def img_thresholding(img, type):
show_image(img)
img_grayscale = color.rgb2gray(img)
if type == 'global':
thresh = threshold_otsu(img_grayscale)
else:
thresh = threshold_local(img_grayscale, block_size=35, offset=10)
img_binary = img_grayscale > thresh
img_binary2 = img_grayscale < thresh
show_image(img_binary)
show_image(img_binary2)
import sys
import numpy as np
sys.path.append('./helpers')
from settings import show_image, nda_import_image
nda_flipped_horiz_vert_seville = nda_import_image(
'./dataset/chapter 1/sevilleup(2).jpg')
nda_flipped_horiz_seville = np.flipud(nda_flipped_horiz_vert_seville)
show_image(nda_flipped_horiz_seville)
nda_seville = np.fliplr(nda_flipped_horiz_seville)
show_image(nda_seville)
import sys
from skimage import data, color
sys.path.append('./helpers')
from settings import show_image
img_rocket = data.rocket()
img_grey_scaled_rocket = color.rgb2gray(img_rocket)
show_image(img_rocket)
show_image(img_grey_scaled_rocket)
> Import the appropriate thresholding and rgb2gray() functions.
> Turn the image to grayscale.
> Obtain the optimal thresh.
> Obtain the binary image by applying thresholding.
"""
import sys
from skimage.filters import try_all_threshold, threshold_li
from skimage.color import rgb2gray
from matplotlib import pyplot as plt
sys.path.append('./helpers')
from settings import nda_import_image, show_image
str_tools_image_path = './dataset/chapter 1/shapes52.jpg'
img_tools = nda_import_image(str_tools_image_path)
def apply_thresholding_test(img):
img_grayscale = rgb2gray(img)
fig, ax = try_all_threshold(img_grayscale, verbose=False)
plt.show()
apply_thresholding_test(img_tools)
img_tools_grayscale = rgb2gray(img_tools)
li_thresh = threshold_li(img_tools_grayscale)
img_tools_binary = img_tools_grayscale > li_thresh
show_image(img_tools_binary)
colored images to grayscale. For that we will use the rgb2gray() function
learned in previous video. Which has already been imported for you.
Instructions:
> Import the otsu threshold function
> Make the image grayscale using rgb2gray
> Obtain the optimal threshold value with otsu
> Apply thresholding to the image
> Show the image
"""
import sys
from skimage.filters import threshold_otsu
from skimage import color
sys.path.append('./helpers')
from settings import show_image, nda_import_image
img_chess_pieces = nda_import_image('./dataset/chapter 1/bw.jpg')
show_image(img_chess_pieces)
img_chess_pieces_gray = color.rgb2gray(img_chess_pieces)
thresh = threshold_otsu(img_chess_pieces_gray)
img_binary = img_chess_pieces_gray > thresh
img_binary2 = img_chess_pieces_gray < thresh
show_image(img_binary)
show_image(img_binary2)