updates

utils/utils.py

@@ -71,7 +71,8 @@ def weights_init_normal(m):
         torch.nn.init.constant_(m.bias.data, 0.0)
 
 
-def xyxy2xywh(x):  # Convert bounding box format from [x1, y1, x2, y2] to [x, y, w, h]
+def xyxy2xywh(x):
+    # Convert bounding box format from [x1, y1, x2, y2] to [x, y, w, h]
     y = torch.zeros(x.shape) if x.dtype is torch.float32 else np.zeros(x.shape)
     y[:, 0] = (x[:, 0] + x[:, 2]) / 2
     y[:, 1] = (x[:, 1] + x[:, 3]) / 2
@@ -80,7 +81,8 @@ def xyxy2xywh(x):  # Convert bounding box format from [x1, y1, x2, y2] to [x, y,
     return y
 
 
-def xywh2xyxy(x):  # Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]
+def xywh2xyxy(x):
+    # Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]
     y = torch.zeros(x.shape) if x.dtype is torch.float32 else np.zeros(x.shape)
     y[:, 0] = (x[:, 0] - x[:, 2] / 2)
     y[:, 1] = (x[:, 1] - x[:, 3] / 2)
@@ -89,6 +91,18 @@ def xywh2xyxy(x):  # Convert bounding box format from [x, y, w, h] to [x1, y1, x
     return y
 
 
+def scale_coords(img_size, coords, img0_shape):
+    # Rescale x1, y1, x2, y2 from 416 to image size
+    gain = float(img_size) / max(img0_shape)  # gain  = old / new
+    pad_x = (img_size - img0_shape[1] * gain) / 2  # width padding
+    pad_y = (img_size - img0_shape[0] * gain) / 2  # height padding
+    coords[:, [0, 2]] -= pad_x
+    coords[:, [1, 3]] -= pad_y
+    coords[:, :4] /= gain
+    coords[:, :4] = torch.round(torch.clamp(coords[:, :4], min=0))
+    return coords
+
+
 def ap_per_class(tp, conf, pred_cls, target_cls):
     """ Compute the average precision, given the recall and precision curves.
     Method originally from https://github.com/rafaelpadilla/Object-Detection-Metrics.