yolov4.cpp

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//
// Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// 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.

//modified 12-31-2021 Q-engineering

#include "net.h"

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream>
#include <stdio.h>
#include <vector>

#define YOLOV4_TINY 1 //0 or undef for yolov4

ncnn::Net yolov4;
int target_size;
const float mean_vals[3] = {0, 0, 0};
const float norm_vals[3] = {1 / 255.f, 1 / 255.f, 1 / 255.f};

const char* class_names[] = {
    "background", "person", "bicycle",
    "car", "motorbike", "aeroplane", "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", "sofa", "pottedplant", "bed", "diningtable",
    "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard",
    "cell phone", "microwave", "oven", "toaster", "sink",
    "refrigerator", "book", "clock", "vase", "scissors",
    "teddy bear", "hair drier", "toothbrush"
};

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

static int detect_yolov4(const cv::Mat& bgr, std::vector<Object>& objects)
{
    int img_w = bgr.cols;
    int img_h = bgr.rows;

    ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR, bgr.cols, bgr.rows, target_size, target_size);

    in.substract_mean_normalize(mean_vals, norm_vals);

    ncnn::Extractor ex = yolov4.create_extractor();
    ex.set_num_threads(4);

    ex.input("data", in);

    ncnn::Mat out;
    ex.extract("output", out);

    //     printf("%d %d %d\n", out.w, out.h, out.c);
    objects.clear();
    for (int i = 0; i < out.h; i++)
    {
        const float* values = out.row(i);

        Object object;
        object.label = values[0];
        object.prob = values[1];
        object.rect.x = values[2] * img_w;
        object.rect.y = values[3] * img_h;
        object.rect.width = values[4] * img_w - object.rect.x;
        object.rect.height = values[5] * img_h - object.rect.y;

        objects.push_back(object);
    }

    return 0;
}

static void draw_objects(cv::Mat& bgr, const std::vector<Object>& objects)
{
    for (size_t i = 0; i < objects.size(); i++)
    {
        const Object& obj = objects[i];

//        fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob,
//                obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height);

        cv::rectangle(bgr, 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 > bgr.cols)
            x = bgr.cols - label_size.width;

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

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

int main(int argc, char** argv)
{
    if (argc != 2)
    {
        fprintf(stderr, "Usage: %s [imagepath]\n", argv[0]);
        return -1;
    }

    const char* imagepath = argv[1];

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

    // original pretrained model from https://github.com/AlexeyAB/darknet
    // the ncnn model https://drive.google.com/drive/folders/1YzILvh0SKQPS_lrb33dmGNq7aVTKPWS0?usp=sharing
    // the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models
#if YOLOV4_TINY
    yolov4.load_param("yolov4-tiny-opt.param");
    yolov4.load_model("yolov4-tiny-opt.bin");
    target_size = 416;
#else
    yolov4.load_param("yolov4-opt.param");
    yolov4.load_model("yolov4-opt.bin");
    target_size = 608;
#endif
    yolov4.opt.num_threads=4;
    
    std::vector<Object> objects;
    detect_yolov4(m, objects);
    draw_objects(m, objects);

    cv::imshow("RPi4 - 1.95 GHz - 2 GB ram",m);
//    cv::imwrite("test.jpg",m);
    cv::waitKey(0);

    return 0;
}