fix bug for rec nms

deploy/configs/inference_cartoon.yaml

@@ -2,7 +2,7 @@ Global:
   infer_imgs: "./recognition_demo_data_v1.0/test_cartoon"
   det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer/"
   rec_inference_model_dir: "./models/cartoon_rec_ResNet50_iCartoon_v1.0_infer/"
-  rec_nms_thresold: 0.1
+  rec_nms_thresold: 0.05
 
   batch_size: 1
   image_shape: [3, 640, 640]

deploy/configs/inference_logo.yaml

@@ -2,7 +2,7 @@ Global:
   infer_imgs: "./recognition_demo_data_v1.0/gallery/test_logo"
   det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer/"
   rec_inference_model_dir: "./models/logo_rec_ResNet50_Logo3K_v1.0_infer/"
-  rec_nms_thresold: 0.3
+  rec_nms_thresold: 0.05
 
   batch_size: 1
   image_shape: [3, 640, 640]

deploy/configs/inference_product.yaml

@@ -2,7 +2,7 @@ Global:
   infer_imgs: "./recognition_demo_data_v1.0/test_product/daoxiangcunjinzhubing_6.jpg"
   det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer"
   rec_inference_model_dir: "./models/product_ResNet50_vd_aliproduct_v1.0_infer"
-  rec_nms_thresold: 0.3
+  rec_nms_thresold: 0.05
   
   batch_size: 1
   image_shape: [3, 640, 640]

deploy/configs/inference_vehicle.yaml

@@ -2,7 +2,7 @@ Global:
   infer_imgs: "./recognition_demo_data_v1.0/test_vehicle/"
   det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer/"
   rec_inference_model_dir: "./models/vehicle_cls_ResNet50_CompCars_v1.0_infer/"
-  rec_nms_thresold: 0.3
+  rec_nms_thresold: 0.05
 
   batch_size: 1
   image_shape: [3, 640, 640]

deploy/python/predict_system.py

@@ -56,7 +56,7 @@ class SystemPredictor(object):
         })
         return results
 
-    def nms_to_rec_results(self, results, thresh=0.3):
+    def nms_to_rec_results(self, results, thresh=0.1):
         filtered_results = []
         x1 = np.array([r["bbox"][0] for r in results]).astype("float32")
         y1 = np.array([r["bbox"][1] for r in results]).astype("float32")
@@ -66,22 +66,19 @@ class SystemPredictor(object):
 
         areas = (x2 - x1 + 1) * (y2 - y1 + 1)
         order = scores.argsort()[::-1]
-
         while order.size > 0:
             i = order[0]
             xx1 = np.maximum(x1[i], x1[order[1:]])
-            yy1 = np.minimum(y1[i], y1[order[1:]])
+            yy1 = np.maximum(y1[i], y1[order[1:]])
             xx2 = np.minimum(x2[i], x2[order[1:]])
-            yy2 = np.maximum(y2[i], y2[order[1:]])
+            yy2 = np.minimum(y2[i], y2[order[1:]])
 
             w = np.maximum(0.0, xx2 - xx1 + 1)
             h = np.maximum(0.0, yy2 - yy1 + 1)
             inter = w * h
             ovr = inter / (areas[i] + areas[order[1:]] - inter)
-
             inds = np.where(ovr <= thresh)[0]
             order = order[inds + 1]
-
             filtered_results.append(results[i])
 
         return filtered_results

deploy/utils/draw_bbox.py

@@ -28,7 +28,8 @@ def draw_bbox_results(image,
     draw = ImageDraw.Draw(image)
     font = ImageFont.truetype(font_path, 20, encoding="utf-8")
 
-    color = (0, 255, 0)
+#     color = (0, 255, 0)
+    color = (0, 102, 255)
 
     for result in results:
         # empty results
@@ -40,9 +41,10 @@ def draw_bbox_results(image,
         th = 20
         tw = int(len(result["rec_docs"]) * 20) + 60
         start_y = max(0, ymin - th)
-        draw.rectangle(
-            [(xmin + 1, start_y), (xmin + tw + 1, start_y + th)],
-            outline=color)
+        
+#         draw.rectangle(
+#             [(xmin + 1, start_y), (xmin + tw + 1, start_y + th)],
+#             outline=color)
 
         draw.text((xmin + 1, start_y), text, fill=color, font=font)