def getNeuralCode(directory, layer="conv5_1", gpu=-1):
model = "illust2vec_ver200.caffemodel"
#use illust2vec_ver200
print('illust2vec_ver200 is being loaded!')
#calculate load time
timeMemory = time()
func = caffe.CaffeFunction(model)
print('illust2vec_ver200 was loaded!')
print('It took ' + str(int(time() - timeMemory)) + " secondes")
#gpu mode
if gpu >= 0:
cuda.init(gpu)
func.to_gpu()
in_size = 224
# Constant mean over spatial pixels
mean_image = np.load("illust2vec_image_mean.npy")
print("neural code is extraced from layer " + layer)
def neuralCode(x): #推測関数
y, = func(inputs={'data': x}, outputs=[layer],
train=False)
return y.data[0]
cropwidth = 256 - in_size
start = cropwidth // 2
stop = start + in_size
mean_image = mean_image[:, start:stop, start:stop].copy()
target_shape = (256, 256)
output_side_length=256
numPic = 0
#count pictures
for folderPath in directory:
#search pictures
picturePath = [picture for picture in os.listdir(folderPath)
if re.findall(r"\.png$|\.jpg$|\.JPG$|\.PNG$|\.JPEG$",picture)]
print("you have " + str(len(picturePath)) + " pictures in " + folderPath)
numPic = numPic + len(picturePath)
print("you have totally " + str(numPic) + " pictures")
count = 0
answer = {}
for folderPath in directory:
#search pictures
picturePath = [picture for picture in os.listdir(folderPath)
if re.findall(r"\.png$|\.jpg$|\.JPG$|\.PNG$|\.JPEG$",picture)]
for picture in picturePath:
timeMemory = time()
count = count + 1
#load image file
image = cv2.imread(folderPath + "/" + picture)
#resize and crop
height, width, depth = image.shape
new_height = output_side_length
new_width = output_side_length
if height > width:
new_height = output_side_length * height / width
else:
new_width = output_side_length * width / height
resized_img = cv2.resize(image, (new_width, new_height))
height_offset = (new_height - output_side_length) / 2
width_offset = (new_width - output_side_length) / 2
image= resized_img[height_offset:height_offset + output_side_length,
width_offset:width_offset + output_side_length]
#subtract mean image
image = image.transpose(2, 0, 1)
image = image[:, start:stop, start:stop].astype(np.float32)
image -= mean_image
x_batch = np.ndarray(
(1, 3, in_size,in_size), dtype=np.float32)
x_batch[0]=image
if gpu >= 0:
x_batch=cuda.to_gpu(x_batch)
#get neural code
x = chainer.Variable(x_batch, volatile=True)
answer[folderPath + "/" + picture] = neuralCode(x)
sen = overwrite.bar(count,numPic)
overwrite.overwrite(sen)
return answer
#obtain url
#put image id at the end
apiUrl = "http://www.image-net.org/api/text/imagenet.synset.geturls?wnid="
files = [re.sub(r"\.txt$", "", name) for name in os.listdir(baseUrl) if re.findall(r"\.txt$",name)]
setSynset = set(synset)
fileUrl = list(setSynset.difference(set(files)))
#get all urls
numId = len(synset)
count = numId - len(fileUrl)
print("getting all urls....")
for picId in fileUrl:
count = count + 1
sen = overwrite.bar(count, numId)
sen = sen + " " + str(picId)
overwrite.overwrite(sen)
cmd = "wget " + apiUrl + picId + " -O tmp -q"
subprocess.call(cmd, shell=True)
cmd = "mv tmp " + baseUrl + picId + ".txt"
subprocess.call(cmd, shell=True)
print("urls are all prepared!")
def checkExistance(path):
if os.path.exists(path):
return True
else:
return False
def makeDirectory(path):