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| import os
import numpy as np
import pandas as pd
import cv2 as cv
from PIL import Image
import xmltodict
import shutil
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from matplotlib import pyplot as plt
originial_dataset_dir = 'D:/download/original_data'
base_dir = 'Vegetables/'
input_dim = 224
LR = 0.003
BATCH_SIZE = 16
EPOCH = 30
save_dir = './model/1.pth'
def image_preparation(original_dir, base_dir, labels):
'''
文件准备, 将文件复制到训练\测试集目录
INPUT -> 原始数据集地址, 数据集存放地址, 分类列表
'''
base_dir = base_dir
if not os.path.exists(base_dir):
os.mkdir(base_dir)
train_dir = os.path.join(base_dir, 'traindata')
if not os.path.exists(train_dir):
os.mkdir(train_dir)
test_dir = os.path.join(base_dir, 'testdata')
if not os.path.exists(test_dir):
os.mkdir(test_dir)
names = locals()
for label in labels:
names["train_"+str(label)+"dir"] = os.path.join(train_dir, str(label))
if not os.path.exists(names["train_"+str(label)+"dir"]):
os.mkdir(names["train_"+str(label)+"dir"])
names["test_"+str(label)+"dir"] = os.path.join(test_dir, str(label))
if not os.path.exists(names["test_"+str(label)+"dir"]):
os.mkdir(names["test_"+str(label)+"dir"])
fnames = [os.path.splitext(i)[0] for i in os.listdir(original_dir) if i.split('_')[0] == str(label)]
for fname in fnames[:int(len(fnames)*0.5)]:
img_src = os.path.join(original_dir, '%s.jpg' % (fname))
img_dst = os.path.join(names["train_"+str(label)+"dir"], '%s.jpg' % (fname))
shutil.copyfile(img_src, img_dst)
xml_src = os.path.join(original_dir, '%s.xml' % (fname))
xml_dst = os.path.join(names["train_"+str(label)+"dir"], '%s.xml' % (fname))
shutil.copyfile(xml_src, xml_dst)
for fname in fnames[int(len(fnames)*0.5):]:
img_src = os.path.join(original_dir, '%s.jpg' % (fname))
img_dst = os.path.join(names["test_"+str(label)+"dir"], '%s.jpg' % (fname))
shutil.copyfile(img_src, img_dst)
xml_src = os.path.join(original_dir, '%s.xml' % (fname))
xml_dst = os.path.join(names["test_"+str(label)+"dir"], '%s.xml' % (fname))
shutil.copyfile(xml_src, xml_dst)
print('total train '+str(label)+' images:', len(os.listdir(names["train_"+str(label)+"dir"])))
print('total test '+str(label)+' images:', len(os.listdir(names["test_"+str(label)+"dir"])))
image_preparation(originial_dataset_dir, base_dir, ['cucumber','eggplant','mushroom'])
n_classes = 0
for fn in os.listdir(os.path.join(base_dir, 'traindata')):
n_classes += 1
class LocationDataSet(Dataset):
def __init__(self, root_dir, transform=None, input_dim=224):
cates = ['cucumber', 'eggplant', 'mushroom']
class_binary_label = pd.get_dummies(cates).values
self.transform = transform
self.imgs = []
self.bboxes = []
self.classes = []
for cate in cates:
cate_dir = os.path.join(root_dir, str(cate))
fnames = [os.path.splitext(i)[0] for i in os.listdir(cate_dir) if i.split('_')[0] == str(cate)]
for fname in fnames:
img_path = os.path.join(cate_dir, '%s.jpg' % (fname))
img = Image.open(img_path)
xml_path = os.path.join(cate_dir, '%s.xml' % (fname))
x = xmltodict.parse(open(xml_path, 'rb'))
bndbox = x['annotation']['object']['bndbox']
bndbox = np.array([float(bndbox['xmin']), float(bndbox['ymin']), float(bndbox['xmax']), float(bndbox['ymax'])])
bndbox = bndbox / input_dim
self.imgs.append(img)
self.bboxes.append(np.hstack((bndbox, class_binary_label[cates.index(cate)])))
self.classes.append(cate)
def __getitem__(self, idx):
img = self.imgs[idx]
if self.transform:
sample = self.transform(img)
else:
sample = img
return sample, torch.Tensor(self.bboxes[idx]).float()
def __len__(self):
return len(self.imgs)
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
trainset = LocationDataSet(os.path.join(base_dir, 'traindata'), transform=transform, input_dim=input_dim)
trainloader = torch.utils.data.DataLoader(
trainset,
batch_size=BATCH_SIZE,
shuffle=True
)
testset = LocationDataSet(os.path.join(base_dir, 'testdata'), transform=transform, input_dim=input_dim)
testloader = torch.utils.data.DataLoader(
testset,
batch_size=BATCH_SIZE,
shuffle=False
)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(3, 6, kernel_size=5, padding=2, bias=False),
nn.LeakyReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.conv2 = nn.Sequential(
nn.Conv2d(6, 16, kernel_size=5, padding=2, bias=False),
nn.LeakyReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.conv3 = nn.Sequential(
nn.Conv2d(16, 120, kernel_size=5, padding=2, bias=False),
nn.LeakyReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.classifier = nn.Sequential(
nn.Linear(120*28*28, 120),
nn.LeakyReLU(),
nn.Dropout(),
nn.Linear(120, 84),
nn.LeakyReLU(),
nn.Dropout(),
nn.Linear(84, n_classes+4)
)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = x.view(x.size(0), 120*28*28)
out = self.classifier(x)
return out
class LocationLoss(nn.Module):
'''
损失函数（同时使用了平方差和交并比）
'''
def calculate_iou(self, target_boxes, pred_boxes):
x_min = torch.max(target_boxes[:, 0], pred_boxes[:, 0])
y_min = torch.max(target_boxes[:, 1], pred_boxes[:, 1])
x_max = torch.min(target_boxes[:, 2], pred_boxes[:, 2])
y_max = torch.min(target_boxes[:, 3], pred_boxes[:, 3])
intersection = torch.max(torch.zeros(x_max.shape).cuda(), x_max - x_min) * torch.max(torch.zeros(y_max.shape).cuda(), y_max - y_min)
boxAArea = (target_boxes[:, 2] - target_boxes[:, 0]) * (target_boxes[:, 3] - target_boxes[:, 1])
boxBArea = (pred_boxes[:, 2] - pred_boxes[:, 0]) * (pred_boxes[:, 3] - pred_boxes[:, 1])
iou = intersection / (boxAArea + boxBArea - intersection)
return iou
def forward(self, target_boxes, pred_boxes):
mseloss = nn.MSELoss().forward(target_boxes, pred_boxes)
iouloss = torch.mean(1 - self.calculate_iou(target_boxes, pred_boxes))
return mseloss + iouloss
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = Net().to(device)
criterion = LocationLoss()
optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9)
def train_work(mode=None):
'''
训练阶段
'''
net.train()
if mode == 'Update' and os.path.exists(save_dir):
net.load_state_dict(torch.load(save_dir))
loss_over_time = []
for epoch in range(EPOCH):
running_loss = 0.0
i = 0
for data in trainloader:
i += 1
images, labels = data
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
outputs = net(images)
loss = criterion.forward(labels, outputs)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 10 == 9:
avg_loss = running_loss/10
loss_over_time.append(avg_loss)
print('[%d] loss: %.03f' % (epoch + 1, avg_loss))
running_loss = 0.0
with torch.no_grad():
correct = 0
total = 0
for data in trainloader:
images, labels = data
images, labels = images.to(device), labels.to(device)
outputs = net(images)
predicted = torch.nn.functional.one_hot(torch.argmax(outputs[:, 4:7], dim=1), num_classes=n_classes)
correct += torch.mean((predicted.float() == labels[:, 4:7].float()).float()).item()
total += 1
print('第%d个epoch的识别准确率为：%d%%' % (epoch + 1, (100 * correct / total)))
print('Finished Training')
torch.save(net.state_dict(), save_dir)
return loss_over_time
def show_loss(training_loss):
'''
可视化损失变化
'''
plt.plot(training_loss)
plt.xlabel('10\'s of batches')
plt.ylabel('loss')
plt.ylim(0, 2.5)
plt.show()
def show_predicted():
'''
展示预测结果
'''
img, label = testloader.dataset.__getitem__(0)
image = img.unsqueeze(0).to(device)
label = label.unsqueeze(0)
net.load_state_dict(torch.load(save_dir))
net.eval()
outputs = net(image)
predict_cate = torch.argmax(outputs[:, 4:7], dim=1)
truth_cate = torch.argmax(label[:, 4:7], dim=1)
img = img * 0.5 + 0.5
pil_transform = transforms.ToPILImage()
truth_rect = label[:, :4] * input_dim
predict_rect = outputs[:, :4] * input_dim
print(truth_rect)
print(predict_rect)
origin = np.array(pil_transform(img), dtype=np.uint8)
x_min, y_min, x_max, y_max = truth_rect.squeeze()[:4]
cv.rectangle(origin, (x_min, y_min), (x_max, y_max),
(0, 255, 0), thickness=2)
x_min, y_min, x_max, y_max = predict_rect.squeeze()[:4]
cv.rectangle(origin, (x_min, y_min), (x_max, y_max),
(0, 0, 255), thickness=2)
cv.imwrite('box_detector.png', origin)
if __name__ == "__main__":
training_loss = train_work('Update')
show_loss(training_loss)
show_predicted()
|
