
            j--;
        }
    }

#pragma omp parallel sections
    {
#pragma omp section
        {
            if (left < j) qsort_descent_inplace(faceobjects, left, j);
        }
#pragma omp section
        {
            if (i < right) qsort_descent_inplace(faceobjects, i, right);
        }
    }
}

static void qsort_descent_inplace(std::vector<Object>& faceobjects)
{
    if (faceobjects.empty())
        return;

    qsort_descent_inplace(faceobjects, 0, faceobjects.size() - 1);
}

static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold)
{
    picked.clear();

    const int n = faceobjects.size();

    std::vector<float> areas(n);
    for (int i = 0; i < n; i++)
    {
        areas[i] = faceobjects[i].rect.area();
    }

    for (int i = 0; i < n; i++)
    {
        const Object& a = faceobjects[i];

        int keep = 1;
        for (int j = 0; j < (int)picked.size(); j++)
        {
            const Object& b = faceobjects[picked[j]];

            // intersection over union
            float inter_area = intersection_area(a, b);
            float union_area = areas[i] + areas[picked[j]] - inter_area;
            // float IoU = inter_area / union_area
            if (inter_area / union_area > nms_threshold)
                keep = 0;
        }

        if (keep)
            picked.push_back(i);
    }
}

static void generate_proposals(int stride, const float* feat, float prob_threshold, std::vector<Object>& objects,
                               int letterbox_cols, int letterbox_rows)
{
    static float anchors[18] = {10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326};

    int anchor_num = 3;
    int feat_w = letterbox_cols / stride;
    int feat_h = letterbox_rows / stride;
    int cls_num = 80;
    int anchor_group;
    if (stride == 8)
        anchor_group = 1;
    if (stride == 16)
        anchor_group = 2;
    if (stride == 32)
        anchor_group = 3;
    for (int h = 0; h <= feat_h - 1; h++)
    {
        for (int w = 0; w <= feat_w - 1; w++)
        {
            for (int a = 0; a <= anchor_num - 1; a++)
            {
                //process cls score
                int class_index = 0;
                float class_score = -FLT_MAX;
                for (int s = 0; s <= cls_num - 1; s++)
                {
                    float score = feat[a * feat_w * feat_h * (cls_num + 5) + h * feat_w * (cls_num + 5) + w * (cls_num + 5) + s + 5];
                    if (score > class_score)
                    {
                        class_index = s;
                        class_score = score;
                    }
                }
                //process box score
                float box_score = feat[a * feat_w * feat_h * (cls_num + 5) + (h * feat_w) * (cls_num + 5) + w * (cls_num + 5) + 4];
                float final_score = sigmoid(box_score) * sigmoid(class_score);
                if (final_score >= prob_threshold)
                {
                    int loc_idx = a * feat_h * feat_w * (cls_num + 5) + h * feat_w * (cls_num + 5) + w * (cls_num + 5);
                    float dx = sigmoid(feat[loc_idx + 0]);
                    float dy = sigmoid(feat[loc_idx + 1]);
                    float dw = sigmoid(feat[loc_idx + 2]);
                    float dh = sigmoid(feat[loc_idx + 3]);
                    float pred_cx = (dx * 2.0f - 0.5f + w) * stride;
                    float pred_cy = (dy * 2.0f - 0.5f + h) * stride;
                    float anchor_w = anchors[(anchor_group - 1) * 6 + a * 2 + 0];
                    float anchor_h = anchors[(anchor_group - 1) * 6 + a * 2 + 1];
                    float pred_w = dw * dw * 4.0f * anchor_w;
                    float pred_h = dh * dh * 4.0f * anchor_h;
                    float x0 = pred_cx - pred_w * 0.5f;
                    float y0 = pred_cy - pred_h * 0.5f;
                    float x1 = pred_cx + pred_w * 0.5f;
                    float y1 = pred_cy + pred_h * 0.5f;

                    Object obj;
                    obj.rect.x = x0;
                    obj.rect.y = y0;
                    obj.rect.width = x1 - x0;
                    obj.rect.height = y1 - y0;
                    obj.label = class_index;
                    obj.prob = final_score;
                    objects.push_back(obj);
                }
            }
        }
    }
}

static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& objects)
{
    static const char* class_names[] = {
        "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
        "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
        "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
        "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
        "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
        "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
        "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
        "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
        "hair drier", "toothbrush"};

    cv::Mat image = bgr.clone();

    for (size_t i = 0; i < objects.size(); i++)
    {
        const Object& obj = objects[i];

        fprintf(stderr, "%2d: %3.0f%%, [%4.0f, %4.0f, %4.0f, %4.0f], %s\n", obj.label, obj.prob * 100, obj.rect.x,
                obj.rect.y, obj.rect.x + obj.rect.width, obj.rect.y + obj.rect.height, class_names[obj.label]);

        cv::rectangle(image, obj.rect, cv::Scalar(255, 0, 0));

        char text[256];
        sprintf(text, "%s %.1f%%", class_names[obj.label], obj.prob * 100);

        int baseLine = 0;
        cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);

        int x = obj.rect.x;
        int y = obj.rect.y - label_size.height - baseLine;
        if (y < 0)
            y = 0;
        if (x + label_size.width > image.cols)
            x = image.cols - label_size.width;

        cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)),
                      cv::Scalar(255, 255, 255), -1);

        cv::putText(image, text, cv::Point(x, y + label_size.height), cv::FONT_HERSHEY_SIMPLEX, 0.5,
                    cv::Scalar(0, 0, 0));
    }

    cv::imwrite("yolov5_timvx_letterbox_out.jpg", image);
}

void show_usage()
{
    fprintf(
        stderr,
        "[Usage]:  [-h]\n    [-m model_file] [-i image_file] [-r repeat_count] [-t thread_count]\n");
}

void get_input_data_focus_uint8(const char* image_file, uint8_t* input_data, int letterbox_rows, int letterbox_cols, const float* mean,
                                const float* scale, float input_scale, int zero_point)
{
    cv::Mat sample = cv::imread(image_file, 1);
    cv::Mat img;

    if (sample.channels() == 1)
        cv::cvtColor(sample, img, cv::COLOR_GRAY2RGB);
    else
        cv::cvtColor(sample, img, cv::COLOR_BGR2RGB);

    /* letterbox process to support different letterbox size */
    float scale_letterbox;
    int resize_rows;
    int resize_cols;
    if ((letterbox_rows * 1.0 / img.rows) < (letterbox_cols * 1.0 / img.cols))
    {
        scale_letterbox = letterbox_rows * 1.0 / img.rows;
    }
    else
    {
        scale_letterbox = letterbox_cols * 1.0 / img.cols;
    }
    resize_cols = int(scale_letterbox * img.cols);
    resize_rows = int(scale_letterbox * img.rows);

    cv::resize(img, img, cv::Size(resize_cols, resize_rows));
    img.convertTo(img, CV_32FC3);
    // Generate a gray image for letterbox using opencv
    cv::Mat img_new(letterbox_cols, letterbox_rows, CV_32FC3, cv::Scalar(0.5 / scale[0] + mean[0], 0.5 / scale[1] + mean[1], 0.5 / scale[2] + mean[2]));
    int top = (letterbox_rows - resize_rows) / 2;
    int bot = (letterbox_rows - resize_rows + 1) / 2;
    int left = (letterbox_cols - resize_cols) / 2;
    int right = (letterbox_cols - resize_cols + 1) / 2;
    // Letterbox filling
    cv::copyMakeBorder(img, img_new, top, bot, left, right, cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0));

    img_new.convertTo(img_new, CV_32FC3);
    float* img_data = (float*)img_new.data;
    std::vector<float> input_temp(3 * letterbox_cols * letterbox_rows);

    /* nhwc to nchw */
    for (int h = 0; h < letterbox_rows; h++)
    {
        for (int w = 0; w < letterbox_cols; w++)
        {
            for (int c = 0; c < 3; c++)
            {
                int in_index = h * letterbox_cols * 3 + w * 3 + c;
                int out_index = c * letterbox_rows * letterbox_cols + h * letterbox_cols + w;
                input_temp[out_index] = (img_data[in_index] - mean[c]) * scale[c];
            }
        }
    }

    /* focus process */
    for (int i = 0; i < 2; i++) // corresponding to rows
    {
        for (int g = 0; g < 2; g++) // corresponding to cols
        {
            for (int c = 0; c < 3; c++)
            {
                for (int h = 0; h < letterbox_rows / 2; h++)
                {
                    for (int w = 0; w < letterbox_cols / 2; w++)
                    {
                        int in_index = i + g * letterbox_cols + c * letterbox_cols * letterbox_rows + h * 2 * letterbox_cols + w * 2;
                        int out_index = i * 2 * 3 * (letterbox_cols / 2) * (letterbox_rows / 2) + g * 3 * (letterbox_cols / 2) * (letterbox_rows / 2) + c * (letterbox_cols / 2) * (letterbox_rows / 2) + h * (letterbox_cols / 2) + w;

                        /* quant to uint8 */
                        int udata = (round)(input_temp[in_index] / input_scale + (float)zero_point);
                        if (udata > 255)
                            udata = 255;
                        else if (udata < 0)
                            udata = 0;

                        input_data[out_index] = udata;
                    }
                }
            }
        }
    }
}

void get_input_data_nofocus_uint8(const char* image_file, uint8_t* input_data, int letterbox_rows, int letterbox_cols, const float* mean,
                                  const float* scale, float input_scale, int zero_point)
{
    cv::Mat sample = cv::imread(image_file, 1);
    cv::Mat img;

    if (sample.channels() == 1)
        cv::cvtColor(sample, img, cv::COLOR_GRAY2RGB);
    else
        cv::cvtColor(sample, img, cv::COLOR_BGR2RGB);

    /* letterbox process to support different letterbox size */
    float scale_letterbox;
    int resize_rows;
    int resize_cols;
    if ((letterbox_rows * 1.0 / img.rows) < (letterbox_cols * 1.0 / img.cols))
    {
        scale_letterbox = letterbox_rows * 1.0 / img.rows;
    }
    else
    {
        scale_letterbox = letterbox_cols * 1.0 / img.cols;
    }
    resize_cols = int(scale_letterbox * img.cols);
    resize_rows = int(scale_letterbox * img.rows);

    cv::resize(img, img, cv::Size(resize_cols, resize_rows));
    img.convertTo(img, CV_32FC3);
    // Generate a gray image for letterbox using opencv
    cv::Mat img_new(letterbox_cols, letterbox_rows, CV_32FC3, cv::Scalar(0.5 / scale[0] + mean[0], 0.5 / scale[1] + mean[1], 0.5 / scale[2] + mean[2]));
    int top = (letterbox_rows - resize_rows) / 2;
    int bot = (letterbox_rows - resize_rows + 1) / 2;
    int left = (letterbox_cols - resize_cols) / 2;
    int right = (letterbox_cols - resize_cols + 1) / 2;
    // Letterbox filling
    cv::copyMakeBorder(img, img_new, top, bot, left, right, cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0));

    img_new.convertTo(img_new, CV_32FC3);
    float* img_data = (float*)img_new.data;

    /* nhwc to nchw */
    for (int h = 0; h < letterbox_rows; h++)
    {
        for (int w = 0; w < letterbox_cols; w++)
        {
            for (int c = 0; c < 3; c++)
            {
                int in_index = h * letterbox_cols * 3 + w * 3 + c;
                int out_index = c * letterbox_rows * letterbox_cols + h * letterbox_cols + w;
                int udata = (round)((img_data[in_index] - mean[c]) * scale[c] / input_scale + (float)zero_point);

                if (udata > 255)
                    udata = 255;
                else if (udata < 0)
                    udata = 0;

                input_data[out_index] = udata;
            }
        }
    }
}

int main(int argc, char* argv[])
{
    const char* model_file = nullptr;
    const char* image_file = nullptr;
    int img_c = 3;
    const float mean[3] = {0, 0, 0};
    const float scale[3] = {0.003921, 0.003921, 0.003921};

    // set default letterbox size
    int letterbox_rows = 640;
    int letterbox_cols = 640;

    int repeat_count = 1;
    int num_thread = 1;

    int res;
    while ((res = getopt(argc, argv, "m:i:r:t:h:")) != -1)
    {
        switch (res)
        {
        case 'm':
            model_file = optarg;
            break;
        case 'i':
            image_file = optarg;
            break;
        case 'r':
            repeat_count = atoi(optarg);
            break;
        case 't':
            num_thread = atoi(optarg);
            break;
        case 'h':
            show_usage();
            return 0;
        default:
            break;
        }
    }

    /* check files */
    if (nullptr == model_file)
    {
        fprintf(stderr, "Error: Tengine model file not specified!\n");
        show_usage();
        return -1;
    }

    if (nullptr == image_file)
    {
        fprintf(stderr, "Error: Image file not specified!\n");
        show_usage();
        return -1;
    }

    if (!check_file_exist(model_file) || !check_file_exist(image_file))
        return -1;

    cv::Mat img = cv::imread(image_file, 1);
    if (img.empty())
    {
        fprintf(stderr, "cv::imread %s failed\n", image_file);
        return -1;
    }

    /* set runtime options */
    struct options opt;
    opt.num_thread = num_thread;
    opt.cluster = TENGINE_CLUSTER_ALL;
    opt.precision = TENGINE_MODE_UINT8;
    opt.affinity = 0;

    /* inital tengine */
    if (init_tengine() != 0)
    {
        fprintf(stderr, "Initial tengine failed.\n");
        return -1;
    }
    fprintf(stderr, "tengine-lite library version: %s\n", get_tengine_version());

    /* create VeriSilicon TIM-VX backend */
    context_t timvx_context = create_context("timvx", 1);
    int rtt = set_context_device(timvx_context, "TIMVX", nullptr, 0);
    if (0 > rtt)
    {
        fprintf(stderr, " add_context_device VSI DEVICE failed.\n");
        return -1;
    }

    /* create graph, load tengine model xxx.tmfile */
    graph_t graph = create_graph(timvx_context, "tengine", model_file);
    if (graph == nullptr)
    {
        fprintf(stderr, "Create graph failed.\n");
        return -1;
    }

    int img_size = letterbox_rows * letterbox_cols * img_c;
#if YOLOV5_VERSION6
    int dims[] = {1, 3, letterbox_rows, letterbox_cols};
#else
    int dims[] = {1, 12, int(letterbox_rows / 2), int(letterbox_cols / 2)};
#endif
    std::vector<uint8_t> input_data(img_size);

    tensor_t input_tensor = get_graph_input_tensor(graph, 0, 0);
    if (input_tensor == nullptr)
    {
        fprintf(stderr, "Get input tensor failed\n");
        return -1;
    }

    if (set_tensor_shape(input_tensor, dims, 4) < 0)
    {
        fprintf(stderr, "Set input tensor shape failed\n");
        return -1;
    }

    if (set_tensor_buffer(input_tensor, input_data.data(), img_size) < 0)
    {
        fprintf(stderr, "Set input tensor buffer failed\n");
        return -1;
    }

    /* prerun graph, set work options(num_thread, cluster, precision) */
    if (prerun_graph_multithread(graph, opt) < 0)
    {
        fprintf(stderr, "Prerun multithread graph failed.\n");
        return -1;
    }

    /* prepare process input data, set the data mem to input tensor */
    float input_scale = 0.f;
    int input_zero_point = 0;
    get_tensor_quant_param(input_tensor, &input_scale, &input_zero_point, 1);

#if YOLOV5_VERSION6
    get_input_data_nofocus_uint8(image_file, input_data.data(), letterbox_rows, letterbox_cols, mean, scale, input_scale, input_zero_point);
#else
    get_input_data_focus_uint8(image_file, input_data.data(), letterbox_rows, letterbox_cols, mean, scale, input_scale, input_zero_point);
#endif

    /* run graph */
    double min_time = DBL_MAX;
    double max_time = DBL_MIN;
    double total_time = 0.;
    for (int i = 0; i < repeat_count; i++)
    {
        double start = get_current_time();
        if (run_graph(graph, 1) < 0)
        {
            fprintf(stderr, "Run graph failed\n");
            return -1;
        }
        double end = get_current_time();
        double cur = end - start;
        total_time += cur;
        min_time = std::min(min_time, cur);
        max_time = std::max(max_time, cur);
    }
    fprintf(stderr, "Repeat %d times, thread %d, avg time %.2f ms, max_time %.2f ms, min_time %.2f ms\n", repeat_count, num_thread,
            total_time / repeat_count, max_time, min_time);
    fprintf(stderr, "--------------------------------------\n");

    /* yolov5 postprocess */
    // 0: 1, 3, 20, 20, 85
    // 1: 1, 3, 40, 40, 85
    // 2: 1, 3, 80, 80, 85
    tensor_t p8_output = get_graph_output_tensor(graph, 0, 0);
    tensor_t p16_output = get_graph_output_tensor(graph, 1, 0);
    tensor_t p32_output = get_graph_output_tensor(graph, 2, 0);

    /* dequant output data */
    float p8_scale = 0.f;
    float p16_scale = 0.f;
    float p32_scale = 0.f;
    int p8_zero_point = 0;
    int p16_zero_point = 0;
    int p32_zero_point = 0;

    get_tensor_quant_param(p8_output, &p8_scale, &p8_zero_point, 1);
    get_tensor_quant_param(p16_output, &p16_scale, &p16_zero_point, 1);
    get_tensor_quant_param(p32_output, &p32_scale, &p32_zero_point, 1);

    int p8_count = get_tensor_buffer_size(p8_output) / sizeof(uint8_t);
    int p16_count = get_tensor_buffer_size(p16_output) / sizeof(uint8_t);
    int p32_count = get_tensor_buffer_size(p32_output) / sizeof(uint8_t);

    std::vector<float> p8_data(p8_count);
    std::vector<float> p16_data(p16_count);
    std::vector<float> p32_data(p32_count);

    uint8_t* p8_data_u8 = (uint8_t*)get_tensor_buffer(p8_output);
    uint8_t* p16_data_u8 = (uint8_t*)get_tensor_buffer(p16_output);
    uint8_t* p32_data_u8 = (uint8_t*)get_tensor_buffer(p32_output);

    for (int c = 0; c < p8_count; c++)
    {
        p8_data[c] = ((float)p8_data_u8[c] - (float)p8_zero_point) * p8_scale;
    }

    for (int c = 0; c < p16_count; c++)
    {
        p16_data[c] = ((float)p16_data_u8[c] - (float)p16_zero_point) * p16_scale;
    }

    for (int c = 0; c < p32_count; c++)
    {
        p32_data[c] = ((float)p32_data_u8[c] - (float)p32_zero_point) * p32_scale;
    }

    /* postprocess */
    const float prob_threshold = 0.25f;
    const float nms_threshold = 0.45f;

    std::vector<Object> proposals;
    std::vector<Object> objects8;
    std::vector<Object> objects16;
    std::vector<Object> objects32;
    std::vector<Object> objects;

    generate_proposals(32, p32_data.data(), prob_threshold, objects32, letterbox_cols, letterbox_rows);
    proposals.insert(proposals.end(), objects32.begin(), objects32.end());
    generate_proposals(16, p16_data.data(), prob_threshold, objects16, letterbox_cols, letterbox_rows);
    proposals.insert(proposals.end(), objects16.begin(), objects16.end());
    generate_proposals(8, p8_data.data(), prob_threshold, objects8, letterbox_cols, letterbox_rows);
    proposals.insert(proposals.end(), objects8.begin(), objects8.end());

    qsort_descent_inplace(proposals);
    std::vector<int> picked;
    nms_sorted_bboxes(proposals, picked, nms_threshold);

    /* yolov5 draw the result */

    float scale_letterbox;
    int resize_rows;
    int resize_cols;
    if ((letterbox_rows * 1.0 / img.rows) < (letterbox_cols * 1.0 / img.cols))
    {
        scale_letterbox = letterbox_rows * 1.0 / img.rows;
    }
    else
    {
        scale_letterbox = letterbox_cols * 1.0 / img.cols;
    }
    resize_cols = int(scale_letterbox * img.cols);
    resize_rows = int(scale_letterbox * img.rows);

    int tmp_h = (letterbox_rows - resize_rows) / 2;
    int tmp_w = (letterbox_cols - resize_cols) / 2;

    float ratio_x = (float)img.rows / resize_rows;
    float ratio_y = (float)img.cols / resize_cols;

    int count = picked.size();
    fprintf(stderr, "detection num: %d\n", count);

    objects.resize(count);
    for (int i = 0; i < count; i++)
    {
        objects[i] = proposals[picked[i]];
        float x0 = (objects[i].rect.x);
        float y0 = (objects[i].rect.y);
        float x1 = (objects[i].rect.x + objects[i].rect.width);
        float y1 = (objects[i].rect.y + objects[i].rect.height);

        x0 = (x0 - tmp_w) * ratio_x;
        y0 = (y0 - tmp_h) * ratio_y;
        x1 = (x1 - tmp_w) * ratio_x;
        y1 = (y1 - tmp_h) * ratio_y;

        x0 = std::max(std::min(x0, (float)(img.cols - 1)), 0.f);
        y0 = std::max(std::min(y0, (float)(img.rows - 1)), 0.f);
        x1 = std::max(std::min(x1, (float)(img.cols - 1)), 0.f);
        y1 = std::max(std::min(y1, (float)(img.rows - 1)), 0.f);

        objects[i].rect.x = x0;
        objects[i].rect.y = y0;
        objects[i].rect.width = x1 - x0;
        objects[i].rect.height = y1 - y0;
    }

    draw_objects(img, objects);

    /* release tengine */
    postrun_graph(graph);
    destroy_graph(graph);
    release_tengine();
}