def execute(outputsize):
faces_dir_path = "data/train_set/48_48_faces_web_augmented"
bkgs_dir_path = "data/train_set/48_48_nonfaces_aflw"
target_path = "data/train_set/13"
faces_dir=join(target_path,"faces")
nonfaces_dir = join(target_path,"nonfaces")
os.makedirs(nonfaces_dir)
os.makedirs(faces_dir)
img_faces = [ f for f in listdir(faces_dir_path) if isfile(join(faces_dir_path,f)) and f.endswith("png") ]
img_bkgs = [ f for f in listdir(bkgs_dir_path) if isfile(join(bkgs_dir_path,f)) and f.endswith("jpg") ]
for i, img_name in enumerate(img_faces):
img_path = join(faces_dir_path,img_name)
img = imread(img_path)
resized_img = resize(img,outputsize)
ubyte_img = img_as_ubyte(resized_img)
imsave(join(faces_dir,img_name), ubyte_img)
print "processed "+ img_path
for i, img_name in enumerate(img_bkgs):
img_path = join(bkgs_dir_path,img_name)
img = imread(img_path)
gray_img = rgb2gray(img)
resized_img = resize(gray_img,outputsize)
ubyte_img = img_as_ubyte(resized_img)
imsave(join(nonfaces_dir,img_name), ubyte_img)
print "processed "+ img_path
def convert_to_gray():
bkgs_dir_path = "data/train_set/13/nonfaces"
target_path = "data/train_set/13/nonfaces_gray"
os.makedirs(target_path)
img_bkgs = [ f for f in listdir(bkgs_dir_path) if isfile(join(bkgs_dir_path,f)) and f.endswith("jpg") ]
for i, img_name in enumerate(img_bkgs):
img_path = join(bkgs_dir_path,img_name)
img = imread(img_path)
gray_img = rgb2gray(img)
imsave(join(target_path,img_name), gray_img)
def execute(outputsize):
faces_dir_path = "data/train_set/48_48_faces_web_augmented"
bkgs_dir_path = "data/train_set/48_48_nonfaces_aflw"
target_path = "data/train_set/13"
faces_dir = join(target_path, "faces")
nonfaces_dir = join(target_path, "nonfaces")
os.makedirs(nonfaces_dir)
os.makedirs(faces_dir)
img_faces = [
f for f in listdir(faces_dir_path)
if isfile(join(faces_dir_path, f)) and f.endswith("png")
]
img_bkgs = [
f for f in listdir(bkgs_dir_path)
if isfile(join(bkgs_dir_path, f)) and f.endswith("jpg")
]
for i, img_name in enumerate(img_faces):
img_path = join(faces_dir_path, img_name)
img = imread(img_path)
resized_img = resize(img, outputsize)
ubyte_img = img_as_ubyte(resized_img)
imsave(join(faces_dir, img_name), ubyte_img)
print "processed " + img_path
for i, img_name in enumerate(img_bkgs):
img_path = join(bkgs_dir_path, img_name)
img = imread(img_path)
gray_img = rgb2gray(img)
resized_img = resize(gray_img, outputsize)
ubyte_img = img_as_ubyte(resized_img)
imsave(join(nonfaces_dir, img_name), ubyte_img)
print "processed " + img_path
def convert_to_gray():
bkgs_dir_path = "data/train_set/13/nonfaces"
target_path = "data/train_set/13/nonfaces_gray"
os.makedirs(target_path)
img_bkgs = [
f for f in listdir(bkgs_dir_path)
if isfile(join(bkgs_dir_path, f)) and f.endswith("jpg")
]
for i, img_name in enumerate(img_bkgs):
img_path = join(bkgs_dir_path, img_name)
img = imread(img_path)
gray_img = rgb2gray(img)
imsave(join(target_path, img_name), gray_img)
def main():
img = imread("HJoceanSmall.png")
# imshow(img)
reduced_img = img
start_time = time.time()
i = 10
while i > 0:
seam = find_seam(reduced_img)
reduced_img = remove_seam(reduced_img, seam)
# imsave(str(i), reduced_img)
i = i-1
stop_time = time.time()
plot_seam(reduced_img)
show()
print "Time taken :", stop_time - start_time
def resize_imgs_in_dir(outputsize):
img_dir_path = "data/newnonfaces/48_48_non_faces_aflw"
target_path = "data/newnonfaces/13/nonfaces"
os.makedirs(target_path)
img_faces = [ f for f in listdir(img_dir_path) if isfile(join(img_dir_path,f)) and f.endswith("jpg") ]
for i, img_name in enumerate(img_faces):
img_path = join(img_dir_path,img_name)
img = imread(img_path)
resized_img = resize(img,outputsize)
ubyte_img = img_as_ubyte(resized_img)
imsave(join(target_path,img_name), ubyte_img)
print "processed "+ img_path
def resize_imgs_in_dir(outputsize):
img_dir_path = "data/newnonfaces/48_48_non_faces_aflw"
target_path = "data/newnonfaces/13/nonfaces"
os.makedirs(target_path)
img_faces = [
f for f in listdir(img_dir_path)
if isfile(join(img_dir_path, f)) and f.endswith("jpg")
]
for i, img_name in enumerate(img_faces):
img_path = join(img_dir_path, img_name)
img = imread(img_path)
resized_img = resize(img, outputsize)
ubyte_img = img_as_ubyte(resized_img)
imsave(join(target_path, img_name), ubyte_img)
print "processed " + img_path
i = img_as_ubyte(i_f2)
# To display this image
plt.imshow(i, cmap=plt.cm.gray) # For showing gray-scale images
#ndim(i)
plt.show()
# <headingcell level=3>
# 2. Importing & displaying using io.imread() and io.imshow()
# <codecell>
phantom = img_as_ubyte(
io.imread(
'/Users/chintak/Repositories/scikit-image/skimage/data/phantom.png',
as_grey=True))
# 'as_grey=True' ensures that the image is taken as a 2D rather than a 3D array with equal R,G,B values for a point
io.imshow(phantom)
plt.show()
# <headingcell level=2>
# EROSION
# <rawcell>
# Usage : erosion(image, selem, out=None, shift_x=False, shift_y=False)
#
# Return greyscale morphological erosion of an image.
#
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from skimage import data
from skimage.filters import threshold_otsu
from skimage.segmentation import clear_border
from skimage.measure import label
from skimage.morphology import closing, square, disk
from skimage.measure import regionprops
from skimage.color import label2rgb
import skimage.io._io as io
image = io.imread('test.jpg', as_grey=True)
# apply threshold
thresh = threshold_otsu(image)
bw = closing(image > thresh, disk(15))
# remove artifacts connected to image border
cleared = bw.copy()
clear_border(cleared)
# label image regions
label_image = label(cleared)
borders = np.logical_xor(bw, cleared)
label_image[borders] = -1
image_label_overlay = label2rgb(label_image, image=image)
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
ax.imshow(image_label_overlay)
def load_func(fname, **kwargs):
kwargs.setdefault('dtype', dtype)
return imread(fname, **kwargs)
i_f2 = i_f[:,:,0]
# To convert to uint8 data type
i = img_as_ubyte(i_f2)
# To display this image
plt.imshow(i, cmap=plt.cm.gray) # For showing gray-scale images
#ndim(i)
plt.show()
# <headingcell level=3>
# 2. Importing & displaying using io.imread() and io.imshow()
# <codecell>
phantom = img_as_ubyte(io.imread('/Users/chintak/Repositories/scikit-image/skimage/data/phantom.png', as_grey=True))
# 'as_grey=True' ensures that the image is taken as a 2D rather than a 3D array with equal R,G,B values for a point
io.imshow(phantom)
plt.show()
# <headingcell level=2>
# EROSION
# <rawcell>
# Usage : erosion(image, selem, out=None, shift_x=False, shift_y=False)
#
# Return greyscale morphological erosion of an image.
#
# Morphological erosion sets a pixel at (i,j) to the **minimum over all pixels