update binary model config

deploy/configs/inference_product_binary.yaml → deploy/configs/inference_general_binary.yaml

 Global:
-  infer_imgs: "./recognition_demo_data_v1.1/test_product/daoxiangcunjinzhubing_6.jpg"
-  det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer"
-  rec_inference_model_dir: "./models/product_MV3_x1_0_aliproduct_bin_v1.0_infer"
+  infer_imgs: "./drink_dataset_v1.0/test_images/001.jpeg"
+  det_inference_model_dir: "./models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer"
+  rec_inference_model_dir: "./models/general_PPLCNet_x2_5_lite_binary_v1.0_infer"
   rec_nms_thresold: 0.05
   
   batch_size: 1
@@ -11,7 +11,6 @@ Global:
   labe_list:
   - foreground
 
-  # inference engine config
   use_gpu: True
   enable_mkldnn: True
   cpu_num_threads: 10
@@ -49,19 +48,18 @@ RecPreProcess:
 RecPostProcess:
   main_indicator: Binarize
   Binarize:
-    method: "round"
+    method: "sign"
 
 # indexing engine config
 IndexProcess:
-  index_method: "Flat"    # supported: HNSW32, Flat
-  index_dir: "./recognition_demo_data_v1.1/gallery_product/index_binary"
-  image_root: "./recognition_demo_data_v1.1/gallery_product/"
-  data_file:  "./recognition_demo_data_v1.1/gallery_product/data_file.txt"
+  index_method: "Flat"  # supported: HNSW32, Flat
+  image_root: "./drink_dataset_v1.0/gallery/"
+  index_dir: "./drink_dataset_v1.0/index_bin"
+  data_file:  "./drink_dataset_v1.0/gallery/drink_label.txt"
   index_operation: "new"  # suported: "append", "remove", "new"
   delimiter: "\t"
   dist_type: "hamming"
   embedding_size: 512
   batch_size: 32
-  binary_index: true
   return_k: 5
-  score_thres: 0
\ No newline at end of file
+  hamming_radius: 100
\ No newline at end of file

deploy/python/predict_system.py

@@ -47,14 +47,14 @@ class SystemPredictor(object):
             index_dir, "vector.index")), "vector.index not found ..."
         assert os.path.exists(os.path.join(
             index_dir, "id_map.pkl")), "id_map.pkl not found ... "
-        
-        if config['IndexProcess'].get("binary_index", False):
+
+        if config['IndexProcess'].get("dist_type") == "hamming":
             self.Searcher = faiss.read_index_binary(
                 os.path.join(index_dir, "vector.index"))
         else:
             self.Searcher = faiss.read_index(
                 os.path.join(index_dir, "vector.index"))
-                
+
         with open(os.path.join(index_dir, "id_map.pkl"), "rb") as fd:
             self.id_map = pickle.load(fd)
 
@@ -102,6 +102,7 @@ class SystemPredictor(object):
 
         # st2: add the whole image for recognition to improve recall
         results = self.append_self(results, img.shape)
+        #print(results)
 
         # st3: recognition process, use score_thres to ensure accuracy
         for result in results:
@@ -111,12 +112,19 @@ class SystemPredictor(object):
             rec_results = self.rec_predictor.predict(crop_img)
             preds["bbox"] = [xmin, ymin, xmax, ymax]
             scores, docs = self.Searcher.search(rec_results, self.return_k)
-            
+
             # just top-1 result will be returned for the final
-            if scores[0][0] >= self.config["IndexProcess"]["score_thres"]:
-                preds["rec_docs"] = self.id_map[docs[0][0]].split()[1]
-                preds["rec_scores"] = scores[0][0]
-                output.append(preds)
+            if self.config["IndexProcess"]["dist_type"] == "hamming":
+                if scores[0][0] <= self.config["IndexProcess"][
+                        "hamming_radius"]:
+                    preds["rec_docs"] = self.id_map[docs[0][0]].split()[1]
+                    preds["rec_scores"] = scores[0][0]
+                    output.append(preds)
+            else:
+                if scores[0][0] >= self.config["IndexProcess"]["score_thres"]:
+                    preds["rec_docs"] = self.id_map[docs[0][0]].split()[1]
+                    preds["rec_scores"] = scores[0][0]
+                    output.append(preds)
 
         # st5: nms to the final results to avoid fetching duplicate results
         output = self.nms_to_rec_results(

docs/zh_CN/quick_start/quick_start_recognition.md

@@ -42,6 +42,7 @@
 | ------------  | ------------- | -------- | ------- |
 | 轻量级通用主体检测模型 | 通用场景  |[tar 格式文件下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar) [zip 格式文件下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.zip) | - |
 | 轻量级通用识别模型 | 通用场景  | [tar 格式下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar) [zip 格式文件下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.zip) | [inference_general.yaml](../../../deploy/configs/inference_general.yaml) |
+| 轻量级通用识别二值模型 | 存储受限场景  | [tar.gz 格式下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_binary_v1.0_infer.tar.gz) | [inference_general_binary.yaml](../../../deploy/configs/inference_general_binary.yaml) |
 
 注意：由于部分解压缩软件在解压上述 `tar` 格式文件时存在问题，建议非命令行用户下载 `zip` 格式文件并解压。`tar` 格式文件建议使用命令 `tar xf xxx.tar` 解压。
 
@@ -339,4 +340,3 @@ wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognit
 按照上述步骤下载模型和测试数据后，您可以进行相关方向识别模型的测试。
 
 * 更多关于主体检测的介绍可以参考：[主体检测教程文档](../image_recognition_pipeline/mainbody_detection.md)；关于特征提取的介绍可以参考：[特征提取教程文档](../image_recognition_pipeline/feature_extraction.md)；关于向量检索的介绍可以参考：[向量检索教程文档](../image_recognition_pipeline/vector_search.md)。
-