def compare(comparator_type, sample_url):
from improc.features.comparator import ChiSquaredComparator, \
EuclideanComparator, ManhattanComparator, ChebyshevComparator, \
CosineComparator, HammingComparator
import improc.features.query as feature_query
if comparator_type == "euclidean":
comparator = EuclideanComparator()
elif comparator_type == "manhattan":
comparator = ManhattanComparator()
elif comparator_type == "chisquared":
comparator = ChiSquaredComparator()
elif comparator_type == "hamming":
comparator = HammingComparator()
elif comparator_type == "chebsy":
comparator = ChebyshevComparator()
elif comparator_type == "cosine":
comparator = CosineComparator()
else:
raise Exception(comparator_type)
result_harlick = feature_query.do(sample_harlick, items["harlick"],
comparator)
detail_result(comparator_type, "harlick", sample_url, result_harlick)
result_rgb_histogram = feature_query.do(sample_rgb_histogram,
items["rgb_histogram"], comparator)
detail_result(comparator_type, "rgb_histogram", sample_url,
result_rgb_histogram)
result_zernike = feature_query.do(sample_zernike, items["zernike"],
comparator)
detail_result(comparator_type, "zernite", sample_url, result_zernike)
def compare(comparator_type, sample_url):
from improc.features.comparator import ChiSquaredComparator, \
EuclideanComparator, ManhattanComparator, ChebyshevComparator, \
CosineComparator, HammingComparator
import improc.features.query as feature_query
if comparator_type == "euclidean":
comparator = EuclideanComparator()
elif comparator_type == "manhattan":
comparator = ManhattanComparator()
elif comparator_type == "chisquared":
comparator = ChiSquaredComparator()
elif comparator_type == "hamming":
comparator = HammingComparator()
elif comparator_type == "chebsy":
comparator = ChebyshevComparator()
elif comparator_type == "cosine":
comparator = CosineComparator()
else:
raise Exception(comparator_type)
result_harlick = feature_query.do(
sample_harlick, items["harlick"], comparator)
detail_result(comparator_type, "harlick", sample_url, result_harlick)
result_rgb_histogram = feature_query.do(
sample_rgb_histogram, items["rgb_histogram"], comparator)
detail_result(comparator_type, "rgb_histogram", sample_url,
result_rgb_histogram)
result_zernike = feature_query.do(
sample_zernike, items["zernike"], comparator)
detail_result(comparator_type, "zernite", sample_url, result_zernike)
def test_dataset(key, total):
descriptor_info = load_dataset("datasets/%s" % key, samples_info[0])
for i, x in enumerate(samples_info):
sample_image = cv2.imread(x["sample_name"])
image_key = "%s_%s" % (key, x["sample_name"][:-10])
methodToCall = getattr(descriptor_definitions, key)
descriptor = methodToCall()
plt.subplot(len(dd.items()), len(samples_info), i + total + 1), plt.imshow(descriptor.do_preprocess(sample_image), 'gray')
# plt.title("%s_%s" % (key[0], image_key))
sample_description = descriptor.describe(sample_image)
res = do(sample_description, descriptor_info["data"],
EuclideanComparator())
process_results(res, x, key, descriptor)
return i + total
def test_dataset(key, total):
descriptor_info = load_dataset("datasets/%s" % key, samples_info[0])
for i, x in enumerate(samples_info):
sample_image = cv2.imread(x["sample_name"])
image_key = "%s_%s" % (key, x["sample_name"][:-10])
methodToCall = getattr(descriptor_definitions, key)
descriptor = methodToCall()
plt.subplot(len(dd.items()), len(samples_info),
i + total + 1), plt.imshow(
descriptor.do_preprocess(sample_image), 'gray')
# plt.title("%s_%s" % (key[0], image_key))
sample_description = descriptor.describe(sample_image)
res = do(sample_description, descriptor_info["data"],
EuclideanComparator())
process_results(res, x, key, descriptor)
return i + total
for doc in docs:
for i in doc["detail"]["images"]:
path = "/Users/rdefeo/Development/getter/detail/data/images/%s" % i["path"]
key = "%s_%s" % (str(doc["_id"]["_id"]), str(i["_id"]))
img = mh.imread(path)
# img = cv2.resize(img, size)
items[key] = descriptor.describe(img)
name = "536f5a1ea26d15820c9211cb.jpg"
base_path = "/Users/rdefeo/Development/getter/detail/data/images/%s" % name
print base_path
base = mh.imread(base_path)
# base = cv2.resize(base, size)
sample = descriptor.describe(base)
result = feature_query.do(sample, items, ChiSquaredComparator())
for x in result["results"][:10]:
print x
# f = mh.imread('test_data/1.jpg', as_grey=True)#mh.demos.load('luispedro', as_grey=True)
# img = mahotas.imread('test_data/1.jpg')
# d = mahotas.features.haralick(img).mean(0)
#
#
# # import numpy as np
# # import mahotas
# # import pylab as p
# #
for i in doc["detail"]["images"]:
path = "/Users/rdefeo/Development/getter/detail/data/images/%s" % i[
"path"]
key = "%s_%s" % (str(doc["_id"]["_id"]), str(i["_id"]))
img = mh.imread(path)
# img = cv2.resize(img, size)
items[key] = descriptor.describe(img)
name = "536f5a1ea26d15820c9211cb.jpg"
base_path = "/Users/rdefeo/Development/getter/detail/data/images/%s" % name
print base_path
base = mh.imread(base_path)
# base = cv2.resize(base, size)
sample = descriptor.describe(base)
result = feature_query.do(sample, items, ChiSquaredComparator())
for x in result["results"][:10]:
print x
# f = mh.imread('test_data/1.jpg', as_grey=True)#mh.demos.load('luispedro', as_grey=True)
# img = mahotas.imread('test_data/1.jpg')
# d = mahotas.features.haralick(img).mean(0)
#
#
# # import numpy as np
# # import mahotas
# # import pylab as p
# #