def process_individual_frames(self, PATH_TO_DATA, annotations, list_of_layers, sampling_rate, LCD):
i = 0
label_map = {}
frm_map = {}
X = {}
map_index_data = pickle.load(open(annotations, "rb"))
for index in map_index_data:
segments = map_index_data[index]
print "Processing images for label " + str(index)
for seg in segments:
print str(seg)
frm_num = seg[0]
while frm_num <= seg[1]:
print frm_num
frm_map[i] = frm_num
label_map[i] = index
im = caffe.io.load_image(utils.get_full_image_path(PATH_TO_DATA, frm_num))
self.net.blobs['data'].data[...] = self.transformer.preprocess('data', im)
out = self.net.forward()
for layer in list_of_layers:
if layer == 'input':
data = cv2.imread(full_image_path)
else:
data = self.net.blobs[layer].data[0]
data = utils.flatten(data)
utils.dict_insert(layer, data, X)
frm_num += sampling_rate
i += 1
return X, label_map, frm_map
def process_individual_frames(self, PATH_TO_DATA, annotations,
list_of_layers, sampling_rate):
X = {}
map_index_data = pickle.load(open(annotations, "rb"))
segments = utils.get_chronological_sequences(map_index_data)
for seg in segments:
print str(seg)
frm_num = seg[0]
while frm_num <= seg[1]:
print frm_num, annotations
im = caffe.io.load_image(
utils.get_full_image_path(PATH_TO_DATA, frm_num))
self.net.blobs['data'].data[...] = self.transformer.preprocess(
'data', im)
out = self.net.forward()
for layer in list_of_layers:
if layer == 'input':
data = cv2.imread(full_image_path)
else:
data = self.net.blobs[layer].data[0]
data = utils.flatten(data)
utils.dict_insert(layer, data, X)
frm_num += sampling_rate
return X
def process_individual_frames_2(self, PATH_TO_DATA, annotations, list_of_layers, sampling_rate):
X = {}
map_index_data = pickle.load(open(annotations, "rb"))
segments = utils.get_chronological_sequences(map_index_data)
for seg in segments:
print str(seg)
frm_num = seg[0]
while frm_num <= seg[1]:
print frm_num, annotations
im = caffe.io.load_image(utils.get_full_image_path(PATH_TO_DATA, frm_num))
self.net.blobs['data'].data[...] = self.transformer.preprocess('data', im)
out = self.net.forward()
for layer in list_of_layers:
if layer == 'input':
data = cv2.imread(full_image_path)
else:
data = self.net.blobs[layer].data[0]
data = utils.flatten(data)
utils.dict_insert(layer, data, X)
frm_num += sampling_rate
return X
def process_batch(self, PATH_TO_DATA, annotations, list_of_layers, sampling_rate, batch_size, LCD):
i = 0
label_map = {}
frm_map = {}
X = {}
map_index_data = pickle.load(open(annotations, "rb"))
for index in map_index_data:
segments = map_index_data[index]
# print "Processing images for label " + str(index)
for seg in segments:
# print str(seg)
frm_num = seg[0]
b = 1 #Running count of num frames in batch
batch_data = {}
while frm_num <= (seg[1] + batch_size):
# Initialize Batch
if b == 1:
label_map[i] = index
frm_map[i] = frm_num
# Process frames and build up features in batches
im = caffe.io.load_image(utils.get_full_image_path(PATH_TO_DATA, frm_num))
self.net.blobs['data'].data[...] = self.transformer.preprocess('data', im)
out = self.net.forward()
for layer in list_of_layers:
if LCD:
if layer == 'input':
print "ERROR: Cannot do LCD on input layer"
sys.exit()
data = self.net.blobs[layer].data[0]
data = lcd.LCD(data)
utils.dict_insert(layer, data, batch_data, axis = 0)
else:
if layer == 'input':
data = cv2.imread(full_image_path)
else:
data = self.net.blobs[layer].data[0]
data = utils.flatten(data)
utils.dict_insert(layer, data, batch_data, axis = 1)
if b == batch_size:
print("Batch %3d" % i)
b = 0
i += 1
# Concatenate with main data dictionary
for layer in list_of_layers:
data = encoding.encode_VLAD(batch_data[layer] , 5)
utils.dict_insert(layer, data, X)
batch_data = {}
b += 1
frm_num += sampling_rate
return X, label_map, frm_map