From e0610558089929371ef39f4159d55d2005ecac6b Mon Sep 17 00:00:00 2001 From: co63oc Date: Wed, 5 Mar 2025 19:09:08 +0800 Subject: [PATCH] Fix typos (#14808) --- README_en.md | 2 +- .../data_loader/modules/make_shrink_map.py | 16 ++++++++-------- deploy/cpp_infer/src/args.cpp | 4 ++-- docs/applications/光功率计数码管字符识别.md | 2 +- docs/applications/液晶屏读数识别.md | 2 +- docs/index.en.md | 2 +- docs/paddlex/overview.en.md | 2 +- docs/ppocr/model_train/tricks.en.md | 2 +- docs/ppstructure/infer_deploy/index.en.md | 2 +- docs/update.en.md | 2 +- ppocr/data/imaug/east_process.py | 2 +- ppocr/data/imaug/make_pse_gt.py | 8 ++++---- ppocr/data/imaug/make_shrink_map.py | 14 +++++++------- ppocr/modeling/heads/rec_aster_head.py | 4 ++-- ppocr/modeling/heads/self_attention.py | 4 ++-- ppstructure/pdf2word/pdf2word.py | 2 +- test_tipc/test_inference_cpp.sh | 2 +- 17 files changed, 36 insertions(+), 36 deletions(-) diff --git a/README_en.md b/README_en.md index 99db86a572..7a25c32df3 100644 --- a/README_en.md +++ b/README_en.md @@ -31,7 +31,7 @@ PaddleOCR is being oversight by a [PMC](https://github.com/PaddlePaddle/PaddleOC - **🔥 2024.10.18 release PaddleOCR v2.9, including**: - PaddleX, an All-in-One development tool based on PaddleOCR's advanced technology, supports low-code full-process development capabilities in the OCR field: - - 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 pipelines, which can be quickly experienced through a simple **Python API one-click call**. In addition, the same set of APIs also supports a total of **200+ models** in image classification, object detection, image segmentation, and time series forcasting, forming 20+ single-function modules, making it convenient for developers to use **model combinations**. + - 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 pipelines, which can be quickly experienced through a simple **Python API one-click call**. In addition, the same set of APIs also supports a total of **200+ models** in image classification, object detection, image segmentation, and time series forecasting, forming 20+ single-function modules, making it convenient for developers to use **model combinations**. - 🚀 [**High Efficiency and Low barrier of entry**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/overview.html): Provides two methods based on **unified commands** and **GUI** to achieve simple and efficient use, combination, and customization of models. Supports multiple deployment methods such as **high-performance inference, service-oriented deployment, and edge deployment**. Additionally, for various mainstream hardware such as **NVIDIA GPU, Kunlunxin XPU, Ascend NPU, Cambricon MLU, and Haiguang DCU**, models can be developed with **seamless switching**. diff --git a/benchmark/PaddleOCR_DBNet/data_loader/modules/make_shrink_map.py b/benchmark/PaddleOCR_DBNet/data_loader/modules/make_shrink_map.py index b3fea40f39..000ffed317 100644 --- a/benchmark/PaddleOCR_DBNet/data_loader/modules/make_shrink_map.py +++ b/benchmark/PaddleOCR_DBNet/data_loader/modules/make_shrink_map.py @@ -24,12 +24,12 @@ def shrink_polygon_pyclipper(polygon, shrink_ratio): subject = [tuple(l) for l in polygon] padding = pyclipper.PyclipperOffset() padding.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON) - shrinked = padding.Execute(-distance) - if shrinked == []: - shrinked = np.array(shrinked) + shrunk = padding.Execute(-distance) + if shrunk == []: + shrunk = np.array(shrunk) else: - shrinked = np.array(shrinked[0]).reshape(-1, 2) - return shrinked + shrunk = np.array(shrunk[0]).reshape(-1, 2) + return shrunk class MakeShrinkMap: @@ -69,12 +69,12 @@ class MakeShrinkMap: cv2.fillPoly(mask, polygon.astype(np.int32)[np.newaxis, :, :], 0) ignore_tags[i] = True else: - shrinked = self.shrink_func(polygon, self.shrink_ratio) - if shrinked.size == 0: + shrunk = self.shrink_func(polygon, self.shrink_ratio) + if shrunk.size == 0: cv2.fillPoly(mask, polygon.astype(np.int32)[np.newaxis, :, :], 0) ignore_tags[i] = True continue - cv2.fillPoly(gt, [shrinked.astype(np.int32)], 1) + cv2.fillPoly(gt, [shrunk.astype(np.int32)], 1) data["shrink_map"] = gt data["shrink_mask"] = mask diff --git a/deploy/cpp_infer/src/args.cpp b/deploy/cpp_infer/src/args.cpp index a408ba4204..f1d3ae2e1e 100644 --- a/deploy/cpp_infer/src/args.cpp +++ b/deploy/cpp_infer/src/args.cpp @@ -15,10 +15,10 @@ #include // common args -DEFINE_bool(use_gpu, false, "Infering with GPU or CPU."); +DEFINE_bool(use_gpu, false, "Inferring with GPU or CPU."); DEFINE_bool(use_tensorrt, false, "Whether use tensorrt."); DEFINE_int32(gpu_id, 0, "Device id of GPU to execute."); -DEFINE_int32(gpu_mem, 4000, "GPU id when infering with GPU."); +DEFINE_int32(gpu_mem, 4000, "GPU id when inferring with GPU."); DEFINE_int32(cpu_threads, 10, "Num of threads with CPU."); DEFINE_bool(enable_mkldnn, false, "Whether use mkldnn with CPU."); DEFINE_string(precision, "fp32", "Precision be one of fp32/fp16/int8"); diff --git a/docs/applications/光功率计数码管字符识别.md b/docs/applications/光功率计数码管字符识别.md index b44e2dba05..79b4937b2c 100644 --- a/docs/applications/光功率计数码管字符识别.md +++ b/docs/applications/光功率计数码管字符识别.md @@ -53,7 +53,7 @@ python3 -m pip install paddleocr ![](./images/8dca91f016884e16ad9216d416da72ea08190f97d87b4be883f15079b7ebab9a.jpeg) ```bash linenums="1" -paddleocr --lang=ch --det=Fase --image_dir=data +paddleocr --lang=ch --det=False --image_dir=data ``` 得到如下测试结果： diff --git a/docs/applications/液晶屏读数识别.md b/docs/applications/液晶屏读数识别.md index 439e1effba..b413d01500 100644 --- a/docs/applications/液晶屏读数识别.md +++ b/docs/applications/液晶屏读数识别.md @@ -182,7 +182,7 @@ python tools/eval.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml -o G ```python linenums="1" cd ./pretrained_models/ -# transform teacher params in best_accuracy.pdparams into teacher_dml.paramers +# transform teacher params in best_accuracy.pdparams into teacher_dml.pdparams import paddle # load pretrained model diff --git a/docs/index.en.md b/docs/index.en.md index 7054d00495..b3e18d68da 100644 --- a/docs/index.en.md +++ b/docs/index.en.md @@ -32,7 +32,7 @@ PaddleOCR is being oversight by a [PMC](https://github.com/PaddlePaddle/PaddleOC - **🔥 2024.10.18 release PaddleOCR v2.9, including**: - PaddleX, an All-in-One development tool based on PaddleOCR's advanced technology, supports low-code full-process development capabilities in the OCR field: - - 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 pipelines, which can be quickly experienced through a simple **Python API one-click call**. In addition, the same set of APIs also supports a total of **200+ models** in image classification, object detection, image segmentation, and time series forcasting, forming 20+ single-function modules, making it convenient for developers to use **model combinations**. + - 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 pipelines, which can be quickly experienced through a simple **Python API one-click call**. In addition, the same set of APIs also supports a total of **200+ models** in image classification, object detection, image segmentation, and time series forecasting, forming 20+ single-function modules, making it convenient for developers to use **model combinations**. - 🚀 [**High Efficiency and Low barrier of entry**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/overview.html): Provides two methods based on **unified commands** and **GUI** to achieve simple and efficient use, combination, and customization of models. Supports multiple deployment methods such as **high-performance inference, service-oriented deployment, and edge deployment**. Additionally, for various mainstream hardware such as **NVIDIA GPU, Kunlunxin XPU, Ascend NPU, Cambricon MLU, and Haiguang DCU**, models can be developed with **seamless switching**. diff --git a/docs/paddlex/overview.en.md b/docs/paddlex/overview.en.md index f21bd382a3..d7fca37cb6 100644 --- a/docs/paddlex/overview.en.md +++ b/docs/paddlex/overview.en.md @@ -2,7 +2,7 @@ The All-in-One development tool [PaddleX](https://github.com/PaddlePaddle/PaddleX/tree/release/3.0-beta1), based on the advanced technology of PaddleOCR, supports **low-code full-process** development capabilities in the OCR field. Through low-code development, simple and efficient model use, combination, and customization can be achieved. This will significantly **reduce the time consumption** of model development, **lower its development difficulty**, and greatly accelerate the application and promotion speed of models in the industry. Features include: -* 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 pipelines, which can be quickly experienced through a simple **Python API one-click call**. In addition, the same set of APIs also supports a total of **200+ models** in image classification, object detection, image segmentation, and time series forcasting, forming 20+ single-function modules, making it convenient for developers to use **model combinations**. +* 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 pipelines, which can be quickly experienced through a simple **Python API one-click call**. In addition, the same set of APIs also supports a total of **200+ models** in image classification, object detection, image segmentation, and time series forecasting, forming 20+ single-function modules, making it convenient for developers to use **model combinations**. * 🚀 [**High Efficiency and Low barrier of entry**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/overview.html): Provides two methods based on **unified commands** and **GUI** to achieve simple and efficient use, combination, and customization of models. Supports multiple deployment methods such as **high-performance inference, service-oriented deployment, and edge deployment**. Additionally, for various mainstream hardware such as **NVIDIA GPU, Kunlunxin XPU, Ascend NPU, Cambricon MLU, and Haiguang DCU**, models can be developed with **seamless switching**. diff --git a/docs/ppocr/model_train/tricks.en.md b/docs/ppocr/model_train/tricks.en.md index 4addbeed34..c91d7d13e5 100644 --- a/docs/ppocr/model_train/tricks.en.md +++ b/docs/ppocr/model_train/tricks.en.md @@ -60,4 +60,4 @@ Here we have sorted out some Chinese OCR training and prediction tricks, which a - **Tips** - There are two possible methods for space recognition. (1) Optimize the text detection. For spliting the text at the space in detection results, it needs to divide the text line with space into many segments when label the data for detection. (2) Optimize the text recognition. The space character is introduced into the recognition dictionary. Label the blank line in the training data for text recognition. In addition, we can also concat multiple word lines to synthesize the training data with spaces. PaddleOCR currently uses the second method. + There are two possible methods for space recognition. (1) Optimize the text detection. For splitting the text at the space in detection results, it needs to divide the text line with space into many segments when label the data for detection. (2) Optimize the text recognition. The space character is introduced into the recognition dictionary. Label the blank line in the training data for text recognition. In addition, we can also concat multiple word lines to synthesize the training data with spaces. PaddleOCR currently uses the second method. diff --git a/docs/ppstructure/infer_deploy/index.en.md b/docs/ppstructure/infer_deploy/index.en.md index dc07190ef2..9a9f29593d 100644 --- a/docs/ppstructure/infer_deploy/index.en.md +++ b/docs/ppstructure/infer_deploy/index.en.md @@ -10,7 +10,7 @@ Paddle provides a variety of deployment schemes to meet the deployment requireme ![img](./images/deployment-20240704135743247.png) -PP-OCR has supported muti deployment schemes. Click the link to get the specific tutorial. +PP-OCR has supported multi deployment schemes. Click the link to get the specific tutorial. - [Python Inference](./python_infer.en.md) - [C++ Inference](./cpp_infer.en.md) diff --git a/docs/update.en.md b/docs/update.en.md index 367c2b2581..6510f1c3d3 100644 --- a/docs/update.en.md +++ b/docs/update.en.md @@ -9,7 +9,7 @@ hide: - **🔥 2024.10.18 release PaddleOCR v2.9, including**: * PaddleX, an All-in-One development tool based on PaddleOCR's advanced technology, supports low-code full-process development capabilities in the OCR field: - * 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 pipelines, which can be quickly experienced through a simple **Python API one-click call**. In addition, the same set of APIs also supports a total of **200+ models** in image classification, object detection, image segmentation, and time series forcasting, forming 20+ single-function modules, making it convenient for developers to use **model combinations**. + * 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 pipelines, which can be quickly experienced through a simple **Python API one-click call**. In addition, the same set of APIs also supports a total of **200+ models** in image classification, object detection, image segmentation, and time series forecasting, forming 20+ single-function modules, making it convenient for developers to use **model combinations**. * 🚀 [**High Efficiency and Low barrier of entry**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/overview.html): Provides two methods based on **unified commands** and **GUI** to achieve simple and efficient use, combination, and customization of models. Supports multiple deployment methods such as **high-performance inference, service-oriented deployment, and edge deployment**. Additionally, for various mainstream hardware such as **NVIDIA GPU, Kunlunxin XPU, Ascend NPU, Cambricon MLU, and Haiguang DCU**, models can be developed with **seamless switching**. diff --git a/ppocr/data/imaug/east_process.py b/ppocr/data/imaug/east_process.py index 3f4189acfd..7e5bfa0fec 100644 --- a/ppocr/data/imaug/east_process.py +++ b/ppocr/data/imaug/east_process.py @@ -169,7 +169,7 @@ class EASTProcessTrain(object): used for generate the score map :param poly: the text poly :param r: r in the paper - :return: the shrinked poly + :return: the shrunk poly """ # shrink ratio R = 0.3 diff --git a/ppocr/data/imaug/make_pse_gt.py b/ppocr/data/imaug/make_pse_gt.py index 2b8c78b713..7be1bb82e7 100644 --- a/ppocr/data/imaug/make_pse_gt.py +++ b/ppocr/data/imaug/make_pse_gt.py @@ -88,17 +88,17 @@ class MakePseGt(object): subject = [tuple(l) for l in poly] pco = pyclipper.PyclipperOffset() pco.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON) - shrinked = np.array(pco.Execute(-distance)) + shrunk = np.array(pco.Execute(-distance)) - if len(shrinked) == 0 or shrinked.size == 0: + if len(shrunk) == 0 or shrunk.size == 0: if ignore_tags is not None: ignore_tags[i] = True continue try: - shrinked = np.array(shrinked[0]).reshape(-1, 2) + shrunk = np.array(shrunk[0]).reshape(-1, 2) except: if ignore_tags is not None: ignore_tags[i] = True continue - cv2.fillPoly(text_kernel, [shrinked.astype(np.int32)], i + 1) + cv2.fillPoly(text_kernel, [shrunk.astype(np.int32)], i + 1) return text_kernel, ignore_tags diff --git a/ppocr/data/imaug/make_shrink_map.py b/ppocr/data/imaug/make_shrink_map.py index d57585ef3b..0e2f3d4f5d 100644 --- a/ppocr/data/imaug/make_shrink_map.py +++ b/ppocr/data/imaug/make_shrink_map.py @@ -64,7 +64,7 @@ class MakeShrinkMap(object): subject = [tuple(l) for l in polygon] padding = pyclipper.PyclipperOffset() padding.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON) - shrinked = [] + shrunk = [] # Increase the shrink ratio every time we get multiple polygon returned back possible_ratios = np.arange(self.shrink_ratio, 1, self.shrink_ratio) @@ -77,18 +77,18 @@ class MakeShrinkMap(object): * (1 - np.power(ratio, 2)) / polygon_shape.length ) - shrinked = padding.Execute(-distance) - if len(shrinked) == 1: + shrunk = padding.Execute(-distance) + if len(shrunk) == 1: break - if shrinked == []: + if shrunk == []: cv2.fillPoly(mask, polygon.astype(np.int32)[np.newaxis, :, :], 0) ignore_tags[i] = True continue - for each_shirnk in shrinked: - shirnk = np.array(each_shirnk).reshape(-1, 2) - cv2.fillPoly(gt, [shirnk.astype(np.int32)], 1) + for each_shrink in shrunk: + shrink = np.array(each_shrink).reshape(-1, 2) + cv2.fillPoly(gt, [shrink.astype(np.int32)], 1) data["shrink_map"] = gt data["shrink_mask"] = mask diff --git a/ppocr/modeling/heads/rec_aster_head.py b/ppocr/modeling/heads/rec_aster_head.py index c80199f64e..60eb995d58 100644 --- a/ppocr/modeling/heads/rec_aster_head.py +++ b/ppocr/modeling/heads/rec_aster_head.py @@ -48,13 +48,13 @@ class AsterHead(nn.Layer): in_channels, out_channels, sDim, attDim, max_len_labels ) self.time_step = time_step - self.embeder = Embedding(self.time_step, in_channels) + self.embedder = Embedding(self.time_step, in_channels) self.beam_width = beam_width self.eos = self.num_classes - 3 def forward(self, x, targets=None, embed=None): return_dict = {} - embedding_vectors = self.embeder(x) + embedding_vectors = self.embedder(x) if self.training: rec_targets, rec_lengths, _ = targets diff --git a/ppocr/modeling/heads/self_attention.py b/ppocr/modeling/heads/self_attention.py index 4f3ad3216c..c02b9cd588 100644 --- a/ppocr/modeling/heads/self_attention.py +++ b/ppocr/modeling/heads/self_attention.py @@ -166,7 +166,7 @@ class Encoder(nn.Layer): ), ) ) - self.processer = PrePostProcessLayer( + self.processor = PrePostProcessLayer( preprocess_cmd, d_model, prepostprocess_dropout ) @@ -174,7 +174,7 @@ class Encoder(nn.Layer): for encoder_layer in self.encoder_layers: enc_output = encoder_layer(enc_input, attn_bias) enc_input = enc_output - enc_output = self.processer(enc_output) + enc_output = self.processor(enc_output) return enc_output diff --git a/ppstructure/pdf2word/pdf2word.py b/ppstructure/pdf2word/pdf2word.py index 065f1d9ca8..1a1bc27c31 100644 --- a/ppstructure/pdf2word/pdf2word.py +++ b/ppstructure/pdf2word/pdf2word.py @@ -508,7 +508,7 @@ class APP_Image2Doc(QWidget): self.pb.setRange(0, max) def handleEndsignalSignal(self): - # enble buttons + # enable buttons self.openFileButton.setEnabled(True) self.startCNButton.setEnabled(True) self.startENButton.setEnabled(True) diff --git a/test_tipc/test_inference_cpp.sh b/test_tipc/test_inference_cpp.sh index aadaa8b077..c84f22574a 100644 --- a/test_tipc/test_inference_cpp.sh +++ b/test_tipc/test_inference_cpp.sh @@ -66,7 +66,7 @@ function func_cpp_inference(){ for batch_size in ${cpp_batch_size_list[*]}; do precision="fp32" if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then - precison="int8" + precision="int8" fi _save_log_path="${_log_path}/cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log" set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")