Imported Upstream version 20240102upstream/20240102upstream

1 files changed, 158 insertions, 0 deletions

diff --git a/examples/yolov2.cpp b/examples/yolov2.cpp
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+// Tencent is pleased to support the open source community by making ncnn available.
+//
+// Copyright (C) 2018 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_yolov2(const cv::Mat& bgr, std::vector<Object>& objects)
+{
+    ncnn::Net yolov2;
+
+    yolov2.opt.use_vulkan_compute = true;
+
+    // original pretrained model from https://github.com/eric612/MobileNet-YOLO
+    // https://github.com/eric612/MobileNet-YOLO/blob/master/models/yolov2/mobilenet_yolo_deploy.prototxt
+    // https://github.com/eric612/MobileNet-YOLO/blob/master/models/yolov2/mobilenet_yolo_deploy_iter_80000.caffemodel
+    // the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models
+    if (yolov2.load_param("mobilenet_yolo.param"))
+        exit(-1);
+    if (yolov2.load_model("mobilenet_yolo.bin"))
+        exit(-1);
+
+    const int target_size = 416;
+
+    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);
+
+    // the Caffe-YOLOv2-Windows style
+    // X' = X * scale - mean
+    const float mean_vals[3] = {1.0f, 1.0f, 1.0f};
+    const float norm_vals[3] = {0.007843f, 0.007843f, 0.007843f};
+    in.substract_mean_normalize(0, norm_vals);
+    in.substract_mean_normalize(mean_vals, 0);
+
+    ncnn::Extractor ex = yolov2.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<Object>& 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<Object> objects;
+    detect_yolov2(m, objects);
+
+    draw_objects(m, objects);
+
+    return 0;
+}