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>pull/7278/head
Evezerest
GitHub
parent
dd063fc98b
commit
7e4e87dd6d
deploy
android_demo/app/src/main
cpp
java/com/baidu/paddle/lite/demo/ocr
cpp_infer/docs
slim/quantization
ppocr
data/imaug
metrics
ppstructure
kie/tools
table/table_metric
tools
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@ -47,7 +47,7 @@ str_to_cpu_mode(const std::string &cpu_mode) {
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std::string upper_key;
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std::transform(cpu_mode.cbegin(), cpu_mode.cend(), upper_key.begin(),
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::toupper);
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auto index = cpu_mode_map.find(upper_key);
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auto index = cpu_mode_map.find(upper_key.c_str());
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if (index == cpu_mode_map.end()) {
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LOGE("cpu_mode not found %s", upper_key.c_str());
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return paddle::lite_api::LITE_POWER_HIGH;
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@ -116,4 +116,4 @@ Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_release(
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ppredictor::OCR_PPredictor *ppredictor =
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(ppredictor::OCR_PPredictor *)java_pointer;
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delete ppredictor;
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}
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}
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@ -54,7 +54,7 @@ public class OCRPredictorNative {
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}
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public void destory() {
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if (nativePointer > 0) {
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if (nativePointer != 0) {
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release(nativePointer);
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nativePointer = 0;
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}
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@ -109,8 +109,10 @@ CUDA_LIB、CUDNN_LIB、TENSORRT_DIR、WITH_GPU、WITH_TENSORRT
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运行之前，将下面文件拷贝到`build/Release/`文件夹下
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1. `paddle_inference/paddle/lib/paddle_inference.dll`
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2. `opencv/build/x64/vc15/bin/opencv_world455.dll`
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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`
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2. `paddle_inference/third_party/install/onnxruntime/lib/onnxruntime.dll`
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3. `paddle_inference/third_party/install/paddle2onnx/lib/paddle2onnx.dll`
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4. `opencv/build/x64/vc15/bin/opencv_world455.dll`
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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`
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### Step4: 预测
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@ -73,4 +73,4 @@ python deploy/slim/quantization/export_model.py -c configs/det/ch_ppocr_v2.0/ch_
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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.
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The derived model can be converted through the `opt tool` of PaddleLite.
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For quantitative model deployment, please refer to [Mobile terminal model deployment](../../lite/readme_en.md)
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For quantitative model deployment, please refer to [Mobile terminal model deployment](../../lite/readme.md)
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@ -35,10 +35,12 @@ class CopyPaste(object):
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point_num = data['polys'].shape[1]
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src_img = data['image']
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src_polys = data['polys'].tolist()
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src_texts = data['texts']
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src_ignores = data['ignore_tags'].tolist()
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ext_data = data['ext_data'][0]
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ext_image = ext_data['image']
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ext_polys = ext_data['polys']
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ext_texts = ext_data['texts']
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ext_ignores = ext_data['ignore_tags']
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indexs = [i for i in range(len(ext_ignores)) if not ext_ignores[i]]
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@ -53,7 +55,7 @@ class CopyPaste(object):
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src_img = cv2.cvtColor(src_img, cv2.COLOR_BGR2RGB)
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ext_image = cv2.cvtColor(ext_image, cv2.COLOR_BGR2RGB)
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src_img = Image.fromarray(src_img).convert('RGBA')
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for poly, tag in zip(select_polys, select_ignores):
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for idx, poly, tag in zip(select_idxs, select_polys, select_ignores):
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box_img = get_rotate_crop_image(ext_image, poly)
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src_img, box = self.paste_img(src_img, box_img, src_polys)
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@ -62,6 +64,7 @@ class CopyPaste(object):
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for _ in range(len(box), point_num):
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box.append(box[-1])
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src_polys.append(box)
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src_texts.append(ext_texts[idx])
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src_ignores.append(tag)
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src_img = cv2.cvtColor(np.array(src_img), cv2.COLOR_RGB2BGR)
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h, w = src_img.shape[:2]
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@ -70,6 +73,7 @@ class CopyPaste(object):
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src_polys[:, :, 1] = np.clip(src_polys[:, :, 1], 0, h)
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data['image'] = src_img
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data['polys'] = src_polys
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data['texts'] = src_texts
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data['ignore_tags'] = np.array(src_ignores)
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return data
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@ -12,7 +12,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import Levenshtein
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from rapidfuzz.distance import Levenshtein
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import string
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@ -46,8 +46,7 @@ class RecMetric(object):
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if self.is_filter:
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pred = self._normalize_text(pred)
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target = self._normalize_text(target)
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norm_edit_dis += Levenshtein.distance(pred, target) / max(
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len(pred), len(target), 1)
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norm_edit_dis += Levenshtein.normalized_distance(pred, target)
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if pred == target:
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correct_num += 1
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all_num += 1
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@ -20,7 +20,7 @@ from shapely.geometry import Polygon
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import numpy as np
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from collections import defaultdict
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import operator
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import Levenshtein
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from rapidfuzz.distance import Levenshtein
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import argparse
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import json
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import copy
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@ -9,7 +9,7 @@
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# Apache 2.0 License for more details.
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import distance
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from rapidfuzz.distance import Levenshtein
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from apted import APTED, Config
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from apted.helpers import Tree
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from lxml import etree, html
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@ -39,17 +39,6 @@ class TableTree(Tree):
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class CustomConfig(Config):
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@staticmethod
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def maximum(*sequences):
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"""Get maximum possible value
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"""
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return max(map(len, sequences))
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def normalized_distance(self, *sequences):
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"""Get distance from 0 to 1
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"""
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return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)
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def rename(self, node1, node2):
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"""Compares attributes of trees"""
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#print(node1.tag)
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@ -58,23 +47,12 @@ class CustomConfig(Config):
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if node1.tag == 'td':
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if node1.content or node2.content:
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#print(node1.content, )
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return self.normalized_distance(node1.content, node2.content)
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return Levenshtein.normalized_distance(node1.content, node2.content)
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return 0.
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class CustomConfig_del_short(Config):
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@staticmethod
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def maximum(*sequences):
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"""Get maximum possible value
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"""
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return max(map(len, sequences))
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def normalized_distance(self, *sequences):
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"""Get distance from 0 to 1
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"""
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return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)
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def rename(self, node1, node2):
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"""Compares attributes of trees"""
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if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan):
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@ -90,21 +68,10 @@ class CustomConfig_del_short(Config):
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node1_content = ['####']
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if len(node2_content) < 3:
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node2_content = ['####']
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return self.normalized_distance(node1_content, node2_content)
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return Levenshtein.normalized_distance(node1_content, node2_content)
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return 0.
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class CustomConfig_del_block(Config):
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@staticmethod
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def maximum(*sequences):
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"""Get maximum possible value
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"""
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return max(map(len, sequences))
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def normalized_distance(self, *sequences):
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"""Get distance from 0 to 1
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"""
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return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)
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def rename(self, node1, node2):
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"""Compares attributes of trees"""
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if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan):
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@ -120,7 +87,7 @@ class CustomConfig_del_block(Config):
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while ' ' in node2_content:
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print(node2_content.index(' '))
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node2_content.pop(node2_content.index(' '))
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return self.normalized_distance(node1_content, node2_content)
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return Levenshtein.normalized_distance(node1_content, node2_content)
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return 0.
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class TEDS(object):
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@ -6,7 +6,7 @@ lmdb
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tqdm
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numpy
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visualdl
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python-Levenshtein
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rapidfuzz
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opencv-contrib-python==4.4.0.46
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cython
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lxml
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@ -120,11 +120,14 @@ def sorted_boxes(dt_boxes):
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_boxes = list(sorted_boxes)
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for i in range(num_boxes - 1):
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if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \
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(_boxes[i + 1][0][0] < _boxes[i][0][0]):
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tmp = _boxes[i]
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_boxes[i] = _boxes[i + 1]
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_boxes[i + 1] = tmp
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for j in range(i, 0, -1):
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if abs(_boxes[j + 1][0][1] - _boxes[j][0][1]) < 10 and \
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(_boxes[j + 1][0][0] < _boxes[j][0][0]):
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tmp = _boxes[j]
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_boxes[j] = _boxes[j + 1]
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_boxes[j + 1] = tmp
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else:
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break
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return _boxes
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@ -549,7 +549,7 @@ def text_visual(texts,
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def base64_to_cv2(b64str):
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import base64
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data = base64.b64decode(b64str.encode('utf8'))
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data = np.fromstring(data, np.uint8)
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data = np.frombuffer(data, np.uint8)
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data = cv2.imdecode(data, cv2.IMREAD_COLOR)
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return data
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@ -88,6 +88,29 @@ def draw_kie_result(batch, node, idx_to_cls, count):
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cv2.imwrite(save_path, vis_img)
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logger.info("The Kie Image saved in {}".format(save_path))
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def write_kie_result(fout, node, data):
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"""
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Write infer result to output file, sorted by the predict label of each line.
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The format keeps the same as the input with additional score attribute.
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"""
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import json
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label = data['label']
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annotations = json.loads(label)
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max_value, max_idx = paddle.max(node, -1), paddle.argmax(node, -1)
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node_pred_label = max_idx.numpy().tolist()
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node_pred_score = max_value.numpy().tolist()
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res = []
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for i, label in enumerate(node_pred_label):
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pred_score = '{:.2f}'.format(node_pred_score[i])
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pred_res = {
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'label': label,
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'transcription': annotations[i]['transcription'],
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'score': pred_score,
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'points': annotations[i]['points'],
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}
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res.append(pred_res)
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res.sort(key=lambda x: x['label'])
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fout.writelines([json.dumps(res, ensure_ascii=False) + '\n'])
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def main():
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global_config = config['Global']
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@ -114,7 +137,7 @@ def main():
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warmup_times = 0
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count_t = []
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with open(save_res_path, "wb") as fout:
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with open(save_res_path, "w") as fout:
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with open(config['Global']['infer_img'], "rb") as f:
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lines = f.readlines()
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for index, data_line in enumerate(lines):
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@ -139,6 +162,8 @@ def main():
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node = F.softmax(node, -1)
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count_t.append(time.time() - st)
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draw_kie_result(batch, node, idx_to_cls, index)
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write_kie_result(fout, node, data)
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fout.close()
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logger.info("success!")
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logger.info("It took {} s for predict {} images.".format(
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np.sum(count_t), len(count_t)))
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Reference in New Issue