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fun_test.py
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fun_test.py
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import os
import pickle
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
import glob
import torch
import torchvision
from torch.autograd import Variable
from fun_dataset import VaganDataset
import scipy.misc
from imutils import face_utils
import numpy as np
import argparse
import imutils
import dlib
import cv2
from collections import OrderedDict
import multiprocessing
import math
from scipy.ndimage import gaussian_filter
from numpy.lib.stride_tricks import as_strided as ast
from skimage.measure import compare_ssim as ssim_f
from PIL import Image
import numpy as np
from skimage import feature
import math
from scipy.ndimage import correlate
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--cuda",
default=True)
parser.add_argument("--dataset_dir",
type=str,
default="/mnt/disk1/dat/lchen63/grid/data/pickle/")
parser.add_argument("--model_dir",
type=str,
default="/mnt/disk1/dat/lchen63/grid/model/model2/model1_stage1_generator_7.pth")
parser.add_argument("--sample_dir",
type=str,
default="/mnt/disk1/dat/lchen63/grid/test_result/model2/")
parser.add_argument("--batch_size",
type=int,
default=8)
parser.add_argument("--num_thread",
type=int,
default=40)
return parser.parse_args()
##################CPDB##############
def block_process(A, block):
block_contrast = np.zeros((A.shape[0]/block[0], A.shape[1]/block[1]), dtype=np.int32)
flatten_contrast = list()
for i in range(0, A.shape[0], block[0]):
for j in range(0, A.shape[1], block[1]):
block_view = A[i:i+block[0], j:j+block[1]]
block_view = np.max(block_view) - np.min(block_view)
flatten_contrast.append(block_view)
block_contrast = np.array(flatten_contrast).reshape(block_contrast.shape)
return block_contrast
def cpbd_compute(image):
if isinstance(image, str):
image = Image.open(image)
image = image.convert('L')
img = np.array(image, dtype=np.float32)
m, n = img.shape
threshold = 0.002
beta = 3.6
rb = 64
rc = 64
max_blk_row_idx = int(m/rb)
max_blk_col_idx = int(n/rc)
widthjnb = np.array([np.append(5 * np.ones((1, 51)), 3*np.ones((1, 205)))])
total_num_edges = 0
hist_pblur = np.zeros(101, dtype=np.float64)
input_image_canny_edge = feature.canny(img)
input_image_sobel_edge = matlab_sobel_edge(img)
width = marziliano_method(input_image_sobel_edge, img)
# print width
for i in range(1, max_blk_row_idx+1):
for j in range(1, max_blk_col_idx+1):
rows = slice(rb*(i-1), rb*i)
cols = slice(rc*(j-1), rc*j)
decision = get_edge_blk_decision(input_image_canny_edge[rows, cols], threshold)
if decision == 1:
local_width = width[rows, cols]
local_width = local_width[np.nonzero(local_width)]
blk_contrast = block_process(img[rows, cols], [rb, rc]) + 1
blk_jnb = widthjnb[0, int(blk_contrast)-1]
prob_blur_detection = 1 - math.e ** (-np.power(np.abs(np.true_divide(local_width, blk_jnb)), beta))
for k in range(1, local_width.size+1):
temp_index = int(round(prob_blur_detection[k-1] * 100)) + 1
hist_pblur[temp_index-1] = hist_pblur[temp_index-1] + 1
total_num_edges = total_num_edges + 1
if total_num_edges != 0:
hist_pblur = hist_pblur / total_num_edges
else:
hist_pblur = np.zeros(hist_pblur.shape)
sharpness_metric = np.sum(hist_pblur[0:63])
return sharpness_metric
def marziliano_method(E, A):
# print E
edge_with_map = np.zeros(A.shape)
gy, gx = np.gradient(A)
M, N = A.shape
angle_A = np.zeros(A.shape)
for m in range(1, M+1):
for n in range(1, N+1):
if gx[m-1, n-1] != 0:
angle_A[m-1, n-1] = math.atan2(gy[m-1,n-1], gx[m-1,n-1]) * (180/np.pi)
if gx[m-1, n-1] == 0 and gy[m-1, n-1] == 0:
angle_A[m-1, n-1] = 0
if gx[m-1, n-1] == 0 and gy[m-1, n-1] == np.pi/2:
angle_A[m-1, n-1] = 90
if angle_A.size != 0:
angle_Arnd = 45 * np.round(angle_A/45.0)
# print angle_Arnd
count = 0
for m in range(2, M):
for n in range(2, N):
if E[m-1, n-1] == 1:
if angle_Arnd[m-1, n-1] == 180 or angle_Arnd[m-1, n-1] == -180:
count += 1
for k in range(0, 101):
posy1 = n-1-k
posy2 = n - 2 - k
if posy2 <= 0:
break
if A[m-1, posy2-1] - A[m-1, posy1-1] <= 0:
break
width_count_side1 = k + 1
for k in range(0, 101):
negy1 = n + 1 + k
negy2 = n + 2 + k
if negy2 > N:
break
if A[m-1, negy2-1] > A[m-1, negy1-1]:
break
width_count_side2 = k + 1
edge_with_map[m-1, n-1] = width_count_side1 + width_count_side2
elif angle_Arnd[m-1, n-1] == 0:
count += 1
for k in range(0, 101):
posy1 = n+1+k
posy2 = n + 2 + k
if posy2 > N:
break
# print m, posy2
if A[m-1, posy2-1] <= A[m-1, posy1-1]:
break
width_count_side1 = k + 1
for k in range(0, 101):
negy1 = n -1-k
negy2 = n -2 -k
if negy2 <=0:
break
if A[m-1, negy2-1] >= A[m-1, negy1-1]:
break
width_count_side2 = k + 1
edge_with_map[m-1, n-1] = width_count_side1 + width_count_side2
return edge_with_map
def get_edge_blk_decision(im_in, T):
m, n = im_in.shape
L = m * n
im_edge_pixels = np.sum(im_in)
im_out = im_edge_pixels > (L * T)
return im_out
def matlab_sobel_edge(img):
mask = np.array([[1, 0, -1], [2, 0, -2], [1, 0, -1]]) / 8.0
bx = correlate(img, mask)
b = bx*bx
# print b
b = b > 4.0
return np.array(b, dtype=np.int)
################################################################################################
def _load(generator, directory):
# paths = glob.glob(os.path.join(directory, "*.pth"))
path = directory
print generator
print path
state_dict = torch.load(path)
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:]
new_state_dict[name] = v
# load params
generator.load_state_dict(new_state_dict)
# print torch.load(path).keys()
# gen_path = [path for path in paths if "generator" in path][0]
# generator.load_state_dict(torch.load(path))
print('Number of model parameters: {}'.format(
sum([p.data.nelement() for p in generator.parameters()])))
print("Load pretrained [{}]".format(path))
def _sample( config):
dataset = VaganDataset(config.dataset_dir, output_shape=[64, 64], train=False)
num_test = len(dataset)
# num_test=100
real_path = os.path.join(config.sample_dir,'image/real64')
fake_path = os.path.join(config.sample_dir,'image/fake64')
if not os.path.exists(config.sample_dir):
os.mkdir(config.sample_dir)
if not os.path.exists(os.path.join(config.sample_dir,'image')):
os.mkdir(os.path.join(config.sample_dir,'image'))
if not os.path.exists(os.path.join(config.sample_dir,'image/real64')):
os.mkdir(os.path.join(config.sample_dir,'image/real64'))
if not os.path.exists(os.path.join(config.sample_dir,'image/fake64')):
os.mkdir(os.path.join(config.sample_dir,'image/fake64'))
paths = []
# print type(dataset)
# dataset = dataset[0:10]
stage1_generator = Generator()
_load(stage1_generator,config.model_dir)
examples, ims, landmarks, embeds, captions = [], [], [],[],[]
for idx in range(num_test):
example,im, landmark, embed, caption = dataset[idx]
examples.append(example)
ims.append(im)
embeds.append(embed)
captions.append(caption)
landmarks.append(landmark)
examples = torch.stack(examples,0)
# landmarks = torch.stack(landmarks,0)
ims = torch.stack(ims, 0)
embeds = torch.stack(embeds, 0)
if config.cuda:
examples= Variable( examples).cuda()
# landmarks = Variable( landmarks).cuda()
embeds = Variable(embeds).cuda()
stage1_generator = stage1_generator.cuda()
else:
examples =Variable(examples)
embeds = Variable(embeds)
landmarks = Variable(landmarks)
# embeds = embeds.view(embeds.size()[0],embeds.size()[1],embeds.size()[2],embeds.size()[3])
# print embeds.size()
# print '-------'
# # embeds = embeds.view(len(indices), -1)
# print embeds.size()
batch_size = config.batch_size
for i in range(num_test/batch_size):
example = examples[i*batch_size: i*batch_size + batch_size]
# landmark = landmarks[i*batch_size: i*batch_size + batch_size]
embed = embeds[i * batch_size : i * batch_size +batch_size]
print '---------------------' + str(i) + '/' + str(num_test/batch_size)
fake_ims_stage1 = stage1_generator(example, embed)
# fake_ims_stage1 = stage1_generator(example, embed)
# print fake_ims_stage1
# fake_ims_stage1 = fake_ims_stage1.view(len(dataset)*16,3,64,64)
# ims = ims.view(len(dataset),3,64,64)
for inx in range(batch_size):
real_ims = ims[inx + i * batch_size]
# real_ims = real_ims.permute(1,2, 3, 0)
fake_ims = fake_ims_stage1[inx]
# fake_ims = fake_ims.permute(1,2,3,0)
real_ims = real_ims.cpu().permute(1,2,3,0).numpy()
# print fake_ims
# print fake_ims.size()
# print fake_ims.data.cpu()
# print type(fake_ims.data.cpu())
# print fake_ims.data.cpu().size()
fake_ims =fake_ims.data.cpu().permute(1,2,3,0).numpy()
# print '------'
# print real_ims.shape
# print fake_ims.shape
fff={}
rp = []
fp =[]
for j in range(real_ims.shape[0]):
real = real_ims[j]
fake= fake_ims[j]
# real = real_ims[:,i,:,:].cpu().permute(2,3,0).numpy()
# fake = fake_ims[:,i,:,:].data.cpu().permute(2,3,0).numpy()
# print captions[inx][i]
temp = captions[ inx+ i * batch_size][j].split('/')
if not os.path.exists(os.path.join(fake_path,temp[-2])):
os.mkdir(os.path.join(fake_path,temp[-2]))
if not os.path.exists(os.path.join(real_path,temp[-2])):
os.mkdir(os.path.join(real_path,temp[-2]))
real_name = os.path.join(real_path,temp[-2]) + '/' + temp[-1][:-4] + '.jpg'
fake_name = os.path.join(fake_path,temp[-2]) + '/' + temp[-1][:-4] + '.jpg'
scipy.misc.imsave(real_name,real)
scipy.misc.imsave(fake_name,fake)
rp.append(real_name)
fp.append(fake_name)
fff["real_path"] = rp
fff["fake_path"] = fp
paths.append(fff)
# print os.path.join(fake_path,temp[-2])
real_im = ims[ i * batch_size : i * batch_size + batch_size]
fake_store = fake_ims_stage1.data.permute(0,2,1,3,4).contiguous().view(config.batch_size*16,3,64,64)
torchvision.utils.save_image(fake_store,
"{}/fake_{}.png".format(os.path.join(fake_path,temp[-2]),i), nrow=16,normalize=True)
real_store = real_im.permute(0,2,1,3,4).contiguous().view(config.batch_size*16,3,64,64)
torchvision.utils.save_image(real_store,
"{}/real_{}.png".format(os.path.join(real_path,temp[-2]),i), nrow=16,normalize=True)
with open(os.path.join(config.sample_dir,'image/test_result.pkl'), 'wb') as handle:
pickle.dump(paths, handle, protocol=pickle.HIGHEST_PROTOCOL)
def generating_landmark_lips(test_inf):
image = cv2.imread(os.path.join(config.sample_dir,'bg.jpg'))
image_real = image.copy()
image_fake = image.copy()
# original = np.array([181,237])
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('/mnt/disk0/dat/lchen63/grid/data/shape_predictor_68_face_landmarks.dat')
for inx in range(len(test_inf)):
real_paths = test_inf[inx]["real_path"]
fake_paths = test_inf[inx]["fake_path"]
# print len(real_paths)
for i in range(len(real_paths)):
rp = real_paths[i]
print rp
fp = fake_paths[i]
# print fp
temp_r = rp.split('/')
# temp_f = fp.split('/')
if not os.path.exists( os.path.join(config.sample_dir,'landmark/real64/' + temp_r[-2])):
os.mkdir( os.path.join(config.sample_dir,'landmark/real64/' + temp_r[-2]))
if not os.path.exists(os.path.join(config.sample_dir,'landmark/fake64/' + temp_r[-2])):
os.mkdir(os.path.join(config.sample_dir,'landmark/fake64/' + temp_r[-2]))
lm_r = os.path.join(config.sample_dir,'landmark/real64/' + temp_r[-2] + '/' + temp_r[-1][:-4] + '.npy' )
lm_f = os.path.join(config.sample_dir,'landmark/fake64/' + temp_r[-2] + '/' + temp_r[-1][:-4] + '.npy' )
i_lm_r = os.path.join(config.sample_dir,'landmark/real64/' + temp_r[-2] + '/' + temp_r[-1][:-4] + '.jpg' )
i_lm_f = os.path.join(config.sample_dir,'landmark/fake64/' + temp_r[-2] + '/' + temp_r[-1][:-4] + '.jpg' )
real_mask = cv2.imread(rp)
fake_mask = cv2.imread(fp)
image_real[237:301,181:245,:] = real_mask
image_fake[237:301,181:245,:] = fake_mask
real_gray = cv2.cvtColor(image_real, cv2.COLOR_BGR2GRAY)
real_rects = detector(real_gray, 1)
fake_gray = cv2.cvtColor(image_fake,cv2.COLOR_BGR2GRAY)
fake_rects = detector(fake_gray, 1)
if real_rects is None or fake_rects is None:
print '--------------------------------'
for (i,rect) in enumerate(fake_rects):
shape = predictor(fake_gray, rect)
shape = face_utils.shape_to_np(shape)
for (name, (i, j)) in face_utils.FACIAL_LANDMARKS_IDXS.items():
# print name
if name != 'mouth':
continue
clone = image_fake.copy()
for (x, y) in shape[i:j]:
cv2.circle(clone, (x, y), 1, (0, 255, 0), -1)
cv2.imwrite(i_lm_f, clone)
mouth_land = shape[i:j].copy()
original = np.sum(mouth_land,axis=0) / 20.0
# print (mouth_land)
mouth_land = mouth_land - original
np.save(lm_f,mouth_land)
for (i,rect) in enumerate(real_rects):
shape = predictor(real_gray, rect)
shape = face_utils.shape_to_np(shape)
for (name, (i, j)) in face_utils.FACIAL_LANDMARKS_IDXS.items():
# print name
if name != 'mouth':
continue
clone = image_real.copy()
for (x, y) in shape[i:j]:
cv2.circle(clone, (x, y), 1, (0, 255, 0), -1)
cv2.imwrite(i_lm_r, clone)
mouth_land = shape[i:j].copy()
original = np.sum(mouth_land,axis=0) / 20.0
# print (mouth_land)
mouth_land = mouth_land - original
np.save(lm_r,mouth_land)
def generate_landmarks(pickle_path):
num_thread = config.num_thread
test_inf = pickle.load(open(pickle_path, "rb"))
print test_inf[0]
datas = []
batch_size = len(test_inf)/num_thread
temp = []
if not os.path.exists( os.path.join(config.sample_dir,'landmark')):
os.mkdir( os.path.join(config.sample_dir,'landmark'))
if not os.path.exists( os.path.join(config.sample_dir,'landmark/real64')):
os.mkdir( os.path.join(config.sample_dir,'landmark/real64'))
if not os.path.exists( os.path.join(config.sample_dir,'landmark/fake64')):
os.mkdir( os.path.join(config.sample_dir,'landmark/fake64'))
for i,d in enumerate(test_inf):
temp.append(d)
if (i+1) % batch_size ==0:
datas.append(temp)
temp = []
for i in range(num_thread):
process = multiprocessing.Process(target = generating_landmark_lips,args = (datas[i],))
process.start()
def compare_landmarks(path):
fake_path = os.path.join(path + 'fake64')
real_path = os.path.join(path + 'real64')
# fakes = os.walk(fake_path)
rps = []
fps = []
print fake_path
for root, dirs, files in os.walk(fake_path):
for name in files:
# print name
if name[-3:] == 'npy':
rps.append(real_path + '/' + name.split('_')[0] + '/' + name)
fps.append(fake_path + '/' + name.split('_')[0] + '/' + name)
dis_txt = open(path + 'distance.txt','w')
distances = []
# print len(rps)
for inx in range(len(rps)):
rp = np.load(rps[inx])
fp = np.load(fps[inx])
# print rp.shape
# print fp.shape
dis = (rp-fp)**2
dis = np.sum(dis,axis=1)
dis = np.sqrt(dis)
print dis
dis = np.sum(dis,axis=0)
distances.append(dis)
dis_txt.write(rps[inx] + '\t' + str(dis) + '\n')
average_distance = sum(distances) / len(rps)
print average_distance
def psnr_f(img1, img2):
mse = np.mean( (img1 - img2) ** 2 )
if mse == 0:
return 100
PIXEL_MAX = 255.0
return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
def compare_ssim(pickle_path):
test_inf = pickle.load(open(pickle_path, "rb"))
print test_inf[0]
dis_txt = open(config.sample_dir + 'ssim.txt','w')
ssims = []
psnrs =[]
for i,d in enumerate(test_inf):
fake_paths = d['fake_path']
real_paths = d['real_path']
for inx in range(len(fake_paths)):
f_i = cv2.imread(fake_paths[inx])
r_i = cv2.imread(real_paths[inx])
f_i = cv2.cvtColor(f_i, cv2.COLOR_BGR2GRAY)
r_i = cv2.cvtColor(r_i, cv2.COLOR_BGR2GRAY)
ssim = ssim_f(f_i,r_i)
psnr = psnr_f(f_i,r_i)
psnrs.append(psnr)
ssims.append(ssim)
print "ssim: {:.4f},\t psnr: {:.4f}".format( ssim, psnr)
dis_txt.write(fake_paths[inx] + '\t {:.4f} \t {:.4f}'.format( ssim, psnr) + '\n')
average_ssim = sum(ssims) / len(ssims)
average_psnr = sum(psnrs) / len(psnrs)
print "Aeverage: \t ssim: {:.4f},\t psnr: {:.4f}".format( average_ssim, average_psnr)
return average_ssim, average_psnr
def compare_cpdb(pickle_path):
test_inf = pickle.load(open(pickle_path, "rb"))
print test_inf[0]
dis_txt = open(config.sample_dir + 'cpdb.txt','w')
r_cpdb = []
f_cpdb = []
for i,d in enumerate(test_inf):
fake_paths = d['fake_path']
# real_paths = d['real_path']
for inx in range(len(fake_paths)):
# real_cpdb = cpbd_compute(real_paths[inx])
fake_cpdb = cpbd_compute(fake_paths[inx])
# r_cpdb.append(real_cpdb)
f_cpdb.append(fake_cpdb)
print "fake: {:.4f}".format( fake_cpdb)
dis_txt.write(fake_paths[inx] + '\t fake: {:.4f}'.format( fake_cpdb) + '\n')
# average_r = sum(r_cpdb) / len(r_cpdb)
average_f = sum(f_cpdb) / len(f_cpdb)
print "Aeverage: \t fake: {:.4f}".format( average_f)
return average_f
def main(config):
_sample( config)
# p = os.path.join( config.sample_dir , 'image/test_result.pkl')
# average_ssim, average_psnr = compare_ssim(p)
# generate_landmarks(p)
# average_f = compare_cpdb(p)
# compare_landmarks(os.path.join(config.sample_dir ,'landmark/'))
# print "Aeverage: \t fake: {:.4f}".format( average_f)
# print "Aeverage: \t ssim: {:.4f},\t psnr: {:.4f}".format( average_ssim, average_psnr)
if __name__ == "__main__":
config = parse_args()
from model_base import Generator
main(config)