add flexible configuration for disable det model

deploy/python/predict_system.py

@@ -31,7 +31,14 @@ class SystemPredictor(object):
 
         self.config = config
         self.rec_predictor = RecPredictor(config)
-        self.det_predictor = DetPredictor(config)
+
+        if not config["Global"]["det_inference_model_dir"]:
+            logger.info(
+                f"find 'Global.det_inference_model_dir' empty({config['Global']['det_inference_model_dir']}), so det_predictor is disabled"
+            )
+            self.det_predictor = None
+        else:
+            self.det_predictor = DetPredictor(config)
 
         assert 'IndexProcess' in config.keys(), "Index config not found ... "
         self.return_k = self.config['IndexProcess']['return_k']
@@ -92,7 +99,10 @@ class SystemPredictor(object):
     def predict(self, img):
         output = []
         # st1: get all detection results
-        results = self.det_predictor.predict(img)
+        if self.det_predictor:
+            results = self.det_predictor.predict(img)
+        else:
+            results = []
 
         # st2: add the whole image for recognition to improve recall
         results = self.append_self(results, img.shape)

docs/en/PPShiTu/PPShiTuV2_introduction.md

@@ -198,6 +198,21 @@ The final output is as follows.
 [{'bbox': [437, 71, 660, 728], 'rec_docs': '元气森林', 'rec_scores': 0.7740249}, {'bbox': [221, 72, 449, 701], 'rec_docs': '元气森林', 'rec_scores': 0.6950992}, {'bbox': [794, 104, 979, 652], 'rec_docs': '元气森林', 'rec_scores': 0.6305153}]
 ```
 
+The recognition process supports flexible configuration. Users can choose not to use the object detection model, but directly input a single whole image into the feature extraction model, and calculate the feature vector for subsequent retrieval, thereby reducing the time-consuming of the overall recognition process. It can be achieved by the script below
+```shell
+# Use the following command to use the GPU for whole-image prediction
+python3.7 python/predict_system.py -c configs/inference_general.yaml -o Global.det_inference_model_dir=None
+
+# Use the following command to use the CPU for whole-image prediction
+python3.7 python/predict_system.py -c configs/inference_general.yaml -o Global.use_gpu=False -o Global.det_inference_model_dir=None
+```
+
+The final output is as follows
+```log
+INFO: find 'Global.det_inference_model_dir' empty(), so det_predictor is disabled
+[{'bbox': [0, 0, 1200, 802], 'rec_docs': '元气森林', 'rec_scores': 0.5696486}]
+```
+
 #### 4.3.2 multi images prediction
 
 If you want to predict the images in the folder, you can directly modify the `Global.infer_imgs` field in the configuration file, or you can modify the corresponding configuration through the following -o parameter.

docs/zh_CN/quick_start/quick_start_recognition.md

@@ -223,6 +223,20 @@ python3.7 python/predict_system.py -c configs/inference_general.yaml -o Global.u
 
 ![](../../images/recognition/drink_data_demo/output/100.jpeg)
 
+识别流程支持灵活配置，用户可以选择不使用主体检测模型，而直接将单幅整图输入到特征提取模型，计算特征向量供后续检索使用，从而减少整体识别流程的耗时。可以按照以下命令直接进行整图识别
+```shell
+# 使用下面的命令使用 GPU 进行整图预测
+python3.7 python/predict_system.py -c configs/inference_general.yaml -o Global.det_inference_model_dir=None
+
+# 使用下面的命令使用 CPU 进行整图预测
+python3.7 python/predict_system.py -c configs/inference_general.yaml -o Global.use_gpu=False -o Global.det_inference_model_dir=None
+```
+
+最终输出结果如下
+```log
+INFO: find 'Global.det_inference_model_dir' empty(), so det_predictor is disabled
+[{'bbox': [0, 0, 1200, 802], 'rec_docs': '元气森林', 'rec_scores': 0.5696486}]
+```
 
 <a name="基于文件夹的批量识别"></a>