// 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 #include #include #endif #include #include struct Object { cv::Rect_ rect; int label; float prob; }; static int detect_squeezenet(const cv::Mat& bgr, std::vector& objects) { ncnn::Net squeezenet; squeezenet.opt.use_vulkan_compute = true; // original pretrained model from https://github.com/chuanqi305/SqueezeNet-SSD // squeezenet_ssd_voc_deploy.prototxt // https://drive.google.com/open?id=0B3gersZ2cHIxdGpyZlZnbEQ5Snc // the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models squeezenet.load_param("squeezenet_ssd_voc.param"); squeezenet.load_model("squeezenet_ssd_voc.bin"); const int target_size = 300; 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] = {104.f, 117.f, 123.f}; in.substract_mean_normalize(mean_vals, 0); ncnn::Extractor ex = squeezenet.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); } return 0; } static void draw_objects(const cv::Mat& bgr, const std::vector& objects) { static const char* class_names[] = {"background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor" }; cv::Mat image = bgr.clone(); 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)); } 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 objects; detect_squeezenet(m, objects); draw_objects(m, objects); return 0; }