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testSSIM.py
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import os
from conditional_gan import make_generator
import cmd
from pose_dataset import PoseHMDataset
from gan.inception_score import get_inception_score
from skimage.io import imread, imsave
from skimage.measure import compare_ssim
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from tqdm import tqdm
import re
def l1_score(generated_images, reference_images):
score_list = []
for reference_image, generated_image in zip(reference_images, generated_images):
score = np.abs(2 * (reference_image/255.0 - 0.5) - 2 * (generated_image/255.0 - 0.5)).mean()
score_list.append(score)
return np.mean(score_list)
def ssim_score(generated_images, reference_images):
ssim_score_list = []
for reference_image, generated_image in zip(reference_images, generated_images):
ssim = compare_ssim(reference_image, generated_image, gaussian_weights=True, sigma=1.5,
use_sample_covariance=False, multichannel=True,
data_range=generated_image.max() - generated_image.min())
ssim_score_list.append(ssim)
return np.mean(ssim_score_list)
def save_images(input_images, att_images,target_images, generated_images, names, output_folder):
if not os.path.exists(output_folder):
os.makedirs(output_folder)
if len(att_images)>0:
iterList=zip(map(list, zip(*input_images)),map(list, zip(*att_images)), target_images, generated_images, names)
else:
iterList=zip(map(list, zip(*input_images)), target_images, generated_images, names)
for images in iterList:
res_name = str('_'.join(images[-1])) + '.png'
if len(att_images)>0:
imagesList=images[0]+images[1]+list(images[1+1:])
else:
imagesList=images[0]+list(images[1:])
imsave(os.path.join(output_folder, res_name), np.concatenate(imagesList[:-1], axis=1))
def create_masked_image(names, images, annotation_file):
import pose_utils
masked_images = []
df = pd.read_csv(annotation_file, sep=':')
for name, image in zip(names, images):
to = name[1]
ano_to = df[df['name'] == to].iloc[0]
kp_to = pose_utils.load_pose_cords_from_strings(ano_to['keypoints_y'], ano_to['keypoints_x'])
mask = pose_utils.produce_ma_mask(kp_to, image.shape[:2])
masked_images.append(image * mask[..., np.newaxis])
return masked_images
def load_generated_images(images_folder):
input_images = []
target_images = []
generated_images = []
names = []
for img_name in os.listdir(images_folder):
img = imread(os.path.join(images_folder, img_name))
h = img.shape[1] / 3
input_images.append(img[:, :h])
target_images.append(img[:, h:2*h])
generated_images.append(img[:, 2*h:])
m = re.match(r'([A-Za-z0-9_]*.jpg)_([A-Za-z0-9_]*.jpg)', img_name)
fr = m.groups()[0]
to = m.groups()[1]
names.append([fr, to])
return input_images, target_images, generated_images, names
def generate_images(dataset, generator, use_input_pose, nb_inputs=2,nbAtt=0):
input_images = [[] for i in range(nb_inputs)]
att_images = [[] for i in range(nb_inputs)]
target_images = []
generated_images = []
names = []
def deprocess_image(img):
return (255 * ((img + 1) / 2.0)).astype(np.uint8)
colormap=[tuple([int(255*x) for x in plt.get_cmap('jet')(i)[:-1]]) for i in range(255)]
def colorizeGray(img,colmap):
output=np.empty(img.shape[0:2]+(3,),dtype=np.uint8)
for i in range(output.shape[0]):
for j in range(output.shape[1]):
output[i,j,:]=colmap[int(img[i,j])]
return output
for _ in tqdm(range(dataset._file_test.shape[0])):
batch, name = dataset.next_generator_sample_test(with_names=True)
out = generator.predict(batch)
out_index = 2*nb_inputs if use_input_pose else nb_inputs
for i in range(nb_inputs):
input_images[i].append(deprocess_image(batch[i]))
if nbAtt>0:
att_im=colorizeGray(deprocess_image(np.squeeze(out[2+out_index+i]-0.5)).astype(np.uint8),colormap)
att_images[i].append(att_im.reshape(input_images[i][-1].shape))
# out_index = 2 if use_input_pose else 1
out_index = 2*nb_inputs if use_input_pose else nb_inputs
target_images.append(deprocess_image(batch[out_index]))
generated_images.append(deprocess_image(out[out_index]))
names.append([name.iloc[0]['from_0'],name.iloc[0]['from_1'], name.iloc[0]['to']])
input_array = [np.concatenate(input_img, axis=0) for input_img in input_images]
if len(att_images[0])>1:
att_array = [np.concatenate(input_img, axis=0) for input_img in att_images]
else:
att_array= []
target_array = np.concatenate(target_images, axis=0)
generated_array = np.concatenate(generated_images, axis=0)
print [x.shape for x in input_array]
print [x.shape for x in att_array]
return input_array, att_array,target_array, generated_array, names
def test():
args = cmd.args()
if args.load_generated_images:
print ("Loading images...")
input_images, target_images, generated_images, names,att_images = load_generated_images(args.generated_images_dir)
else:
print ("Generate images...")
from keras import backend as K
if args.use_dropout_test:
K.set_learning_phase(1)
dataset = PoseHMDataset(test_phase=True, **vars(args))
generator = make_generator(args.image_size, args.nb_inputs, args.use_input_pose, args.warp_skip, args.disc_type, args.warp_agg,
args.use_bg, args.pose_rep_type,args.fusion_type,args.return_att,args.nb_rec,args.dmax,args.kernel_size_last,args.res_att,args.use3D,args.resDec)
assert (args.generator_checkpoint is not None)
generator.load_weights(args.generator_checkpoint)
input_images, att_images, target_images, generated_images, names = generate_images(dataset, generator, args.use_input_pose,nb_inputs=args.nb_inputs,nbAtt=(args.nb_inputs if args.return_att else 0))
print ("Save images to %s..." % (args.generated_images_dir, ))
save_images(input_images,att_images, target_images, generated_images, names,
args.generated_images_dir)
print ("Compute structured similarity score (SSIM)...")
structured_score = ssim_score(generated_images, target_images)
print ("SSIM score %s" % structured_score)
generated_images_masked = create_masked_image(names, generated_images, args.annotations_file_test)
reference_images_masked = create_masked_image(names, target_images, args.annotations_file_test)
print ("Compute masked SSIM...")
structured_score_masked = ssim_score(generated_images_masked, reference_images_masked)
print ("SSIM score masked %s" % structured_score_masked)
if __name__ == "__main__":
test()