未验证 提交 a0c8968c 编写于 作者: D dyning 提交者: GitHub

Merge pull request #471 from LDOUBLEV/fixocr

fix typo and opt doc
......@@ -26,7 +26,7 @@ wget -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/test_icdar2015_la
提供的标注文件格式为,其中中间是"\t"分隔:
```
" 图像文件名 json.dumps编码的图像标注信息"
ch4_test_images/img_61.jpg [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]], ...}]
ch4_test_images/img_61.jpg [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]]}, {...}]
```
json.dumps编码前的图像标注信息是包含多个字典的list,字典中的 `points` 表示文本框的四个点的坐标(x, y),从左上角的点开始顺时针排列。
`transcription` 表示当前文本框的文字,在文本检测任务中并不需要这个信息。
......
......@@ -21,10 +21,10 @@ cd /home/Projects
如果您希望在CPU环境下使用docker,使用docker而不是nvidia-docker创建docker
sudo docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash
如果您的机器安装的是CUDA9,请运行以下命令创建容器
如果使用CUDA9,请运行以下命令创建容器
sudo nvidia-docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash
如果您的机器安装的是CUDA10,请运行以下命令创建容器
如果使用CUDA10,请运行以下命令创建容器
sudo nvidia-docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda10.0-cudnn7-dev /bin/bash
您也可以访问[DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获取与您机器适配的镜像。
......
......@@ -25,7 +25,7 @@ After decompressing the data set and downloading the annotation file, PaddleOCR/
The provided annotation file format is as follow, seperated by "\t":
```
" Image file name Image annotation information encoded by json.dumps"
ch4_test_images/img_61.jpg [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]], ...}]
ch4_test_images/img_61.jpg [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]]}, {...}]
```
The image annotation after json.dumps() encoding is a list containing multiple dictionaries. The `points` in the dictionary represent the coordinates (x, y) of the four points of the text box, arranged clockwise from the point at the upper left corner.
......
......@@ -18,14 +18,14 @@ cd /home/Projects
# You need to create a docker container for the first run, and do not need to run the current command when you run it again
# Create a docker container named ppocr and map the current directory to the /paddle directory of the container
#If you want to use docker in a CPU environment, use docker instead of nvidia-docker to create docker
#If using CPU, use docker instead of nvidia-docker to create docker
sudo docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash
```
If you have cuda9 installed on your machine, please run the following command to create a container:
If using CUDA9, please run the following command to create a container:
```
sudo nvidia-docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash
```
If you have cuda10 installed on your machine, please run the following command to create a container:
If using CUDA10, please run the following command to create a container:
```
sudo nvidia-docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda10.0-cudnn7-dev /bin/bash
```
......
......@@ -122,9 +122,9 @@ class TextRecognizer(object):
ind = np.argmax(probs, axis=1)
blank = probs.shape[1]
valid_ind = np.where(ind != (blank - 1))[0]
score = np.mean(probs[valid_ind, ind[valid_ind]])
if len(valid_ind) == 0:
continue
score = np.mean(probs[valid_ind, ind[valid_ind]])
# rec_res.append([preds_text, score])
rec_res[indices[beg_img_no + rno]] = [preds_text, score]
else:
......
......@@ -95,7 +95,7 @@ def create_predictor(args, mode):
config.set_cpu_math_library_num_threads(6)
if args.enable_mkldnn:
config.enable_mkldnn()
#config.enable_memory_optim()
config.disable_glog_info()
......@@ -169,7 +169,7 @@ def draw_ocr_box_txt(image, boxes, txts):
img_right = Image.new('RGB', (w, h), (255, 255, 255))
import random
# 每次使用相同的随机种子 ,可以保证两次颜色一致
random.seed(0)
draw_left = ImageDraw.Draw(img_left)
draw_right = ImageDraw.Draw(img_right)
......
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