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process.py
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process.py
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from images_read_write import imread
from operations import rgb_from_gray
import numpy as np
def process_cat(path, out):
from filesystem import list_images
images = list_images(path)
# For each image
res = []
for image_path in images:
# Process one image
r = process_one(image_path)
# Save the result promise in a list
res.append(r)
# Compute the average recursively
from instancing import reduce_list_as_tree
avg = reduce_list_as_tree(average, res)
# write the result
from images_read_write import rgb_write
rgb_write(avg, out)
def process_one(filename):
# read the image
rgb = imread(filename)
# convert to grayscale
if rgb.ndim == 2:
rgb = rgb_from_gray(rgb)
# resize to (64, 64)
#print(rgb.shape)
x = rgb_smooth(rgb, sigma=3)
#print(x.shape)
x = rgb_resize(x, (128, 128))
#print(x.shape)
return x
def rgb_resize(rgb, shape):
from scipy.misc import imresize
res = np.zeros((shape[0], shape[1], 3))
for i in range(3):
res[:,:,i] = imresize(rgb[:,:,i], shape, interp='bilinear', mode='F')
return res
def rgb_smooth(rgb, sigma):
from scipy.ndimage import gaussian_filter
res = np.empty_like(rgb)
for i in range(3):
res[:,:,i] = gaussian_filter(rgb[:,:,i], sigma=sigma)
return res
def average(image1, image2):
return image1*0.5 + image2*0.5