From bb8f28ad405b5b604feed02545fe81889e5e2a26 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Zuzanna=20W=C3=B3jcik?= Date: Mon, 5 Jun 2023 00:45:29 +0200 Subject: [PATCH] implementacja sieci neuronowych --- NeuralNetwork/prediction.py | 147 ++++++++++++++++++++++++++++++++++++ images/Items_test/test1.jpg | Bin 0 -> 10539 bytes images/Items_test/test2.jpg | Bin 0 -> 58120 bytes images/Items_test/test3.jpg | Bin 0 -> 42770 bytes images/Items_test/test4.jpg | Bin 0 -> 61459 bytes letter.py | 4 +- main.py | 12 +-- paczka.py | 3 +- 8 files changed, 158 insertions(+), 8 deletions(-) create mode 100644 NeuralNetwork/prediction.py create mode 100644 images/Items_test/test1.jpg create mode 100644 images/Items_test/test2.jpg create mode 100644 images/Items_test/test3.jpg create mode 100644 images/Items_test/test4.jpg diff --git a/NeuralNetwork/prediction.py b/NeuralNetwork/prediction.py new file mode 100644 index 0000000..b2a63a4 --- /dev/null +++ b/NeuralNetwork/prediction.py @@ -0,0 +1,147 @@ +import torch +import torch.nn as nn +from torchvision.transforms import transforms +import numpy as np +from torch.autograd import Variable +from torchvision.models import squeezenet1_1 +import torch.functional as F +from io import open +import os +from PIL import Image +import pathlib +import glob +from tkinter import Tk, Label +from PIL import Image, ImageTk + +absolute_path = os.path.abspath('NeuralNetwork/src/train_images') +train_path = absolute_path +absolute_path = os.path.abspath('Images/Items_test') +pred_path = absolute_path + +root=pathlib.Path(train_path) +classes=sorted([j.name.split('/')[-1] for j in root.iterdir()]) + + +class DataModel(nn.Module): + def __init__(self, num_classes): + super(DataModel, self).__init__() + #input (batch=256, nr of channels rgb=3 , size=244x244) + + # convolution + self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1) + #shape (256, 12, 224x224) + + # batch normalization + self.bn1 = nn.BatchNorm2d(num_features=12) + #shape (256, 12, 224x224) + self.reul1 = nn.ReLU() + + self.pool=nn.MaxPool2d(kernel_size=2, stride=2) + # reduce image size by factor 2 + # pooling window moves by 2 pixels at a time instead of 1 + # shape (256, 12, 112x112) + + + + self.conv2 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3, stride=1, padding=1) + self.bn2 = nn.BatchNorm2d(num_features=24) + self.reul2 = nn.ReLU() + # shape (256, 24, 112x112) + + self.conv3 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=3, stride=1, padding=1) + #shape (256, 48, 112x112) + self.bn3 = nn.BatchNorm2d(num_features=48) + #shape (256, 48, 112x112) + self.reul3 = nn.ReLU() + + # connected layer + self.fc = nn.Linear(in_features=48*112*112, out_features=num_classes) + + def forward(self, input): + output = self.conv1(input) + output = self.bn1(output) + output = self.reul1(output) + + output = self.pool(output) + output = self.conv2(output) + output = self.bn2(output) + output = self.reul2(output) + + output = self.conv3(output) + output = self.bn3(output) + output = self.reul3(output) + + # output shape matrix (256, 48, 112x112) + #print(output.shape) + #print(self.fc.weight.shape) + + output = output.view(-1, 48*112*112) + output = self.fc(output) + + return output + +script_dir = os.path.dirname(os.path.abspath(__file__)) +file_path = os.path.join(script_dir, 'best_model.pth') +checkpoint=torch.load(file_path) +model = DataModel(num_classes=2) +model.load_state_dict(checkpoint) +model.eval() + +transformer = transforms.Compose([ + transforms.Resize((224, 224)), # Resize images to (224, 224) + transforms.ToTensor(), # Convert images to tensors, 0-255 to 0-1 + # transforms.RandomHorizontalFlip(), # 0.5 chance to flip the image + transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5]) +]) + +def prediction(img_path,transformer): + + image=Image.open(img_path) + + image_tensor=transformer(image).float() + + image_tensor=image_tensor.unsqueeze_(0) + + if torch.cuda.is_available(): + image_tensor.cuda() + + input=Variable(image_tensor) + + output=model(input) + + index=output.data.numpy().argmax() + + pred=classes[index] + + return pred + +def prediction_keys(): + + #funkcja zwracajaca sciezki do kazdego pliku w folderze w postaci listy + + images_path=glob.glob(pred_path+'/*.jpg') + + pred_list=[] + + for i in images_path: + pred_list.append(i) + + return pred_list + +def predict_one(path): + + #wyswietlanie obrazka po kazdym podniesieniu itemu + root = Tk() + root.title("Okno z obrazkiem") + + image = Image.open(path) + photo = ImageTk.PhotoImage(image) + label = Label(root, image=photo) + label.pack() + + root.mainloop() + + #uruchamia sie funkcja spr czy obrazek to paczka czy list + pred_print = prediction(path,transformer) + print('Zdjecie jest: '+pred_print) + return pred_print \ No newline at end of file diff --git a/images/Items_test/test1.jpg b/images/Items_test/test1.jpg new file mode 100644 index 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