Metadata-Version: 2.1
Name: icdar-tools
Version: 0.0.3
Summary: a pip install icdar_tools
Home-page: UNKNOWN
Author: mlib_4_you
Author-email: none.none@gmail.com
License: MIT
Description: These tools are to provide effort by researchers in creating their own working environment
        This is about dealing with {ICDAR} data
        It provides you with initial processing tools for training and testing data.
        It provides tools for calculating the text area using polygon of shapely.
        Save results from images and text locations as a prelude to calculating precision.
        And some other tools we will try to "more examples to explain the use later."
        
        These tools have been quoted and written by the {EAST}.
        Where you can see the original files here.
        https://github.com/argman/EAST/
        
        These tools depend on several libraries you must provide before use.
        Like:
        ```
        -opencv-3.x.x
        -numpy
        -scipy
        -matplotlib
        -shapely
        ```
        
        use Modules!
        
        ```python
        import icdar_tools
        ```
        or 
        ```python
        from icdar_tools import icdar
        from icdar_tools import icd_util
        from icdar_tools import locality_aware_nms
        from icdar_tools import data_util
        ```
        
         - icdar.py
        
        This module is very important as it is found to serve your time instead of betting a lot of effort and time in order to produce already existing tools, in order to handle the data.
        Here you will find everything you need, from the future ICDAR Data Processing
        
        From loading the data and locating the texts inside the images and some other things.
        The following are examples of usage.
        
        1:get_batch()
        ```python
        get_batch(num_workers, **kwargs)
        ```
        The function works to get the coordinates of the text in the images
        Through text files with them in the same path
        It then returns those geometrical coordinates,
        image names, and images derived from the training images specified by the place of the text only.
        
        use:
        
        ```python
        data_generator = icdar.get_batch(num_workers=num_readers,
                                                 training_data_path='path/to_data/icdar15/train/'
                                                 input_size=input_size,
                                                 batch_size=batch_size_per_gpu * len(gpus))
        ```
        
        reutrn
        ```python
        yield images, image_fns, score_maps, geo_maps, training_masks
        ```
        
        2:load_annoataion()
        
        ```python
        text_polys, text_tags = icdar.load_annoataion(txt_file-name)
        ```
        
           
        3:restore_rectangle_rbox()
        ```python
        text_box_restored = icdar.restore_rectangle_rbox(origin, geometry)
        ```
           
           
        **:**
         - icd_util.py
         
         
        1 - get_images()
        The input path should be images
        ```python
        images_list_fullName = icd_util.get_images(path/data/images/)
        ```
        Repetition is a list of all images in the input path
        
           
           
        2 -resize_image()
        
        ```python
        im_resized, (ratio_h, ratio_w) = icd_util.resize_image(image)
        ```
            '''
            resize image to a size multiple of 32 which is required by the network
            :param im: the resized image
            :param max_side_len: limit of max image size to avoid out of memory in gpu
            :return: the resized image and the resize ratio
            '''
         - The default setting of the function
           ```python
           icd_util.resize_image(image, max_side_len=2400)
           ```
        
        3 - detect() \
        Here is the conclusion of the model represented in the geometrical map of coordinates and score
        
        Use the threshold to filter the results that look false
        The borders of the text boxes are then redrawn
        
        return of these boxes and the time of implementation of this processe.
        
        ```python
        boxes, timer = icd_util.detect(score_map=score, geo_map=geometry, timer=timer)
        ```
        
            '''
            restore text boxes from score map and geo map
            :param score_map:
            :param geo_map:
            :param timer:
            :param score_map_thresh: threshhold for score map
            :param box_thresh: threshhold for boxes
            :param nms_thres: threshold for nms
            :return: boxes and time out
            '''
            
            - The default setting of the function
            
           ```python
           icd_util.detect(score_map, geo_map, timer, score_map_thresh=0.8, box_thresh=0.1, nms_thres=0.2):
           ```
           
        - write_result() \
        This function gets the image and its name \
        The file name is written as the text location in the image 
        
        You get the text boxes that are expected for that image \
        writeing text locations in text files \
        drawing squares around those texts in the picture \
        See the font size of the box and font color through passes 
        ```
        color, thickness
        ```
        Finally a place will be written  those 'output_path/'
        
        Images and text files are written into a single folder.
        
        ```python
           icd_util.write_result(img ,boxes ,output_dir ,res_file ,img_fn)
        ```
           
         - The default setting of the function
         ```python
           icd_util.write_result(img ,boxes ,output_dir ,res_file ,img_fn ,color=(255, 255, 0),thickness=1, skip = True)
         ```
         
        ...
        
Keywords: icdar data tools,East tools
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
