mirror of
https://github.com/Tencent/ncnn.git
synced 2026-10-18 21:54:39 +08:00
nihui
451 lines
13 KiB
C++
451 lines
13 KiB
C++
// Copyright 2020 Tencent
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// SPDX-License-Identifier: BSD-3-Clause
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#include "layer.h"
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#include "net.h"
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#if defined(USE_NCNN_SIMPLEOCV)
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#include "simpleocv.h"
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#else
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#include <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#endif
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#include <float.h>
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#include <stdio.h>
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#include <vector>
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#define MAX_STRIDE 32
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struct Object
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{
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cv::Rect_<float> rect;
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int label;
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float prob;
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};
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static inline float intersection_area(const Object& a, const Object& b)
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{
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cv::Rect_<float> inter = a.rect & b.rect;
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return inter.area();
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}
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static void qsort_descent_inplace(std::vector<Object>& objects, int left, int right)
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{
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int i = left;
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int j = right;
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float p = objects[(left + right) / 2].prob;
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while (i <= j)
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{
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while (objects[i].prob > p)
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i++;
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while (objects[j].prob < p)
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j--;
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if (i <= j)
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{
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// swap
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std::swap(objects[i], objects[j]);
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i++;
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j--;
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}
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}
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#pragma omp parallel sections
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{
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#pragma omp section
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{
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if (left < j) qsort_descent_inplace(objects, left, j);
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}
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#pragma omp section
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{
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if (i < right) qsort_descent_inplace(objects, i, right);
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}
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}
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}
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static void qsort_descent_inplace(std::vector<Object>& objects)
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{
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if (objects.empty())
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return;
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qsort_descent_inplace(objects, 0, objects.size() - 1);
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}
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static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold, bool agnostic = false)
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{
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picked.clear();
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const int n = faceobjects.size();
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std::vector<float> areas(n);
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for (int i = 0; i < n; i++)
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{
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areas[i] = faceobjects[i].rect.area();
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}
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for (int i = 0; i < n; i++)
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{
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const Object& a = faceobjects[i];
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int keep = 1;
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for (int j = 0; j < (int)picked.size(); j++)
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{
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const Object& b = faceobjects[picked[j]];
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if (!agnostic && a.label != b.label)
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continue;
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// intersection over union
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float inter_area = intersection_area(a, b);
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float union_area = areas[i] + areas[picked[j]] - inter_area;
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// float IoU = inter_area / union_area
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if (inter_area / union_area > nms_threshold)
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keep = 0;
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}
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if (keep)
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picked.push_back(i);
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}
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}
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static inline float sigmoid(float x)
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{
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return static_cast<float>(1.f / (1.f + exp(-x)));
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}
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static void generate_proposals(const ncnn::Mat& anchors, int stride, const ncnn::Mat& in_pad, const ncnn::Mat& feat_blob, float prob_threshold, std::vector<Object>& objects)
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{
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const int num_grid = feat_blob.h;
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int num_grid_x;
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int num_grid_y;
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if (in_pad.w > in_pad.h)
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{
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num_grid_x = in_pad.w / stride;
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num_grid_y = num_grid / num_grid_x;
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}
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else
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{
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num_grid_y = in_pad.h / stride;
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num_grid_x = num_grid / num_grid_y;
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}
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const int num_class = feat_blob.w - 5;
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const int num_anchors = anchors.w / 2;
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for (int q = 0; q < num_anchors; q++)
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{
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const float anchor_w = anchors[q * 2];
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const float anchor_h = anchors[q * 2 + 1];
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const ncnn::Mat feat = feat_blob.channel(q);
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for (int i = 0; i < num_grid_y; i++)
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{
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for (int j = 0; j < num_grid_x; j++)
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{
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const float* featptr = feat.row(i * num_grid_x + j);
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float box_confidence = sigmoid(featptr[4]);
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if (box_confidence >= prob_threshold)
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{
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// find class index with max class score
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int class_index = 0;
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float class_score = -FLT_MAX;
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for (int k = 0; k < num_class; k++)
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{
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float score = featptr[5 + k];
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if (score > class_score)
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{
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class_index = k;
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class_score = score;
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}
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}
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float confidence = box_confidence * sigmoid(class_score);
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if (confidence >= prob_threshold)
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{
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float dx = sigmoid(featptr[0]);
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float dy = sigmoid(featptr[1]);
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float dw = sigmoid(featptr[2]);
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float dh = sigmoid(featptr[3]);
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float pb_cx = (dx * 2.f - 0.5f + j) * stride;
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float pb_cy = (dy * 2.f - 0.5f + i) * stride;
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float pb_w = pow(dw * 2.f, 2) * anchor_w;
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float pb_h = pow(dh * 2.f, 2) * anchor_h;
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float x0 = pb_cx - pb_w * 0.5f;
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float y0 = pb_cy - pb_h * 0.5f;
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float x1 = pb_cx + pb_w * 0.5f;
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float y1 = pb_cy + pb_h * 0.5f;
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Object obj;
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obj.rect.x = x0;
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obj.rect.y = y0;
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obj.rect.width = x1 - x0;
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obj.rect.height = y1 - y0;
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obj.label = class_index;
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obj.prob = confidence;
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objects.push_back(obj);
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}
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}
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}
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}
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}
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}
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static int detect_yolov7(const cv::Mat& bgr, std::vector<Object>& objects)
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{
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ncnn::Net yolov7;
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yolov7.opt.use_vulkan_compute = true;
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// yolov7.opt.use_bf16_storage = true;
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// original pretrained model from https://github.com/WongKinYiu/yolov7
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// the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models
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yolov7.load_param("yolov7-tiny.param");
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yolov7.load_model("yolov7-tiny.bin");
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const int target_size = 640;
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const float prob_threshold = 0.25f;
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const float nms_threshold = 0.45f;
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int img_w = bgr.cols;
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int img_h = bgr.rows;
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// letterbox pad to multiple of MAX_STRIDE
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int w = img_w;
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int h = img_h;
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float scale = 1.f;
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if (w > h)
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{
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scale = (float)target_size / w;
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w = target_size;
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h = h * scale;
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}
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else
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{
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scale = (float)target_size / h;
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h = target_size;
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w = w * scale;
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}
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ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, img_w, img_h, w, h);
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int wpad = (w + MAX_STRIDE - 1) / MAX_STRIDE * MAX_STRIDE - w;
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int hpad = (h + MAX_STRIDE - 1) / MAX_STRIDE * MAX_STRIDE - h;
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ncnn::Mat in_pad;
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ncnn::copy_make_border(in, in_pad, hpad / 2, hpad - hpad / 2, wpad / 2, wpad - wpad / 2, ncnn::BORDER_CONSTANT, 114.f);
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const float norm_vals[3] = {1 / 255.f, 1 / 255.f, 1 / 255.f};
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in_pad.substract_mean_normalize(0, norm_vals);
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ncnn::Extractor ex = yolov7.create_extractor();
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ex.input("images", in_pad);
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std::vector<Object> proposals;
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// stride 8
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{
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ncnn::Mat out;
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ex.extract("output", out);
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ncnn::Mat anchors(6);
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anchors[0] = 12.f;
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anchors[1] = 16.f;
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anchors[2] = 19.f;
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anchors[3] = 36.f;
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anchors[4] = 40.f;
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anchors[5] = 28.f;
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std::vector<Object> objects8;
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generate_proposals(anchors, 8, in_pad, out, prob_threshold, objects8);
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proposals.insert(proposals.end(), objects8.begin(), objects8.end());
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}
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// stride 16
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{
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ncnn::Mat out;
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ex.extract("288", out);
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ncnn::Mat anchors(6);
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anchors[0] = 36.f;
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anchors[1] = 75.f;
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anchors[2] = 76.f;
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anchors[3] = 55.f;
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anchors[4] = 72.f;
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anchors[5] = 146.f;
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std::vector<Object> objects16;
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generate_proposals(anchors, 16, in_pad, out, prob_threshold, objects16);
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proposals.insert(proposals.end(), objects16.begin(), objects16.end());
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}
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// stride 32
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{
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ncnn::Mat out;
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ex.extract("302", out);
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ncnn::Mat anchors(6);
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anchors[0] = 142.f;
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anchors[1] = 110.f;
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anchors[2] = 192.f;
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anchors[3] = 243.f;
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anchors[4] = 459.f;
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anchors[5] = 401.f;
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std::vector<Object> objects32;
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generate_proposals(anchors, 32, in_pad, out, prob_threshold, objects32);
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proposals.insert(proposals.end(), objects32.begin(), objects32.end());
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}
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// sort all proposals by score from highest to lowest
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qsort_descent_inplace(proposals);
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// apply nms with nms_threshold
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std::vector<int> picked;
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nms_sorted_bboxes(proposals, picked, nms_threshold);
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int count = picked.size();
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objects.resize(count);
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for (int i = 0; i < count; i++)
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{
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objects[i] = proposals[picked[i]];
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// adjust offset to original unpadded
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float x0 = (objects[i].rect.x - (wpad / 2)) / scale;
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float y0 = (objects[i].rect.y - (hpad / 2)) / scale;
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float x1 = (objects[i].rect.x + objects[i].rect.width - (wpad / 2)) / scale;
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float y1 = (objects[i].rect.y + objects[i].rect.height - (hpad / 2)) / scale;
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// clip
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x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f);
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y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f);
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x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f);
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y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f);
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objects[i].rect.x = x0;
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objects[i].rect.y = y0;
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objects[i].rect.width = x1 - x0;
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objects[i].rect.height = y1 - y0;
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}
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return 0;
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}
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static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& objects)
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{
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static const char* class_names[] = {
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"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
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"fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
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"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
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"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
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"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
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"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
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"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
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"microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
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"hair drier", "toothbrush"
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};
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static const unsigned char colors[19][3] = {
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{54, 67, 244},
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{99, 30, 233},
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{176, 39, 156},
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{183, 58, 103},
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{181, 81, 63},
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{243, 150, 33},
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{244, 169, 3},
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{212, 188, 0},
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{136, 150, 0},
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{80, 175, 76},
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{74, 195, 139},
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{57, 220, 205},
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{59, 235, 255},
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{7, 193, 255},
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{0, 152, 255},
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{34, 87, 255},
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{72, 85, 121},
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{158, 158, 158},
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{139, 125, 96}
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};
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int color_index = 0;
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cv::Mat image = bgr.clone();
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for (size_t i = 0; i < objects.size(); i++)
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{
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const Object& obj = objects[i];
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const unsigned char* color = colors[color_index % 19];
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color_index++;
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cv::Scalar cc(color[0], color[1], color[2]);
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fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob,
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obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height);
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cv::rectangle(image, obj.rect, cc, 2);
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char text[256];
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sprintf(text, "%s %.1f%%", class_names[obj.label], obj.prob * 100);
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int baseLine = 0;
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cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
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int x = obj.rect.x;
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int y = obj.rect.y - label_size.height - baseLine;
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if (y < 0)
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y = 0;
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if (x + label_size.width > image.cols)
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x = image.cols - label_size.width;
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cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)),
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cc, -1);
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cv::putText(image, text, cv::Point(x, y + label_size.height),
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cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(255, 255, 255));
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}
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cv::imshow("image", image);
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cv::waitKey(0);
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}
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int main(int argc, char** argv)
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{
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if (argc != 2)
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{
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fprintf(stderr, "Usage: %s [imagepath]\n", argv[0]);
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return -1;
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}
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const char* imagepath = argv[1];
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cv::Mat m = cv::imread(imagepath, 1);
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if (m.empty())
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{
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fprintf(stderr, "cv::imread %s failed\n", imagepath);
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return -1;
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}
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std::vector<Object> objects;
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detect_yolov7(m, objects);
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draw_objects(m, objects);
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return 0;
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}
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