cherry pick PRs from community (#7273)

* Merge pull request #6824 from ChenNima/release/2.5-kie-save-res

[kie]add write_kie_result to kie infer tool

* Merge pull request #6677 from TonyJiangWJ/release/2.5

修复内存泄露问题

* Update native.cpp (#6650)

fix issue 6640

* Merge pull request #6625 from ynjang/ynjang

update sorted_boxes

* fix DeprecationWarning, (#6604)

DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead

* Merge pull request #6585 from maxbachmann/release/2.5

replace GPL licensed components

* Merge pull request #6575 from Eling486/release/2.5

update win doc

* Merge pull request #6477 from MikoyChinese/fix-copy-paste

Fix copy_paste no texts augment.

* Merge pull request #6361 from mohamadmansourX/patch-9

Update README_en.md

Co-authored-by: Double_V <liuvv0203@163.com>
Co-authored-by: shawn <1021362695@qq.com>
Co-authored-by: paopjian <672034519@qq.com>

deploy/android_demo/app/src/main/cpp/native.cpp

@@ -47,7 +47,7 @@ str_to_cpu_mode(const std::string &cpu_mode) {
   std::string upper_key;
   std::transform(cpu_mode.cbegin(), cpu_mode.cend(), upper_key.begin(),
                  ::toupper);
-  auto index = cpu_mode_map.find(upper_key);
+  auto index = cpu_mode_map.find(upper_key.c_str());
   if (index == cpu_mode_map.end()) {
     LOGE("cpu_mode not found %s", upper_key.c_str());
     return paddle::lite_api::LITE_POWER_HIGH;

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OCRPredictorNative.java

@@ -54,7 +54,7 @@ public class OCRPredictorNative {
     }
 
     public void destory() {
-        if (nativePointer > 0) {
+        if (nativePointer != 0) {
             release(nativePointer);
             nativePointer = 0;
         }

deploy/cpp_infer/docs/windows_vs2019_build.md

@@ -109,8 +109,10 @@ CUDA_LIB、CUDNN_LIB、TENSORRT_DIR、WITH_GPU、WITH_TENSORRT
 
 运行之前，将下面文件拷贝到`build/Release/`文件夹下
 1. `paddle_inference/paddle/lib/paddle_inference.dll`
-2. `opencv/build/x64/vc15/bin/opencv_world455.dll`
+2. `paddle_inference/third_party/install/onnxruntime/lib/onnxruntime.dll`
-3. 如果使用openblas版本的预测库还需要拷贝 `paddle_inference/third_party/install/openblas/lib/openblas.dll`
+3. `paddle_inference/third_party/install/paddle2onnx/lib/paddle2onnx.dll`
+4. `opencv/build/x64/vc15/bin/opencv_world455.dll`
+5. 如果使用openblas版本的预测库还需要拷贝 `paddle_inference/third_party/install/openblas/lib/openblas.dll`
 
 ### Step4: 预测
 

deploy/slim/quantization/README_en.md

@@ -73,4 +73,4 @@ python deploy/slim/quantization/export_model.py -c configs/det/ch_ppocr_v2.0/ch_
 The numerical range of the quantized model parameters derived from the above steps is still FP32, but the numerical range of the parameters is int8.
 The derived model can be converted through the `opt tool` of PaddleLite.
 
-For quantitative model deployment, please refer to [Mobile terminal model deployment](../../lite/readme_en.md)
+For quantitative model deployment, please refer to [Mobile terminal model deployment](../../lite/readme.md)

ppocr/data/imaug/copy_paste.py

@@ -35,10 +35,12 @@ class CopyPaste(object):
         point_num = data['polys'].shape[1]
         src_img = data['image']
         src_polys = data['polys'].tolist()
+        src_texts = data['texts']
         src_ignores = data['ignore_tags'].tolist()
         ext_data = data['ext_data'][0]
         ext_image = ext_data['image']
         ext_polys = ext_data['polys']
+        ext_texts = ext_data['texts']
         ext_ignores = ext_data['ignore_tags']
 
         indexs = [i for i in range(len(ext_ignores)) if not ext_ignores[i]]
@@ -53,7 +55,7 @@ class CopyPaste(object):
         src_img = cv2.cvtColor(src_img, cv2.COLOR_BGR2RGB)
         ext_image = cv2.cvtColor(ext_image, cv2.COLOR_BGR2RGB)
         src_img = Image.fromarray(src_img).convert('RGBA')
-        for poly, tag in zip(select_polys, select_ignores):
+        for idx, poly, tag in zip(select_idxs, select_polys, select_ignores):
             box_img = get_rotate_crop_image(ext_image, poly)
 
             src_img, box = self.paste_img(src_img, box_img, src_polys)
@@ -62,6 +64,7 @@ class CopyPaste(object):
                 for _ in range(len(box), point_num):
                     box.append(box[-1])
                 src_polys.append(box)
+                src_texts.append(ext_texts[idx])
                 src_ignores.append(tag)
         src_img = cv2.cvtColor(np.array(src_img), cv2.COLOR_RGB2BGR)
         h, w = src_img.shape[:2]
@@ -70,6 +73,7 @@ class CopyPaste(object):
         src_polys[:, :, 1] = np.clip(src_polys[:, :, 1], 0, h)
         data['image'] = src_img
         data['polys'] = src_polys
+        data['texts'] = src_texts
         data['ignore_tags'] = np.array(src_ignores)
         return data
 

ppocr/metrics/rec_metric.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import Levenshtein
+from rapidfuzz.distance import Levenshtein
 import string
 
 
@@ -46,8 +46,7 @@ class RecMetric(object):
             if self.is_filter:
                 pred = self._normalize_text(pred)
                 target = self._normalize_text(target)
-            norm_edit_dis += Levenshtein.distance(pred, target) / max(
+            norm_edit_dis += Levenshtein.normalized_distance(pred, target)
-                len(pred), len(target), 1)
             if pred == target:
                 correct_num += 1
             all_num += 1

ppstructure/kie/tools/eval_with_label_end2end.py

@@ -20,7 +20,7 @@ from shapely.geometry import Polygon
 import numpy as np
 from collections import defaultdict
 import operator
-import Levenshtein
+from rapidfuzz.distance import Levenshtein
 import argparse
 import json
 import copy

ppstructure/table/table_metric/table_metric.py

@@ -9,7 +9,7 @@
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 # Apache 2.0 License for more details.
 
-import distance
+from rapidfuzz.distance import Levenshtein
 from apted import APTED, Config
 from apted.helpers import Tree
 from lxml import etree, html
@@ -39,17 +39,6 @@ class TableTree(Tree):
 
 
 class CustomConfig(Config):
-    @staticmethod
-    def maximum(*sequences):
-        """Get maximum possible value
-        """
-        return max(map(len, sequences))
-
-    def normalized_distance(self, *sequences):
-        """Get distance from 0 to 1
-        """
-        return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)
-
     def rename(self, node1, node2):
         """Compares attributes of trees"""
         #print(node1.tag)
@@ -58,23 +47,12 @@ class CustomConfig(Config):
         if node1.tag == 'td':
             if node1.content or node2.content:
                 #print(node1.content, )
-                return self.normalized_distance(node1.content, node2.content)
+                return Levenshtein.normalized_distance(node1.content, node2.content)
         return 0.
 
 
 
 class CustomConfig_del_short(Config):
-    @staticmethod
-    def maximum(*sequences):
-        """Get maximum possible value
-        """
-        return max(map(len, sequences))
-
-    def normalized_distance(self, *sequences):
-        """Get distance from 0 to 1
-        """
-        return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)
-
     def rename(self, node1, node2):
         """Compares attributes of trees"""
         if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan):
@@ -90,21 +68,10 @@ class CustomConfig_del_short(Config):
                     node1_content = ['####']
                 if len(node2_content) < 3:
                     node2_content = ['####']   
-                return self.normalized_distance(node1_content, node2_content)
+                return Levenshtein.normalized_distance(node1_content, node2_content)
         return 0.
 
 class CustomConfig_del_block(Config):
-    @staticmethod
-    def maximum(*sequences):
-        """Get maximum possible value
-        """
-        return max(map(len, sequences))
-
-    def normalized_distance(self, *sequences):
-        """Get distance from 0 to 1
-        """
-        return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)
-
     def rename(self, node1, node2):
         """Compares attributes of trees"""
         if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan):
@@ -120,7 +87,7 @@ class CustomConfig_del_block(Config):
                 while ' ' in node2_content:
                     print(node2_content.index(' '))
                     node2_content.pop(node2_content.index(' '))
-                return self.normalized_distance(node1_content, node2_content)
+                return Levenshtein.normalized_distance(node1_content, node2_content)
         return 0.
 
 class TEDS(object):

requirements.txt

@@ -6,7 +6,7 @@ lmdb
 tqdm
 numpy
 visualdl
-python-Levenshtein
+rapidfuzz
 opencv-contrib-python==4.4.0.46
 cython
 lxml

tools/infer/predict_system.py

@@ -120,11 +120,14 @@ def sorted_boxes(dt_boxes):
     _boxes = list(sorted_boxes)
 
     for i in range(num_boxes - 1):
-        if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \
+        for j in range(i, 0, -1):
-                (_boxes[i + 1][0][0] < _boxes[i][0][0]):
+            if abs(_boxes[j + 1][0][1] - _boxes[j][0][1]) < 10 and \
-            tmp = _boxes[i]
+                    (_boxes[j + 1][0][0] < _boxes[j][0][0]):
-            _boxes[i] = _boxes[i + 1]
+                tmp = _boxes[j]
-            _boxes[i + 1] = tmp
+                _boxes[j] = _boxes[j + 1]
+                _boxes[j + 1] = tmp
+            else:
+                break
     return _boxes
 
 

tools/infer/utility.py

@@ -549,7 +549,7 @@ def text_visual(texts,
 def base64_to_cv2(b64str):
     import base64
     data = base64.b64decode(b64str.encode('utf8'))
-    data = np.fromstring(data, np.uint8)
+    data = np.frombuffer(data, np.uint8)
     data = cv2.imdecode(data, cv2.IMREAD_COLOR)
     return data
 

tools/infer_kie.py

@@ -88,6 +88,29 @@ def draw_kie_result(batch, node, idx_to_cls, count):
     cv2.imwrite(save_path, vis_img)
     logger.info("The Kie Image saved in {}".format(save_path))
 
+def write_kie_result(fout, node, data):
+    """
+    Write infer result to output file, sorted by the predict label of each line.
+    The format keeps the same as the input with additional score attribute.
+    """
+    import json
+    label = data['label']
+    annotations = json.loads(label)
+    max_value, max_idx = paddle.max(node, -1), paddle.argmax(node, -1)
+    node_pred_label = max_idx.numpy().tolist()
+    node_pred_score = max_value.numpy().tolist()
+    res = []
+    for i, label in enumerate(node_pred_label):
+        pred_score = '{:.2f}'.format(node_pred_score[i])
+        pred_res = {
+                'label': label,
+                'transcription': annotations[i]['transcription'],
+                'score': pred_score,
+                'points': annotations[i]['points'],
+            }
+        res.append(pred_res)
+    res.sort(key=lambda x: x['label'])
+    fout.writelines([json.dumps(res, ensure_ascii=False) + '\n'])
 
 def main():
     global_config = config['Global']
@@ -114,7 +137,7 @@ def main():
 
     warmup_times = 0
     count_t = []
-    with open(save_res_path, "wb") as fout:
+    with open(save_res_path, "w") as fout:
         with open(config['Global']['infer_img'], "rb") as f:
             lines = f.readlines()
             for index, data_line in enumerate(lines):
@@ -139,6 +162,8 @@ def main():
                 node = F.softmax(node, -1)
                 count_t.append(time.time() - st)
                 draw_kie_result(batch, node, idx_to_cls, index)
+                write_kie_result(fout, node, data)
+        fout.close()
     logger.info("success!")
     logger.info("It took {} s for predict {} images.".format(
         np.sum(count_t), len(count_t)))