Milujeme BI-SVZ

import os
import io

import cv2
import numpy as np
import matplotlib.pyplot as plt

from improutils import *

%matplotlib inline
np.set_printoptions(formatter={'float': lambda x: "{0:0.3f}".format(x)})

from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
def create_slider(min, max, description):
    return widgets.IntRangeSlider( min=min, max=max, step=1,value=[min,max], 
                                   description=description.ljust(30,'\xa0'), 
                                   continuous_update=False, 
                                   orientation='horizontal',
                                   style=dict(description_width='initial'),
                                   layout=widgets.Layout(width='auto', grid_area='header'),
                                  )

### interact slider template for segmentation

# @interact(value=create_slider(min=0, max=255, description='Value'))
# def _(value):
#     mask = ...(red, value[0], value[1]) ###
#     plot_images(mask, red, titles=['Segmentation', 'Original'])

def find_holes(img_bin,min_area = 0, max_area=1000000,fill = True):
    """
    Finds inner contours of holes in binary image and filters them using their area. Then it draws binary image from filtered contours. It counts contours as well.

    Parameters
    ----------
    img_bin : ndarray
        Input binary image.
    min_area : int
        Size of contour that is used to filter all smaller contours out.
    max_area : int
        Size of contour that is used to filter all larger contours out.
    Returns
    -------
    contour_drawn : ndarray
        Output binary image with drawn filled filtered contours.
    count : int
        Number of found and filtered contours.
    contours : list
        Found contours.
    """

    contours, hierarchy = cv2.findContours(img_bin,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_SIMPLE)
    # filter out only hole contours (contours that are inside another contour)
    # for more info about hierarchy retrieval modes visit: https://docs.opencv.org/4.x/d9/d8b/tutorial_py_contours_hierarchy.html
    hole_indices = [ i for i in range(len(hierarchy[0])) if hierarchy[0,i,-1] != -1]    
    # filter out contours by area
    contours = [contours[hole_index] for hole_index in hole_indices if min_area < cv2.contourArea(contours[hole_index]) <= max_area]
    # draw contours
    thick = cv2.FILLED
    if not fill: thick = 2
    contours_drawn = cv2.drawContours(np.zeros(img_bin.shape, dtype=np.uint8), contours, -1, color=(255, 255, 255),thickness=thick)
    return contours_drawn, len(contours), contours

def ocr(img_bin):
    """
    Detects text in the file.

    Parameters
    ----------
    img_bin : ndarray
        Input binary image. White objects on black background.
    Returns
    -------
    The recognized text in the image.
    """
    # Tesseract works with black objects on white background.
    config = r' -l eng --psm 6'
    img_bin = negative(img_bin)
    return pytesseract.image_to_string(img_bin, config=config)