<gh_stars>10-100
#encoding = utf-8
import torch
import os
import copy
from PIL import Image
import shutil
import numpy as np
import dlib
import cv2
import sys
from config_mask import config
import torchvision.transforms as transforms
from torch.nn.modules.distance import PairwiseDistance
pwd = os.path.abspath(__file__+'../../')
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
pwd = os.path.abspath('./')
version = 'V9'
mask = True  #是否给人脸戴口罩
img1_path = os.path.join(pwd, 'Layer_show', 'George_W_Bush_0001.jpg')
# img1_path = os.path.join(pwd, 'Layer_show', 'Michael_Douglas_0003.jpg')
img2_path = os.path.join(pwd, 'Layer_show', 'George_W_Bush_0003.jpg')

if version=='V1':
    from Models.CBAM_Face_attention_Resnet_maskV1 import resnet18_cbam, resnet50_cbam, resnet101_cbam, resnet34_cbam, \
        resnet152_cbam
elif version=='V6':
    from Models.Resnet34 import resnet34 as resnet34_cbam
elif version=='V2':
    from Models.CBAM_Face_attention_Resnet_maskV2 import resnet18_cbam, resnet50_cbam, resnet101_cbam, resnet34_cbam, \
        resnet152_cbam
elif version=='V8':
    from Models.Resnet34_attention import resnet34 as resnet34_cbam
elif (version=='V3') or (version=='V9'):
    from Models.CBAM_Face_attention_Resnet_notmaskV3 import resnet18_cbam, resnet50_cbam, resnet101_cbam, resnet34_cbam, \
        resnet152_cbam

if config['model'] == 18:
    model = resnet18_cbam(pretrained=False, showlayer= True, num_classes=128)
elif config['model'] == 34:
    model = resnet34_cbam(pretrained=False, showlayer= True, num_classes=128)
elif config['model'] == 50:
    model = resnet50_cbam(pretrained=False, showlayer= True, num_classes=128)
elif config['model'] == 101:
    model = resnet101_cbam(pretrained=False, showlayer= True, num_classes=128)
elif config['model'] == 152:
    model = resnet152_cbam(pretrained=False, showlayer= True, num_classes=128)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_path = os.path.join(pwd, 'Model_training_checkpoints')
x = [int(i.split('_')[4]) for i in os.listdir(model_path) if version in i]
x.sort()
for i in os.listdir(model_path):
    if (len(x)!=0) and ('epoch_'+str(x[-1]) in i) and (version in i):
        model_pathi = os.path.join(model_path, i)
        break
if version=='V1':
    model_pathi = os.path.join(model_path, 'model_34_triplet_epoch_30_rocNotMasked0.819_rocMasked0.764maskV1.pt')
elif version=='V2':
    model_pathi = os.path.join(model_path, 'model_34_triplet_epoch_27_rocNotMasked0.919_rocMasked0.798notmaskV2.pt')
elif version=='V3':
    model_pathi = os.path.join(model_path, 'model_34_triplet_epoch_97_rocNotMasked0.951_rocMasked0.766notmaskV3.pt')
elif version=='V6':
    model_pathi = os.path.join(model_path, 'model_34_triplet_epoch_63_rocNotMasked0.922_rocMasked0.834maskV6.pt')
elif version=='V8':
    model_pathi = os.path.join(model_path, 'model_34_triplet_epoch_39_rocNotMasked0.926_rocMasked0.856maskV8.pt')
elif version=='V9':
    model_pathi = os.path.join(model_path, 'model_34_triplet_epoch_19_rocNotMasked0.918_rocMasked0.831notmaskV9.pt')
print(model_path)
if os.path.exists(model_pathi) and (version in model_pathi):
    if torch.cuda.is_available():
        model_state = torch.load(model_pathi)
    else:
        model_state = torch.load(model_pathi, map_location='cpu')
    model.load_state_dict(model_state['model_state_dict'])
    start_epoch = model_state['epoch']
    print('loaded %s' % model_pathi)
else:
    print('不存在预训练模型！')
    sys.exit(0)

if torch.cuda.is_available():
    model.cuda()

model.eval()

test_data_transforms = transforms.Compose([
    transforms.Resize([config['image_size'], config['image_size']]), # resize
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.5, 0.5, 0.5],
        std=[0.5, 0.5, 0.5]
    )
])

isame = 1
threshold = 0.9
detector = dlib.get_frontal_face_detector()
predicter_path=config['predicter_path']
predictor = dlib.shape_predictor(predicter_path)
img_size = config['image_size']
font = cv2.FONT_HERSHEY_SIMPLEX

masked = os.path.join(pwd, 'Layer_show', 'mask')
notmasked = os.path.join(pwd, 'Layer_show', 'notmask')

delete = input('是否删除文件？ Y or N')
if (delete.upper()=='Y') and (mask==True):
    os.system('rm -rf %s'%masked)
    os.mkdir(masked)
elif (delete.upper()=='Y') and (mask==False):
    os.system('rm -rf %s'%notmasked)
    os.mkdir(notmasked)

def preprocess(image_path, detector, predictor, img_size, cl, mask=True):
    image = dlib.load_rgb_image(image_path)
    face_img, TF = None, 0
    # 人脸对齐、切图
    dets = detector(image, 1)
    if len(dets) == 1:
        faces = dlib.full_object_detections()
        faces.append(predictor(image, dets[0]))
        images = dlib.get_face_chips(image, faces, size=img_size)

        image = np.array(images[0]).astype(np.uint8)
        # face_img = Image.fromarray(image).convert('RGB')  #
        face_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        # 生成人脸mask
        dets = detector(image, 1)
        if len(dets) == 1:
            point68 = predictor(image, dets[0])
            landmarks = list()
            INDEX = [0, 2, 14, 16, 17, 18, 19, 24, 25, 26]
            eyebrow_list = [19, 24]
            eyes_list = [36, 45]
            eyebrow = 0
            eyes = 0

            for eb, ey in zip(eyebrow_list, eyes_list):
                eyebrow += point68.part(eb).y
                eyes += point68.part(ey).y
            add_pixel = int(eyes / 2 - eyebrow / 2)

            for idx in INDEX:
                x = point68.part(idx).x
                if idx in eyebrow_list:
                    y = (point68.part(idx).y - 2 * add_pixel) if (point68.part(idx).y - 2 * add_pixel) > 0 else 0
                else:
                    y = point68.part(idx).y
                landmarks.append((x, y))
            belows = []
            for i in range(2, 15, 1):
                belows.append([point68.part(i).x, point68.part(i).y])
            belows = np.array(belows)
            colors = [(200, 183, 144), (163, 150, 134), (172, 170, 169), \
                      (167, 168, 166), (173, 171, 170), (161, 161, 160), \
                      (170, 162, 162)]
            # cl = np.random.choice(len(colors), 1)[0]
            # cl = 0
            if mask:
                cv2.fillConvexPoly(face_img, belows, colors[cl])
            else:
                pass
    return Image.fromarray(face_img).convert('RGB')

def ishowm(ima, imb):
    imgone = np.asarray(ima)
    imgtwo = np.asarray(imb)
    imgone = cv2.cvtColor(imgone, cv2.COLOR_RGB2BGR)
    imgtwo = cv2.cvtColor(imgtwo, cv2.COLOR_RGB2BGR)
    cv2.putText(imgtwo, 'lay',(1,25),font,1,[0,0,255],2)
    imgall = np.concatenate([imgone, imgtwo], axis=1)
    if False:
        cv2.namedWindow('images')
        cv2.resizeWindow('images', 600, 600)
        cv2.imshow('images', imgall)
        cv2.waitKey(0)
        cv2.destroyWindow('images')
    return imgall

ima = preprocess(img1_path, detector, predictor, img_size, 1, mask)
imb = preprocess(img2_path, detector, predictor, img_size, 3, mask)
imb_ = copy.deepcopy(imb)
ima_ = copy.deepcopy(ima)
ima, imb = test_data_transforms(ima), test_data_transforms(imb)
if torch.cuda.is_available():
    data_a = ima.unsqueeze(0).cuda()
    data_b = imb.unsqueeze(0).cuda()
else:
    data_a, data_b = ima.unsqueeze(0), imb.unsqueeze(0)

imgall=ishowm(ima_, imb_)

output_a, output_b = model(data_a), model(data_b)
output_a = torch.div(output_a, torch.norm(output_a))
output_b = torch.div(output_b, torch.norm(output_b))
l2_distance = PairwiseDistance(2)#.cuda()
distance = l2_distance.forward(output_a, output_b)
print('从两张图片提取出来的特征向量的欧氏距离是：%1.3f' % distance)
cv2.putText(imgall, 'dis:%1.4f'%distance, (1, 19), font, 0.6, [0, 0, 255], 1)
imgall = Image.fromarray(imgall).convert('RGB')
if mask:
    imgall.save(os.path.join(pwd, 'Layer_show', 'mask', 'dis%1.3f_faceshow_%s.jpg'%(distance, version)))
    path = os.path.join(os.path.join(pwd,'Layer_show'))
    for i in  os.listdir(path):
        if os.path.isfile(os.path.join(path, i)) and '000' not in i:
            shutil.move(os.path.join(path,i),os.path.join(path,'mask',i))
else:
    imgall.save(os.path.join(pwd, 'Layer_show', 'notmask', 'dis%1.3f_faceshow_%s.jpg'%(distance, version)))
    path = os.path.join(os.path.join(pwd, 'Layer_show'))
    for i in os.listdir(path):
        if os.path.isfile(os.path.join(path, i)) and '000' not in i:
            shutil.move(os.path.join(path, i), os.path.join(path, 'notmask', i))

#encoding = utf-8
import torch
import os
import copy
from PIL import Image
import shutil
import numpy as np
import dlib
import cv2
import sys
from config_mask import config
import torchvision.transforms as transforms
from torch.nn.modules.distance import PairwiseDistance
pwd = os.path.abspath(__file__+'../../')
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
pwd = os.path.abspath('./')
version = 'V9'
mask = True  #是否给人脸戴口罩
img1_path = os.path.join(pwd, 'Layer_show', 'George_W_Bush_0001.jpg')
# img1_path = os.path.join(pwd, 'Layer_show', 'Michael_Douglas_0003.jpg')
img2_path = os.path.join(pwd, 'Layer_show', 'George_W_Bush_0003.jpg')

if version=='V1':
    from Models.CBAM_Face_attention_Resnet_maskV1 import resnet18_cbam, resnet50_cbam, resnet101_cbam, resnet34_cbam, \
        resnet152_cbam
elif version=='V6':
    from Models.Resnet34 import resnet34 as resnet34_cbam
elif version=='V2':
    from Models.CBAM_Face_attention_Resnet_maskV2 import resnet18_cbam, resnet50_cbam, resnet101_cbam, resnet34_cbam, \
        resnet152_cbam
elif version=='V8':
    from Models.Resnet34_attention import resnet34 as resnet34_cbam
elif (version=='V3') or (version=='V9'):
    from Models.CBAM_Face_attention_Resnet_notmaskV3 import resnet18_cbam, resnet50_cbam, resnet101_cbam, resnet34_cbam, \
        resnet152_cbam

if config['model'] == 18:
    model = resnet18_cbam(pretrained=False, showlayer= True, num_classes=128)
elif config['model'] == 34:
    model = resnet34_cbam(pretrained=False, showlayer= True, num_classes=128)
elif config['model'] == 50:
    model = resnet50_cbam(pretrained=False, showlayer= True, num_classes=128)
elif config['model'] == 101:
    model = resnet101_cbam(pretrained=False, showlayer= True, num_classes=128)
elif config['model'] == 152:
    model = resnet152_cbam(pretrained=False, showlayer= True, num_classes=128)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_path = os.path.join(pwd, 'Model_training_checkpoints')
x = [int(i.split('_')[4]) for i in os.listdir(model_path) if version in i]
x.sort()
for i in os.listdir(model_path):
    if (len(x)!=0) and ('epoch_'+str(x[-1]) in i) and (version in i):
        model_pathi = os.path.join(model_path, i)
        break
if version=='V1':
    model_pathi = os.path.join(model_path, 'model_34_triplet_epoch_30_rocNotMasked0.819_rocMasked0.764maskV1.pt')
elif version=='V2':
    model_pathi = os.path.join(model_path, 'model_34_triplet_epoch_27_rocNotMasked0.919_rocMasked0.798notmaskV2.pt')
elif version=='V3':
    model_pathi = os.path.join(model_path, 'model_34_triplet_epoch_97_rocNotMasked0.951_rocMasked0.766notmaskV3.pt')
elif version=='V6':
    model_pathi = os.path.join(model_path, 'model_34_triplet_epoch_63_rocNotMasked0.922_rocMasked0.834maskV6.pt')
elif version=='V8':
    model_pathi = os.path.join(model_path, 'model_34_triplet_epoch_39_rocNotMasked0.926_rocMasked0.856maskV8.pt')
elif version=='V9':
    model_pathi = os.path.join(model_path, 'model_34_triplet_epoch_19_rocNotMasked0.918_rocMasked0.831notmaskV9.pt')
print(model_path)
if os.path.exists(model_pathi) and (version in model_pathi):
    if torch.cuda.is_available():
        model_state = torch.load(model_pathi)
    else:
        model_state = torch.load(model_pathi, map_location='cpu')
    model.load_state_dict(model_state['model_state_dict'])
    start_epoch = model_state['epoch']
    print('loaded %s' % model_pathi)
else:
    print('不存在预训练模型！')
    sys.exit(0)

if torch.cuda.is_available():
    model.cuda()

model.eval()

test_data_transforms = transforms.Compose([
    transforms.Resize([config['image_size'], config['image_size']]), # resize
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.5, 0.5, 0.5],
        std=[0.5, 0.5, 0.5]
    )
])

isame = 1
threshold = 0.9
detector = dlib.get_frontal_face_detector()
predicter_path=config['predicter_path']
predictor = dlib.shape_predictor(predicter_path)
img_size = config['image_size']
font = cv2.FONT_HERSHEY_SIMPLEX

masked = os.path.join(pwd, 'Layer_show', 'mask')
notmasked = os.path.join(pwd, 'Layer_show', 'notmask')

delete = input('是否删除文件？ Y or N')
if (delete.upper()=='Y') and (mask==True):
    os.system('rm -rf %s'%masked)
    os.mkdir(masked)
elif (delete.upper()=='Y') and (mask==False):
    os.system('rm -rf %s'%notmasked)
    os.mkdir(notmasked)

def preprocess(image_path, detector, predictor, img_size, cl, mask=True):
    image = dlib.load_rgb_image(image_path)
    face_img, TF = None, 0
    # 人脸对齐、切图
    dets = detector(image, 1)
    if len(dets) == 1:
        faces = dlib.full_object_detections()
        faces.append(predictor(image, dets[0]))
        images = dlib.get_face_chips(image, faces, size=img_size)

        image = np.array(images[0]).astype(np.uint8)
        # face_img = Image.fromarray(image).convert('RGB')  #
        face_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        # 生成人脸mask
        dets = detector(image, 1)
        if len(dets) == 1:
            point68 = predictor(image, dets[0])
            landmarks = list()
            INDEX = [0, 2, 14, 16, 17, 18, 19, 24, 25, 26]
            eyebrow_list = [19, 24]
            eyes_list = [36, 45]
            eyebrow = 0
            eyes = 0

            for eb, ey in zip(eyebrow_list, eyes_list):
                eyebrow += point68.part(eb).y
                eyes += point68.part(ey).y
            add_pixel = int(eyes / 2 - eyebrow / 2)

            for idx in INDEX:
                x = point68.part(idx).x
                if idx in eyebrow_list:
                    y = (point68.part(idx).y - 2 * add_pixel) if (point68.part(idx).y - 2 * add_pixel) > 0 else 0
                else:
                    y = point68.part(idx).y
                landmarks.append((x, y))
            belows = []
            for i in range(2, 15, 1):
                belows.append([point68.part(i).x, point68.part(i).y])
            belows = np.array(belows)
            colors = [(200, 183, 144), (163, 150, 134), (172, 170, 169), \
                      (167, 168, 166), (173, 171, 170), (161, 161, 160), \
                      (170, 162, 162)]
            # cl = np.random.choice(len(colors), 1)[0]
            # cl = 0
            if mask:
                cv2.fillConvexPoly(face_img, belows, colors[cl])
            else:
                pass
    return Image.fromarray(face_img).convert('RGB')

def ishowm(ima, imb):
    imgone = np.asarray(ima)
    imgtwo = np.asarray(imb)
    imgone = cv2.cvtColor(imgone, cv2.COLOR_RGB2BGR)
    imgtwo = cv2.cvtColor(imgtwo, cv2.COLOR_RGB2BGR)
    cv2.putText(imgtwo, 'lay',(1,25),font,1,[0,0,255],2)
    imgall = np.concatenate([imgone, imgtwo], axis=1)
    if False:
        cv2.namedWindow('images')
        cv2.resizeWindow('images', 600, 600)
        cv2.imshow('images', imgall)
        cv2.waitKey(0)
        cv2.destroyWindow('images')
    return imgall

ima = preprocess(img1_path, detector, predictor, img_size, 1, mask)
imb = preprocess(img2_path, detector, predictor, img_size, 3, mask)
imb_ = copy.deepcopy(imb)
ima_ = copy.deepcopy(ima)
ima, imb = test_data_transforms(ima), test_data_transforms(imb)
if torch.cuda.is_available():
    data_a = ima.unsqueeze(0).cuda()
    data_b = imb.unsqueeze(0).cuda()
else:
    data_a, data_b = ima.unsqueeze(0), imb.unsqueeze(0)

imgall=ishowm(ima_, imb_)

output_a, output_b = model(data_a), model(data_b)
output_a = torch.div(output_a, torch.norm(output_a))
output_b = torch.div(output_b, torch.norm(output_b))
l2_distance = PairwiseDistance(2)#.cuda()
distance = l2_distance.forward(output_a, output_b)
print('从两张图片提取出来的特征向量的欧氏距离是：%1.3f' % distance)
cv2.putText(imgall, 'dis:%1.4f'%distance, (1, 19), font, 0.6, [0, 0, 255], 1)
imgall = Image.fromarray(imgall).convert('RGB')
if mask:
    imgall.save(os.path.join(pwd, 'Layer_show', 'mask', 'dis%1.3f_faceshow_%s.jpg'%(distance, version)))
    path = os.path.join(os.path.join(pwd,'Layer_show'))
    for i in  os.listdir(path):
        if os.path.isfile(os.path.join(path, i)) and '000' not in i:
            shutil.move(os.path.join(path,i),os.path.join(path,'mask',i))
else:
    imgall.save(os.path.join(pwd, 'Layer_show', 'notmask', 'dis%1.3f_faceshow_%s.jpg'%(distance, version)))
    path = os.path.join(os.path.join(pwd, 'Layer_show'))
    for i in os.listdir(path):
        if os.path.isfile(os.path.join(path, i)) and '000' not in i:
            shutil.move(os.path.join(path, i), os.path.join(path, 'notmask', i))
#encoding = utf-8 #是否给人脸戴口罩 # img1_path = os.path.join(pwd, 'Layer_show', 'Michael_Douglas_0003.jpg') # resize # 人脸对齐、切图 # face_img = Image.fromarray(image).convert('RGB')  # # 生成人脸mask # cl = np.random.choice(len(colors), 1)[0] # cl = 0 #.cuda()