def index_results(dist_filelist, ref_filelist):
psnr_list = []
ssim_list = []
uiqm_list = []
for dist_file in dist_filelist: #遍历
dist_file_dir = os.path.join(dist_path, dist_file) #参考文件绝对路径
if os.path.isdir(dist_file_dir): #如果是文件夹，跳过
continue
dist_img = scipy.misc.imread(dist_file_dir,
flatten=True).astype(numpy.float32)
#filename = os.path.splitext(dist_file)[0] + os.path.splitext(dist_file)[1] #ref filename
filename = os.path.splitext(dist_file)[0] + '.jpg' #ref filename
ref_img = scipy.misc.imread(ref_path + '\\' + filename,
flatten=True).astype(
numpy.float32) #读取参考图像对应的测试图像
psnr_data = psnr.psnr(ref_img, dist_img) #psnr指标
ssim_data = ssim.ssim_exact(ref_img / 255, dist_img / 255) #ssim指标
dist_img = cv2.imread(dist_path + '\\' +
os.path.splitext(dist_file)[0] + '.jpg') #UIQM指标
uiqm_data = uqim_utils.getUIQM(dist_img) #UIQM
print("img:" + str(filename) + " psnr:" + str(psnr_data) + " ssim:" +
str(ssim_data) + " UIQM:" + str(uiqm_data))
data = str(filename) + " psnr:" + str(psnr_data) + " ssim:" + str(
ssim_data) + " UIQM:" + str(uiqm_data)
psnr_list.append(psnr_data)
ssim_list.append(ssim_data)
uiqm_list.append(uiqm_data)
average = "psnr_average:" + str(
sum(psnr_list) / len(psnr_list)) + " ssim_average:" + str(
sum(ssim_list) / len(ssim_list)) + " UIQM:" + str(
sum(uiqm_list) / len(uiqm_list))
return average
if os.path.isdir(dist_file_dir): #如果是文件夹，跳过
continue
dist_img = scipy.misc.imread(dist_file_dir,
flatten=True).astype(numpy.float32)
#filename = os.path.splitext(dist_file)[0] + os.path.splitext(dist_file)[1] #ref filename
filename = os.path.splitext(dist_file)[0] + '.png' #ref filename
ref_img = scipy.misc.imread(ref_path + '\\' + filename,
flatten=True).astype(
numpy.float32) #读取参考图像对应的测试图像
psnr_data = psnr.psnr(ref_img, dist_img) #psnr指标
ssim_data = ssim.ssim_exact(ref_img / 255, dist_img / 255) #ssim指标
dist_img = cv2.imread(dist_path + '\\' + os.path.splitext(dist_file)[0] +
'.png') #UIQM指标
uiqm_data = uqim_utils.getUIQM(dist_img) #UIQM
print("img:" + str(filename) + " psnr:" + str(psnr_data) + " ssim:" +
str(ssim_data) + " UIQM:" + str(uiqm_data))
data = str(filename) + " psnr:" + str(psnr_data) + " ssim:" + str(
ssim_data) + " UIQM:" + str(uiqm_data)
psnr_list.append(psnr_data)
ssim_list.append(ssim_data)
uiqm_list.append(uiqm_data)
average = "psnr_average:" + str(
reco_values = []
plt.figure(figsize=(8, 8))
for quality in range(0, 101, 1):
subprocess.check_call('gm convert %s -quality %d %s' %
(ref_file, quality, dist_file),
shell=True)
file_size = os.path.getsize(dist_file)
dist = scipy.misc.imread(dist_file, flatten=True).astype(numpy.float32)
quality_values.append(quality)
size_values.append(int(file_size / 1024))
vifp_values.append(vifp.vifp_mscale(ref, dist))
ssim_values.append(ssim.ssim_exact(ref / 255, dist / 255))
psnr_values.append(psnr.psnr(ref, dist))
# niqe_values.append( niqe.niqe(dist/255) )
reco_values.append(reco.reco(ref / 255, dist / 255))
plt.plot(quality_values, vifp_values, label='VIFP')
plt.plot(quality_values, ssim_values, label='SSIM')
# plt.plot(niqe_values, label='NIQE')
plt.plot(quality_values, reco_values, label='RECO')
plt.plot(quality_values, numpy.asarray(psnr_values) / 100.0, label='PSNR/100')
plt.legend(loc='lower right')
plt.xlabel('JPEG Quality')
plt.ylabel('Metric')
plt.savefig('jpg_demo_quality.png')
plt.figure(figsize=(8, 8))
args = parse_args()
pred_imgs_path = os.path.join(args.output_path,
args.model_flag + '_' + args.exp_num)
imgs_list_file = '/home/chenli/code/SRN-Deblur/test_datalist.txt'
imgs_datalist = open(imgs_list_file, 'rt').read().splitlines()
imgs_datalist = list(map(lambda x: x.split(' '), imgs_datalist))
imgsName = [x[1] for x in imgs_datalist]
psnr_values = []
ssim_values = []
for imgName in imgsName:
real = scipy.misc.imread(imgName, flatten=True).astype(numpy.float32)
split_name = imgName.split('/')
pred_imgName = os.path.join(pred_imgs_path, split_name[-3], split_name[-2],
split_name[-1])
pred = scipy.misc.imread(pred_imgName, flatten=True).astype(numpy.float32)
ssim_value = ssim.ssim_exact(real / 255, pred / 255)
psnr_value = psnr.psnr(real, pred)
print('psnr:%.5f ssim:%.5f' % (psnr_value, ssim_value))
ssim_values.append(ssim_value)
psnr_values.append(psnr_value)
final_psnr_value = numpy.mean(psnr_values)
final_ssim_value = numpy.mean(ssim_values)
print('final psnr:%.5f final ssim:%.5f' % (final_psnr_value, final_ssim_value))
# cps_img = scipy.misc.imread(cps_path, flatten=False, mode='RGB').astype(numpy.float32)
# reconst_img = scipy.misc.imread(reconst_path, flatten=False, model='RGB').astype(numpy.float32)
# cps_path =
name = os.path.splitext(os.path.basename(reconst_path))[0]
cps_path = glob.glob(os.path.join(stream_dir, model, name+'*'))[0]
stream_file_size = os.path.getsize(cps_path)
reconst_img = cv2.imread(reconst_path).astype(numpy.float32)
# print cps_img.shape #exit()
path_list.append(reconst_path)
size_values.append( int(stream_file_size/1024) ) ## kb
# vifp_values.append( vifp.vifp_mscale(ref_img, cps_img) )
ssim_values.append( ssim.ssim_exact(ref_img/255, reconst_img/255) )
psnr_values.append( psnr.psnr(ref_img, reconst_img) )
# reco_values.append( reco.reco(ref_img/255, cps_img/255) )
# cps_path_list = glob.glob(os.path.join(stream_dir, model, ref_name+'*'))
# # print ref_path
# for cps_path in cps_path_list:
# file_size = os.path.getsize(cps_path)
# # reconst_path = os.path.join(reconst_dir, model, ref_name) ## have to be same format with reference image
# print size_values;exit()
for i in range(len(size_values)):
# csv_writer.writerow
dist1_file):
print(dist1_file)
dist1 = scipy.misc.imread(
dist1_file, flatten=True).astype(numpy.float32)
dist2 = scipy.misc.imread(
dist2_file, flatten=True).astype(numpy.float32)
dist3 = scipy.misc.imread(
dist3_file, flatten=True).astype(numpy.float32)
dist4 = scipy.misc.imread(
dist4_file, flatten=True).astype(numpy.float32)
sharp = scipy.misc.imread(
sharp_file, flatten=True).astype(numpy.float32)
# print(ref_file + ' ' + dist_file + ':')
num += 1
# vifp1 = vifp.vifp_mscale(ref, dist)
ssim1 = ssim.ssim_exact(sharp / 255, dist1 / 255)
ssim2 = ssim.ssim_exact(sharp / 255, dist2 / 255)
ssim3 = ssim.ssim_exact(sharp / 255, dist3 / 255)
ssim4 = ssim.ssim_exact(sharp / 255, dist4 / 255)
psnr1 = psnr.psnr(sharp, dist1)
psnr2 = psnr.psnr(sharp, dist2)
psnr3 = psnr.psnr(sharp, dist3)
psnr4 = psnr.psnr(sharp, dist4)
# reco1 = reco.reco(ref / 255, dist / 255)
# vifp_sum += vifp1
# reco_sum += reco1
# ref_values.append(ref_file)
# dist_values.append(dist_file)
# vifp_values.append(vifp1)
# ssim_values.append(ssim1)
# psnr_values.append(psnr1)
print "Frame=%d VIFP=%f SSIM=%f" % (frame_num, vifp_value, ssim_value)
frame_num += 1
else:
# Inputs are image files
ref = scipy.misc.imread(ref_file, flatten=True).astype(numpy.float32)
dist = scipy.misc.imread(dist_file, flatten=True).astype(numpy.float32)
width, height = ref.shape[1], ref.shape[0]
print "Comparing %s to %s, resolution %d x %d" % (ref_file, dist_file,
width, height)
vifp_value = vifp.vifp_mscale(ref, dist)
print "VIFP=%f" % (vifp_value)
ssim_value = ssim.ssim_exact(ref / 255, dist / 255)
print "SSIM=%f" % (ssim_value)
# FIXME this is buggy, disable for now
# ssim_value2 = ssim.ssim(ref/255, dist/255)
# print "SSIM approx=%f" % (ssim_value2)
psnr_value = psnr.psnr(ref, dist)
print "PSNR=%f" % (psnr_value)
# niqe_value = niqe.niqe(dist/255)
# print "NIQE=%f" % (niqe_value)
reco_value = reco.reco(ref / 255, dist / 255)
print "RECO=%f" % (reco_value)
ssim_value = ssim.ssim(ref, dist)
print "Frame=%d VIFP=%f SSIM=%f" % (frame_num, vifp_value, ssim_value)
frame_num += 1
else:
# Inputs are image files
ref = scipy.misc.imread(ref_file, flatten=True).astype(numpy.float32)
dist = scipy.misc.imread(dist_file, flatten=True).astype(numpy.float32)
width, height = ref.shape[1], ref.shape[0]
print "Comparing %s to %s, resolution %d x %d" % (ref_file, dist_file, width, height)
vifp_value = vifp.vifp_mscale(ref, dist)
print "VIFP=%f" % (vifp_value)
ssim_value = ssim.ssim_exact(ref / 255, dist / 255)
print "SSIM=%f" % (ssim_value)
# FIXME this is buggy, disable for now
# ssim_value2 = ssim.ssim(ref/255, dist/255)
# print "SSIM approx=%f" % (ssim_value2)
psnr_value = psnr.psnr(ref, dist)
print "PSNR=%f" % (psnr_value)
# niqe_value = niqe.niqe(dist/255)
# print "NIQE=%f" % (niqe_value)
reco_value = reco.reco(ref / 255, dist / 255)
print "RECO=%f" % (reco_value)
print type(ref_img), ref_img.shape,
ori_shape = ref_img.shape
ori_size = 1
for dim in ori_shape:
ori_size = ori_size * dim
ori_size = int(ori_size / 1024)
print cps_path_list
for cps_path in cps_path_list:
file_size = os.path.getsize(cps_path)
cps_img = scipy.misc.imread(cps_path, flatten=False,
mode='RGB').astype(numpy.float32)
print cps_img.shape #exit()
size_values.append(int(file_size / 1024)) ## kb
# vifp_values.append( vifp.vifp_mscale(ref_img, cps_img) )
ssim_values.append(ssim.ssim_exact(ref_img / 255, cps_img / 255))
psnr_values.append(psnr.psnr(ref_img, cps_img))
# reco_values.append( reco.reco(ref_img/255, cps_img/255) )
# print size_values;exit()
for i in range(len(size_values)):
csv_writer.writerow
output_row = [
os.path.basename(ref_path),
os.path.basename(cps_path_list[i]),
str(ori_shape),
str(ori_size),
str(size_values[i]),
str(float(ori_size) / size_values[i]),
str(psnr_values[i]),
str(ssim_values[i])
]
if '_Restored_Train.png' in os.path.split(name)[1]:
ref_file = os.path.join(root, name)
dist_file = ref_file.replace('Restored', 'Blurred')
sharp_file = ref_file.replace('Restored', 'Sharp')
if os.path.isfile(ref_file) and os.path.isfile(
dist_file) and os.path.isfile(sharp_file):
ref = scipy.misc.imread(ref_file, flatten=True).astype(
numpy.float32)
dist = scipy.misc.imread(
dist_file, flatten=True).astype(numpy.float32)
sharp = scipy.misc.imread(
sharp_file, flatten=True).astype(numpy.float32)
# print(ref_file + ' ' + dist_file + ':')
num += 1
# vifp1 = vifp.vifp_mscale(ref, dist)
ssim1 = ssim.ssim_exact(sharp / 255, ref / 255)
psnr1 = psnr.psnr(sharp, ref)
ssim2 = ssim.ssim_exact(sharp / 255, dist / 255)
psnr2 = psnr.psnr(sharp, dist)
# reco1 = reco.reco(ref / 255, dist / 255)
# vifp_sum += vifp1
ssim1_sum += ssim1
psnr1_sum += psnr1
ssim2_sum += ssim2
psnr2_sum += psnr2
# reco_sum += reco1
# ref_values.append(ref_file)
# dist_values.append(dist_file)
# vifp_values.append(vifp1)
# ssim_values.append(ssim1)
# psnr_values.append(psnr1)
psnr_values = []
niqe_values = []
reco_values = []
plt.figure(figsize=(8, 8))
for quality in range(0, 101, 1):
subprocess.check_call('gm convert %s -quality %d %s'%(ref_file, quality, dist_file), shell=True)
file_size = os.path.getsize(dist_file)
dist = scipy.misc.imread(dist_file, flatten=True).astype(numpy.float32)
quality_values.append( quality )
size_values.append( int(file_size/1024) )
vifp_values.append( vifp.vifp_mscale(ref, dist) )
ssim_values.append( ssim.ssim_exact(ref/255, dist/255) )
psnr_values.append( psnr.psnr(ref, dist) )
# niqe_values.append( niqe.niqe(dist/255) )
reco_values.append( reco.reco(ref/255, dist/255) )
plt.plot(quality_values, vifp_values, label='VIFP')
plt.plot(quality_values, ssim_values, label='SSIM')
# plt.plot(niqe_values, label='NIQE')
plt.plot(quality_values, reco_values, label='RECO')
plt.plot(quality_values, numpy.asarray(psnr_values)/100.0, label='PSNR/100')
plt.legend(loc='lower right')
plt.xlabel('JPEG Quality')
plt.ylabel('Metric')
plt.savefig('jpg_demo_quality.png')
plt.figure(figsize=(8, 8))
