def measure_UIQMs(dir_name, im_res=(256, 256)):
paths = sorted(glob(join(dir_name, "*.*")))
uqims = []
for img_path in paths:
im = Image.open(img_path).resize(im_res)
uiqm = getUIQM(np.array(im))
uqims.append(uiqm)
return np.array(uqims)
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
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(
sum(psnr_list) / len(psnr_list)) + " ssim_average:" + str(
sum(ssim_list) / len(ssim_list)) + " UIQM:" + str(
sum(uiqm_list) / len(uiqm_list))
print(average)
import cv2 import uqim_utils #img_path = r'D:\github\cv-paper-reproduction\PSNR_test\An Underwater Image Enhancement Benchmark Dataset and Beyond\result\4 Original Image.jpg' #img_path = r'D:\github\cv-paper-reproduction\PSNR_test\InputImages\real1.jpg' img_path = r'D:\github\video-quality\102_img_.png' original = cv2.imread(img_path) UQIM_value = uqim_utils.getUIQM(original) print(round(UQIM_value,3))