tags = {
'READY': 1,
'DONE': 2,
'EXIT': 3,
'START': 4,
'START_2': 5,
'DONE_2': 6,
'WAIT': 7
}
status = MPI.Status() # get MPI status object
if rank == 0:
timekeeper = TimeKeeper()
timekeeper.time_now('start', True)
session = DatabaseAccessor().session
files = []
tasks = []
# Master process executes code below
# Find files
# For the moment skip any clever filtering of existing descriptor existance
# TODO come back and add this in
if DO_TASKS['find_files']:
if DO_FILTER_DESCRIPTORS:
filtered = DESCRIPTORS
def iterall(files_list, match_thresh=1.5):
timekeeper = TimeKeeper()
timekeeper.time_now('start', True)
detector = cv2.SIFT()
all_descriptors = {}
descriptor_distances = {}
desc_strings = {}
weighted_strings = {}
desc_man = ImageDescriptorManager(match_threshold=match_thresh, detector=detector)
wrote_sift = 0
for img_idx in files_list:
img_path = files_list[img_idx]
# print str(img_idx) + ' : ' + img_path
# if os.path.isfile(img_path + '.sift'):
# print 'Found sift file : ' + img_path + '.sift'
# # print '.',
# # with open(img_path + '.sift', 'rb') as f:
# # keypoints, descriptors = unpickle_keypoints( cPickle.load(f) )
# else:
# print 'Creating sift : ' + img_path + '.sift'
# img = cv2.imread(img_path)
# img_gray = cv2.cvtColor( img, cv2.COLOR_BGR2GRAY )
# keypoints, descriptors = detector.detectAndCompute(img_gray, None)
#
# key_desc_temp = pickle_keypoints(keypoints, descriptors)
#
# with open(img_path + '.sift', 'wb') as f:
# cPickle.dump(key_desc_temp, f, protocol=cPickle.HIGHEST_PROTOCOL)
# del keypoints
# del descriptors
# wrote_sift += 1
already_existed = touch_descriptor(img_path, detector)
if not already_existed:
wrote_sift += 1
# all_descriptors[idx] = descriptors
print 'Had to create ' + str(wrote_sift) + ' .sift files.'
timekeeper.time_now('After check SIFT')
for filea in files_list:
img_desc = ImageDescriptor(filea, files_list[filea])
desc_man.add_descriptor(img_desc)
time_delta = timekeeper.time_now('After create ImageDescriptors', True)
print '\nAvg ' + str(float(time_delta) / len(files_list)) + ' secs per img load'
for man_img_idx in desc_man.image_descriptors:
man_img = desc_man.image_descriptors[man_img_idx]
print '***\n'
print 'img sig : ' + man_img.get_image_signature()
print man_img.get_weighted_signature()
print 'Image ' + man_img.img_path + ' is most similar to ' + files_list[list(man_img.get_sorted_weights())[0]]
for i in files_list:
if files_list[i] == man_img.img_path:
print str(man_img.img_path) + ' : ' + str(i)
for j in files_list:
if files_list[j] == files_list[list(man_img.get_sorted_weights())[0]]:
print 'Similar to image : ' + str(j)
print '\n'
print timekeeper.time_now('After similarities', True)
# for file_a in files_list:
# print '\nComparing file ' + str(file_a) + ' to : '
#
# descriptor_orders = {}
# weighted_orders = {}
# with open(files_list[file_a] + '.sift', 'rb') as fa:
# keypoints_a, descriptors_a = unpickle_keypoints( cPickle.load(fa) )
#
# for file_b in files_list:
# print file_b,
# if file_a != file_b:
# with open(files_list[file_b] + '.sift', 'rb') as fb:
# keypoints_b, descriptors_b = unpickle_keypoints( cPickle.load(fb) )
#
# matches_size = find_matches(str(file_a) + ',' + str(file_b),
# descriptors_a,
# descriptors_b,
# match_thresh)
# descriptor_orders[str(file_b)] = matches_size
#
# weighted_orders[str(file_b)] = (str(file_b) + '=' + str(matches_size)) + ':'
# # weighted_orders[str(file_b)] = (str(file_b) * matches_size)
#
# descriptor_distances[str(file_a) + ':' + str(file_b)] = matches_size
#
# print '\nImage ' + files_list[file_a] + ' is most similar to ' \
# + files_list[int(list(sorted(descriptor_orders, key=descriptor_orders.get, reverse=True))[0])] + '\n'
#
# desc_arr = list(sorted(descriptor_orders, key=descriptor_orders.get, reverse=True))
#
# item_string = ''
# for item in desc_arr:
# item_string += str(weighted_orders[item])
# weighted_strings[file_a] = item_string
# desc_strings[file_a] = ' '.join(desc_arr)
# print 'Array of img_idx for file ' + str(file_a) + ', closest match first : \n' + desc_strings[file_a]
# print '\n****\nArray of distances between images : \n' + str(descriptor_distances)
# for ws in weighted_strings:
# print str(ws) + ' : ' + weighted_strings[ws]
# print '\n****\nAll images description strings : \n' + str(desc_strings)
# for s in desc_strings:
# for st in desc_strings:
# dist = nltk.metrics.edit_distance(
# desc_strings[s],
# desc_strings[st])
# if (len(files_list) * 0.3) < dist < (len(files_list) * 0.8):
# print '\n****\nClosest image pair signatures within 0.8 of each other:'
# print str(s) + ' : ' + desc_strings[s]
# print str(st) + ' : ' + desc_strings[st]
# print 'Deviation of sigs (lower is better) : ' + str(dist)
# print files_list[s]
# print files_list[st]
# print '\n'
print '\n****\nAll images description strings : \n' + str(desc_strings)
sigs = desc_man.get_all_image_signatures()
for s in sigs:
for st in sigs:
dist = nltk.metrics.edit_distance(
sigs[s],
sigs[st])
print sigs[s] + '\n' + sigs[st] + '\n' + str(dist)
if (len(files_list) * 0.3) < dist < (len(files_list) * 0.8):
print '\n****\nClosest image pair signatures within 0.8 of each other:'
print str(s) + ' : ' + sigs[s]
print str(st) + ' : ' + sigs[st]
print 'Deviation of sigs (lower is better) : ' + str(dist)
print files_list[s]
print files_list[st]
print '\n'
print timekeeper.time_now('After description strings', True)
# new_desc = ImageDescriptor(len(files_list), './portrait/11040852736_83cc5c2155_z.jpg')
# desc_man.quick_init(new_desc)
# print '\n' + new_desc.get_image_signature()
# print new_desc.get_weighted_signature()
timekeeper.time_now('Finish', True)
res = 0
for a in range(0, len(values)):
res += weights[a] * values[a]
transformed_img.itemset((x,y), res)
return transformed_img
# print x
def np_hist_to_cv(np_histogram_output):
counts, bin_edges = np_histogram_output
return counts.ravel().astype('float32')
tk = TimeKeeper()
tk.time_now('start')
filename = 'animals/11175682543_4db70a7f6f_z.jpg'
img = cv2.imread(filename, 0)
transformed_img = cv2.imread(filename, 0)
tk.time_now('read img')
transformed_img = calculate_lbp(img, transformed_img)
tk.time_now('lpb')
# cv2.imshow('image', img)
# cv2.imshow('thresholded image', transformed_img)
