fix en (#942)

* fix en quick start

* add en image

deploy/utils/draw_bbox.py

@@ -39,7 +39,8 @@ def draw_bbox_results(image,
         xmin, ymin, xmax, ymax = result["bbox"]
         text = "{}, {:.2f}".format(result["rec_docs"], result["rec_scores"])
         th = font_size
-        tw = int(len(result["rec_docs"]) * font_size) + 60
+        tw = font.getsize(text)[0]
+        # tw = int(len(result["rec_docs"]) * font_size) + 60
         start_y = max(0, ymin - th)
 
         draw.rectangle(

docs/en/tutorials/quick_start_recognition_en.md

@@ -89,7 +89,7 @@ wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/infere
 cd ..
 
 # Download the demo data and unzip it
-wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_v1.0.tar && tar -xf recognition_demo_data_v1.0.tar
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_en_v1.0.tar && tar -xf recognition_demo_data_en_v1.0.tar
 ```
 
 Once unpacked, the `recognition_demo_data_v1.0` folder should have the following file structure.
@@ -137,7 +137,7 @@ Run the following command to identify and retrieve the image `./recognition_demo
 # use the following command to predict using GPU.
 python3.7 python/predict_system.py -c configs/inference_product.yaml
 # use the following command to predict using CPU
-python3.7 python/predict_system.py -c configs/inference_product.yaml
+python3.7 python/predict_system.py -c configs/inference_product.yaml -o Global.use_gpu=False
 ```
 
 **Note:** Program lib used to build index is compliled on our machine, if error occured because of the environment, you can refer to [vector search tutorial](../../../deploy/vector_search/README.md) to rebuild the lib.
@@ -153,7 +153,7 @@ The image to be retrieved is shown below.
 The final output is shown below.
 
 ```
-[{'bbox': [287, 129, 497, 326], 'rec_docs': '稻香村金猪饼', 'rec_scores': 0.8309420943260193}, {'bbox': [99, 242, 313, 426], 'rec_docs': '稻香村金猪饼', 'rec_scores': 0.7245652079582214}]
+[{'bbox': [287, 129, 497, 326], 'rec_docs': 'Daoxaingcun Golden Piggie Cake', 'rec_scores': 0.8309420347213745}, {'bbox': [99, 242, 313, 426], 'rec_docs': 'Daoxaingcun Golden Piggie Cake', 'rec_scores': 0.7245651483535767}]
 ```
 
 
@@ -163,7 +163,7 @@ where bbox indicates the location of the detected object, rec_docs indicates the
 The detection result is also saved in the folder `output`, for this image, the visualization result is as follows.
 
 <div align="center">
-<img src="../../images/recognition/product_demo/result/daoxiangcunjinzhubing_6.jpg"  width = "400" />
+<img src="../../images/recognition/product_demo/result/daoxiangcunjinzhubing_6_en.jpg"  width = "400" />
 </div>
 
 
@@ -182,13 +182,12 @@ The results on the screen are shown as following.
 
 ```
 ...
-[{'bbox': [37, 29, 123, 89], 'rec_docs': '香奈儿包', 'rec_scores': 0.6163763999938965}, {'bbox': [153, 96, 235, 175], 'rec_docs': '香奈儿包', 'rec_scores': 0.5279821157455444}]
-[{'bbox': [735, 562, 1133, 851], 'rec_docs': '香奈儿包', 'rec_scores': 0.5588355660438538}]
-[{'bbox': [124, 50, 230, 129], 'rec_docs': '香奈儿包', 'rec_scores': 0.6980369687080383}]
-[{'bbox': [0, 0, 275, 183], 'rec_docs': '香奈儿包', 'rec_scores': 0.5818190574645996}]
-[{'bbox': [400, 1179, 905, 1537], 'rec_docs': '香奈儿包', 'rec_scores': 0.9814301133155823}]
-[{'bbox': [544, 4, 1482, 932], 'rec_docs': '香奈儿包', 'rec_scores': 0.5143815279006958}]
-[{'bbox': [29, 42, 194, 183], 'rec_docs': '香奈儿包', 'rec_scores': 0.9543638229370117}]
+[{'bbox': [37, 29, 123, 89], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.6163763999938965}, {'bbox': [153, 96, 235, 175], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.5279821157455444}]
+[{'bbox': [735, 562, 1133, 851], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.5588355660438538}]
+[{'bbox': [124, 50, 230, 129], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.6980369687080383}]
+[{'bbox': [0, 0, 275, 183], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.5818190574645996}]
+[{'bbox': [400, 1179, 905, 1537], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.9814301133155823}, {'bbox': [295, 713, 820, 1046], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.9496176242828369}, {'bbox': [153, 236, 694, 614], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.8395382761955261}]
+[{'bbox': [544, 4, 1482, 932], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.5143815279006958}]
 ...
 ```
 
@@ -238,12 +237,12 @@ cp recognition_demo_data_v1.0/gallery_product/data_file.txt recognition_demo_dat
 Then add some new lines into the new label file, which is shown as follows.
 
 ```
-gallery/anmuxi/001.jpg	安慕希酸奶
-gallery/anmuxi/002.jpg	安慕希酸奶
-gallery/anmuxi/003.jpg	安慕希酸奶
-gallery/anmuxi/004.jpg	安慕希酸奶
-gallery/anmuxi/005.jpg	安慕希酸奶
-gallery/anmuxi/006.jpg	安慕希酸奶
+gallery/anmuxi/001.jpg	Anmuxi Ambrosial Yogurt
+gallery/anmuxi/002.jpg	Anmuxi Ambrosial Yogurt
+gallery/anmuxi/003.jpg	Anmuxi Ambrosial Yogurt
+gallery/anmuxi/004.jpg	Anmuxi Ambrosial Yogurt
+gallery/anmuxi/005.jpg	Anmuxi Ambrosial Yogurt
+gallery/anmuxi/006.jpg	Anmuxi Ambrosial Yogurt
 ```
 
 Each line can be splited into two fields. The first field denotes the relative image path, and the second field denotes its label. The `delimiter` is `tab` here.
@@ -274,11 +273,11 @@ python3.7 python/predict_system.py -c configs/inference_product.yaml -o Global.i
 The output is as follows:
 
 ```
-[{'bbox': [243, 80, 523, 522], 'rec_docs': '安慕希酸奶', 'rec_scores': 0.5570770502090454}]
+[{'bbox': [243, 80, 523, 522], 'rec_docs': 'Anmuxi Ambrosial Yogurt', 'rec_scores': 0.5570770502090454}]
 ```
 
-The final recognition result is `安慕希酸奶`, which is corrrect, the visualization result is as follows.
+The final recognition result is `Anmuxi Ambrosial Yogurt`, which is corrrect, the visualization result is as follows.
 
 <div align="center">
-<img src="../../images/recognition/product_demo/result/anmuxi.jpg"  width = "400" />
+<img src="../../images/recognition/product_demo/result/anmuxi_en.jpg"  width = "400" />
 </div>