718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
423 lines
13 KiB
C++
423 lines
13 KiB
C++
// Tencent is pleased to support the open source community by making ncnn available.
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//
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// Copyright (C) 2022 THL A29 Limited, a Tencent company. All rights reserved.
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//
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// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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// in compliance with the License. You may obtain a copy of the License at
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//
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// https://opensource.org/licenses/BSD-3-Clause
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//
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// Unless required by applicable law or agreed to in writing, software distributed
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// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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// CONDITIONS OF ANY KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations under the License.
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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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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>& faceobjects, 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 = faceobjects[(left + right) / 2].prob;
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while (i <= j)
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{
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while (faceobjects[i].prob > p)
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i++;
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while (faceobjects[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(faceobjects[i], faceobjects[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(faceobjects, 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(faceobjects, 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>& faceobjects)
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{
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if (faceobjects.empty())
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return;
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qsort_descent_inplace(faceobjects, 0, faceobjects.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)
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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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// 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_x = feat_blob.w;
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const int num_grid_y = feat_blob.h;
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const int num_anchors = anchors.w / 2;
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const int num_class = 80;
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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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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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// 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 = feat_blob.channel(q * 85 + 5 + k).row(i)[j];
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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 box_score = feat_blob.channel(q * 85 + 4).row(i)[j];
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float confidence = sigmoid(box_score) * sigmoid(class_score);
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if (confidence >= prob_threshold)
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{
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// yolov5/models/yolo.py Detect forward
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// y = x[i].sigmoid()
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// y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy
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// y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
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float dx = sigmoid(feat_blob.channel(q * 85 + 0).row(i)[j]);
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float dy = sigmoid(feat_blob.channel(q * 85 + 1).row(i)[j]);
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float dw = sigmoid(feat_blob.channel(q * 85 + 2).row(i)[j]);
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float dh = sigmoid(feat_blob.channel(q * 85 + 3).row(i)[j]);
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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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static int detect_yolov5(const cv::Mat& bgr, std::vector<Object>& objects)
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{
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ncnn::Net yolov5;
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yolov5.opt.use_vulkan_compute = true;
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// yolov5.opt.use_bf16_storage = true;
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// original pretrained model from https://github.com/ultralytics/yolov5
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// the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models
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yolov5.load_param("yolov5s.ncnn.param");
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yolov5.load_model("yolov5s.ncnn.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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// yolov5/models/common.py DetectMultiBackend
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const int max_stride = 64;
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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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// pad to target_size rectangle
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// yolov5/utils/datasets.py letterbox
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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 = yolov5.create_extractor();
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ex.input("in0", in_pad);
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std::vector<Object> proposals;
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// anchor setting from yolov5/models/yolov5s.yaml
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// stride 8
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{
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ncnn::Mat out;
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ex.extract("out0", out);
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ncnn::Mat anchors(6);
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anchors[0] = 10.f;
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anchors[1] = 13.f;
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anchors[2] = 16.f;
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anchors[3] = 30.f;
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anchors[4] = 33.f;
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anchors[5] = 23.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("out1", out);
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ncnn::Mat anchors(6);
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anchors[0] = 30.f;
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anchors[1] = 61.f;
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anchors[2] = 62.f;
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anchors[3] = 45.f;
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anchors[4] = 59.f;
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anchors[5] = 119.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("out2", out);
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ncnn::Mat anchors(6);
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anchors[0] = 116.f;
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anchors[1] = 90.f;
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anchors[2] = 156.f;
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anchors[3] = 198.f;
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anchors[4] = 373.f;
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anchors[5] = 326.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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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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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, cv::Scalar(255, 0, 0));
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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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cv::Scalar(255, 255, 255), -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(0, 0, 0));
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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_yolov5(m, objects);
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draw_objects(m, objects);
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return 0;
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}
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