Update android demo (#368)

demo_android_ncnn/README.md

@@ -13,10 +13,15 @@ Download ncnn-android-vulkan.zip from ncnn repo or build ncnn-android from sourc
 ## Step2.
 Unzip ncnn-android-vulkan.zip into demo_android_ncnn/app/src/main/cpp or change the ncnn_DIR path to yours in demo_android_ncnn/app/src/main/cpp/CMakeLists.txt
 
+```bash
+# e.g. change to `ncnn-20211208-android-vulkan` if download version 200211208
+set(ncnn_DIR ${CMAKE_SOURCE_DIR}/ncnn-20211208-android-vulkan/${ANDROID_ABI}/lib/cmake/ncnn)
+```
+
 ## Step3.
-Copy the NanoDet ncnn model file (nanodet_m.param and nanodet_m.bin) from models folder into demo_android_ncnn/app/src/main/assets
+Copy the NanoDet ncnn model file and rename to nanodet.param and nanodet.bin from models folder into demo_android_ncnn/app/src/main/assets
 
-* [NanoDet ncnn model download link](https://github.com/RangiLyu/nanodet/releases/download/v0.3.0/nanodet_m_ncnn_model.zip)
+* [NanoDet ncnn model download link](https://drive.google.com/file/d/1cuVBJiFKwyq1-l3AwHoP2boTesUQP-6K/view?usp=sharing)
 
 If you want to run yolov4-tiny and yolov5s, download them and also put in demo_android_ncnn/app/src/main/assets.
 
@@ -28,6 +33,13 @@ Open demo_android_ncnn folder with Android Studio and then build it.
 # Screenshot
 ![](Android_demo.jpg)
 
+# Notice
+
+* The FPS in the app includes pre-process, post-process and visualization, not equal to the model inference time.
+
+* If meet error like `No version of NDK matched the requested version`, set `android { ndkVersion` to your ndk version.
+
+* If you want to use custom model, remember to change the hyperparams in `demo_android_ncnn/app/src/main/cpp/NanoDet.h` the same with your training config.
 
 # Reference
 

demo_android_ncnn/app/src/main/cpp/CMakeLists.txt

@@ -1,6 +1,6 @@
 cmake_minimum_required(VERSION 3.10)
 
-set(ncnn_DIR ${CMAKE_SOURCE_DIR}/ncnn-20201218-android-vulkan/${ANDROID_ABI}/lib/cmake/ncnn)
+set(ncnn_DIR ${CMAKE_SOURCE_DIR}/ncnn-20211208-android-vulkan/${ANDROID_ABI}/lib/cmake/ncnn)
 find_package(ncnn REQUIRED)
 
 add_library(yolov5 SHARED

demo_android_ncnn/app/src/main/cpp/NanoDet.cpp

@@ -79,7 +79,7 @@ NanoDet::~NanoDet()
 
 void NanoDet::preprocess(JNIEnv *env, jobject image, ncnn::Mat& in)
 {
- in = ncnn::Mat::from_android_bitmap_resize(env, image, ncnn::Mat::PIXEL_RGBA2BGR, input_size, input_size);
+ in = ncnn::Mat::from_android_bitmap_resize(env, image, ncnn::Mat::PIXEL_RGBA2BGR, input_size[1], input_size[0]);
 // in = ncnn::Mat::from_pixels(image.data, ncnn::Mat::PIXEL_BGR, img_w, img_h);
  //in = ncnn::Mat::from_pixels_resize(image.data, ncnn::Mat::PIXEL_BGR, img_w, img_h, this->input_width, this->input_height);
 
@@ -91,8 +91,8 @@ void NanoDet::preprocess(JNIEnv *env, jobject image, ncnn::Mat& in)
 std::vector<BoxInfo> NanoDet::detect(JNIEnv *env, jobject image, float score_threshold, float nms_threshold) {
  AndroidBitmapInfo img_size;
  AndroidBitmap_getInfo(env, image, &img_size);
- float width_ratio = (float) img_size.width / (float) this->input_size;
- float height_ratio = (float) img_size.height / (float) this->input_size;
+ float width_ratio = (float) img_size.width / (float) this->input_size[1];
+ float height_ratio = (float) img_size.height / (float) this->input_size[0];
 
  ncnn::Mat input;
  this->preprocess(env, image, input);
@@ -167,8 +167,8 @@ void NanoDet::decode_infer(ncnn::Mat& feats, std::vector<CenterPrior>& center_pr
 
 BoxInfo NanoDet::disPred2Bbox(const float*& dfl_det, int label, float score, int x, int y, int stride, float width_ratio, float height_ratio)
 {
- float ct_x = (x + 0.5) * stride;
- float ct_y = (y + 0.5) * stride;
+ float ct_x = x * stride;
+ float ct_y = y * stride;
  std::vector<float> dis_pred;
  dis_pred.resize(4);
  for (int i = 0; i < 4; i++)
@@ -187,8 +187,8 @@ BoxInfo NanoDet::disPred2Bbox(const float*& dfl_det, int label, float score, int
  }
  float xmin = (std::max)(ct_x - dis_pred[0], .0f) * width_ratio;
  float ymin = (std::max)(ct_y - dis_pred[1], .0f) * height_ratio;
- float xmax = (std::min)(ct_x + dis_pred[2], (float)this->input_size) * width_ratio;
- float ymax = (std::min)(ct_y + dis_pred[3], (float)this->input_size) * height_ratio;
+ float xmax = (std::min)(ct_x + dis_pred[2], (float)this->input_size[1]) * width_ratio;
+ float ymax = (std::min)(ct_y + dis_pred[3], (float)this->input_size[0]) * height_ratio;
 
  //std::cout << xmin << "," << ymin << "," << xmax << "," << xmax << "," << std::endl;
  return BoxInfo { xmin, ymin, xmax, ymax, score, label };

demo_android_ncnn/app/src/main/cpp/NanoDet.h

@@ -49,7 +49,7 @@ class NanoDet{
 
  ncnn::Net *Net;
  // modify these parameters to the same with your config if you want to use your own model
- int input_size[2] = {416, 416}; // input height and width
+ int input_size[2] = {320, 320}; // input height and width
  int num_class = 80; // number of classes. 80 for COCO
  int reg_max = 7; // `reg_max` set in the training config. Default: 7.
  std::vector<int> strides = { 8, 16, 32, 64 }; // strides of the multi-level feature.

demo_android_ncnn/app/src/main/cpp/jni_interface.cpp

@@ -31,7 +31,7 @@ extern "C" JNIEXPORT void JNICALL
 Java_com_rangi_nanodet_NanoDet_init(JNIEnv *env, jclass, jobject assetManager, jboolean useGPU) {
  if (NanoDet::detector == nullptr) {
  AAssetManager *mgr = AAssetManager_fromJava(env, assetManager);
- NanoDet::detector = new NanoDet(mgr, "nanodet_m.param", "nanodet_m.bin", useGPU);
+ NanoDet::detector = new NanoDet(mgr, "nanodet.param", "nanodet.bin", useGPU);
  }
 }