return i
print ("Loading model ...")
data = np.loadtxt(os.path.join(args.d, args.features), delimiter = ',') / 255
labels = np.loadtxt(os.path.join(args.d, args.labels), np.uint8)
lines = [i.strip().split(" ") for i in open(os.path.join(args.d, args.labelmapping))]
labelmap = dict([(int(n), l) for l, n in lines])
clf = KNeighborsClassifier(n_neighbors = 7, algorithm = 'brute')
clf.fit(data, labels)
img = resize_rgb_image(read_rgb_image(args.i), (32, 32))
d = []
for i, details in normed_windows(img, [1.0], details = True):
arr = flatten(i) / 255
arr = np.reshape(arr, (1, -1))
t = clf.predict(arr)
c = t[0]
print (c, labelmap[c])
distances, idx = clf.kneighbors(arr)
distances = [d for d, p in zip(distances.flatten(), idx.flatten()) if labels[p] == c]
s = sum(distances) / len(distances)
d.append([s, c, details])
d = sorted(d)
for s, c, details in d[:3]:
print (s, labelmap[c], details)
img = box(img, details[1], details[2], details[0])
write_rgb_image(args.o, img)
#!/usr/bin/env python3
from tools.image import read_rgb_image, write_rgb_image
import argparse
import numpy as np
from skimage import io
p = argparse.ArgumentParser(description = 'Extract an image from a 9x9 reCAPTCHA image.')
p.add_argument('-i', help = 'input image', required = True)
p.add_argument('-y', help = 'row', required = True, type = int)
p.add_argument('-x', help = 'column', required = True, type = int)
p.add_argument('-o', help = 'output image', default = None)
p.add_argument('-v', help = 'show output image', default = False, action = 'store_true')
args = p.parse_args()
def read_image(fname, row, col):
i = read_rgb_image(fname)
assert(i.shape[0] == 300)
assert(i.shape[1] == 300)
i = i[row * 100:row * 100 + 100, col * 100:col * 100 + 100]
return i
i = read_image(args.i, args.y, args.x)
if args.o != None:
write_rgb_image(args.o, i)
if args.v:
io.imshow(i)
from skimage import io
p = argparse.ArgumentParser(
description='Extract an image from a 9x9 reCAPTCHA image.')
p.add_argument('-i', help='input image', required=True)
p.add_argument('-y', help='row', required=True, type=int)
p.add_argument('-x', help='column', required=True, type=int)
p.add_argument('-o', help='output image', default=None)
p.add_argument('-v',
help='show output image',
default=False,
action='store_true')
args = p.parse_args()
def read_image(fname, row, col):
i = read_rgb_image(fname)
assert (i.shape[0] == 300)
assert (i.shape[1] == 300)
i = i[row * 100:row * 100 + 100, col * 100:col * 100 + 100]
return i
i = read_image(args.i, args.y, args.x)
if args.o != None:
write_rgb_image(args.o, i)
if args.v:
io.imshow(i)
