147 lines
4.1 KiB
Python
147 lines
4.1 KiB
Python
import torch
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import torch.nn as nn
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from torchvision.transforms import transforms
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import numpy as np
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from torch.autograd import Variable
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from torchvision.models import squeezenet1_1
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import torch.functional as F
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from io import open
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import os
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from PIL import Image
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import pathlib
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import glob
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from tkinter import Tk, Label
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from PIL import Image, ImageTk
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absolute_path = os.path.abspath('NeuralNetwork/src/train_images')
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train_path = absolute_path
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absolute_path = os.path.abspath('Images/Items_test')
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pred_path = absolute_path
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root=pathlib.Path(train_path)
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classes=sorted([j.name.split('/')[-1] for j in root.iterdir()])
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class DataModel(nn.Module):
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def __init__(self, num_classes):
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super(DataModel, self).__init__()
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#input (batch=256, nr of channels rgb=3 , size=244x244)
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# convolution
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self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
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#shape (256, 12, 224x224)
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# batch normalization
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self.bn1 = nn.BatchNorm2d(num_features=12)
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#shape (256, 12, 224x224)
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self.reul1 = nn.ReLU()
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self.pool=nn.MaxPool2d(kernel_size=2, stride=2)
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# reduce image size by factor 2
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# pooling window moves by 2 pixels at a time instead of 1
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# shape (256, 12, 112x112)
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self.conv2 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3, stride=1, padding=1)
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self.bn2 = nn.BatchNorm2d(num_features=24)
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self.reul2 = nn.ReLU()
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# shape (256, 24, 112x112)
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self.conv3 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=3, stride=1, padding=1)
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#shape (256, 48, 112x112)
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self.bn3 = nn.BatchNorm2d(num_features=48)
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#shape (256, 48, 112x112)
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self.reul3 = nn.ReLU()
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# connected layer
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self.fc = nn.Linear(in_features=48*112*112, out_features=num_classes)
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def forward(self, input):
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output = self.conv1(input)
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output = self.bn1(output)
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output = self.reul1(output)
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output = self.pool(output)
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output = self.conv2(output)
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output = self.bn2(output)
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output = self.reul2(output)
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output = self.conv3(output)
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output = self.bn3(output)
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output = self.reul3(output)
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# output shape matrix (256, 48, 112x112)
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#print(output.shape)
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#print(self.fc.weight.shape)
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output = output.view(-1, 48*112*112)
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output = self.fc(output)
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return output
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script_dir = os.path.dirname(os.path.abspath(__file__))
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file_path = os.path.join(script_dir, 'best_model.pth')
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checkpoint=torch.load(file_path)
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model = DataModel(num_classes=2)
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model.load_state_dict(checkpoint)
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model.eval()
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transformer = transforms.Compose([
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transforms.Resize((224, 224)), # Resize images to (224, 224)
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transforms.ToTensor(), # Convert images to tensors, 0-255 to 0-1
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# transforms.RandomHorizontalFlip(), # 0.5 chance to flip the image
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transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5])
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])
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def prediction(img_path,transformer):
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image=Image.open(img_path)
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image_tensor=transformer(image).float()
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image_tensor=image_tensor.unsqueeze_(0)
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if torch.cuda.is_available():
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image_tensor.cuda()
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input=Variable(image_tensor)
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output=model(input)
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index=output.data.numpy().argmax()
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pred=classes[index]
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return pred
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def prediction_keys():
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#funkcja zwracajaca sciezki do kazdego pliku w folderze w postaci listy
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images_path=glob.glob(pred_path+'/*.jpg')
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pred_list=[]
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for i in images_path:
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pred_list.append(i)
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return pred_list
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def predict_one(path):
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#wyswietlanie obrazka po kazdym podniesieniu itemu
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root = Tk()
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root.title("Okno z obrazkiem")
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image = Image.open(path)
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photo = ImageTk.PhotoImage(image)
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label = Label(root, image=photo)
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label.pack()
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root.mainloop()
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#uruchamia sie funkcja spr czy obrazek to paczka czy list
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pred_print = prediction(path,transformer)
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print('Zdjecie jest: '+pred_print)
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return pred_print |