danila_lib v1.3.7
python library for Danila
To install project made
pip install danila-lib
To use in your project
from danila.danila import Danila
All use methods are in
class Danila
main method returns dict {'number', 'prod', 'year'} for openCV rama img or 'no_rama'
def text_recognize(self, img):
steps for algorythm
returns string - class of rama, img - openCV frame
def rama_classify(self, img):
returns openCV frame with rama from openCV frame
def rama_detect(self, img):
returns openCV image with cut_rama
def rama_cut(self, img):
returns openCV cut rama with drawn text areas
def text_detect_cut(self, img):
returns openCV img with drawn text areas
def text_detect(self, img):
in package data/neuro there is module Rama_classify_class
class Rama_classify_class
reads CNN taught model and includes it in class example
def __init__():
makes grey NumPy Array(1,512,512) of doubles[0..1] from openCV image
def prepare_img(img : openCV frame): NumPy Array(1,512,512)[0..1]
classify openCV img with CNN, returns list with double[0..1] values
def work_img(img : openCV frame): Double[0..1] list
classify openCV img with CNN, returns Class_im
def classify(img : openCV frame): Class_im
in package data/neuro there is module Rama_detect_class
class Rama_detect_class
reads yolov5 taught model from yandex-disk and includes it in class example
def __init__(self, model_path, model_name, yolo_path):
получить JSON с результатами yolo
def work_img(self, img_path):
получить координаты прямоугольника с рамой
def rama_detect(self, img_path):
in package data/neuro there is module Rama_text_detect_class
class Rama_text_detect_class
reads yolov5 taught model from yandex-disk and includes it in class example
def __init__(self, model_path, model_name, yolo_path):
find text areas on img from img_path with yolov5, returns yolojson
def work_img(self, img_path):
find text areas on img from img_path with yolov5, returns dict with rects for each text class
def text_detect(self, img_path):
draw img_text_areas on img, returns opencv img
def draw_text_areas_in_opencv(self, image_text_areas, img):
in package data/neuro there is module Letters_recognize
class Letters_recognize:
main_method takes all image_text_areas from image_rama_cut and recognize text
def work_image_cut(self, image_text_areas, image_rama_cut, number_length, prod_length, year_length):
read CNN model from yandex and put into object
def __init__(self):
cut text_areas imgs for each Rect from rect_array returns openCv imgs list
def make_cuts(self, img_rama_cut, rect_array):
for every text_class recognize text from all areas of text_class, length is depends on class and prod, returns string
def work_image_cuts(self, number_image_cuts, length):
recognize one word of given length from one img, returns str
def work_img_word(self, image_number, letter_number):
prepare img of one letter for CNN, returns np_array(1,28,28,1) of Double[0..1]
def prepare_img_letter(self, image_letter):
recognize img of one letter with CNN, returns list[10] of p
def work_img_letter(self, image_initial):
recognize img of one letter with CNN, returns letter in str
def classify_letter(self, image_letter):
in package data/result Rect module for rectangle operations
прочитать из json результата йоло
@staticmethod
def get_rect_from_yolo_json(yolo_json):
makes Rect object from xmin, xmax, ymin, ymax
def __init__(self, xmin=0, xmax=0, ymin=0, ymax=0):
Найти IOU между этим прямоугольником и другим, данным в объекте
def IoU(self, rect):
makes string from object
def __str__(self):
find intersection square between object and other rectangle
def intersection(self, rect):
find union RECT between object and other rectangle
def union(self, rect):
in package data/result Class_im
class Class_im(Enum):
rama_no_spring = 0
rama_spring = 1
in package data/result class Text_area
def __init__(self, dict_text_area):
self.class_im = Class_text(dict_text_area['class'])
self.rect = Rect(...)
in package data/result class image_text_areas
class contains dict with Rects list for each text_class
class Image_text_areas:
makes dict {Class_text.number : [], Class_text.prod : [], Class_text.text : [], Class_text.year : []}
def __init__(self):
add text area to dict
def add_area(self, text_area):
add list of text areas
def fill_in_with_areas(self, areas):
delete all cases in which two areas are intersected
def correct_intersections(self):
changes Rects coordinates from cut_img to whole_img from rama Rect
def explore_to_whole_image(self, rama_rect):
exapmles of using you can find
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Release files for danila-lib 3.1.0
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