add check_install

doc/doc_ch/algorithm_det_sast.md

@@ -73,9 +73,9 @@ python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Gl
 
 ```
 
-SAST文本检测模型推理，需要设置参数`--det_algorithm="SAST"`，同时，还需要增加参数`--det_sast_polygon=True`，可以执行如下命令：
+SAST文本检测模型推理，需要设置参数`--det_algorithm="SAST"`，同时，还需要增加参数`--det_box_type=poly`，可以执行如下命令：
 ```
-python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_sast_tt/" --det_sast_polygon=True
+python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_sast_tt/" --det_box_type='poly'
 ```
 可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
 

doc/doc_ch/inference_args.md

@@ -70,7 +70,7 @@ SAST算法相关参数如下
 | :--: | :--: | :--: | :--: |
 |  det_sast_score_thresh | float | 0.5 | SAST后处理中的得分阈值 |
 |  det_sast_nms_thresh | float | 0.5 | SAST后处理中nms的阈值 |
-|  det_sast_polygon | bool | False | 是否多边形检测，弯曲文本场景（如Total-Text）设置为True |
+|  det_box_type | str | quad | 是否多边形检测，弯曲文本场景（如Total-Text）设置为'poly' |
 
 PSE算法相关参数如下
 
@@ -79,7 +79,7 @@ PSE算法相关参数如下
 |  det_pse_thresh | float | 0.0 | 对输出图做二值化的阈值 |
 |  det_pse_box_thresh | float | 0.85 | 对box进行过滤的阈值，低于此阈值的丢弃 |
 |  det_pse_min_area | float | 16 | box的最小面积，低于此阈值的丢弃 |
-|  det_pse_box_type | str | "box" | 返回框的类型，box:四点坐标，poly: 弯曲文本的所有点坐标 |
+|  det_box_type | str | "quad" | 返回框的类型，quad:四点坐标，poly: 弯曲文本的所有点坐标 |
 |  det_pse_scale | int | 1 | 输入图像相对于进后处理的图的比例，如`640*640`的图像，网络输出为`160*160`，scale为2的情况下，进后处理的图片shape为`320*320`。这个值调大可以加快后处理速度，但是会带来精度的下降 |
 
 * 文本识别模型相关

doc/doc_en/algorithm_det_sast_en.md

@@ -74,10 +74,10 @@ First, convert the model saved in the SAST text detection training process into
 python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/det_sast_tt
 ```
 
-For SAST curved text detection model inference, you need to set the parameter `--det_algorithm="SAST"` and `--det_sast_polygon=True`, run the following command:
+For SAST curved text detection model inference, you need to set the parameter `--det_algorithm="SAST"` and `--det_box_type=poly`, run the following command:
 
 ```
-python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_sast_tt/" --det_sast_polygon=True
+python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_sast_tt/" --det_box_type='poly'
 ```
 
 The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:

doc/doc_en/inference_args_en.md

@@ -70,7 +70,7 @@ The relevant parameters of the SAST algorithm are as follows
 | :--: | :--: | :--: | :--: |
 |  det_sast_score_thresh | float | 0.5 | Score thresholds in SAST postprocess |
 |  det_sast_nms_thresh | float | 0.5 | Thresholding of nms in SAST postprocess |
-|  det_sast_polygon | bool | False | Whether polygon detection, curved text scene (such as Total-Text) is set to True |
+|  det_box_type | str | 'quad' | Whether polygon detection, curved text scene (such as Total-Text) is set to 'poly' |
 
 The relevant parameters of the PSE algorithm are as follows
 
@@ -79,7 +79,7 @@ The relevant parameters of the PSE algorithm are as follows
 |  det_pse_thresh | float | 0.0 | Threshold for binarizing the output image |
 |  det_pse_box_thresh | float | 0.85 | Threshold for filtering boxes, below this threshold is discarded |
 |  det_pse_min_area | float | 16 | The minimum area of the box, below this threshold is discarded |
-|  det_pse_box_type | str | "box" | The type of the returned box, box: four point coordinates, poly: all point coordinates of the curved text |
+|  det_box_type | str | "quad" | The type of the returned box, quad: four point coordinates, poly: all point coordinates of the curved text |
 |  det_pse_scale | int | 1 | The ratio of the input image relative to the post-processed image, such as an image of `640*640`, the network output is `160*160`, and when the scale is 2, the shape of the post-processed image is `320*320`. Increasing this value can speed up the post-processing speed, but it will bring about a decrease in accuracy |
 
 * Text recognition model related parameters

ppocr/data/imaug/ct_process.py

@@ -19,7 +19,13 @@
 import paddle
 
 import numpy as np
-import Polygon as plg
+from ppocr.utils.utility import check_install
+try:
+    import Polygon as plg
+except:
+    check_install("Polygon3")
+    import Polygon as plg
+
 import scipy.io as scio
 
 from PIL import Image

ppocr/postprocess/east_postprocess.py

@@ -22,6 +22,7 @@
 import paddle
 
 import os
+from ppocr.utils.utility import check_install
 import sys
 
 
@@ -78,11 +79,12 @@ def detect(self,
         boxes[:, 8] = score_map[xy_text[:, 0], xy_text[:, 1]]
 
         try:
+            check_install("lanms")
             import lanms
             boxes = lanms.merge_quadrangle_n9(boxes, nms_thresh)
         except:
             print(
-                'you should install lanms by pip3 install lanms-nova to speed up nms_locality'
+                'you should install lanms by pip3 install lanms to speed up nms_locality'
             )
             boxes = nms_locality(boxes.astype(np.float64), nms_thresh)
         if boxes.shape[0] == 0:

ppocr/utils/e2e_metric/Deteval.py

@@ -15,7 +15,11 @@
 import json
 import numpy as np
 import scipy.io as io
-import Polygon as plg
+try:
+    import Polygon as plg
+except:
+    check_install("Polygon3")
+    import Polygon as plg
 from ppocr.utils.e2e_metric.polygon_fast import iod, area_of_intersection, area
 
 

ppocr/utils/utility.py

@@ -19,6 +19,9 @@
 import random
 import numpy as np
 import paddle
+import importlib.util
+import sys
+import subprocess
 
 
 def print_dict(d, logger, delimiter=0):
@@ -131,6 +134,25 @@ def set_seed(seed=1024):
     paddle.seed(seed)
 
 
+def check_install(module_name):
+    spec = importlib.util.find_spec(module_name)
+    if spec is None:
+        print(f'Warnning! The {module_name} module is NOT installed')
+        print(
+            f'Try install {module_name} module automatically. You can also try to install manually.'
+        )
+        python = sys.executable
+        try:
+            subprocess.check_call(
+                [python, '-m', 'pip', 'install', 'requests'],
+                stdout=subprocess.DEVNULL)
+            print(f'The {module_name} module is now installed')
+        except subprocess.CalledProcessError as exc:
+            print(f"Install {module_name} failed, please install manually")
+    else:
+        print(f"{module_name} has been installed.")
+
+
 class AverageMeter:
     def __init__(self):
         self.reset()

requirements.txt

@@ -15,5 +15,4 @@ premailer
 openpyxl
 attrdict
 Polygon3
-lanms-neo==1.0.2
 PyMuPDF==1.19.0