Update example code to sort by reading order

README.md

@@ -64,25 +64,21 @@ def run_doclayout_onnx():
                   input_names[1]: input_blob,
                   input_names[2]: [[scale_h, scale_w]]}
 
-    # shape=(300, 8), First 6 values are [label_index, score, xmin, ymin, xmax, ymax]
+    # shape=(300, 7), Values are [label_index, score, xmin, ymin, xmax, ymax, read_order]
     output = model.run(output_names, input_feed)[0]
 
     # Filter out low-confidence boxes
     boxes = output[output[:, 1] > 0.5]
     print('--- DocLayoutV3 ONNX Output: ---')
-    print_doclayout_res(boxes[np.argsort(boxes[:, 2])])
+    # Sort by reading order
+    print_doclayout_res(boxes[np.argsort(boxes[:, 6])])
 
 
 def print_doclayout_res(boxes):
-    print('cls_id\tscore\txmin\tymin\txmax\tymax')
-    if isinstance(boxes, np.ndarray):
-        for box in boxes:
-            print(f"{box[0]:.0f}\t{box[1]:.3f}\t{box[2]:.2f}\t{box[3]:.2f}\t{box[4]:.2f}\t{box[5]:.2f}")
-    else:
-        for box in sorted(boxes, key=lambda x: x['coordinate'][0]):
-            xmin, ymin, xmax, ymax = box['coordinate']
-            print(f"{box['cls_id']:.0f}\t{box['score']:.3f}"
-                  f"\t{xmin:.2f}\t{ymin:.2f}\t{xmax:.2f}\t{ymax:.2f}")
+    print('cls_id\tscore\txmin\tymin\txmax\tymax\tread_order')
+    for box in boxes:
+        print(f"{box[0]:.0f}\t\t{box[1]:.3f}\t{box[2]:.2f}\t{box[3]:.2f}\t{box[4]:.2f}\t{box[5]:.2f}\t{box[6]:.0f}")
+
 
 if __name__ == '__main__':
     run_doclayout_onnx()