口罩二分类

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+/shelf/
+/workspace.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/face_mask_classifier.iml" filepath="$PROJECT_DIR$/.idea/face_mask_classifier.iml" />
+    </modules>
+  </component>
+</project>
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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>
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+import os
+import random
+
+import cv2
+import torch
+from PIL import Image
+from torch.utils.data import Dataset, DataLoader
+from utils import *
+from torchvision import transforms
+
+classes_names = ['normal', 'mask']
+
+
+class FaceMaskDataset(Dataset):
+    def __init__(self, root_path):
+        self.transform = transforms.Compose([
+            transforms.ToTensor()
+        ])
+        self.dataset = []
+        class_names = os.listdir(root_path)
+        for cls in class_names:
+            image_names = os.listdir(os.path.join(root_path, cls))
+            for image in image_names:
+                self.dataset.append([os.path.join(root_path, cls, image), classes_names.index(cls)])
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, index):
+        lights=[0.6,0.8,1,1.2,1.4,1.6]
+        data = self.dataset[index]
+        image_path = data[0]
+        image_data = keep_resize_image(image_path)
+        image_data=cv2.convertScaleAbs(image_data,alpha=lights[random.randint(0,4)])
+        image_label = data[1]
+        return self.transform(image_data), image_label
+
+
+if __name__ == '__main__':
+    import tqdm
+    d = FaceMaskDataset('image')
+    for i in d:
+        i
+

+import os
+
+import cv2
+
+import torch
+from PIL import Image
+import numpy as np
+from torchvision import transforms
+from net import *
+
+
+
+def video(net,):
+    cap=cv2.VideoCapture(0)
+    while True:
+        _, frame = cap.read()
+        image = Image.fromarray(frame)
+        w, h = image.size
+        temp = max(w, h)
+        mask = Image.new('RGB', (temp, temp))
+        if w >= h:
+            mask.paste(image, (0, (w - h) // 2))
+        else:
+            mask.paste(image, ((h - w) // 2, 0))
+        mask = mask.resize((128, 128))
+        mask = np.array(mask)
+        mask = cv2.cvtColor(mask, cv2.COLOR_RGB2BGR)
+        mask_image = torch.unsqueeze(transform(mask), dim=0)
+        out = net(mask_image)
+        print(out)
+        out=torch.argmax(out,dim=1)
+        result = classes_names[int(out.item())]
+        cv2.putText(frame, result, (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), thickness=2)
+        cv2.imshow('frame', frame)
+
+        if cv2.waitKey(1) & 0XFF == ord('q'):
+            break
+    cap.release()
+    cv2.destroyAllWindows()
+
+def image_cls(net,path):
+    frame=cv2.imread(path)
+    image = Image.fromarray(frame)
+    w, h = image.size
+    temp = max(w, h)
+    mask = Image.new('RGB', (temp, temp))
+    if w >= h:
+        mask.paste(image, (0, (w - h) // 2))
+    else:
+        mask.paste(image, ((h - w) // 2, 0))
+    mask = mask.resize((128, 128))
+    mask = np.array(mask)
+    mask = cv2.cvtColor(mask, cv2.COLOR_RGB2BGR)
+    mask_image = torch.unsqueeze(transform(mask), dim=0)
+    out = net(mask_image)
+    print(out)
+    out = torch.argmax(out, dim=1)
+    result = classes_names[int(out.item())]
+    cv2.putText(frame, result, (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), thickness=2)
+    cv2.imshow('frame', frame)
+    cv2.waitKey(0)
+
+if __name__ == '__main__':
+    transform = transforms.Compose([
+        transforms.ToTensor()
+    ])
+    net = FaceMaskNet()
+    weights_path = r'params/new_face_mobilenet_v2.pth'
+    classes_names = ['normal', 'mask']
+
+    if os.path.exists(weights_path):
+        net.load_state_dict(torch.load(weights_path, map_location='cuda:0'))
+        print('successfully loading weights!')
+    net.eval()
+
+    image_cls(net,'image/img_1.png')
+
+

+import torch
+from torch import nn
+from torchvision import models
+
+
+class FaceMaskNet(nn.Module):
+    def __init__(self):
+        super(FaceMaskNet, self).__init__()
+        self.layer = nn.Sequential(
+            models.mobilenet_v2(pretrained=True)
+        )
+        self.classifier = nn.Sequential(
+            nn.Linear(1000, 2)
+        )
+
+    def forward(self, x):
+        return self.classifier(self.layer(x))
+
+
+
+if __name__ == '__main__':
+    net = FaceMaskNet()
+    x = torch.randn(5, 3, 128, 128)
+    print(net(x).shape)

+import os
+
+import cv2
+import torch
+
+from net import *
+from dataset import *
+
+transform = transforms.Compose([
+            transforms.ToTensor()
+        ])
+
+data=FaceMaskDataset('/data2/face_mask')
+d = DataLoader(data, batch_size=1000, shuffle=True)
+with torch.no_grad():
+    for i,(image,label) in enumerate(d):
+        net=FaceMaskNet().cuda()
+        net.load_state_dict(torch.load('params/face_mobilenet_v2.pth'))
+        net.eval()
+        out=net(image.cuda())
+        out=torch.argmax(out,dim=1)
+        acc=torch.mean(torch.eq(label.cuda(), out).float()).item()
+        print(acc)

+import os.path
+
+from torch import nn, optim
+import torch
+from dataset import *
+from torch.utils.data import random_split
+from net import *
+import tqdm
+import time
+
+if __name__ == '__main__':
+    train_rate=0.8
+    batch_size=50
+    device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    print(device)
+    epochs=50
+
+    datasets = FaceMaskDataset('/data2/new_face_mask')
+    train_datasets, test_datasets = random_split(
+        datasets,
+        [int(len(datasets) * train_rate), len(datasets) - int(len(datasets) * train_rate)],
+    )
+    print(f'train_datasets:{len(train_datasets)}   test_datasets:{len(test_datasets)}')
+    train_data_loader = DataLoader(train_datasets, batch_size=batch_size, shuffle=True)
+    test_data_loader = DataLoader(test_datasets, batch_size=batch_size, shuffle=True)
+    loss_fun = nn.CrossEntropyLoss()
+
+    net = FaceMaskNet().to(device)
+    if os.path.exists('params/new_face_mobilenet_v2.pth'):
+        net.load_state_dict(torch.load('params/new_face_mobilenet_v2.pth'))
+        print('successfully loading weights!')
+    opt = optim.Adam(net.parameters())
+
+
+    for epoch in range(1, epochs):
+
+        with tqdm.tqdm(train_data_loader) as t1:
+            for i, (image_data, image_label) in enumerate(train_data_loader):
+                net.train()
+                image_data, image_label = image_data.to(device), image_label.to(device)
+                out = net(image_data)
+                train_loss = loss_fun(out, image_label)
+                opt.zero_grad()
+                train_loss.backward()
+                opt.step()
+                t1.set_description(f'Epoch {epoch} train')
+                t1.set_postfix(train_loss=train_loss.item(),
+                               train_acc=torch.mean(torch.eq(image_label, torch.argmax(out,dim=1)).float()).item())
+                time.sleep(0.1)
+                t1.update(1)
+                if (i+1) % 10 == 0:
+                    torch.save(net.state_dict(), 'params/new_face_mobilenet_v2.pth')
+                    print(f'epoch : {epoch}  {i}  successfully save weights!')
+
+
+        acc, temp = 0, 0
+        with torch.no_grad():
+            net.eval()
+            with tqdm.tqdm(test_data_loader) as t2:
+                for j, (image_data, image_label) in enumerate(test_data_loader):
+                    image_data, image_label = image_data.to(device), image_label.to(device)
+                    out = net(image_data)
+                    test_loss = loss_fun(out, image_label)
+
+                    t2.set_description(f'Epoch {epoch} test')
+                    out = torch.argmax(out, dim=1)
+                    t2.set_postfix(test_loss=test_loss.item(),
+                                  test_acc=torch.mean(torch.eq(image_label, out).float()).item())
+                    time.sleep(0.1)
+                    t2.update(1)
+                    acc += torch.mean(torch.eq(image_label, out).float()).item()
+                    temp += 1
+                print(f'epoch : {epoch}  avg acc : ', acc / temp)
+
+        # acc,temp=0,0
+        # with torch.no_grad():
+        #     net.eval()
+        #     for i, (image_data, image_label) in enumerate(tqdm.tqdm(test_data_loader)):
+        #         image_data, image_label = image_data.to(device), image_label.to(device)
+        #         out = net(image_data)
+        #         test_loss = loss_fun(out, image_label)
+        #
+        #         out = torch.argmax(out, dim=1)
+        #
+        #         acc += torch.mean(torch.eq(image_label, out).float()).item()
+        #         temp+=1
+        #         if i % 5 == 0:
+        #             print(f'epoch : {epoch}  {i}  test_loss : ', test_loss.item())
+        #             print(f'epoch : {epoch}  {i}  test acc  : ',torch.mean(torch.eq(image_label, out).float()).item())
+        #     print(f'epoch : {epoch}  avg acc : ',acc/temp)
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+import cv2
+import numpy as np
+from PIL import Image
+# 填充黑边，等比缩放
+def keep_resize_image(image_path,size=(128,128)):
+    image=Image.open(image_path)
+    w,h=image.size
+    temp=max(w,h)
+    mask=Image.new('RGB',(temp,temp))
+    if w>=h:
+        mask.paste(image,(0,(w-h)//2))
+    else:
+        mask.paste(image,((h-w)//2,0))
+    mask=mask.resize(size)
+    mask=np.array(mask)
+    mask=cv2.cvtColor(mask,cv2.COLOR_RGB2BGR)
+    return mask
+if __name__ == '__main__':
+    keep_resize_image('image/mask/img.png')
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