修复box_line_optimized bug

models/line_detect/predict2.py

@@ -61,11 +61,11 @@ def box_line_optimized(pred):
             possible_matches = list(idx.intersection((i[0], i[1], i[2], i[3])))
 
             for j in possible_matches:
-                line_j = lines[0, j].cpu().numpy() / 128 * 512  # 调整比例
-                if (line_j[0][0] >= i[0] and line_j[1][0] >= i[0] and
-                        line_j[0][0] <= i[2] and line_j[1][0] <= i[2] and
-                        line_j[0][1] >= i[1] and line_j[1][1] >= i[1] and
-                        line_j[0][1] <= i[3] and line_j[1][1] <= i[3]):
+                line_j = lines[0, j].cpu().numpy() / 128 * 512
+                if (line_j[0][1] >= i[0] and line_j[1][1] >= i[0] and  # 注意这里交换了x和y
+                        line_j[0][1] <= i[2] and line_j[1][1] <= i[2] and
+                        line_j[0][0] >= i[1] and line_j[1][0] >= i[1] and
+                        line_j[0][0] <= i[3] and line_j[1][0] <= i[3]):
 
                     if scores[j] > score_max:
                         tmp = line_j
@@ -147,7 +147,7 @@ if __name__ == '__main__':
     print(f'start to predict:{t_start}')
     model = linenet_resnet50_fpn().to(device)
     pt_path = r"F:\BaiduNetdiskDownload\resnet50_best_e8.pth"
-    img_path = r"I:\datasets\wirenet_1000\images\val\00037040_0.png"
+    img_path = r"I:\datasets\wirenet_1000\images\val\00035148_0.png"
     predict(pt_path, model, img_path)
     t_end = time.time()
     # print(f'predict used:{t_end - t_start}')