if filt == None:
return arr
elif filt == 'raw,sobel':
i = ip.unflatten_rgb_image(arr, d, d)
i = ip.sobel_scipy(i)
i = ip.gray_as_rgb(i)
return ip.flatten_rgb_image(i)
raise Exception('unknown filter')
if args.filter == None:
qi = np.int32(qi)
elif args.filter == 'raw,sobel':
qi = np.int32(do_filter(qi, args.filter))
else:
print >> sys.stderr, "unknown filter"
sys.exit(1)
if args.filterout != None:
ip.write_rgb_image(args.filterout, ip.unflatten_rgb_image(np.uint8(qi), d, d))
# -----------------------------
def compute_distance(datachunks):
return [np.linalg.norm(qi - do_filter(np.fromstring(c, np.uint8), args.filter)) for c in datachunks]
c = 0
for result in process(db.groups(400), compute_distance):
for k in result:
print k, c
c += 1
#!/usr/bin/env python
import argparse
from imageprocessing import read_rgb_image, write_rgb_image, gray_as_rgb, sobel_scipy, rgb_as_gray, sobel
p = argparse.ArgumentParser()
p.add_argument('-i', required = True)
p.add_argument('-o', required = True)
p.add_argument('-f', required = True)
args = p.parse_args()
i = read_rgb_image(args.i)
o = None
if args.f == 'sobel':
o = gray_as_rgb(sobel(i))
elif args.f == 'gray':
o = gray_as_rgb(rgb_as_gray(i))
else:
raise Exception('unknown filter')
write_rgb_image(args.o, o)
i = ip.sobel_scipy(i)
i = ip.gray_as_rgb(i)
return ip.flatten_rgb_image(i)
raise Exception('unknown filter')
if args.filter == None:
qi = np.int32(qi)
elif args.filter == 'raw,sobel':
qi = np.int32(do_filter(qi, args.filter))
else:
print >> sys.stderr, "unknown filter"
sys.exit(1)
if args.filterout != None:
ip.write_rgb_image(args.filterout,
ip.unflatten_rgb_image(np.uint8(qi), d, d))
# -----------------------------
def compute_distance(datachunks):
return [
np.linalg.norm(qi - do_filter(np.fromstring(c, np.uint8), args.filter))
for c in datachunks
]
c = 0
for result in process(db.groups(400), compute_distance):
for k in result:
print k, c
