erap129

import sys
from PyQt6.QtWidgets import QApplication, QHBoxLayout, QMainWindow, QPushButton, QSpinBox, QVBoxLayout, QWidget, QFileDialog, QLabel, QErrorMessage
from PyQt6.QtGui import QImage, QPixmap
import cv2
import numpy as np


class ImageWidget(QLabel):
    def __init__(self, parent=None):
        super().__init__(parent)

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def resize_image(image_data, max_img_width, max_img_height):
    scale_percent = min(max_img_width / image_data.shape[1], max_img_height / image_data.shape[0])
    width = int(image_data.shape[1] * scale_percent)
    height = int(image_data.shape[0] * scale_percent)
    newSize = (width, height)
    image_resized = cv2.resize(image_data, newSize, None, None, None, cv2.INTER_AREA)
    return image_resized


def pixmap_from_cv_image(cv_image):

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def choose_source_image(self):
        self.source_filename = QFileDialog.getOpenFileName()[0]
        self.source_image_data = cv2.imread(self.source_filename)
        source_image_resized = resize_image(self.source_image_data, self.max_img_width, self.max_img_height)
        self.source_image.setPixmap(pixmap_from_cv_image(source_image_resized))

def process_image(self):
    if self.source_image_data is None:
        error_dialog = QErrorMessage()
        error_dialog.showMessage('No image selected')

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bottom_bar_layout = QHBoxLayout()
self.save_button = QPushButton('Save as file')
self.save_button.clicked.connect(self.save_as_file)        
self.save_button.setFixedWidth(300)
bottom_bar_layout.addWidget(self.save_button)
self.percent_traced_label = QLabel()
bottom_bar_layout.addWidget(self.percent_traced_label)

main_layout.addLayout(top_bar_layout)
main_layout.addLayout(image_bar_layout)

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top_bar_layout.addWidget(select_image_button)
top_bar_layout.addWidget(self.r_select)
top_bar_layout.addWidget(self.g_select)
top_bar_layout.addWidget(self.b_select)
top_bar_layout.addWidget(self.threshold_select)
top_bar_layout.addWidget(process_image_button)

self.source_image = ImageWidget()
self.result_image = ImageWidget()
self.source_image.setMaximumSize(self.max_img_width, self.max_img_height)

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select_image_button = QPushButton('Select Image')
process_image_button = QPushButton('Process!')
select_image_button.clicked.connect(self.choose_source_image)
process_image_button.clicked.connect(self.process_image)
for btn in [select_image_button, process_image_button]:
    btn.setFixedHeight(30)
    btn.setFixedWidth(100)
self.r_select = QSpinBox()
self.g_select = QSpinBox()
self.b_select = QSpinBox()

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class MainWindow(QMainWindow):
    def __init__(self):
        super(MainWindow, self).__init__()

        self.setWindowTitle("Image Tracer")

        main_layout = QVBoxLayout()
        top_bar_layout = QHBoxLayout()
        image_bar_layout = QHBoxLayout()
        self.source_filename = None

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,unit_number,time,sensor_1,sensor_2,sensor_3,sensor_4,sensor_5,sensor_6,sensor_7,sensor_8,sensor_9,sensor_10,sensor_11,sensor_12,sensor_13,sensor_14,sensor_15,sensor_16,sensor_17,sensor_18,sensor_19,sensor_20,sensor_21,sensor_22,sensor_23,sensor_24,sensor_25,sensor_26
0,1,1,-0.0007,-0.0004,100.0,518.67,641.82,1589.7,1400.6,14.62,21.61,554.36,2388.06,9046.19,1.3,47.47,521.66,2388.02,8138.62,8.4195,0.03,392,2388,100.0,39.06,23.419,,
1,1,2,0.0019,-0.0003,100.0,518.67,642.15,1591.82,1403.14,14.62,21.61,553.75,2388.04,9044.07,1.3,47.49,522.28,2388.07,8131.49,8.4318,0.03,392,2388,100.0,39.0,23.4236,,
2,1,3,-0.0043,0.0003,100.0,518.67,642.35,1587.99,1404.2,14.62,21.61,554.26,2388.08,9052.94,1.3,47.27,522.42,2388.03,8133.23,8.4178,0.03,390,2388,100.0,38.95,23.3442,,
3,1,4,0.0007,0.0,100.0,518.67,642.35,1582.79,1401.87,14.62,21.61,554.45,2388.11,9049.48,1.3,47.13,522.86,2388.08,8133.83,8.3682,0.03,392,2388,100.0,38.88,23.3739,,
4,1,5,-0.0019,-0.0002,100.0,518.67,642.37,1582.85,1406.22,14.62,21.61,554.0,2388.06,9055.15

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from tqdm import tqdm

def get_predictions_and_labels_lstm(dataset):
  predictions = []
  labels = []
  for item in tqdm(test_dataset):
    X, y = item
    _, output = trained_model(X.unsqueeze(dim=0))
    predictions.append(output.item())
    labels.append(y.item())

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checkpoint_callback = ModelCheckpoint(
    dirpath='checkpoints',
    filename='best-checkpoint',
    save_top_k=1,
    verbose=True,
    monitor='val_loss',
    mode='min'
)

logger = TensorBoardLogger('lightning_logs', name='RUL')