diff --git a/deploy/android_demo/app/src/main/cpp/native.cpp b/deploy/android_demo/app/src/main/cpp/native.cpp index ced932556f09244d1e9e962e7b75461203a7cc3a..4961e5ecf141bb50701ecf9c3654a54f062937ce 100644 --- a/deploy/android_demo/app/src/main/cpp/native.cpp +++ b/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; @@ -116,4 +116,4 @@ Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_release( ppredictor::OCR_PPredictor *ppredictor = (ppredictor::OCR_PPredictor *)java_pointer; delete ppredictor; -} \ No newline at end of file +} diff --git a/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OCRPredictorNative.java b/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OCRPredictorNative.java index 622da2a3f9a1233167e777e62b687c1f246df01f..41fa183dea1d968582dbedf4e831c55b043ae00f 100644 --- a/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OCRPredictorNative.java +++ b/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; } diff --git a/deploy/cpp_infer/docs/windows_vs2019_build.md b/deploy/cpp_infer/docs/windows_vs2019_build.md index 4f391d925008b4bffcbd123e937eb608f502c646..bcaefa46f83a30a4c232add78dc2e9f521b9f84f 100644 --- a/deploy/cpp_infer/docs/windows_vs2019_build.md +++ b/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` -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` +2. `paddle_inference/third_party/install/onnxruntime/lib/onnxruntime.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 diff --git a/deploy/slim/quantization/README_en.md b/deploy/slim/quantization/README_en.md index 33b2c4784afa4be68c8b9db1a02d83013c886655..c6796ae9dc256496308e432023c45ef1026c3d92 100644 --- a/deploy/slim/quantization/README_en.md +++ b/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) diff --git a/ppocr/data/imaug/copy_paste.py b/ppocr/data/imaug/copy_paste.py index 0b3386c896792bd670cd2bfc757eb3b80f22bac4..79343da60fd40f8dc0ffe8927398b70cb751b532 100644 --- a/ppocr/data/imaug/copy_paste.py +++ b/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 diff --git a/ppocr/metrics/rec_metric.py b/ppocr/metrics/rec_metric.py index d858ae28e999d546847727243b35f2ac902e1026..9863978116b1340fa809e8919a6a37d598d6bbdf 100644 --- a/ppocr/metrics/rec_metric.py +++ b/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( - len(pred), len(target), 1) + norm_edit_dis += Levenshtein.normalized_distance(pred, target) if pred == target: correct_num += 1 all_num += 1 diff --git a/ppstructure/kie/tools/eval_with_label_end2end.py b/ppstructure/kie/tools/eval_with_label_end2end.py index b13ffb568fd9610fee5d5a246c501ed5b90de91a..b0fd84363f450dfb7e4ef18e53adc17ef088cf18 100644 --- a/ppstructure/kie/tools/eval_with_label_end2end.py +++ b/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 diff --git a/ppstructure/table/table_metric/table_metric.py b/ppstructure/table/table_metric/table_metric.py index 9aca98ad785d4614a803fa5a277a6e4a27b3b078..923a9c0071d083de72a2a896d6f62037373d4e73 100755 --- a/ppstructure/table/table_metric/table_metric.py +++ b/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): diff --git a/requirements.txt b/requirements.txt index b15176db3eb42c381c1612f404fd15c6b020b3dc..976d29192abbbf89b8ee6064c0b4ec48d43ad268 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,7 +6,7 @@ lmdb tqdm numpy visualdl -python-Levenshtein +rapidfuzz opencv-contrib-python==4.4.0.46 cython lxml diff --git a/tools/infer/predict_system.py b/tools/infer/predict_system.py index 252ed1aaf2eb441415dff3bb823814936ab5f24f..e0f2c41fa2aba23491efee920afbd76db1ec84e0 100755 --- a/tools/infer/predict_system.py +++ b/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 \ - (_boxes[i + 1][0][0] < _boxes[i][0][0]): - tmp = _boxes[i] - _boxes[i] = _boxes[i + 1] - _boxes[i + 1] = tmp + for j in range(i, 0, -1): + if abs(_boxes[j + 1][0][1] - _boxes[j][0][1]) < 10 and \ + (_boxes[j + 1][0][0] < _boxes[j][0][0]): + tmp = _boxes[j] + _boxes[j] = _boxes[j + 1] + _boxes[j + 1] = tmp + else: + break return _boxes diff --git a/tools/infer/utility.py b/tools/infer/utility.py index 1355ca62e5c6b50177d1c7c2f6d7a016e168f545..a547bbdba4332bfbd3a7f18e5187f356c2df0964 100644 --- a/tools/infer/utility.py +++ b/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 diff --git a/tools/infer_kie.py b/tools/infer_kie.py index 346e2e0aeeee695ab49577b6b13dcc058150df1a..9375434cc887b08dfa746420a6c73c58c6e04797 100755 --- a/tools/infer_kie.py +++ b/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)))