def test_good_image_features_rgb(self):
test_image = np.random.rand(64, 64, 3).tolist()
response = image_features(test_image)
self.assertTrue(isinstance(response, list))
self.assertEqual(len(response), 2048)
self.check_range(response)
def get_indicoio_features(data_dir, local_path):
if (local_path == 'slides'):
pathin = data_dir + 'new' + local_path + '/'
else:
pathin = data_dir + local_path + '/'
names = os.listdir(pathin)
l = len(names)
new_names = []
for i in range(l):
new_names += [local_path[:-1] + str(i) + '.jpg']
names = new_names
paths = []
i = 0
for f in names:
i = i + 1
paths += [pathin + f]
if (i % 100) == 0:
print(i)
features = []
for i in range(0, len(paths), 20):
features += indicoio.image_features(paths[i:(i + 20)])
print((i + 20), " done")
print("reading " + str(local_path) + " done")
#write to files
pathout = data_dir + local_path + '_features.npy'
np.save(pathout, np.asarray(features))
def test_good_image_features_rgb(self):
test_image = np.random.rand(64, 64, 3).tolist()
response = image_features(test_image)
self.assertTrue(isinstance(response, list))
self.assertEqual(len(response), 2048)
self.check_range(response)
def test_good_image_features_rgb(self):
test_image = os.path.normpath(os.path.join(DIR, "data/48by48rgb.png"))
response = image_features(test_image)
self.assertTrue(isinstance(response, list))
self.assertEqual(len(response), 2048)
self.check_range(response)
def test_int_RGBA_numpy_arrays(self):
test_image = np.random.randint(0, 255, size=(48,48, 3))
response = image_features(test_image)
self.assertTrue(isinstance(response, list))
self.assertEqual(len(response), 2048)
self.check_range(response)
def test_float_RGBA_numpy_arrays(self):
test_image = np.random.random(size=(48,48,4))
response = image_features(test_image)
self.assertTrue(isinstance(response, list))
self.assertEqual(len(response), 4096)
self.check_range(response)
def test_float_RGBA_numpy_arrays(self):
test_image = np.random.random(size=(48, 48, 4))
response = image_features(test_image)
self.assertTrue(isinstance(response, list))
self.assertEqual(len(response), 4096)
self.check_range(response)
def test_image_features_greyscale(self):
np = self._require_numpy()
test_image = os.path.normpath(os.path.join(DIR, "data/48by48.png"))
response = image_features(test_image)
self.assertTrue(isinstance(response, list))
self.assertEqual(len(response), 4096)
self.check_range(response)
def test_image_features_greyscale(self):
np = self._require_numpy()
test_image = os.path.normpath(os.path.join(DIR, "data/48by48.png"))
response = image_features(test_image)
self.assertTrue(isinstance(response, list))
self.assertEqual(len(response), 4096)
self.check_range(response)
def test_int_numpy_arrays(self):
np = self._require_numpy()
test_image = np.random.randint(0, 255, size=(48, 48))
response = image_features(test_image)
self.assertTrue(isinstance(response, list))
self.assertEqual(len(response), 4096)
self.check_range(response)
def test_int_numpy_arrays(self):
np = self._require_numpy()
test_image = np.random.randint(0, 255, size=(48,48))
response = image_features(test_image)
self.assertTrue(isinstance(response, list))
self.assertEqual(len(response), 4096)
self.check_range(response)
def _send(self):
"""
Does a get on the computed link
Will set storage fields to returned values
"""
assert self.request and isinstance(self.request, dict), \
"Trying to make request before having a valid request dictionary."
response = None
indicoio.config.api_key = self.config.api_key
try:
response = indicoio.image_features(self.request["url"])
except Exception as exc:
log.exception(exc)
self.set_error(-1, connection_error=True)
self._update_from_response(response)
def _send(self):
"""
Does a get on the computed link
Will set storage fields to returned values
"""
assert self.request and isinstance(self.request, dict), \
"Trying to make request before having a valid request dictionary."
response = None
indicoio.config.api_key = self.config.api_key
try:
response = indicoio.image_features(self.request["url"])
except Exception as exc:
log.exception(exc)
self.set_error(-1, connection_error=True)
self._update_from_response(response)
def test_batch_image_features_rgb(self):
test_data = [os.path.normpath(os.path.join(DIR, "data/48by48rgb.png"))]
response = image_features(test_data)
self.assertTrue(isinstance(response, list))
self.assertTrue(isinstance(response[0], list))
self.assertEqual(len(response[0]), 4096)
def map_frame_to_slide(data_dir):
slidespath = data_dir + 'slides/'
framespath = data_dir + 'frames/'
slidenames = os.listdir(slidespath)
framenames = os.listdir(framespath)
l = len(framenames)
new_framenames = []
for i in range(l):
new_framenames += ['frame' + str(i) + '.jpg']
framenames = new_framenames
l = len(slidenames)
new_slidenames = []
for i in range(l):
new_slidenames += ['slide' + str(i) + '.jpg']
slidenames = new_slidenames
print(slidenames)
#print (srt_parse)
slides = []
for s in slidenames:
slides += [Image.open(slidespath + s)]
slides = indicoio.image_features(slides)
slides = zip(slidenames, slides)
print("reading slides done")
frames = []
i = 0
for f in framenames:
i = i + 1
frames += [(framespath + f)]
if (i % 100) == 0:
print(i)
frame_features = []
for i in range(0, len(frames), 20):
frame_features += indicoio.image_features(frames[i:(i + 20)])
print((i + 20), " done")
frames = zip(framenames, frame_features)
print("reading frames done")
prev_mapping = 'slide0.jpg'
prev_max = 0
max_index = 0
#map each frame to a slide:
output = []
for f in frames:
max_sim = 0
mapping = 'slide0.jpg'
for s in slides[max((max_index - 2), 0):]:
s1 = 1 - spatial.distance.cosine(f[1], s[1])
print(s[0], s1)
if (s1 > max_sim):
max_sim = s1
mapping = s[0]
if (max_sim < 0.50):
mapping = prev_mapping
max_sim = prev_max
print(f[0], mapping, max_sim)
output += [(f[0], mapping, max_sim)]
max_index = int(mapping.split('slide')[1].split('.jpg')[0])
prev_mapping = mapping
prev_max = max_sim
#write output
target = open(data_dir + 'frame_mappings.txt', 'w')
for o in output:
target.write(str(o) + '\n')
target.flush()
target.close()
def make_feats(paths):
chunks = [paths[x:x + 100] for x in xrange(0, len(paths), 100)]
feats = []
for chunk in chunks:
feats.extend(indicoio.image_features(chunk, batch=True, version=3))
return feats
def test_batch_image_features_rgb(self):
test_data = [os.path.normpath(os.path.join(DIR, "data/48by48rgb.png"))]
response = image_features(test_data, api_key=self.api_key)
self.assertTrue(isinstance(response, list))
self.assertTrue(isinstance(response[0], list))
self.assertEqual(len(response[0]), 2048)
def test_batch_image_features_greyscale(self):
test_data = [os.path.normpath(os.path.join(DIR, "data/48by48.png"))]
response = image_features(test_data, api_key=self.api_key)
self.assertTrue(isinstance(response, list))
self.assertTrue(isinstance(response[0], list))
self.assertEqual(len(response[0]), 2048)
def make_feats(paths):
chunks=[paths[x:x+100] for x in xrange(0, len(paths), 100)]
feats = []
for chunk in chunks:
feats.extend(indicoio.image_features(chunk, batch=True, version=3))
return feats
def test_batch_image_features_greyscale(self):
test_data = [os.path.normpath(os.path.join(DIR, "data/48by48.png"))]
response = image_features(test_data)
self.assertTrue(isinstance(response, list))
self.assertTrue(isinstance(response[0], list))
self.assertEqual(len(response[0]), 4096)
def test_image_features_v2(self):
test_data = os.path.normpath(os.path.join(DIR, "data", "fear.png"))
response = image_features(test_data, api_key = self.api_key, version="2")
self.assertIsInstance(response, list)
self.assertEqual(len(response), 4096)
import indicoio
indicoio.config.api_key = 'b8cfad3e06d63c45f6d45f63f4bb9981'
from scipy.misc import imsave, imread
from indicoio import image_features
import json
import os
# Compute image features for all images and save to json.
features = []
i = 0
path = 'image/'
for home, dirs, files in os.walk(path):
for f in files:
print i
if(i>0):
s = 'image/%s' %f
filename = '%s' %f
print(s)
print(filename)
img = imread(s)
new = [filename,image_features(img)]
features.append(new)
i = i + 1
json.dump(features, open('features.json', 'wb'))
def test_image_features_v2(self):
test_data = os.path.normpath(os.path.join(DIR, "data", "fear.png"))
response = image_features(test_data, version="2")
self.assertIsInstance(response, list)
self.assertEqual(len(response), 4096)
# -*- coding: utf-8 -*-
from scipy.misc import imsave, imread
from indicoio import image_features
import numpy, os, json
root = 'bcn'
data = []
for f in os.listdir('bcn'):
import time
start = time.time()
img = imread(root + '/' + f)
features = image_features(img)
data.append({'file':f,'features':features})
end = time.time()
print end - start
json.dump(data, open('features.json', 'wb'))
from scipy.misc import imsave, imread
from indicoio import image_features
import json
# Compute image features for all images and save to json.
features = []
for i in range(250):
img = imread('imgs/%s.png'%i)
features.append(image_features(img))
json.dump(features, open('features.json', 'wb'))
import indicoio
indicoio.config.api_key = 'b8cfad3e06d63c45f6d45f63f4bb9981'
from scipy.misc import imsave, imread
from indicoio import image_features
import json
import os
#read image
imagein = imread('input.jpg')
feature = image_features(imagein)
#initial
text = open('features.json').read()
dict = json.loads(text)
t = 0
maxnum = 5
maxnumdot = maxnum - 1
imagenumber = 10
max = []
minname = []
mindistance = []
#compare to find the similarity
for i in range(0, maxnumdot + 1):
max.append(i)
minname.append(dict[i][0])
mindistance.append(0)
for j in range(0, 2047 + 1):
mindistance[i] = mindistance[i] + (dict[i][1][j] - feature[j]) * (dict[i][1][j] - feature[j])
from scipy.misc import imsave, imread
from indicoio import image_features
import json
# Compute image features for all images and save to json.
features = []
for i in range(250):
img = imread('imgs/%s.png' % i)
features.append(image_features(img))
json.dump(features, open('features.json', 'wb'))