def _read_jpg():
dumm = glob.glob('/Users/HANEL/Desktop/' + '*.png')
print(len(dumm))
filename_queue = tf.train.string_input_producer(dumm)
# filename_queue = tf.train.string_input_producer(['/Users/HANEL/Desktop/tf.png', '/Users/HANEL/Desktop/ft.png'])
reader = tf.WholeFileReader()
key, value = reader.read(filename_queue)
my_img = tf.image.decode_png(value)
# my_img_flip = tf.image.flip_up_down(my_img)
init_op = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init_op)
# Start populating the filename queue.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in range(1):
gunel = my_img.eval()
print(gunel.shape)
Image._showxv(Image.fromarray(np.asarray(gunel)))
coord.request_stop()
coord.join(threads)
def _read_jpg():
dumm = glob.glob('/Users/HANEL/Desktop/' + '*.png')
print(len(dumm))
filename_queue = tf.train.string_input_producer(dumm)
# filename_queue = tf.train.string_input_producer(['/Users/HANEL/Desktop/tf.png', '/Users/HANEL/Desktop/ft.png'])
reader = tf.WholeFileReader()
key, value = reader.read(filename_queue)
my_img = tf.image.decode_png(value)
# my_img_flip = tf.image.flip_up_down(my_img)
init_op = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init_op)
# Start populating the filename queue.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in range(1):
gunel = my_img.eval()
print(gunel.shape)
Image._showxv(Image.fromarray(np.asarray(gunel)))
coord.request_stop()
coord.join(threads)
def test_showxv_deprecation(self):
class TestViewer(ImageShow.Viewer):
def show_image(self, image, **options):
return True
viewer = TestViewer()
ImageShow.register(viewer, -1)
im = Image.new("RGB", (50, 50), "white")
with pytest.warns(DeprecationWarning):
Image._showxv(im)
# Restore original state
ImageShow._viewers.pop(0)
r1 = (i - center1) * 2 + (j - center2) * 2
# euclidean distancefrom origin
r = math.sqrt(r1)
# using cut_off radius to eliminate high freq
# if r > d_0:
# for ideal low pass
# H[i, j] = 0.0
# for butterworth low pass
# H[i, j] = 1 / (1 + (r / d_0) ** t1)
# for Gaussian low pass
# H[i, j] = math.exp(-r * 2 / t1 * 2)
if 0 < r < d_0:
# for ideal high pass
# H[i, j] = 1.0
# for butterworth high pass filter
H[i, j] = 1 / (1 + (d_0 / r)**t1)
# for gaussian high pass
# H[i, j] = 1 - math.exp(-r * 2 / t1 * 2)
# # converting H to image
H = Image.fromarray(H)
# performing convolution
con = d * H
# computing mag of inverse FFT
e = abs(fftim.ifft2(con))
# from array to image
f = Image.fromarray(e)
Image._showxv(f, " lowpass filter")
def showImg(save_path):
Image._showxv(Image.open(save_path), 'Generated QrCode')
from img_preprocess import grayscale_avg
from sobel_filter import sobel_filter_horizontal
from sobel_filter import sobel_filter_vertical
from sobel_filter import sobel_filter
from gaussian_filter import gaussian_filter
from laplacian_of_gaussian_filter import log_filter
from PIL import Image
import numpy as np
# read the RGB images
img = Image.open('data/checkerbox.jpg') # converts the input image to np.array of 3 dimensions (height, width, 3) where 3 stands for RGB channels
Image._showxv(img,title="original")
img_gray = grayscale_avg(np.asarray(img)) # converts the input image to gayscale using average method
# Image._show(Image.fromarray(img_gray),title="grayscaled")
# Image._show(Image.fromarray(gaussian_filter(img_gray)),title="gaussian filter")
# Image._show(Image.fromarray(sobel_filter_vertical(img_gray)),title="sobel vertical filter")
# Image._show(Image.fromarray(sobel_filter_horizontal(img_gray)),title="sobel horizontal filter")
Image._show(Image.fromarray(sobel_filter(img_gray)),title="sobel filter")
Image._show(Image.fromarray(log_filter(img_gray)),title="log filter")
