code added
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Adam Wojdyla
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commit
b67569536f
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@ -1,3 +1,6 @@
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# AI-Tech-WKO-Projekt
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# Flats datasets
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## Struktura
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Surowe pliki ze zdjęciami nie są trzymane w repozytorium.
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Repozytorium do projektu z przedmiotu 'Widzenie komputerowe'
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Przy pierwszym użyciu surowe dane należy umieścić w katalogu `images/raw`, które później będą
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preprocesowane przez skrypty i umieszczone odpowiedno w `images/test` i `images/train`.
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Binary file not shown.
Binary file not shown.
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import os
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from abc import ABC
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from pathlib import Path
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from typing import Callable
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import cv2 as cv
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import numpy as np
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import torch
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from skimage.io import imread
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from sklearn.preprocessing import LabelEncoder
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from torch.utils.data import Dataset, DataLoader
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from torchvision.transforms import transforms
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def _load_data(input_dir: str, new_size: int | None = None):
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image_dir = Path(input_dir)
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categories_name = {}
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i = 0
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for file in os.listdir(image_dir):
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directory = os.path.join(image_dir, file)
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if os.path.isdir(directory):
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categories_name[i] = file
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i += 1
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folders = [directory for directory in image_dir.iterdir() if directory.is_dir()]
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train_img = []
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categories_count = len(folders)
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labels = []
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for directory in folders:
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count = 0
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for obj in directory.iterdir():
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try:
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img = imread(obj)
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if new_size is not None:
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img = cv.resize(img, (new_size, new_size), interpolation=cv.INTER_AREA)
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img = img / 255
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train_img.append(img)
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labels.append(os.path.basename(os.path.normpath(directory)))
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count += 1
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except ValueError:
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# This can happen when a file is broken, so let's omit it.
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print(f'Broken file: {obj}')
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return {
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"values": np.array(train_img),
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"categories_count": categories_count,
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"labels": labels,
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"categories_name": categories_name
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}
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class FlatsDataset(Dataset):
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def __init__(self, data, device):
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self.device = device
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self.x = []
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for d in data['values']:
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self.x.append(transforms.ToTensor()(d))
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self.y = torch.LongTensor(LabelEncoder().fit_transform(data['labels']))
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def __len__(self):
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return len(self.x)
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def __getitem__(self, ind):
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return self.x[ind], self.y[ind]
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class FlatsDatasetLoader(Dataset, ABC):
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def __init__(
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self,
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images_dir: str,
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resize_to: int or None = None,
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batch_size: int = 512,
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device: str = 'cpu',
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file_loader: Callable[[str, int or None], dict] = _load_data
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):
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self.images_dir = images_dir
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self.resize_to = resize_to
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self.batch_size = batch_size
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self.device = device
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self.loader = file_loader
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self.train_loader = None
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self.test_loader = None
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self.classes_count = 0
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self.label_names = {}
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def load(self, verbose: bool = True):
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test_dir = os.path.join(self.images_dir, 'test')
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train_dir = os.path.join(self.images_dir, 'train')
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if verbose:
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print('Loading dataset from files...')
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test_raw = self.loader(test_dir, self.resize_to)
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train_raw = self.loader(train_dir, self.resize_to)
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self.classes_count = test_raw['categories_count']
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self.label_names = test_raw['categories_name']
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if verbose:
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print('Done. Creating PyTorch datasets...')
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train_set = FlatsDataset(train_raw, self.device)
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test_set = FlatsDataset(test_raw, self.device)
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self.train_loader = DataLoader(train_set, batch_size=self.batch_size, shuffle=True)
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self.test_loader = DataLoader(test_set, batch_size=self.batch_size, shuffle=False)
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if verbose:
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print('Done.')
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def get_train_loader(self) -> DataLoader:
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return self.train_loader
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def get_test_loader(self) -> DataLoader:
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return self.test_loader
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def get_label_names(self) -> dict:
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return self.label_names
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def get_classes_count(self) -> int:
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return self.classes_count
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@ -0,0 +1,58 @@
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import os
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import shutil
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from pathlib import Path
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def mkdir_if_not_exists(path):
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try:
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os.mkdir(path)
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except FileExistsError:
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pass
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def bulk_copy(file_names: list[str], input_dir, output):
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for i, file in enumerate(file_names):
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shutil.copy(
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os.path.join(input_dir, file), os.path.join(output, str(i) + '.' + file.split('.')[1])
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)
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def split_houzz_dataset(
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raw_path: str,
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train_out_folder: str,
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test_out_folder: str,
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train_test_ratio: float = 0.8
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):
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image_dir = Path(raw_path)
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classes = []
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for maybe_dir in os.listdir(image_dir):
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class_dir = os.path.join(image_dir, maybe_dir)
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if os.path.isdir(class_dir):
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classes.append(maybe_dir)
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print(f'Found {len(classes)} classes')
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for cls in classes:
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mkdir_if_not_exists(os.path.join(train_out_folder, str(cls)))
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mkdir_if_not_exists(os.path.join(test_out_folder, str(cls)))
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|
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raw_folders = [directory for directory in image_dir.iterdir() if directory.is_dir()]
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for raw_directory, cls in zip(raw_folders, classes):
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raw_files = os.listdir(raw_directory)
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||||
print(f'{raw_directory}: {len(raw_files)}')
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split_point = round(len(raw_files) * train_test_ratio)
|
||||
train_files = raw_files[:split_point]
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||||
print(f'\tTrain files: {len(train_files)}')
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||||
test_files = raw_files[split_point + 1:]
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||||
print(f'\tTest files: {len(test_files)}')
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||||
print('Copying... ', end='')
|
||||
bulk_copy(test_files, raw_directory, os.path.join(TEST_OUTPUT, str(cls)))
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||||
bulk_copy(train_files, raw_directory, os.path.join(TRAIN_OUTPUT, str(cls)))
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||||
print('Done.')
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||||
|
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|
||||
if __name__ == '__main__':
|
||||
HOUZZ_DATASET_PATH = 'images/raw/houzz'
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||||
TRAIN_OUTPUT = 'images/train'
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||||
TEST_OUTPUT = 'images/test'
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||||
split_houzz_dataset(HOUZZ_DATASET_PATH, TRAIN_OUTPUT, TEST_OUTPUT)
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||||
|
|
@ -0,0 +1,76 @@
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|||
import argparse
|
||||
|
||||
import cv2 as cv
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||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.utils.data import DataLoader
|
||||
from torchvision.transforms import transforms
|
||||
|
||||
|
||||
def infer(data, network, loss_fn, device_type):
|
||||
x_cpu, y_cpu = data
|
||||
x = x_cpu.to(device_type).float()
|
||||
y = y_cpu.to(device_type).long()
|
||||
output = network(x)
|
||||
loss = loss_fn(output, y)
|
||||
return output, loss
|
||||
|
||||
|
||||
def evaluate(
|
||||
network: nn.Module,
|
||||
test_data: DataLoader,
|
||||
loss_fn,
|
||||
device_type: str
|
||||
) -> [np.ndarray, np.ndarray, list[float]]:
|
||||
"""
|
||||
Test a given model and return true, predicted values and loss
|
||||
"""
|
||||
network.eval()
|
||||
preds, losses = np.array([]), []
|
||||
trues = np.array([])
|
||||
with torch.no_grad():
|
||||
for data in test_data:
|
||||
output, loss = infer(data, network, loss_fn, device_type)
|
||||
trues = np.concatenate((trues, data[1].data.numpy()))
|
||||
preds = np.concatenate(
|
||||
(preds, torch.nn.functional.softmax(output, dim=1)
|
||||
.cpu()
|
||||
.data
|
||||
.numpy()
|
||||
.argmax(axis=1))
|
||||
)
|
||||
losses.append(loss.item())
|
||||
return trues, preds, losses
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
||||
|
||||
parser = argparse.ArgumentParser(description="Load a model and run inference on a source image")
|
||||
parser.add_argument(
|
||||
"-m", "--model", required=True, type=str, help="path to a pickled model to load"
|
||||
)
|
||||
parser.add_argument \
|
||||
("-i", "--image", required=True, type=str, help="path to an image to load"
|
||||
)
|
||||
args = parser.parse_args()
|
||||
model = torch.load(args.model)
|
||||
image = cv.imread(args.image)
|
||||
image = image / 255
|
||||
processed = transforms.ToTensor()(image).to(device)
|
||||
predicted = model(processed.float().unsqueeze(0))
|
||||
labels = {
|
||||
'0': 'ArtDeco',
|
||||
'1': 'Classic',
|
||||
'2': 'Glamour',
|
||||
'3': 'Industrial',
|
||||
'4': 'Minimalistic',
|
||||
'5': 'Modern',
|
||||
'6': 'Rustic',
|
||||
'7': 'Scandinavian',
|
||||
'8': 'Vintage',
|
||||
}
|
||||
print(labels[str(
|
||||
torch.nn.functional.softmax(predicted, dim=1).cpu().data.numpy().argmax(axis=1)[0]
|
||||
)])
|
||||
|
|
@ -0,0 +1,35 @@
|
|||
from statistics import mean
|
||||
|
||||
import numpy as np
|
||||
from sklearn.metrics import precision_recall_fscore_support, accuracy_score
|
||||
|
||||
|
||||
class Metrics:
|
||||
def __init__(self):
|
||||
self.loss = []
|
||||
self.accuracy = []
|
||||
self.precision = []
|
||||
self.recall = []
|
||||
self.f_score = []
|
||||
|
||||
def add_new(self, preds: np.ndarray, trues: np.ndarray, losses: list[float]):
|
||||
self.loss.append(mean(losses))
|
||||
precision, recall, f_scr, _ = precision_recall_fscore_support(
|
||||
trues,
|
||||
preds,
|
||||
average='weighted',
|
||||
zero_division=1
|
||||
)
|
||||
self.precision.append(precision)
|
||||
self.recall.append(recall)
|
||||
self.f_score.append(f_scr)
|
||||
self.accuracy.append(accuracy_score(trues, preds))
|
||||
|
||||
def as_dict(self):
|
||||
return {
|
||||
"loss": self.loss,
|
||||
"acc": self.accuracy,
|
||||
"precision": self.precision,
|
||||
"recall": self.recall,
|
||||
"f": self.f_score
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,107 @@
|
|||
from datetime import timedelta
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import seaborn as sns
|
||||
from torch.utils.data import Dataset
|
||||
from torchvision.transforms import ToPILImage
|
||||
|
||||
import metrics
|
||||
|
||||
|
||||
def plot_metrics(
|
||||
title: str,
|
||||
test_metrics: metrics.Metrics,
|
||||
train_metrics: metrics.Metrics,
|
||||
n_epochs: int,
|
||||
time: int,
|
||||
image_size: int,
|
||||
device: str = 'cpu'
|
||||
):
|
||||
"""
|
||||
Shows a plot from collected metrics
|
||||
:param title: Plot title
|
||||
:param test_metrics: A dict of keyed metric scores with arrays as values.
|
||||
Each metric should have the same # of items.
|
||||
Keys:
|
||||
- l - losses
|
||||
- a - accuracy scores
|
||||
- p - precision scores
|
||||
- r - recall scores
|
||||
- f - f scores
|
||||
:param train_metrics: A dict of keyed metric scores with arrays as values.
|
||||
See `test_metrics` for details
|
||||
:param n_epochs:
|
||||
:param time: Time taken to train the model in seconds
|
||||
:param image_size: A number corresponding to the size of images used to train the model
|
||||
:param device: What was used to train the model
|
||||
:return:
|
||||
"""
|
||||
plt.style.use('classic')
|
||||
sns.set()
|
||||
|
||||
fig, axis = plt.subplot_mosaic([['l', 'l'],
|
||||
['a', 'p'],
|
||||
['r', 'f']],
|
||||
constrained_layout=True, figsize=(10, 10))
|
||||
axis['l'].plot(test_metrics.loss)
|
||||
axis['l'].plot(train_metrics.loss)
|
||||
axis['l'].set_yscale('log')
|
||||
axis['l'].set_title("Loss")
|
||||
|
||||
axis['a'].plot(test_metrics.accuracy)
|
||||
axis['a'].plot(train_metrics.accuracy)
|
||||
axis['a'].set_title("Accuracy")
|
||||
|
||||
axis['p'].plot(test_metrics.precision)
|
||||
axis['p'].plot(train_metrics.precision)
|
||||
axis['p'].set_title("Precision")
|
||||
|
||||
axis['r'].plot(test_metrics.recall)
|
||||
axis['r'].plot(train_metrics.recall)
|
||||
axis['r'].set_title("Recall")
|
||||
|
||||
axis['f'].plot(test_metrics.f_score)
|
||||
axis['f'].plot(train_metrics.f_score)
|
||||
axis['f'].set_title("F-score")
|
||||
fig.tight_layout()
|
||||
fig.subplots_adjust(top=0.90, bottom=0.05)
|
||||
fig.suptitle(title, fontsize=24)
|
||||
fig.legend(axis, labels=['test', 'train'], loc="lower center")
|
||||
|
||||
plt.text(0.30, 0.93,
|
||||
f'{device}, {image_size}x{image_size}, {n_epochs} iteracji, czas treningu: '
|
||||
f'{str(timedelta(seconds=time)).split(".", maxsplit=1)[0]}',
|
||||
fontsize=14,
|
||||
transform=plt.gcf().transFigure)
|
||||
plt.show()
|
||||
|
||||
|
||||
def show_missclassified(
|
||||
dataset: Dataset,
|
||||
preds: np.ndarray,
|
||||
label_names: dict,
|
||||
count_per_class: int = 5
|
||||
):
|
||||
results = {}
|
||||
for i in label_names.keys():
|
||||
results[i] = []
|
||||
|
||||
indexes = np.random.permutation(len(preds))
|
||||
for i in indexes:
|
||||
pred = preds[i]
|
||||
image_tensor, true = dataset[i]
|
||||
if len(results[pred]) < count_per_class and pred != int(true):
|
||||
results[pred].append({
|
||||
"image": ToPILImage()(image_tensor),
|
||||
"actual": int(true)
|
||||
})
|
||||
sns.reset_orig()
|
||||
plt.figure(figsize=[20, 30])
|
||||
for row, (label, images) in enumerate(results.items()):
|
||||
for i, image in enumerate(images):
|
||||
plt.subplot(len(label_names.keys()), count_per_class, row * count_per_class + i + 1)
|
||||
plt.imshow(image["image"], interpolation="bicubic")
|
||||
plt.title(f'{label_names[label]}, expected {label_names[image["actual"]]}')
|
||||
plt.axis('off')
|
||||
plt.show()
|
||||
Loading…
Reference in New Issue