add R-FCN example

examples/CMakeLists.txt

@@ -13,6 +13,9 @@ target_link_libraries(squeezenet ncnn ${OpenCV_LIBS})
 add_executable(fasterrcnn fasterrcnn.cpp)
 target_link_libraries(fasterrcnn ncnn ${OpenCV_LIBS})
 
+add_executable(rfcn rfcn.cpp)
+target_link_libraries(rfcn ncnn ${OpenCV_LIBS})
+
 add_executable(yolov2 yolov2.cpp)
 target_link_libraries(yolov2 ncnn ${OpenCV_LIBS})
 

examples/rfcn.cpp

@@ -0,0 +1,351 @@
+// 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 <math.h>
+#include <stdio.h>
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+#include "net.h"
+
+struct Object
+{
+    cv::Rect_<float> rect;
+    int label;
+    float prob;
+};
+
+static inline float intersection_area(const Object& a, const Object& b)
+{
+    cv::Rect_<float> inter = a.rect & b.rect;
+    return inter.area();
+}
+
+static void qsort_descent_inplace(std::vector<Object>& objects, int left, int right)
+{
+    int i = left;
+    int j = right;
+    float p = objects[(left + right) / 2].prob;
+
+    while (i <= j)
+    {
+        while (objects[i].prob > p)
+            i++;
+
+        while (objects[j].prob < p)
+            j--;
+
+        if (i <= j)
+        {
+            // swap
+            std::swap(objects[i], objects[j]);
+
+            i++;
+            j--;
+        }
+    }
+
+    #pragma omp parallel sections
+    {
+        #pragma omp section
+        {
+            if (left < j) qsort_descent_inplace(objects, left, j);
+        }
+        #pragma omp section
+        {
+            if (i < right) qsort_descent_inplace(objects, i, right);
+        }
+    }
+}
+
+static void qsort_descent_inplace(std::vector<Object>& objects)
+{
+    if (objects.empty())
+        return;
+
+    qsort_descent_inplace(objects, 0, objects.size() - 1);
+}
+
+static void nms_sorted_bboxes(const std::vector<Object>& objects, std::vector<int>& picked, float nms_threshold)
+{
+    picked.clear();
+
+    const int n = objects.size();
+
+    std::vector<float> areas(n);
+    for (int i = 0; i < n; i++)
+    {
+        areas[i] = objects[i].rect.area();
+    }
+
+    for (int i = 0; i < n; i++)
+    {
+        const Object& a = objects[i];
+
+        int keep = 1;
+        for (int j = 0; j < (int)picked.size(); j++)
+        {
+            const Object& b = objects[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);
+    }
+}
+
+static int detect_rfcn(const cv::Mat& bgr, std::vector<Object>& objects)
+{
+    ncnn::Net rfcn;
+
+    // original pretrained model from https://github.com/YuwenXiong/py-R-FCN
+    // https://github.com/YuwenXiong/py-R-FCN/blob/master/models/pascal_voc/ResNet-50/rfcn_end2end/test_agnostic.prototxt
+    // https://1drv.ms/u/s!AoN7vygOjLIQqUWHpY67oaC7mopf
+    // resnet50_rfcn_final.caffemodel
+    rfcn.load_param("rfcn_end2end.param");
+    rfcn.load_model("rfcn_end2end.bin");
+
+    const int target_size = 224;
+
+    const int max_per_image = 100;
+    const float confidence_thresh = 0.6f;// CONF_THRESH
+
+    const float nms_threshold = 0.3f;// NMS_THRESH
+
+    // scale to target detect size
+    int w = bgr.cols;
+    int h = bgr.rows;
+    float scale = 1.f;
+    if (w < h)
+    {
+        scale = (float)target_size / w;
+        w = target_size;
+        h = h * scale;
+    }
+    else
+    {
+        scale = (float)target_size / h;
+        h = target_size;
+        w = w * scale;
+    }
+
+    ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR, bgr.cols, bgr.rows, w, h);
+
+    const float mean_vals[3] = { 102.9801f, 115.9465f, 122.7717f };
+    in.substract_mean_normalize(mean_vals, 0);
+
+    ncnn::Mat im_info(3);
+    im_info[0] = h;
+    im_info[1] = w;
+    im_info[2] = scale;
+
+    // step1, extract feature and all rois
+    ncnn::Extractor ex1 = rfcn.create_extractor();
+
+    ex1.input("data", in);
+    ex1.input("im_info", im_info);
+
+    ncnn::Mat rfcn_cls;
+    ncnn::Mat rfcn_bbox;
+    ncnn::Mat rois;// all rois
+    ex1.extract("rfcn_cls", rfcn_cls);
+    ex1.extract("rfcn_bbox", rfcn_bbox);
+    ex1.extract("rois", rois);
+
+    // step2, extract bbox and score for each roi
+    std::vector< std::vector<Object> > class_candidates;
+    for (int i = 0; i < rois.c; i++)
+    {
+        ncnn::Extractor ex2 = rfcn.create_extractor();
+
+        ncnn::Mat roi = rois.channel(i);// get single roi
+        ex2.input("rfcn_cls", rfcn_cls);
+        ex2.input("rfcn_bbox", rfcn_bbox);
+        ex2.input("rois", roi);
+
+        ncnn::Mat bbox_pred;
+        ncnn::Mat cls_prob;
+        ex2.extract("bbox_pred", bbox_pred);
+        ex2.extract("cls_prob", cls_prob);
+
+        int num_class = cls_prob.w;
+        class_candidates.resize(num_class);
+
+        // find class id with highest score
+        int label = 0;
+        float score = 0.f;
+        for (int i=0; i<num_class; i++)
+        {
+            float class_score = cls_prob[i];
+            if (class_score > score)
+            {
+                label = i;
+                score = class_score;
+            }
+        }
+
+        // ignore background or low score
+        if (label == 0 || score <= confidence_thresh)
+            continue;
+
+//         fprintf(stderr, "%d = %f\n", label, score);
+
+        // unscale to image size
+        float x1 = roi[0] / scale;
+        float y1 = roi[1] / scale;
+        float x2 = roi[2] / scale;
+        float y2 = roi[3] / scale;
+
+        float pb_w = x2 - x1 + 1;
+        float pb_h = y2 - y1 + 1;
+
+        // apply bbox regression
+        float dx = bbox_pred[4];
+        float dy = bbox_pred[4 + 1];
+        float dw = bbox_pred[4 + 2];
+        float dh = bbox_pred[4 + 3];
+
+        float cx = x1 + pb_w * 0.5f;
+        float cy = y1 + pb_h * 0.5f;
+
+        float obj_cx = cx + pb_w * dx;
+        float obj_cy = cy + pb_h * dy;
+
+        float obj_w = pb_w * exp(dw);
+        float obj_h = pb_h * exp(dh);
+
+        float obj_x1 = obj_cx - obj_w * 0.5f;
+        float obj_y1 = obj_cy - obj_h * 0.5f;
+        float obj_x2 = obj_cx + obj_w * 0.5f;
+        float obj_y2 = obj_cy + obj_h * 0.5f;
+
+        // clip
+        obj_x1 = std::max(std::min(obj_x1, (float)(bgr.cols - 1)), 0.f);
+        obj_y1 = std::max(std::min(obj_y1, (float)(bgr.rows - 1)), 0.f);
+        obj_x2 = std::max(std::min(obj_x2, (float)(bgr.cols - 1)), 0.f);
+        obj_y2 = std::max(std::min(obj_y2, (float)(bgr.rows - 1)), 0.f);
+
+        // append object
+        Object obj;
+        obj.rect = cv::Rect_<float>(obj_x1, obj_y1, obj_x2-obj_x1+1, obj_y2-obj_y1+1);
+        obj.label = label;
+        obj.prob = score;
+
+        class_candidates[label].push_back(obj);
+    }
+
+    // post process
+    objects.clear();
+    for (int i = 0; i < (int)class_candidates.size(); i++)
+    {
+        std::vector<Object>& candidates = class_candidates[i];
+
+        qsort_descent_inplace(candidates);
+
+        std::vector<int> picked;
+        nms_sorted_bboxes(candidates, picked, nms_threshold);
+
+        for (int j = 0; j < (int)picked.size(); j++)
+        {
+            int z = picked[j];
+            objects.push_back(candidates[z]);
+        }
+    }
+
+    qsort_descent_inplace(objects);
+
+    if (max_per_image > 0 && max_per_image < objects.size())
+    {
+        objects.resize(max_per_image);
+    }
+
+    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), CV_FILLED);
+
+        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, CV_LOAD_IMAGE_COLOR);
+    if (m.empty())
+    {
+        fprintf(stderr, "cv::imread %s failed\n", imagepath);
+        return -1;
+    }
+
+    std::vector<Object> objects;
+    detect_rfcn(m, objects);
+
+    draw_objects(m, objects);
+
+    return 0;
+}