diff --git a/deploy/utils/draw_bbox.py b/deploy/utils/draw_bbox.py index 7c8044d1b84a996a4ce4bbe6c62d14a8e15b56ee..9c0bcf61bfc84d5dbb217f1f317dc084bbd5f975 100644 --- a/deploy/utils/draw_bbox.py +++ b/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( diff --git a/docs/en/tutorials/quick_start_recognition_en.md b/docs/en/tutorials/quick_start_recognition_en.md index 7996e56965b5e96fcff7dc4530f667e7d3861785..60ae04f005be72a3115f0509d8ab474cc125c70c 100644 --- a/docs/en/tutorials/quick_start_recognition_en.md +++ b/docs/en/tutorials/quick_start_recognition_en.md @@ -43,7 +43,7 @@ The detection model with the recognition inference model for the 4 directions (L | Product Recignition Model | Product Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/product_ResNet50_vd_Inshop_v1.0_infer.tar) | [inference_product.yaml](../../../deploy/configs/inference_product.yaml) | [build_product.yaml](../../../deploy/configs/build_product.yaml) | -Demo data in this tutorial can be downloaded here: [download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_v1.0.tar). +Demo data in this tutorial can be downloaded here: [download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_en_v1.0.tar). **Attention** @@ -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.
- +
@@ -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.
- +
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