File size: 6,360 Bytes
be903e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | // Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2017 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.
#include "net.h"
#if defined(USE_NCNN_SIMPLEOCV)
#include "simpleocv.h"
#else
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#endif
#include <stdio.h>
#include <vector>
struct Object
{
cv::Rect_<float> rect;
int label;
float prob;
};
static int detect_peleenet(const cv::Mat& bgr, std::vector<Object>& objects, ncnn::Mat& resized)
{
ncnn::Net peleenet;
peleenet.opt.use_vulkan_compute = true;
// model is converted from https://github.com/eric612/MobileNet-YOLO
// and can be downloaded from https://drive.google.com/open?id=1Wt6jKv13sBRMHgrGAJYlOlRF-o80pC0g
// the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models
if (peleenet.load_param("pelee.param"))
exit(-1);
if (peleenet.load_model("pelee.bin"))
exit(-1);
const int target_size = 304;
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);
const float mean_vals[3] = {103.9f, 116.7f, 123.6f};
const float norm_vals[3] = {0.017f, 0.017f, 0.017f};
in.substract_mean_normalize(mean_vals, norm_vals);
ncnn::Extractor ex = peleenet.create_extractor();
ex.input("data", in);
ncnn::Mat out;
ex.extract("detection_out", 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);
}
ncnn::Mat seg_out;
ex.extract("sigmoid", seg_out);
resize_bilinear(seg_out, resized, img_w, img_h);
//resize_bicubic(seg_out,resized,img_w,img_h); // sharpness
return 0;
}
static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& objects, ncnn::Mat map)
{
static const char* class_names[] = {"background",
"person", "rider", "car", "bus",
"truck", "bike", "motor",
"traffic light", "traffic sign", "train"
};
cv::Mat image = bgr.clone();
const int color[] = {128, 255, 128, 244, 35, 232};
const int color_count = sizeof(color) / sizeof(int);
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(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));
}
int width = map.w;
int height = map.h;
int size = map.c;
int img_index2 = 0;
float threshold = 0.45;
const float* ptr2 = map;
for (int i = 0; i < height; i++)
{
unsigned char* ptr1 = image.ptr<unsigned char>(i);
int img_index1 = 0;
for (int j = 0; j < width; j++)
{
float maxima = threshold;
int index = -1;
for (int c = 0; c < size; c++)
{
//const float* ptr3 = map.channel(c);
const float* ptr3 = ptr2 + c * width * height;
if (ptr3[img_index2] > maxima)
{
maxima = ptr3[img_index2];
index = c;
}
}
if (index > -1)
{
int color_index = (index)*3;
if (color_index < color_count)
{
int b = color[color_index];
int g = color[color_index + 1];
int r = color[color_index + 2];
ptr1[img_index1] = b / 2 + ptr1[img_index1] / 2;
ptr1[img_index1 + 1] = g / 2 + ptr1[img_index1 + 1] / 2;
ptr1[img_index1 + 2] = r / 2 + ptr1[img_index1 + 2] / 2;
}
}
img_index1 += 3;
img_index2++;
}
}
cv::imshow("image", image);
cv::waitKey(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;
}
std::vector<Object> objects;
ncnn::Mat seg_out;
detect_peleenet(m, objects, seg_out);
draw_objects(m, objects, seg_out);
return 0;
}
|