Frequently Used Code Snippets

Performing Stroke Width Transformation

# Installation
# !pip install swtloc

# Imports
import swtloc as swt
# Image Path
imgpath = 'examples/images/test_image_5/test_img5.jpg'
# Result Path
respath = 'examples/images/test_image_5/usage_results/'
# Initializing the SWTLocalizer class with the image path
swtl = swt.SWTLocalizer(image_paths=imgpath)
# Accessing the SWTImage Object which is housing this image
swtImgObj = swtl.swtimages[0]
# Performing Stroke Width Transformation
swt_mat = swtImgObj.transformImage(text_mode='db_lf')

Localizing & Annotating Letters and Generating Crops of Letters

# Installation
# !pip install swtloc

# Imports
import swtloc as swt
from cv2 import cv2
import numpy as np
# Image Path
imgpath = 'examples/images/test_image_1/test_img1.jpg'
# Read the image
img = cv2.imread(imgpath)
# Result Path
respath = 'examples/images/test_image_1/usage_results/'
# Initializing the SWTLocalizer class with a pre loaded image
swtl = swt.SWTLocalizer(images=img)
swtImgObj = swtl.swtimages[0]
# Perform Stroke Width Transformation
swt_mat = swtImgObj.transformImage(text_mode='db_lf',
                                   maximum_angle_deviation=np.pi/2,
                                   gaussian_blurr_kernel=(11, 11),
                                   minimum_stroke_width=5,
                                   maximum_stroke_width=50,
                                   display=False)  # NOTE: Set display=True 
# Localizing Letters
localized_letters = swtImgObj.localizeLetters(minimum_pixels_per_cc=950,
                                              maximum_pixels_per_cc=5200)
letter_labels = [int(k) for k in list(localized_letters.keys())]

# Some Other Helpful Letter related functions
# # Query a single letter
from swtloc.configs import (IMAGE_ORIGINAL,
                            IMAGE_SWT_TRANSFORMED)
loc_letter, swt_loc, orig_loc = swtImgObj.getLetter(key=letter_labels[5])

# # Iterating over all the letters
# # Specifically useful for jupyter notebooks - Iterate over all
# # the letters, at the same time visualizing the localizations
letter_gen = swtImgObj.letterIterator()
loc_letter, swt_loc, orig_loc = next(letter_gen)

# # Generating a crop of a single letter on any of the available
# # image codes.
# # Crop on SWT Image
swtImgObj.saveCrop(save_path=respath,crop_of='letters',crop_key=6, crop_on=IMAGE_SWT_TRANSFORMED, crop_type='min_bbox')
# # Crop on Original Image
swtImgObj.saveCrop(save_path=respath,crop_of='letters',crop_key=6, crop_on=IMAGE_ORIGINAL, crop_type='min_bbox')

Localizing & Annotating Words and Generating Crops of Words

# Installation
# !pip install swtloc
# Imports
import swtloc as swt
# Image Path
imgpath = 'images/test_img2/test_img2.jpg'
# Result Path
respath = 'images/test_img2/usage_results/'
# Initializing the SWTLocalizer class with the image path
swtl = swt.SWTLocalizer(image_paths=imgpath)
swtImgObj = swtl.swtimages[0]
# Perform Stroke Width Transformation
swt_mat = swtImgObj.transformImage(maximum_angle_deviation=np.pi/2,
                                   gaussian_blurr_kernel=(9, 9),
                                   minimum_stroke_width=3,
                                   maximum_stroke_width=50,
                                   include_edges_in_swt=False,
                                   display=False)  # NOTE: Set display=True 

# Localizing Letters
localized_letters = swtImgObj.localizeLetters(minimum_pixels_per_cc=400,
                                              maximum_pixels_per_cc=6000,
                                              display=False)  # NOTE: Set display=True 

# Calculate and Draw Words Annotations
localized_words = swtImgObj.localizeWords(display=True)  # NOTE: Set display=True 
word_labels = [int(k) for k in list(localized_words.keys())]

# Some Other Helpful Words related functions
# # Query a single word
from swtloc.configs import (IMAGE_ORIGINAL,
                            IMAGE_SWT_TRANSFORMED)
loc_word, swt_loc, orig_loc = swtImgObj.getWord(key=word_labels[8])

# # Iterating over all the words
# # Specifically useful for jupyter notebooks - Iterate over all
# # the words, at the same time visualizing the localizations
word_gen = swtImgObj.wordIterator()
loc_word, swt_loc, orig_loc = next(word_gen)

# # Generating a crop of a single word on any of the available
# # image codes
# # Crop on SWT Image
swtImgObj.saveCrop(save_path=respath, crop_of='words', crop_key=9, crop_on=IMAGE_SWT_TRANSFORMED, crop_type='bubble')
# # Crop on Original Image
swtImgObj.saveCrop(save_path=respath, crop_of='words', crop_key=9, crop_on=IMAGE_ORIGINAL, crop_type='bubble')