update ch_pp-ocrv3 (#2033)

* update ch_pp-ocrv3

* update README

* update defalut valuex

* add a param

modules/image/text_recognition/ch_pp-ocrv3/README.md

@@ -58,7 +58,7 @@
     ```
   - 通过命令行方式实现文字识别模型的调用，更多请见 [PaddleHub命令行指令](../../../../docs/docs_ch/tutorial/cmd_usage.rst)
 
-- ### 2、代码示例
+- ### 2、预测代码示例
 
   - ```python
     import paddlehub as hub
@@ -87,14 +87,15 @@
 
   - ```python
     def recognize_text(images=[],
-                        paths=[],
-                        use_gpu=False,
-                        output_dir='ocr_result',
-                        visualization=False,
-                        box_thresh=0.5,
-                        text_thresh=0.5,
-                        angle_classification_thresh=0.9,
-                        det_db_unclip_ratio=1.5)
+                       paths=[],
+                       use_gpu=False,
+                       output_dir='ocr_result',
+                       visualization=False,
+                       box_thresh=0.6,
+                       text_thresh=0.5,
+                       angle_classification_thresh=0.9,
+                       det_db_unclip_ratio=1.5,
+                       det_db_score_mode="fast"):
     ```
 
     - 预测API，检测输入图片中的所有中文文本的位置。
@@ -110,6 +111,8 @@
       - visualization (bool): 是否将识别结果保存为图片文件；
       - output\_dir (str): 图片的保存路径，默认设为 ocr\_result；
       - det\_db\_unclip\_ratio: 设置检测框的大小；
+      - det\_db\_score\_mode: 设置检测得分计算方式，“fast” / “slow”
+
     - **返回**
 
       - res (list\[dict\]): 识别结果的列表，列表中每一个元素为 dict，各字段为：
@@ -166,6 +169,10 @@
 
   初始发布
 
+* 1.1.0
+
+  移除 Fluid API
+
   - ```shell
-    $ hub install ch_pp-ocrv3==1.0.0
+    $ hub install ch_pp-ocrv3==1.1.0
     ```

modules/image/text_recognition/ch_pp-ocrv3/module.py

@@ -22,11 +22,7 @@ import time
 import cv2
 import numpy as np
 import paddle
-import paddle.fluid as fluid
 import paddle.inference as paddle_infer
-from paddle.fluid.core import AnalysisConfig
-from paddle.fluid.core import create_paddle_predictor
-from paddle.fluid.core import PaddleTensor
 from PIL import Image
 
 import paddlehub as hub
@@ -35,7 +31,7 @@ from .utils import base64_to_cv2
 from .utils import draw_ocr
 from .utils import get_image_ext
 from .utils import sorted_boxes
-from paddlehub.common.logger import logger
+from paddlehub.utils.utils import logger
 from paddlehub.module.module import moduleinfo
 from paddlehub.module.module import runnable
 from paddlehub.module.module import serving
@@ -43,15 +39,14 @@ from paddlehub.module.module import serving
 
 @moduleinfo(
     name="ch_pp-ocrv3",
-    version="1.0.0",
+    version="1.1.0",
     summary="The module can recognize the chinese texts in an image. Firstly, it will detect the text box positions \
         based on the differentiable_binarization_chn module. Then it classifies the text angle and recognizes the chinese texts. ",
     author="paddle-dev",
     author_email="paddle-dev@baidu.com",
     type="cv/text_recognition")
-class ChPPOCRv3(hub.Module):
-
-    def _initialize(self, text_detector_module=None, enable_mkldnn=False):
+class ChPPOCRv3:
+    def __init__(self, text_detector_module=None, enable_mkldnn=False):
         """
         initialize with the necessary elements
         """
@@ -124,7 +119,7 @@ class ChPPOCRv3(hub.Module):
         if not self._text_detector_module:
             self._text_detector_module = hub.Module(name='ch_pp-ocrv3_det',
                                                     enable_mkldnn=self.enable_mkldnn,
-                                                    version='1.0.0')
+                                                    version='1.1.0')
         return self._text_detector_module
 
     def read_images(self, paths=[]):
@@ -210,10 +205,11 @@ class ChPPOCRv3(hub.Module):
                        use_gpu=False,
                        output_dir='ocr_result',
                        visualization=False,
-                       box_thresh=0.5,
+                       box_thresh=0.6,
                        text_thresh=0.5,
                        angle_classification_thresh=0.9,
-                       det_db_unclip_ratio=1.5):
+                       det_db_unclip_ratio=1.5,
+                       det_db_score_mode="fast"):
         """
         Get the chinese texts in the predicted images.
         Args:
@@ -227,6 +223,7 @@ class ChPPOCRv3(hub.Module):
             text_thresh(float): the threshold of the chinese text recognition confidence
             angle_classification_thresh(float): the threshold of the angle classification confidence
             det_db_unclip_ratio(float): unclip ratio for post processing in DB detection.
+            det_db_score_mode(str): method to calc the final det score, one of fast(using box) and slow(using poly).
         Returns:
             res (list): The result of chinese texts and save path of images.
         """
@@ -253,7 +250,8 @@ class ChPPOCRv3(hub.Module):
         detection_results = self.text_detector_module.detect_text(images=predicted_data,
                                                                   use_gpu=self.use_gpu,
                                                                   box_thresh=box_thresh,
-                                                                  det_db_unclip_ratio=det_db_unclip_ratio)
+                                                                  det_db_unclip_ratio=det_db_unclip_ratio,
+                                                                  det_db_score_mode=det_db_score_mode)
 
         boxes = [np.array(item['data']).astype(np.float32) for item in detection_results]
         all_results = []
@@ -281,7 +279,7 @@ class ChPPOCRv3(hub.Module):
                         rec_res_final.append({
                             'text': text,
                             'confidence': float(score),
-                            'text_box_position': boxes[index].astype(np.int).tolist()
+                            'text_box_position': boxes[index].astype(np.int64).tolist()
                         })
                 result['data'] = rec_res_final
 
@@ -444,6 +442,7 @@ class ChPPOCRv3(hub.Module):
                                       use_gpu=args.use_gpu,
                                       output_dir=args.output_dir,
                                       det_db_unclip_ratio=args.det_db_unclip_ratio,
+                                      det_db_score_mode=args.det_db_score_mode,
                                       visualization=args.visualization)
         return results
 
@@ -467,6 +466,11 @@ class ChPPOCRv3(hub.Module):
                                            type=float,
                                            default=1.5,
                                            help="unclip ratio for post processing in DB detection.")
+        self.arg_config_group.add_argument(
+            '--det_db_score_mode',
+            type=str,
+            default="fast",
+            help="method to calc the final det score, one of fast(using box) and slow(using poly).")
 
     def add_module_input_arg(self):
         """

modules/image/text_recognition/ch_pp-ocrv3/test.py

+import os
+import shutil
+import unittest
+
+import cv2
+import requests
+import paddlehub as hub
+
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+
+class TestHubModule(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls) -> None:
+        img_url = 'https://unsplash.com/photos/KTzZVDjUsXw/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MzM3fHx0ZXh0fGVufDB8fHx8MTY2MzUxMTExMQ&force=true&w=640'
+        if not os.path.exists('tests'):
+            os.makedirs('tests')
+        response = requests.get(img_url)
+        assert response.status_code == 200, 'Network Error.'
+        with open('tests/test.jpg', 'wb') as f:
+            f.write(response.content)
+        cls.module = hub.Module(name="ch_pp-ocrv3")
+
+    @classmethod
+    def tearDownClass(cls) -> None:
+        shutil.rmtree('tests')
+        shutil.rmtree('inference')
+        shutil.rmtree('ocr_result')
+
+    def test_recognize_text1(self):
+        results = self.module.recognize_text(
+            paths=['tests/test.jpg'],
+            use_gpu=False,
+            visualization=False,
+        )
+        self.assertEqual(results[0]['data'], [
+        {
+            'text': 'GIVE.', 'confidence': 0.9509806632995605, 
+            'text_box_position': [[283, 162], [352, 162], [352, 202], [283, 202]]
+        }, 
+        {
+            'text': 'THANKS', 'confidence': 0.9943129420280457, 
+            'text_box_position': [[261, 202], [376, 202], [376, 239], [261, 239]]
+        }])
+
+    def test_recognize_text2(self):
+        results = self.module.recognize_text(
+            images=[cv2.imread('tests/test.jpg')],
+            use_gpu=False,
+            visualization=False,
+        )
+        self.assertEqual(results[0]['data'], [
+        {
+            'text': 'GIVE.', 'confidence': 0.9509806632995605, 
+            'text_box_position': [[283, 162], [352, 162], [352, 202], [283, 202]]
+        }, 
+        {
+            'text': 'THANKS', 'confidence': 0.9943129420280457, 
+            'text_box_position': [[261, 202], [376, 202], [376, 239], [261, 239]]
+        }])
+
+    def test_recognize_text3(self):
+        results = self.module.recognize_text(
+            images=[cv2.imread('tests/test.jpg')],
+            use_gpu=True,
+            visualization=False,
+        )
+        self.assertEqual(results[0]['data'], [
+        {
+            'text': 'GIVE.', 'confidence': 0.9509806632995605, 
+            'text_box_position': [[283, 162], [352, 162], [352, 202], [283, 202]]
+        }, 
+        {
+            'text': 'THANKS', 'confidence': 0.9943129420280457, 
+            'text_box_position': [[261, 202], [376, 202], [376, 239], [261, 239]]
+        }])
+
+    def test_recognize_text4(self):
+        results = self.module.recognize_text(
+            images=[cv2.imread('tests/test.jpg')],
+            use_gpu=False,
+            visualization=True,
+        )
+        self.assertEqual(results[0]['data'], [
+        {
+            'text': 'GIVE.', 'confidence': 0.9509806632995605, 
+            'text_box_position': [[283, 162], [352, 162], [352, 202], [283, 202]]
+        }, 
+        {
+            'text': 'THANKS', 'confidence': 0.9943129420280457, 
+            'text_box_position': [[261, 202], [376, 202], [376, 239], [261, 239]]
+        }])
+
+    def test_recognize_text5(self):
+        self.assertRaises(
+            AttributeError,
+            self.module.recognize_text,
+            images=['tests/test.jpg']
+        )
+
+    def test_recognize_text6(self):
+        self.assertRaises(
+            AssertionError,
+            self.module.recognize_text,
+            paths=['no.jpg']
+        )
+
+    def test_save_inference_model(self):
+        self.module.save_inference_model('./inference/model')
+
+        self.assertTrue(os.path.exists('./inference/model/model.pdmodel'))
+        self.assertTrue(os.path.exists('./inference/model/model.pdiparams'))
+
+        self.assertTrue(os.path.exists('./inference/model/_text_detector_module.pdmodel'))
+        self.assertTrue(os.path.exists('./inference/model/_text_detector_module.pdiparams'))
+
+
+if __name__ == "__main__":
+    unittest.main()

modules/image/text_recognition/ch_pp-ocrv3/utils.py

@@ -69,7 +69,7 @@ def text_visual(texts, scores, font_file, img_h=400, img_w=600, threshold=0.):
         assert len(texts) == len(scores), "The number of txts and corresponding scores must match"
 
     def create_blank_img():
-        blank_img = np.ones(shape=[img_h, img_w], dtype=np.int8) * 255
+        blank_img = np.ones(shape=[img_h, img_w], dtype=np.uint8) * 255
         blank_img[:, img_w - 1:] = 0
         blank_img = Image.fromarray(blank_img).convert("RGB")
         draw_txt = ImageDraw.Draw(blank_img)