diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index e49473d1e..aa5f3f064 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -44,3 +44,6 @@ target_link_libraries(peleenetssd_seg ${NCNN_EXAMPLE_LINK_LIBRARIES}) add_executable(simplepose simplepose.cpp) target_link_libraries(simplepose ${NCNN_EXAMPLE_LINK_LIBRARIES}) + +add_executable(retinaface retinaface.cpp) +target_link_libraries(retinaface ${NCNN_EXAMPLE_LINK_LIBRARIES}) diff --git a/examples/retinaface.cpp b/examples/retinaface.cpp new file mode 100755 index 000000000..84d8eb16f --- /dev/null +++ b/examples/retinaface.cpp @@ -0,0 +1,445 @@ +// Tencent is pleased to support the open source community by making ncnn available. +// +// Copyright (C) 2019 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 +#include +#include +#include +#include + +#include "platform.h" +#include "net.h" +#if NCNN_VULKAN +#include "gpu.h" +#endif // NCNN_VULKAN + +struct FaceObject +{ + cv::Rect_ rect; + cv::Point2f landmark[5]; + float prob; +}; + +static inline float intersection_area(const FaceObject& a, const FaceObject& b) +{ + cv::Rect_ inter = a.rect & b.rect; + return inter.area(); +} + +static void qsort_descent_inplace(std::vector& 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& faceobjects) +{ + if (faceobjects.empty()) + return; + + qsort_descent_inplace(faceobjects, 0, faceobjects.size() - 1); +} + +static void nms_sorted_bboxes(const std::vector& faceobjects, std::vector& picked, float nms_threshold) +{ + picked.clear(); + + const int n = faceobjects.size(); + + std::vector areas(n); + for (int i = 0; i < n; i++) + { + areas[i] = faceobjects[i].rect.area(); + } + + for (int i = 0; i < n; i++) + { + const FaceObject& a = faceobjects[i]; + + int keep = 1; + for (int j = 0; j < (int)picked.size(); j++) + { + const FaceObject& 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); + } +} + +// copy from src/layer/proposal.cpp +static ncnn::Mat generate_anchors(int base_size, const ncnn::Mat& ratios, const ncnn::Mat& scales) +{ + int num_ratio = ratios.w; + int num_scale = scales.w; + + ncnn::Mat anchors; + anchors.create(4, num_ratio * num_scale); + + const float cx = base_size * 0.5f; + const float cy = base_size * 0.5f; + + for (int i = 0; i < num_ratio; i++) + { + float ar = ratios[i]; + + int r_w = round(base_size / sqrt(ar)); + int r_h = round(r_w * ar);//round(base_size * sqrt(ar)); + + for (int j = 0; j < num_scale; j++) + { + float scale = scales[j]; + + float rs_w = r_w * scale; + float rs_h = r_h * scale; + + float* anchor = anchors.row(i * num_scale + j); + + anchor[0] = cx - rs_w * 0.5f; + anchor[1] = cy - rs_h * 0.5f; + anchor[2] = cx + rs_w * 0.5f; + anchor[3] = cy + rs_h * 0.5f; + } + } + + return anchors; +} + +static void generate_proposals(const ncnn::Mat& anchors, int feat_stride, const ncnn::Mat& score_blob, const ncnn::Mat& bbox_blob, const ncnn::Mat& landmark_blob, float prob_threshold, std::vector& faceobjects) +{ + int w = score_blob.w; + int h = score_blob.h; + + // generate face proposal from bbox deltas and shifted anchors + const int num_anchors = anchors.h; + + for (int q=0; q= prob_threshold) + { + // apply center size + float dx = bbox.channel(0)[index]; + float dy = bbox.channel(1)[index]; + float dw = bbox.channel(2)[index]; + float dh = bbox.channel(3)[index]; + + float cx = anchor_x + anchor_w * 0.5f; + float cy = anchor_y + anchor_h * 0.5f; + + float pb_cx = cx + anchor_w * dx; + float pb_cy = cy + anchor_h * dy; + + float pb_w = anchor_w * exp(dw); + float pb_h = anchor_h * exp(dh); + + float x0 = pb_cx - pb_w * 0.5f; + float y0 = pb_cy - pb_h * 0.5f; + float x1 = pb_cx + pb_w * 0.5f; + float y1 = pb_cy + pb_h * 0.5f; + + FaceObject obj; + obj.rect.x = x0; + obj.rect.y = y0; + obj.rect.width = x1 - x0 + 1; + obj.rect.height = y1 - y0 + 1; + obj.landmark[0].x = cx + (anchor_w + 1) * landmark.channel(0)[index]; + obj.landmark[0].y = cy + (anchor_h + 1) * landmark.channel(1)[index]; + obj.landmark[1].x = cx + (anchor_w + 1) * landmark.channel(2)[index]; + obj.landmark[1].y = cy + (anchor_h + 1) * landmark.channel(3)[index]; + obj.landmark[2].x = cx + (anchor_w + 1) * landmark.channel(4)[index]; + obj.landmark[2].y = cy + (anchor_h + 1) * landmark.channel(5)[index]; + obj.landmark[3].x = cx + (anchor_w + 1) * landmark.channel(6)[index]; + obj.landmark[3].y = cy + (anchor_h + 1) * landmark.channel(7)[index]; + obj.landmark[4].x = cx + (anchor_w + 1) * landmark.channel(8)[index]; + obj.landmark[4].y = cy + (anchor_h + 1) * landmark.channel(9)[index]; + obj.prob = prob; + + faceobjects.push_back(obj); + } + + anchor_x += feat_stride; + } + + anchor_y += feat_stride; + } + } + +} + +static int detect_retinaface(const cv::Mat& bgr, std::vector& faceobjects) +{ + ncnn::Net retinaface; + +#if NCNN_VULKAN + retinaface.opt.use_vulkan_compute = true; +#endif // NCNN_VULKAN + + // model is converted from + // https://github.com/deepinsight/insightface/tree/master/RetinaFace#retinaface-pretrained-models + // https://github.com/deepinsight/insightface/issues/669 +// retinaface.load_param("retinaface-R50.param"); +// retinaface.load_model("retinaface-R50.bin"); + retinaface.load_param("mnet.25-opt.param"); + retinaface.load_model("mnet.25-opt.bin"); + + const float prob_threshold = 0.8f; + const float nms_threshold = 0.4f; + + int img_w = bgr.cols; + int img_h = bgr.rows; + + ncnn::Mat in = ncnn::Mat::from_pixels(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, img_w, img_h); + + ncnn::Extractor ex = retinaface.create_extractor(); + + ex.input("data", in); + + std::vector faceproposals; + + // stride 32 + { + ncnn::Mat score_blob, bbox_blob, landmark_blob; + ex.extract("face_rpn_cls_prob_reshape_stride32", score_blob); + ex.extract("face_rpn_bbox_pred_stride32", bbox_blob); + ex.extract("face_rpn_landmark_pred_stride32", landmark_blob); + + const int base_size = 16; + const int feat_stride = 32; + ncnn::Mat ratios(1); + ratios[0] = 1.f; + ncnn::Mat scales(2); + scales[0] = 32.f; + scales[1] = 16.f; + ncnn::Mat anchors = generate_anchors(base_size, ratios, scales); + + std::vector faceobjects32; + generate_proposals(anchors, feat_stride, score_blob, bbox_blob, landmark_blob, prob_threshold, faceobjects32); + + faceproposals.insert(faceproposals.end(), faceobjects32.begin(), faceobjects32.end()); + } + + // stride 16 + { + ncnn::Mat score_blob, bbox_blob, landmark_blob; + ex.extract("face_rpn_cls_prob_reshape_stride16", score_blob); + ex.extract("face_rpn_bbox_pred_stride16", bbox_blob); + ex.extract("face_rpn_landmark_pred_stride16", landmark_blob); + + const int base_size = 16; + const int feat_stride = 16; + ncnn::Mat ratios(1); + ratios[0] = 1.f; + ncnn::Mat scales(2); + scales[0] = 8.f; + scales[1] = 4.f; + ncnn::Mat anchors = generate_anchors(base_size, ratios, scales); + + std::vector faceobjects16; + generate_proposals(anchors, feat_stride, score_blob, bbox_blob, landmark_blob, prob_threshold, faceobjects16); + + faceproposals.insert(faceproposals.end(), faceobjects16.begin(), faceobjects16.end()); + } + + // stride 8 + { + ncnn::Mat score_blob, bbox_blob, landmark_blob; + ex.extract("face_rpn_cls_prob_reshape_stride8", score_blob); + ex.extract("face_rpn_bbox_pred_stride8", bbox_blob); + ex.extract("face_rpn_landmark_pred_stride8", landmark_blob); + + const int base_size = 16; + const int feat_stride = 8; + ncnn::Mat ratios(1); + ratios[0] = 1.f; + ncnn::Mat scales(2); + scales[0] = 2.f; + scales[1] = 1.f; + ncnn::Mat anchors = generate_anchors(base_size, ratios, scales); + + std::vector faceobjects8; + generate_proposals(anchors, feat_stride, score_blob, bbox_blob, landmark_blob, prob_threshold, faceobjects8); + + faceproposals.insert(faceproposals.end(), faceobjects8.begin(), faceobjects8.end()); + } + + // sort all proposals by score from highest to lowest + qsort_descent_inplace(faceproposals); + + // apply nms with nms_threshold + std::vector picked; + nms_sorted_bboxes(faceproposals, picked, nms_threshold); + + int face_count = picked.size(); + + faceobjects.resize(face_count); + for (int i = 0; i < face_count; i++) + { + faceobjects[i] = faceproposals[ picked[i] ]; + + // clip to image size + float x0 = faceobjects[i].rect.x; + float y0 = faceobjects[i].rect.y; + float x1 = x0 + faceobjects[i].rect.width; + float y1 = y0 + faceobjects[i].rect.height; + + x0 = std::max(std::min(x0, (float)img_w - 1), 0.f); + y0 = std::max(std::min(y0, (float)img_h - 1), 0.f); + x1 = std::max(std::min(x1, (float)img_w - 1), 0.f); + y1 = std::max(std::min(y1, (float)img_h - 1), 0.f); + + faceobjects[i].rect.x = x0; + faceobjects[i].rect.y = y0; + faceobjects[i].rect.width = x1 - x0; + faceobjects[i].rect.height = y1 - y0; + } + + return 0; +} + +static void draw_faceobjects(const cv::Mat& bgr, const std::vector& faceobjects) +{ + cv::Mat image = bgr.clone(); + + for (size_t i = 0; i < faceobjects.size(); i++) + { + const FaceObject& obj = faceobjects[i]; + + fprintf(stderr, "%.5f at %.2f %.2f %.2f x %.2f\n", obj.prob, + obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height); + + cv::rectangle(image, obj.rect, cv::Scalar(0, 255, 0)); + + cv::circle(image, obj.landmark[0], 2, cv::Scalar(0, 255, 255), -1); + cv::circle(image, obj.landmark[1], 2, cv::Scalar(0, 255, 255), -1); + cv::circle(image, obj.landmark[2], 2, cv::Scalar(0, 255, 255), -1); + cv::circle(image, obj.landmark[3], 2, cv::Scalar(0, 255, 255), -1); + cv::circle(image, obj.landmark[4], 2, cv::Scalar(0, 255, 255), -1); + + char text[256]; + sprintf(text, "%.1f%%", 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::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; + } + +#if NCNN_VULKAN + ncnn::create_gpu_instance(); +#endif // NCNN_VULKAN + + std::vector faceobjects; + detect_retinaface(m, faceobjects); + +#if NCNN_VULKAN + ncnn::destroy_gpu_instance(); +#endif // NCNN_VULKAN + + draw_faceobjects(m, faceobjects); + + return 0; +}