def load_image_array(image_file, image_size,
image_id, data_dir='Data/datasets/mscoco/train2014',
mode='train'):
img = None
if os.path.exists(image_file):
#print('found' + image_file)
img = skimage.io.imread(image_file)
else:
print('notfound' + image_file)
img = skimage.io.imread('http://mscoco.org/images/%d' % (image_id))
img_path = os.path.join(data_dir, 'COCO_%s2014_%.12d.jpg' % ( mode,
image_id))
skimage.io.imsave(img_path, img)
# GRAYSCALE
if len(img.shape) == 2:
img_new = np.ndarray( (img.shape[0], img.shape[1], 3), dtype = 'uint8')
img_new[:,:,0] = img
img_new[:,:,1] = img
img_new[:,:,2] = img
img = img_new
img_resized = skimage.transform.resize(img, (image_size, image_size))
# FLIP HORIZONTAL WIRH A PROBABILITY 0.5
if random.random() > 0.5:
img_resized = np.fliplr(img_resized)
return img_resized.astype('float32')
python类imsave()的实例源码
def save_concat_images(imgs, img_path):
concated = np.concatenate(imgs, axis=1)
misc.imsave(img_path, concated)
def swap_attribute(src_img, att_img, model_dir, model, gpu):
'''
Input
src_img: the source image that you want to change its attribute
att_img: the attribute image that has certain attribute
model_dir: the directory that contains the checkpoint, ckpt.* files
model: the GeneGAN network that defined in train.py
gpu: for example, '0,1'. Use '' for cpu mode
Output
out1: src_img with attributes
out2: att_img without attributes
'''
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(model_dir)
# print(ckpt)
# print(ckpt.model_checkpoint_path)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
out2, out1 = sess.run([model.Ae, model.Bx], feed_dict={model.Ax: att_img, model.Be: src_img})
misc.imsave('out1.jpg', out1[0])
misc.imsave('out2.jpg', out2[0])
def interpolation(src_img, att_img, inter_num, model_dir, model, gpu):
'''
Input
src_img: the source image that you want to change its attribute
att_img: the attribute image that has certain attribute
inter_num: number of interpolation points
model_dir: the directory that contains the checkpoint, ckpt.* files
model: the GeneGAN network that defined in train.py
gpu: for example, '0,1'. Use '' for cpu mode
Output
out: [src_img, inter1, inter2, ..., inter_{inter_num}]
'''
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(model_dir)
# print(ckpt)
# print(ckpt.model_checkpoint_path)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
out = src_img[0]
for i in range(1, inter_num + 1):
lambda_i = i / float(inter_num)
model.out_i = model.joiner('G_joiner', model.B, model.x * lambda_i)
out_i = sess.run(model.out_i, feed_dict={model.Ax: att_img, model.Be: src_img})
out = np.concatenate((out, out_i[0]), axis=1)
# print(out.shape)
misc.imsave('interpolation.jpg', out)
def interpolation2(src_img, att_img, inter_num, model_dir, model, gpu):
'''
Input
src_img: the source image that you want to change its attribute
att_img: the attribute image that has certain attribute
inter_num: number of interpolation points
model_dir: the directory that contains the checkpoint, ckpt.* files
model: the GeneGAN network that defined in train.py
gpu: for example, '0,1'. Use '' for cpu mode
Output
out: [src_img, inter1, inter2, ..., inter_{inter_num}]
'''
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(model_dir)
# print(ckpt)
# print(ckpt.model_checkpoint_path)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
B, src_feat = sess.run([model.B, model.e], feed_dict={model.Be: src_img})
att_feat = sess.run(model.x, feed_dict={model.Ax: att_img})
out = src_img[0]
for i in range(1, inter_num + 1):
lambda_i = i / float(inter_num)
out_i = sess.run(model.joiner('G_joiner', B, src_feat + (att_feat - src_feat) * lambda_i) )
out = np.concatenate((out, out_i[0]), axis=1)
# print(out.shape)
misc.imsave('interpolation2.jpg', out)
def AddNoize(i):
R = random.randint(0, 1)
if (R==1):
i=np.fliplr(i)#random mirroring
R = random.randint(0, 1)
if (R==1):
R = random.randint(-10, 10)
i= ndimage.interpolation.rotate(i,R)#random rotation
R = random.randint(0, 1)
if (R==1):
crop_left=random.randint(0,15)
crop_right = random.randint(1, 15)
crop_top = random.randint(0, 15)
crop_bot = random.randint(1, 15)
i=i[crop_left:-crop_right,crop_top:-crop_bot,:] #Randomcrop
#Next code is VERY SLOW becoase it use Python to change brightness
#need to optimase it, but i have no time yet:)
R = random.randint(0, 2)
if (R==2): #Random brightness in R channel
d = random.random()+1
i[:, :, 0] = adjust_gamma(i[:,:,0],d)
R = random.randint(0, 2)
if (R==2): #Random brightness in G channel
d = random.random()+1
i[:, :, 1] = adjust_gamma(i[:, :, 1], d)
R = random.randint(0, 2)
if (R==2): #Random brightness in B channel
d = random.random()+1
i[:, :, 2] = adjust_gamma(i[:, :, 2], d)
#misc.imsave("test.jpg",i)
return i
#Prepare data for learning
def save_image(image, image_size, save_dir, name=""):
"""
Save image by unprocessing assuming mean 127.5
:param image:
:param save_dir:
:param name:
:return:
"""
image += 1
image *= 127.5
image = np.clip(image, 0, 255).astype(np.uint8)
image = np.reshape(image, (image_size, image_size, -1))
misc.imsave(os.path.join(save_dir, name + "pred_image.png"), image)
def resize_raw_images(prms):
rawNames,_,_ = get_imnames(prms, isRaw=True)
tgNames,_,_ = get_imnames(prms)
for rn,tn in zip(rawNames, tgNames):
im = scm.imread(rn)
im = scm.imresize(im, [320, 480])
dName = os.path.dirname(tn)
if not os.path.exists(dName):
os.makedirs(dName)
scm.imsave(tn, im)
##
# Write pairs file
def vis_pairs(prms, isSave=False, svIdx=None, svPath=None):
imName1, imName2, euls, trans = read_pairs(prms)
N = len(imName1)
seed = 3
randState = np.random.RandomState(seed)
perm = randState.permutation(N)
fig = plt.figure()
plt.ion()
imName1 = [imName1[i] for i in perm]
imName2 = [imName2[i] for i in perm]
euls = [euls[i] for i in perm]
trans = [trans[i] for i in perm]
titleStr = 'Trans: ' + '%.3f ' * 3 + 'Rot: ' + '%.3f ' * 3
count = 0
numSave = 0
for (im1,im2,eu,tr) in zip(imName1, imName2, euls, trans):
titleName = titleStr % (tuple(tr) + eu)
im1 = scm.imread(im1)
im2 = scm.imread(im2)
print count
if isSave:
if count in svIdx:
imN1 = svPath % (count,1)
imN2 = svPath % (count,2)
scm.imsave(imN1,im1)
scm.imsave(imN2,im2)
numSave += 1
if numSave==len(svIdx):
return
else:
vu.plot_pairs(im1, im2, fig, titleStr=titleName)
cmd = raw_input()
if cmd == 'exit':
return
count += 1
##
def reshape_images(cls, source_folder, target_folder, height=128, width=128,
extensions=('.jpg', '.jpeg', '.png')):
""" copy images and reshape them"""
# check source_folder and target_folder:
cls.check_folder_existance(source_folder, throw_error_if_no_folder=True)
cls.check_folder_existance(target_folder, display_msg=False)
if source_folder[-1] == "/":
source_folder = source_folder[:-1]
if target_folder[-1] == "/":
target_folder = target_folder[:-1]
# read images and reshape:
print("Resizing '", source_folder, "' images...")
for filename in os.listdir(source_folder):
if os.path.isdir(source_folder + '/' + filename):
cls.reshape_images(source_folder + '/' + filename,
target_folder + '/' + filename,
height, width, extensions=extensions)
else:
if extensions == '' and os.path.splitext(filename)[1] == '':
copy2(source_folder + "/" + filename,
target_folder + "/" + filename)
image = ndimage.imread(target_folder + "/" + filename, mode="RGB")
image_resized = misc.imresize(image, (height, width))
misc.imsave(target_folder + "/" + filename, image_resized)
else:
for extension in extensions:
if filename.endswith(extension):
copy2(source_folder + "/" + filename,
target_folder + "/" + filename)
image = ndimage.imread(target_folder + "/" + filename, mode="RGB")
image_resized = misc.imresize(image, (height, width))
misc.imsave(target_folder + "/" + filename, image_resized)
def crop_images(cls, source_folder, target_folder, height=128, width=128,
extensions=('.jpg', '.jpeg', '.png')):
""" copy images and center crop them"""
# check source_folder and target_folder:
cls.check_folder_existance(source_folder, throw_error_if_no_folder=True)
cls.check_folder_existance(target_folder, display_msg=False)
if source_folder[-1] == "/":
source_folder = source_folder[:-1]
if target_folder[-1] == "/":
target_folder = target_folder[:-1]
# read images and crop:
print("Cropping '", source_folder, "' images...")
for filename in os.listdir(source_folder):
if os.path.isdir(source_folder + '/' + filename):
cls.crop_images(source_folder + '/' + filename,
target_folder + '/' + filename,
height, width, extensions=extensions)
else:
if extensions == '' and os.path.splitext(filename)[1] == '':
copy2(source_folder + "/" + filename,
target_folder + "/" + filename)
image = ndimage.imread(target_folder + "/" + filename, mode="RGB")
[width_original, height_original, _] = image.shape
offset_w = (width_original - width) / 2
offset_h = (width_original - width) / 2
image_cropped = image[offset_w : width + offset_w, offset_h : height + offset_h, :]
misc.imsave(target_folder + "/" + filename, image_cropped)
else:
for extension in extensions:
if filename.endswith(extension):
copy2(source_folder + "/" + filename,
target_folder + "/" + filename)
image = ndimage.imread(target_folder + "/" + filename, mode="RGB")
[width_original, height_original, _] = image.shape
offset_w = (width_original - width) / 2
offset_h = (width_original - width) / 2
image_cropped = image[offset_w : width + offset_w, offset_h : height + offset_h, :]
misc.imsave(target_folder + "/" + filename, image_cropped)
def convert_to_grayscale(cls, source_folder, target_folder,
extensions=('.jpg', '.jpeg', '.png')):
""" convert images from RGB to Grayscale"""
# check source_folder and target_folder:
cls.check_folder_existance(source_folder, throw_error_if_no_folder=True)
cls.check_folder_existance(target_folder, display_msg=False)
if source_folder[-1] == "/":
source_folder = source_folder[:-1]
if target_folder[-1] == "/":
target_folder = target_folder[:-1]
# read images and reshape:
print("Convert '", source_folder, "' images to grayscale...")
for filename in os.listdir(source_folder):
if os.path.isdir(source_folder + '/' + filename):
cls.convert_to_grayscale(source_folder + '/' + filename,
target_folder + '/' + filename,
extensions=extensions)
else:
if extensions == '' and os.path.splitext(filename)[1] == '':
copy2(source_folder + "/" + filename,
target_folder + "/" + filename)
image = ndimage.imread(target_folder + "/" + filename, flatten=True)
misc.imsave(target_folder + "/" + filename, image)
else:
for extension in extensions:
if filename.endswith(extension):
copy2(source_folder + "/" + filename,
target_folder + "/" + filename)
image = ndimage.imread(target_folder + "/" + filename, flatten=True)
misc.imsave(target_folder + "/" + filename, image)
def convert_format(cls, source_folder, target_folder,
extensions=('.jpg', '.jpeg', '.png'), new_extension='.jpg'):
""" change images from one format to another (eg. change png files to jpeg) """
# check source_folder and target_folder:
cls.check_folder_existance(source_folder, throw_error_if_no_folder=True)
cls.check_folder_existance(target_folder, display_msg=False)
if source_folder[-1] == "/":
source_folder = source_folder[:-1]
if target_folder[-1] == "/":
target_folder = target_folder[:-1]
# read images and reshape:
print("Change format of '", source_folder, "' files...")
for filename in os.listdir(source_folder):
if os.path.isdir(source_folder + '/' + filename):
cls.convert_format(source_folder + '/' + filename,
target_folder + '/' + filename,
extensions=extensions, new_extension=new_extension)
else:
if extensions == '' and os.path.splitext(filename)[1] == '':
copy2(source_folder + "/" + filename,
target_folder + "/" + filename + new_extension)
image = ndimage.imread(target_folder + "/" + filename + new_extension)
misc.imsave(target_folder + "/" + filename + new_extension, image)
else:
for extension in extensions:
if filename.endswith(extension):
new_filename = os.path.splitext(filename)[0] + new_extension
copy2(source_folder + "/" + filename,
target_folder + "/" + new_filename)
image = ndimage.imread(target_folder + "/" + new_filename)
misc.imsave(target_folder + "/" + new_filename, image)
def save_image(image, save_dir, name, mean=None):
"""
Save image by unprocessing if mean given else just save
:param mean:
:param image:
:param save_dir:
:param name:
:return:
"""
if mean:
image = unprocess_image(image, mean)
misc.imsave(os.path.join(save_dir, name + ".png"), image)
def save_alpha_img(org, mat, name):
w, h, _ = mat.shape
#print(mat[200:210, 200:210])
rmat = np.reshape(mat, (w, h))
amat = np.zeros((w, h, 4), dtype=np.int)
amat[:, :, 3] = rmat * 1000
amat[:, :, 0:3] = org
print(amat[200:205, 200:205])
#im = Image.fromarray(np.uint8(amat))
#im.save(name + '.png')
misc.imsave(name + '.png', amat)
def save_image(image, save_dir, name, mean=None):
"""
Save image by unprocessing if mean given else just save
:param mean:
:param image:
:param save_dir:
:param name:
:return:
"""
if mean:
image = unprocess_image(image, mean)
misc.imsave(os.path.join(save_dir, name + ".png"), image)
# as describe at Sec.4.2
def save_alpha_img(org, mat, name):
w, h = mat.shape[0], mat.shape[1]
#print(mat[200:210, 200:210])
rmat = np.reshape(mat, (w, h))
amat = np.zeros((w, h, 4), dtype=np.int)
amat[:, :, 3] = np.round(rmat * 1000)
amat[:, :, 0:3] = org
#print(amat[200:205, 200:205])
#im = Image.fromarray(np.uint8(amat))
#im.save(name + '.png')
misc.imsave(name + '.png', amat)
def _write_deepdream(images, layer, path_outdir, path_logdir):
is_success = True
images = _im_normlize([images])
layer, units, k = layer
# write into disk
path_out = os.path.join(path_outdir, "deepdream_" + layer.lower().replace("/", "_"))
is_success = make_dir(path_out)
for i in range(len(images)):
for j in range(images[i].shape[0]):
img_save = images[i][j]
if img_save.shape[2] == 1:
img_save = np.squeeze(img_save, axis=2)
imsave(os.path.join(path_out, "image_%d.png" % (units[(i * images[i].shape[0]) + j + k])), img_save, format = "png")
# write into logfile
path_log = os.path.join(path_logdir, layer.lower().replace("/", "_"))
is_success = make_dir(path_log)
with tf.Graph().as_default() as g:
image = tf.placeholder(tf.float32, shape = [None, None, None, None])
image_summary_t = tf.summary.image(name = "One_By_One_DeepDream", tensor = image, max_outputs = config["MAX_FEATUREMAP"])
with tf.Session() as sess:
summary = sess.run(image_summary_t, feed_dict = {image : np.concatenate(images, axis = 0)})
try:
file_writer = tf.summary.FileWriter(path_log, g) # create file writer
# compute and write the summary
file_writer.add_summary(summary)
except:
is_success = False
print("Error occured in writting results into log file.")
finally:
file_writer.close() # close file writer
return is_success
