Merge pull request #316 from LDOUBLEV/fixocr

add static_cast

deploy/lite/crnn_process.cc

@@ -27,12 +27,12 @@ cv::Mat CrnnResizeImg(cv::Mat img, float wh_ratio) {
 
   imgW = int(32 * wh_ratio);
 
-  float ratio = float(img.cols) / float(img.rows);
+  float ratio = static_cast<float>(img.cols) / static_cast<float>(img.rows);
   int resize_w, resize_h;
   if (ceilf(imgH * ratio) > imgW)
     resize_w = imgW;
   else
-    resize_w = int(ceilf(imgH * ratio));
+    resize_w = static_cast<int>(ceilf(imgH * ratio));
   cv::Mat resize_img;
   cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
              cv::INTER_LINEAR);
@@ -76,9 +76,11 @@ cv::Mat GetRotateCropImage(cv::Mat srcimage,
     points[i][1] -= top;
   }
 
-  int img_crop_width = int(sqrt(pow(points[0][0] - points[1][0], 2) +
+  int img_crop_width =
+      static_cast<int>(sqrt(pow(points[0][0] - points[1][0], 2) +
                             pow(points[0][1] - points[1][1], 2)));
-  int img_crop_height = int(sqrt(pow(points[0][0] - points[3][0], 2) +
+  int img_crop_height =
+      static_cast<int>(sqrt(pow(points[0][0] - points[3][0], 2) +
                             pow(points[0][1] - points[3][1], 2)));
 
   cv::Point2f pts_std[4];
@@ -100,7 +102,9 @@ cv::Mat GetRotateCropImage(cv::Mat srcimage,
                       cv::Size(img_crop_width, img_crop_height),
                       cv::BORDER_REPLICATE);
 
-  if (float(dst_img.rows) >= float(dst_img.cols) * 1.5) {
+  const float ratio = 1.5;
+  if (static_cast<float>(dst_img.rows) >=
+      static_cast<float>(dst_img.cols) * ratio) {
     cv::Mat srcCopy = cv::Mat(dst_img.rows, dst_img.cols, dst_img.depth());
     cv::transpose(dst_img, srcCopy);
     cv::flip(srcCopy, srcCopy, 0);

deploy/lite/db_post_process.cc

@@ -42,10 +42,14 @@ cv::RotatedRect Unclip(std::vector<std::vector<float>> box,
 
   ClipperLib::ClipperOffset offset;
   ClipperLib::Path p;
-  p << ClipperLib::IntPoint(int(box[0][0]), int(box[0][1]))
-    << ClipperLib::IntPoint(int(box[1][0]), int(box[1][1]))
-    << ClipperLib::IntPoint(int(box[2][0]), int(box[2][1]))
-    << ClipperLib::IntPoint(int(box[3][0]), int(box[3][1]));
+  p << ClipperLib::IntPoint(static_cast<int>(box[0][0]),
+                            static_cast<int>(box[0][1]))
+    << ClipperLib::IntPoint(static_cast<int>(box[1][0]),
+                            static_cast<int>(box[1][1]))
+    << ClipperLib::IntPoint(static_cast<int>(box[2][0]),
+                            static_cast<int>(box[2][1]))
+    << ClipperLib::IntPoint(static_cast<int>(box[3][0]),
+                            static_cast<int>(box[3][1]));
   offset.AddPath(p, ClipperLib::jtRound, ClipperLib::etClosedPolygon);
 
   ClipperLib::Paths soln;
@@ -149,23 +153,31 @@ float BoxScoreFast(std::vector<std::vector<float>> box_array, cv::Mat pred) {
   float box_x[4] = {array[0][0], array[1][0], array[2][0], array[3][0]};
   float box_y[4] = {array[0][1], array[1][1], array[2][1], array[3][1]};
 
-  int xmin = clamp(int(std::floorf(*(std::min_element(box_x, box_x + 4)))), 0,
+  int xmin = clamp(
+      static_cast<int>(std::floorf(*(std::min_element(box_x, box_x + 4)))), 0,
       width - 1);
-  int xmax = clamp(int(std::ceilf(*(std::max_element(box_x, box_x + 4)))), 0,
-                   width - 1);
-  int ymin = clamp(int(std::floorf(*(std::min_element(box_y, box_y + 4)))), 0,
-                   height - 1);
-  int ymax = clamp(int(std::ceilf(*(std::max_element(box_y, box_y + 4)))), 0,
+  int xmax =
+      clamp(static_cast<int>(std::ceilf(*(std::max_element(box_x, box_x + 4)))),
+            0, width - 1);
+  int ymin = clamp(
+      static_cast<int>(std::floorf(*(std::min_element(box_y, box_y + 4)))), 0,
       height - 1);
+  int ymax =
+      clamp(static_cast<int>(std::ceilf(*(std::max_element(box_y, box_y + 4)))),
+            0, height - 1);
 
   cv::Mat mask;
   mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);
 
   cv::Point root_point[4];
-  root_point[0] = cv::Point(int(array[0][0]) - xmin, int(array[0][1]) - ymin);
-  root_point[1] = cv::Point(int(array[1][0]) - xmin, int(array[1][1]) - ymin);
-  root_point[2] = cv::Point(int(array[2][0]) - xmin, int(array[2][1]) - ymin);
-  root_point[3] = cv::Point(int(array[3][0]) - xmin, int(array[3][1]) - ymin);
+  root_point[0] = cv::Point(static_cast<int>(array[0][0]) - xmin,
+                            static_cast<int>(array[0][1]) - ymin);
+  root_point[1] = cv::Point(static_cast<int>(array[1][0]) - xmin,
+                            static_cast<int>(array[1][1]) - ymin);
+  root_point[2] = cv::Point(static_cast<int>(array[2][0]) - xmin,
+                            static_cast<int>(array[2][1]) - ymin);
+  root_point[3] = cv::Point(static_cast<int>(array[3][0]) - xmin,
+                            static_cast<int>(array[3][1]) - ymin);
   const cv::Point *ppt[1] = {root_point};
   int npt[] = {4};
   cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));
@@ -183,8 +195,8 @@ BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap,
                 std::map<std::string, double> Config) {
   const int min_size = 3;
   const int max_candidates = 1000;
-  const float box_thresh = float(Config["det_db_box_thresh"]);
-  const float unclip_ratio = float(Config["det_db_unclip_ratio"]);
+  const float box_thresh = static_cast<float>(Config["det_db_box_thresh"]);
+  const float unclip_ratio = static_cast<float>(Config["det_db_unclip_ratio"]);
 
   int width = bitmap.cols;
   int height = bitmap.rows;
@@ -233,11 +245,12 @@ BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap,
     std::vector<std::vector<int>> intcliparray;
 
     for (int num_pt = 0; num_pt < 4; num_pt++) {
-      std::vector<int> a{int(clamp(roundf(cliparray[num_pt][0] / float(width) *
-                                          float(dest_width)),
+      std::vector<int> a{
+          static_cast<int>(clamp(
+              roundf(cliparray[num_pt][0] / float(width) * float(dest_width)),
               float(0), float(dest_width))),
-                         int(clamp(roundf(cliparray[num_pt][1] / float(height) *
-                                          float(dest_height)),
+          static_cast<int>(clamp(
+              roundf(cliparray[num_pt][1] / float(height) * float(dest_height)),
               float(0), float(dest_height)))};
       intcliparray.push_back(a);
     }
@@ -254,22 +267,26 @@ FilterTagDetRes(std::vector<std::vector<std::vector<int>>> boxes, float ratio_h,
   int oriimg_w = srcimg.cols;
 
   std::vector<std::vector<std::vector<int>>> root_points;
-  for (int n = 0; n < boxes.size(); n++) {
+  for (int n = 0; n < static_cast<int>(boxes.size()); n++) {
     boxes[n] = OrderPointsClockwise(boxes[n]);
-    for (int m = 0; m < boxes[0].size(); m++) {
+    for (int m = 0; m < static_cast<int>(boxes[0].size()); m++) {
       boxes[n][m][0] /= ratio_w;
       boxes[n][m][1] /= ratio_h;
 
-      boxes[n][m][0] = int(std::min(std::max(boxes[n][m][0], 0), oriimg_w - 1));
-      boxes[n][m][1] = int(std::min(std::max(boxes[n][m][1], 0), oriimg_h - 1));
+      boxes[n][m][0] =
+          static_cast<int>(std::min(std::max(boxes[n][m][0], 0), oriimg_w - 1));
+      boxes[n][m][1] =
+          static_cast<int>(std::min(std::max(boxes[n][m][1], 0), oriimg_h - 1));
     }
   }
 
   for (int n = 0; n < boxes.size(); n++) {
     int rect_width, rect_height;
-    rect_width = int(sqrt(pow(boxes[n][0][0] - boxes[n][1][0], 2) +
+    rect_width =
+        static_cast<int>(sqrt(pow(boxes[n][0][0] - boxes[n][1][0], 2) +
                               pow(boxes[n][0][1] - boxes[n][1][1], 2)));
-    rect_height = int(sqrt(pow(boxes[n][0][0] - boxes[n][3][0], 2) +
+    rect_height =
+        static_cast<int>(sqrt(pow(boxes[n][0][0] - boxes[n][3][0], 2) +
                               pow(boxes[n][0][1] - boxes[n][3][1], 2)));
     if (rect_width <= 10 || rect_height <= 10)
       continue;

deploy/lite/ocr_db_crnn.cc

@@ -22,7 +22,7 @@ using namespace paddle::lite_api; // NOLINT
 using namespace std;
 
 // fill tensor with mean and scale and trans layout: nhwc -> nchw, neon speed up
-void neon_mean_scale(const float *din, float *dout, int size,
+void NeonMeanScale(const float *din, float *dout, int size,
                    const std::vector<float> mean,
                    const std::vector<float> scale) {
   if (mean.size() != 3 || scale.size() != 3) {
@@ -75,14 +75,14 @@ cv::Mat DetResizeImg(const cv::Mat img, int max_size_len,
   int max_wh = w >= h ? w : h;
   if (max_wh > max_size_len) {
     if (h > w) {
-      ratio = float(max_size_len) / float(h);
+      ratio = static_cast<float>(max_size_len) / static_cast<float>(h);
     } else {
-      ratio = float(max_size_len) / float(w);
+      ratio = static_cast<float>(max_size_len) / static_cast<float>(w);
     }
   }
 
-  int resize_h = int(float(h) * ratio);
-  int resize_w = int(float(w) * ratio);
+  int resize_h = static_cast<int>(float(h) * ratio);
+  int resize_w = static_cast<int>(float(w) * ratio);
   if (resize_h % 32 == 0)
     resize_h = resize_h;
   else if (resize_h / 32 < 1 + 1e-5)
@@ -100,8 +100,8 @@ cv::Mat DetResizeImg(const cv::Mat img, int max_size_len,
   cv::Mat resize_img;
   cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
 
-  ratio_hw.push_back(float(resize_h) / float(h));
-  ratio_hw.push_back(float(resize_w) / float(w));
+  ratio_hw.push_back(static_cast<float>(resize_h) / static_cast<float>(h));
+  ratio_hw.push_back(static_cast<float>(resize_w) / static_cast<float>(w));
   return resize_img;
 }
 
@@ -121,7 +121,8 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
   int index = 0;
   for (int i = boxes.size() - 1; i >= 0; i--) {
     crop_img = GetRotateCropImage(srcimg, boxes[i]);
-    float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
+    float wh_ratio =
+        static_cast<float>(crop_img.cols) / static_cast<float>(crop_img.rows);
 
     resize_img = CrnnResizeImg(crop_img, wh_ratio);
     resize_img.convertTo(resize_img, CV_32FC3, 1 / 255.f);
@@ -133,8 +134,7 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
     input_tensor0->Resize({1, 3, resize_img.rows, resize_img.cols});
     auto *data0 = input_tensor0->mutable_data<float>();
 
-    neon_mean_scale(dimg, data0, resize_img.rows * resize_img.cols, mean,
-                    scale);
+    NeonMeanScale(dimg, data0, resize_img.rows * resize_img.cols, mean, scale);
     //// Run CRNN predictor
     predictor_crnn->Run();
 
@@ -147,8 +147,9 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
     auto shape_out = output_tensor0->shape();
 
     std::vector<int> pred_idx;
-    for (int n = int(rec_idx_lod[0][0]); n < int(rec_idx_lod[0][1]); n += 1) {
-      pred_idx.push_back(int(rec_idx[n]));
+    for (int n = static_cast<int>(rec_idx_lod[0][0]);
+         n < static_cast<int>(rec_idx_lod[0][1]); n += 1) {
+      pred_idx.push_back(static_cast<int>(rec_idx[n]));
     }
 
     if (pred_idx.size() < 1e-3)
@@ -169,16 +170,15 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
 
     auto predict_lod = output_tensor1->lod();
 
-    int argmax_idx;
     int blank = predict_shape[1];
     float score = 0.f;
     int count = 0;
-    float max_value = 0.0f;
 
     for (int n = predict_lod[0][0]; n < predict_lod[0][1] - 1; n++) {
-      argmax_idx = int(Argmax(&predict_batch[n * predict_shape[1]],
+      int argmax_idx =
+          static_cast<int>(Argmax(&predict_batch[n * predict_shape[1]],
                                   &predict_batch[(n + 1) * predict_shape[1]]));
-      max_value =
+      float max_value =
           float(*std::max_element(&predict_batch[n * predict_shape[1]],
                                   &predict_batch[(n + 1) * predict_shape[1]]));
 
@@ -214,7 +214,7 @@ RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
   std::vector<float> mean = {0.485f, 0.456f, 0.406f};
   std::vector<float> scale = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
   const float *dimg = reinterpret_cast<const float *>(img_fp.data);
-  neon_mean_scale(dimg, data0, img_fp.rows * img_fp.cols, mean, scale);
+  NeonMeanScale(dimg, data0, img_fp.rows * img_fp.cols, mean, scale);
 
   // Run predictor
   predictor->Run();
@@ -230,12 +230,14 @@ RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
   unsigned char cbuf[shape_out[2] * shape_out[3]];
 
   for (int i = 0; i < int(shape_out[2] * shape_out[3]); i++) {
-    pred[i] = float(outptr[i]);
-    cbuf[i] = (unsigned char)((outptr[i]) * 255);
+    pred[i] = static_cast<float>(outptr[i]);
+    cbuf[i] = static_cast<unsigned char>((outptr[i]) * 255);
   }
 
-  cv::Mat cbuf_map(shape_out[2], shape_out[3], CV_8UC1, (unsigned char *)cbuf);
-  cv::Mat pred_map(shape_out[2], shape_out[3], CV_32F, (float *)pred);
+  cv::Mat cbuf_map(shape_out[2], shape_out[3], CV_8UC1,
+                   reinterpret_cast<unsigned char *> cbuf);
+  cv::Mat pred_map(shape_out[2], shape_out[3], CV_32F,
+                   reinterpret_cast<float *> pred);
 
   const double threshold = double(Config["det_db_thresh"]) * 255;
   const double maxvalue = 255;
@@ -264,7 +266,8 @@ cv::Mat Visualization(cv::Mat srcimg,
   cv::Point rook_points[boxes.size()][4];
   for (int n = 0; n < boxes.size(); n++) {
     for (int m = 0; m < boxes[0].size(); m++) {
-      rook_points[n][m] = cv::Point(int(boxes[n][m][0]), int(boxes[n][m][1]));
+      rook_points[n][m] = cv::Point(static_cast<int>(boxes[n][m][0]),
+                                    static_cast<int>(boxes[n][m][1]));
     }
   }
   cv::Mat img_vis;