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eDOCr-0.0.4
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مشاهده بیشترتوضیحات
OCR for Engineering Mechanical Drawings
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | eDOCr-0.0.4 |
| نام | eDOCr |
| نسخه کتابخانه | 0.0.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Javier Villena Toro |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/eDOCr/ |
| مجوز | - |
# eDOCr
eDOCr is a packaged version of [keras-ocr](https://github.com/faustomorales/keras-ocr) that facilitates end-to-end digitization of mechanical EDs. Developed for Windows OS and using Python as the primary programming language.
## Getting Started
### Installation
`eDOCr` supports Python >= 3.6 and TensorFlow >= 2.0.0.
Install your prefered distribution platform, [Anaconda](https://www.anaconda.com/products/distribution) is recommended.
Open Anaconda Prompt and type the following commands:
```bash
conda create -n edocr -ypip
conda activate edocr
# To install from PyPi
conda install pip
pip install eDOCr
# To install from Source
cd path/to/your/folder
git clone https://github.com/javvi51/eDOCr
cd eDOCr
pip install -r requirements.txt
```
### Using
There are two ways of using eDOCr: from terminal and from your own python file.
#### From Terminal:
We need to locate the ocr_it.py file in our system. If you have installed using pip, it will probably come in ```C:\Users\YOUR_USER\.conda\envs\edocr\Lib\site-packages\eDOCr\ocr_it.py```. If you have installed from source, it will be at your selected folder. All you need to do is:
```bash
python PATH/TO/YOUR/FOLDER/eDOCr/ocr_it.py PATH/TO/YOUR/DRAWING/my_drawing.pdf
```
Additional commands you can use are:
```bash
# Specify the destination path. By default, it is the same as your drawing.
--dest-folder PATH/TO/YOUR/DESTINATION/FOLDER
# Does the drawing have watermark you want to remove? By default, it is not considered.
--water
# Advance Setting: Set a custom threshold distance (in px.) for grouping detections. Default is 20px.
--cluster 25
```
#### From your own python file
More customization is possible using your own python file, such as selecting a different model, alphabet or changing colors.
```bash
# Importing packages
import os
from eDOCr import tools
import cv2
import string
from skimage import io
# Loading image and destination file
dest_DIR = 'tests/test_Results'
file_path = 'tests/test_samples/Candle_holder.jpg'
filename = os.path.splitext(os.path.basename(file_path))[0]
img = cv2.imread(file_path)
# Selecting alphabet and model (Note that alphabet and alphabet model need to match)
GDT_symbols = '⏤⏥○⌭⌒⌓⏊∠⫽⌯⌖◎↗⌰'
FCF_symbols = 'ⒺⒻⓁⓂⓅⓈⓉⓊ'
Extra = '(),.+-±:/°"⌀'
alphabet_dimensions = string.digits + 'AaBCDRGHhMmnx' + Extra
model_dimensions = 'eDOCr/keras_ocr_models/models/recognizer_dimensions.h5'
alphabet_infoblock = string.digits+string.ascii_letters+',.:-/'
model_infoblock = 'eDOCr/keras_ocr_models/models/recognizer_infoblock.h5'
alphabet_gdts = string.digits + ',.⌀ABCD' + GDT_symbols
model_gdts = 'eDOCr/keras_ocr_models/models/recognizer_gdts.h5'
# Selecting personalized color palette and cluster setting
color_palette = {'infoblock': (180, 220, 250), 'gdts': (94, 204, 243), 'dimensions': (93, 206, 175), 'frame': (167, 234, 82), 'flag': (241, 65, 36)}
cluster_t = 20
# eDOCr functions
class_list, img_boxes = tools.box_tree.findrect(img)
boxes_infoblock, gdt_boxes, cl_frame, process_img = tools.img_process.process_rect(class_list, img)
io.imsave(os.path.join(dest_DIR, filename + '_process.jpg'), process_img)
infoblock_dict = tools.pipeline_infoblock.read_infoblocks(boxes_infoblock, img, alphabet_infoblock, model_infoblock)
gdt_dict = tools.pipeline_gdts.read_gdtbox1(gdt_boxes, alphabet_gdts, model_gdts, alphabet_dimensions, model_dimensions)
process_img = os.path.join(dest_DIR, filename + '_process.jpg')
dimension_dict = tools.pipeline_dimensions.read_dimensions(process_img, alphabet_dimensions, model_dimensions, cluster_t)
mask_img = tools.output.mask_the_drawing(img, infoblock_dict, gdt_dict, dimension_dict, cl_frame, color_palette)
# Record the results
io.imsave(os.path.join(dest_DIR, filename + '_boxes.jpg'), img_boxes)
io.imsave(os.path.join(dest_DIR, filename + '_mask.jpg'), mask_img)
tools.output.record_data(dest_DIR, filename, infoblock_dict, gdt_dict, dimension_dict)
```
### Training a model on a custom alphabet
Fonts are not loaded if installing from pip. To train new models, please install from source.
To train a model in a custom alphabet, a python file is provided, so that the only steps needed are:
```bash
# Importing Packages
import os
import string
from eDOCr import keras_ocr
from eDOCr.keras_ocr_models import train_recognizer
# Fixing paths and alphabet
DIR = os.getcwd()
recognizer_basepath = os.path.join(DIR, 'eDOCr/Keras_OCR_models/models')
data_dir = './tests'
alphabet = string.digits + 'AaBCDRGHhMmnx' + '().,+-±:/°"⌀'
# Number of autogenerated samples
samples = 10000
# Load white backgrounds and fonts
backgrounds = []
for i in range(0, samples):
backgrounds.append(os.path.join('./eDOCr/Keras_OCR_models/backgrounds/0.jpg'))
fonts = []
for i in os.listdir(os.path.join(DIR, 'eDOCr/Keras_OCR_models/fonts')):
fonts.append(os.path.join('./eDOCr/Keras_OCR_models/fonts', i))
# Choose a pretrained model if you like
pretrained_model = None
#pretrained_model = os.path.join(recognizer_basepath,'recognizer_dimensions.h5')
# Start Training
train_recognizer.generate_n_train(alphabet, backgrounds, fonts, recognizer_basepath=recognizer_basepath, pretrained_model=pretrained_model)
```
نیازمندی
| مقدار | نام |
|---|---|
| - | opencv-python |
| >=2.0.0 | tensorflow |
| - | Pillow |
| - | shapely |
| - | anytree |
| - | scikit-image |
| - | scikit-learn |
| - | tqdm |
| - | validators |
| - | essential-generators |
| - | imgaug |
| - | fonttools |
| - | editdistance |
| - | pyclipper |
| ==1.0.0 | efficientnet |
| - | pytest |
| - | pdf2image |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl eDOCr-0.0.4:
pip install eDOCr-0.0.4.whl
نصب پکیج tar.gz eDOCr-0.0.4:
pip install eDOCr-0.0.4.tar.gz