bbox calculation changes

tools/demo_api.py

@@ -74,6 +74,11 @@ class Predictor(object):
         img_info["raw_img"] = img
 
         ratio = min(self.test_size[0] / img.shape[0], self.test_size[1] / img.shape[1])
+        pad = [0,0,0,0]
+        pad[1] = ( self.test_size[0] - img.shape[0] * ratio ) / 2
+        pad[0] = ( self.test_size[1] - img.shape[1] * ratio ) / 2
+        pad[2] = pad[0]
+        pad[3] = pad[1]
         img_info["ratio"] = ratio
 
         img, _ = self.preproc(img, None, self.test_size)
@@ -94,7 +99,7 @@ class Predictor(object):
                 self.nmsthre, class_agnostic=True
             )
             logger.info("Infer time: {:.4f}s".format(time.time() - t0))
-        return outputs, img_info
+        return outputs, img_info, pad
 
     def visual(self, output, img_info, cls_conf=0.35):
         ratio = img_info["ratio"]
@@ -163,7 +168,7 @@ def run_detection(predictor, path):
     
     for img_id, image_name in enumerate(files):
         
-        outputs, img_info = predictor.inference(image_name)
+        outputs, img_info, pad = predictor.inference(image_name)
         ratio = img_info["ratio"]
         
         img_entry = {"id": img_id,
@@ -176,7 +181,7 @@ def run_detection(predictor, path):
                 print(output)
                 ann_entry = {"id": id,
                              "image_id": img_id,
-                             "bbox": (output[:4] / ratio).tolist(),
+                             "bbox": ((output[:4] - pad) / ratio).tolist(),
                              "cls": output[6].item(),
                              "score": (output[4] * output[5]).item() }
                 ann_list.append(ann_entry)