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main.py
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main.py
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from compare import *
from db_reader import *
from Cop import *
from ArduinoThread import *
from Arduino_Con import *
from Hata_Goster import *
from PDF_Thread import *
from Pdf_Lister_Thread import *
from post_thread import *
from GirisKayit import *
from Camera import *
from Giris import *
from admin_page import *
from Camera import*
from Yukleniyor import *
from whitedetect import *
from image_dimension_converter import *
from threshold import *
from mail_send import *
import time
import sys
import psutil
import logging
import traceback
import datetime
from PyQt5.QtCore import QObject, QThread, pyqtSignal
from PyQt5.QtWidgets import (
QApplication,
QMainWindow,
qApp
)
# Log ayarları
logging.basicConfig(filename='app_logs.txt')
logging.info("Program başlatıldı.")
class Worker(QObject):
finished = pyqtSignal()
progress = pyqtSignal(int)
def run(self):
while 1:
time.sleep(0.01)
class MainWindow(QMainWindow):
def __init__(self):
super(MainWindow, self).__init__()
self.ui_ = Ui_LoadingScreen()
self.setWindowFlag(Qt.FramelessWindowHint)
self.ui_.setupUi(self)
icon_loading = QPixmap("./Icon/MainWindow/Loadign-Logo.png")
self.ui_.Logo.setPixmap(icon_loading)
self.show()
self.execute()
def execute(self):
self.update_progress(0)
self.thread = QThread()
self.worker = Worker()
self.worker.moveToThread(self.thread)
self.thread.started.connect(self.worker.run)
self.worker.finished.connect(self.thread.quit)
self.worker.finished.connect(self.worker.deleteLater)
self.thread.finished.connect(self.thread.deleteLater)
self.worker.progress.connect(self.update_progress)
self.thread.start()
main(self.worker, self)
def update_progress(self, progress):
self.ui_.progressBar.setValue(progress)
qApp.processEvents()
def main(worker, window):
def loadingbar(x):
worker.progress.emit(x)
sleep(1./120)
loadingbar(5)
import Db_Con as DC
loadingbar(10)
from pylonReader import PylonVideoStream
loadingbar(15)
from Ysa import get_model
loadingbar(20)
from Helper import Helper
loadingbar(25)
from ToolKit import ToolKit
loadingbar(30)
from os import _exit, mkdir, scandir
loadingbar(35)
from torch import tensor, device, cuda, cat
loadingbar(40)
from imutils import resize
loadingbar(45)
from cv2 import cvtColor, COLOR_BGR2RGB, INTER_CUBIC, imwrite, waitKey, destroyAllWindows, imshow
loadingbar(50)
from cv2 import resize as resize_cv2
loadingbar(55)
from PyQt5.QtGui import QImage,QPixmap
loadingbar(60)
from PyQt5.QtCore import QTimer, QTime
loadingbar(65)
from io import BytesIO
loadingbar(70)
from pandas import DataFrame
loadingbar(75)
from pypylon import pylon
loadingbar(80)
import random
global configs
configs = {
1: {
'value1': 0,
'value3': 0,
'value5': [0,0]
},
2: {
'value1': 0,
'value3': 0,
'value5': [0,0]
},
3: {
'value1': 0,
'value3': 0,
'value5': [0,0]
},
4: {
'value1': 0,
'value3': 0,
'value5': [0,0]
},
}
def displayImage():
Tools.Port_Op()
quality = ui2.comboBox_Kalite_No.currentText()
dok_num = ui2.lineEdit_Dok_No.text()
if not quality or quality == "Kalite seçiniz":
ui10.warning_label.setText("Kalite Kodunu giriniz Lütfen. Kapatmak için butona tıklayınız lütfen...")
Tools.QWarning_Window()
elif not dok_num:
Tools.QWarning_Window()
else:
# MainWindow7.show()
ui2.logic_All = 1
MainWindow9.showMaximized()
ui2.Ac_pushButton.setDisabled(True)
ui2.Kayit_pushButton_2.setDisabled(True)
ui2.Off_pushButton.setDisabled(False)
Basler_Cameras()
def Soft_Serial_OPEN():
Tools.Port_Op()
MainWindow7.close()
MainWindow9.showMaximized()
Basler_Cameras()
# if Tools.Trigg_Port_Button==True:
# MainWindow7.close()
# MainWindow9.showMaximized()
# Basler_Cameras()
def Soft_NSerial_OPEN():
Tools.Port_Close()
if Tools.Non_Trigg_Port_Button==True:
MainWindow7.close()
MainWindow9.showMaximized()
Basler_Cameras()
def record_cameras_data():
ui2.logic_All = 1
ui2.Ac_pushButton.setDisabled(True)
ui2.Off_pushButton.setDisabled(False)
Tools.Port_Close()
if Tools.Non_Trigg_Port_Button==True:
MainWindow7.close()
Basler_Cameras(choise = "Record")
def Basler_Cameras(choise: str = "Detection"):
New_Day_Folder()
selected_item = ui2.comboBox_Kalite_No.currentText()
try:
Arduino_Tools.port_ac(Veri_Tabani_Window())
brightnessValue = Veri_Tabani_Window().get_fabric_brightness(selected_item)
sleep(1./5)
Arduino_Tools.setBrightness(str(brightnessValue))
sleep(1./5)
Arduino_Tools.setBrightness(str(brightnessValue))
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
brightnessValue = Veri_Tabani_Window().get_fabric_brightness(selected_item)
time.sleep(0.1)
ui2.statusbar.showMessage(" "*1 + " Port açılamadı !!!", 1500)
faulty_cnt = 0
faulty_cnt_100 = 0
theta_1_center, theta_2_center = [0, 0]
detect_threshold = 3
detect_threshold_100 = 10
detect_cam = 0
threshold = 200
wDetect = False
start_time_faulty_cnt = 0
start_time_faulty_cnt_100 = 0
limited_time_faulty = 6
limited_time_faulty_100 = 120
detect_faulty_fabric = {"Flawed Hole": 0, "Flawed Spot": 0, "Flawed Crack": 0, "Other Errors": 0}
start_time_crack_cnt = 0
limited_time_crack = 18
crack_ready = False
myTime = 0
recordcounter = 0
prev_time = time.time()
position = 0
speed = 0
prev_frame_time = 0
new_frame_time = 0
########################################################################################################################
#Pixel to mm
local_height, vision_weight, vision_height, vision_angle = Veri_Tabani_Window().get_Camera_Local_Settings()
# print(vision_weight)
resolution_pixels_height, resolution_pixels_width, _ = Veri_Tabani_Window().get_last_Heigt_Width()
# print(resolution_pixels_width)
width_converter = WidthConverter(vision_weight, resolution_pixels_width)
########################################################################################################################
tlFactory = pylon.TlFactory.GetInstance()
devices = tlFactory.EnumerateDevices()
cameras = pylon.InstantCameraArray(min(len(devices), 3))
device_indices = []
for serial in Tools.Camera_Serial:
for i, devic in enumerate(devices):
if devic.GetSerialNumber() == serial:
device_indices.append(i)
break
else:
device_indices.append(None)
for i, cam in enumerate(cameras):
# print(cam.GetDeviceInfo().GetSerialNumber())
device_index = device_indices[i]
if device_index is not None:
cam.Attach(tlFactory.CreateDevice(devices[device_index]))
serial_cam = cam.GetDeviceInfo().GetSerialNumber()
print("Using device ", serial_cam)
ui2.statusbar.showMessage(" "*1 + f" Tanımlı kamera: {serial_cam}", 1500)
vs_pylon = PylonVideoStream(cameras, Tools).start()
tensor_temp = tensor([1.0,2.0], device="cuda")
device_temp = device('cuda' if cuda.is_available() else 'cpu')
ui2.statusbar.showMessage( " " * 1 + f" Cuda GPU Durumu: {cuda .is_available()}", 1500)
if cuda.is_available():
tensor_temp = tensor_temp.to(device_temp)
class_thresholds, error_thresholds, type_speed = Veri_Tabani_Window().get_fabric_settings(selected_item)
while 1:
current_time_target = datetime.datetime.now()
if current_time_target.hour == 0 and 0 < current_time_target.minute < 2:
New_Day_Folder()
position, speed = Arduino_Tools.Feedback_src()
position: int = int(position) / 1000
speed: int = int(speed)
if speed > 120:
ui9.text_Dok_Hizi.setText("0")
else:
ui9.text_Dok_Hizi.setText(str(speed))
if position is None:
ui9.text_Metre_Durumu.setText(str(int(0)))
else:
ui9.text_Metre_Durumu.setText(str(int(position)))
Dok_no= ui2.lineEdit_Dok_No.text()
Kalite_no= ui2.comboBox_Kalite_No.currentText()
if vs_pylon.cameras.IsCameraDeviceRemoved() :
vs_pylon.cameras.Close()
pixmap = QPixmap('./Icon/Label Img/CameraOFF.PNG')
ui2.Camera_1.setPixmap(pixmap)
ui2.Camera_2.setPixmap(pixmap)
ui2.Camera_3.setPixmap(pixmap)
ui2.Camera_4.setPixmap(pixmap)
return Basler_Cameras()
try:
frames = vs_pylon.read()
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
return
if ui2.logic_All == 0:
vs_pylon.stop()
for cam in vs_pylon.Active_cameras:
cam.grabResult.Release()
cameras.StopGrabbing()
break
if len(frames) == 0:
print('No camera is detected!')
ui2.statusbar.showMessage(" "*1 + " Tanımlı kamera bulunmamaktadır. Lütfen kamera takılı ise admin panelinden ayarlayınız...", 1500)
ui2.Off_pushButton.setDisabled(True)
vs_pylon.stop()
ui2.Ac_pushButton.setDisabled(False)
return
if len(frames) == 1:
Dok_no= ui2.lineEdit_Dok_No.text()
Kalite_no= ui2.comboBox_Kalite_No.currentText()
model_name = frames[0][1]
model_image = frames[0][0]
model_image = model_image[:-1, :-1, :]
model_image, cloth_width = ImageProcessor(image=model_image, trim_size=75).trim_image()
try:
cloth_width = int(round(width_converter.convert(cloth_width) / 10, 2))
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
cloth_width=0
copy_model_image = np.copy(model_image)
height, width, channel = model_image.shape
zoom_value_5 = Tools.zoom_value_5()
zoom_value_3 = Tools.zoom_value_3()
zoom_value_1 = Tools.zoom_value_1()
if ui2.radioButton_Camera_I.isChecked()==True and model_name== Tools.Camera_Serial[0]:
model_image = model_image[zoom_value_5[1]+ zoom_value_3: -zoom_value_5[1]+height + zoom_value_3, zoom_value_5[0]+zoom_value_1 : width - (zoom_value_5[0])+ zoom_value_1 ]
if ui2.radioButton_Camera_II.isChecked()==True and model_name==Tools.Camera_Serial[1]:
model_image = model_image[zoom_value_5[1]+ zoom_value_3: -zoom_value_5[1]+height + zoom_value_3, zoom_value_5[0]+zoom_value_1 : width - (zoom_value_5[0])+ zoom_value_1 ]
new_frame_time = time.time()
fps = 1/(new_frame_time-prev_frame_time)
prev_frame_time = new_frame_time
fps = str(int(fps))
results = model(model_image, size=2048)
results.render()
out = cvtColor(results.ims[0], COLOR_BGR2RGB)
outh1 = int((43*height)/256)
outh2 = int((213*height)/256)
out[outh1,:] = 0
out[outh2,:] = 0
df=results.pandas().xyxy[0]
df=DataFrame(df)
myTime+=1
def y_detect(cy, theta_1_center, theta_2_center, cx):
if 0 <= cy <= model_image.shape[0] * 1:
detect_cam = 1
cv2.line(out, (int(theta_1_center), (detect_cam-1) * height), (int(theta_1_center), height*detect_cam), (0, 255, 0), thickness=2)
cv2.line(out, (int(theta_2_center), (detect_cam-1) * height), (int(theta_2_center), height*detect_cam), (0, 255, 0), thickness=2)
faulty_meter = round(width_converter.convert(cx) / 10, 2)
return detect_cam, out, faulty_meter
elif model_image.shape[0] * 1 < cy <= model_image.shape[0] * 2:
detect_cam = 2
cv2.line(out, (int(theta_1_center), (detect_cam-1) * height), (int(theta_1_center), height*detect_cam), (0, 255, 0), thickness=2)
cv2.line(out, (int(theta_2_center), (detect_cam-1) * height), (int(theta_2_center), height*detect_cam), (0, 255, 0), thickness=2)
faulty_meter = round(width_converter.convert(cx + width) / 10, 2)
return detect_cam, out, faulty_meter
elif model_image.shape[0] * 2 < cy <= model_image.shape[0] * 3:
detect_cam = 3
cv2.line(out, (int(theta_1_center), (detect_cam-1) * height), (int(theta_1_center), height*detect_cam), (0, 255, 0), thickness=2)
cv2.line(out, (int(theta_2_center), (detect_cam-1) * height), (int(theta_2_center), height*detect_cam), (0, 255, 0), thickness=2)
faulty_meter = round(width_converter.convert(cx + width * 2) / 10, 2)
return detect_cam, out, faulty_meter
return detect_cam
single_frame = resize(out, width=1400)
height_s, width_s, channel_s = single_frame.shape
step_s = channel_s * width_s
if len(df)!=0:
for detect in range(len(df.iloc[:]['name'])):
Save_image="./Database"+"/"+helper.Db_path_time(choice="Now-Day")+"/"+"Cam"+"/"+"images"+"/"+df.iloc[:]['name'][detect]+"-"+helper.Db_path_time(choice="Now-Time")+"-"+".jpg"
Save_crop_image = "./Database"+"/"+helper.Db_path_time(choice="Now-Day")+"/"+"Cam"+"/"+"images"+"/"+"cropped"+"/"+df.iloc[:]['name'][detect]+"-"+helper.Db_path_time(choice="Now-Time")+"-"+".jpg"
x1=int(df.iloc[:]['xmin'][detect])
x2=int(df.iloc[:]['xmax'][detect])
y1=int(df.iloc[:]['ymin'][detect])
y2=int(df.iloc[:]['ymax'][detect])
cx = (x1 + x2)/ 2
cy = (y1 + y2)/ 2
if(x1<=0):x1=0
if(y1<=0):y1=0
if (x1<=56):x1=56
if(y1<=56):y1=56
yc = (y1+y2)/2
if yc>outh1 and yc<outh2:
new_x1, new_x2, new_y1, new_y2 = Tools.expand(copy_model_image.shape, x1, x2, y1, y2, [10.5, 5.5, 1.1])
try:
crop = copy_model_image[new_y1:new_y2,new_x1:new_x2]
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
crop = copy_model_image[y1:y2,x1:x2]
if Tools.Trigg_Port_Button == True:
try:
src, speed = Arduino_Tools.Feedback_src()
src: int = int(int(src) / 1000)
speed: int = int(speed)
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
ui2.statusbar.showMessage(" "*1 + "Seri Port Hatası Metre bilgileri 0 Olarak Ayarlandı", 1500)
src=0
if Tools.Trigg_Port_Button==False:
src=0
x = abs(x2-x1)
y = abs(y2-y1)
x = round(width_converter.convert(x), 2)
y = round(width_converter.convert(y), 2)
xy = round(x * y, 2)
if (str(df.at[detect, 'name']) in ['dikis']):
start_time_crack_cnt = time.time()
crack_ready = False
if (str(df.at[detect, 'name']) in ['delik', 'leke', 'kirik']):
if faulty_cnt == 0:
start_time_faulty_cnt = time.time()
theta_1_center = max(0, cx - threshold)
theta_2_center = min(model_image.shape[1], cx + threshold)
y_detect_1, single_frame, faulty_meter = y_detect(cy, theta_1_center, theta_2_center, cx)
print(f"yakalanan kamera: {y_detect_1}")
y_detect_2, single_frame, faulty_meter = y_detect(cy, theta_1_center, theta_2_center, cx)
if faulty_cnt_100 == 0:
start_time_faulty_cnt_100 = time.time()
try:
crop = resize_cv2(crop, (320,320), interpolation = INTER_CUBIC)
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
crop = np.zeros((320, 320), dtype=np.uint8)
faulty_meter = int(round(width_converter.convert(cx) / 10, 2) )
image2 = QtGui.QImage(crop.data, crop.shape[1], crop.shape[0], QtGui.QImage.Format_RGB888).rgbSwapped()
image3 = QtGui.QImage(single_frame.data, single_frame.shape[1], single_frame.shape[0], QtGui.QImage.Format_RGB888).rgbSwapped()
ui6.Goster_Label.setPixmap(QtGui.QPixmap.fromImage(image2))
detect_images.insert(0, image2)
show_images.insert(0, single_frame)
detect_Faulty_Windows.insert(0, image3)
detect_Hata_Eni.insert(0, str(x))
detect_Hata_Boyu.insert(0, str(y))
detect_Hata_Alan.insert(0, str(faulty_meter))
detect_Hata_Metre.insert(0, str(src))
detect_Hata_Sinif.insert(0, str(df.iloc[:]['name'][detect]))
# detect_images için değişiklik
if len(detect_images) > max_data_count:
del detect_images[max_data_count:]
for index in range(len(detect_images)):
index2 = index % 6
index3 = page_num[index2]
try:
if str(df.at[detect, 'name']) in error_thresholds:
Hata_Goster_labels[index2].setPixmap(QtGui.QPixmap.fromImage(detect_images[index3]))
Hata_Eni_labels[index2].setText(detect_Hata_Eni[index3])
Hata_Boyu_labels[index2].setText(detect_Hata_Boyu[index3])
Hata_Alan_labels[index2].setText(detect_Hata_Alan[index3])
Hata_Metre_Labels[index2].setText(detect_Hata_Metre[index3])
Hata_Sinif_labels[index2].setText(detect_Hata_Sinif[index3])
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
pass
# detect_Faulty_Windows için değişiklik
if len(detect_Faulty_Windows) > max_data_count:
del detect_Faulty_Windows[max_data_count:]
for index in range(len(detect_Faulty_Windows)):
index2 = index % 4
try:
if str(df.at[detect, 'name']) in error_thresholds:
Hata_Faultys_Window_labels[index2].setPixmap(QtGui.QPixmap.fromImage(detect_Faulty_Windows[index2]))
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
pass
if str(df.iloc[:]['name'][detect])=='delik':
detect_faulty_fabric["Flawed Hole"] += 1
elif str(df.iloc[:]['name'][detect])=='leke':
detect_faulty_fabric["Flawed Spot"] += 1
elif str(df.iloc[:]['name'][detect])=='kirik':
detect_faulty_fabric["Flawed Crack"] += 1
waitKey(2)
postOut = cvtColor(crop, COLOR_BGR2RGB)
img = Image.fromarray(postOut, "RGB")
img_byte_arr = BytesIO()
img.save(img_byte_arr, format='PNG')
img_byte_arr = img_byte_arr.getvalue()
if len(helper.last_images)>=5:
del helper.last_images[0]
helper.last_images.append(crop)
else:
helper.last_images.append(crop)
imwrite(Save_image, copy_model_image)
imwrite(Save_crop_image, crop)
waitKey(1)
Hata_Koordinant = [x1, x2, y1, y2]
Hata_Koordinant = ", ".join(str(coord) for coord in Hata_Koordinant)
helper.append_db(df, detect, Save_image, str(src), str(x), str(y), str(xy), Dok_no, Kalite_no, Hata_Koordinant, cloth_width, faulty_meter)
if (str(df.at[detect, 'name']) in error_thresholds) and (theta_1_center <= cx <= theta_2_center) and (y_detect_1 == y_detect_2) and crack_ready == True:
faulty_cnt += 1
if (str(df.at[detect, 'name']) in error_thresholds) and (theta_1_center <= cx <= theta_2_center) and (y_detect_1 == y_detect_2):
faulty_cnt += 1
if (str(df.at[detect, 'name']) in error_thresholds):
faulty_cnt_100 +=1
elif not (theta_1_center <= cx <= theta_2_center):
faulty_cnt = 0
if faulty_cnt >= detect_threshold:
MainWindow11.show()
faulty_cnt = 0
Arduino_Tools.kirmizi_led_ac()
sleep(1./5)
Arduino_Tools.setBrightness(str(brightnessValue))
sleep(1./5)
Arduino_Tools.setBrightness(str(brightnessValue))
print("Kırmızı Led Aktif")
if faulty_cnt_100 >= detect_threshold_100:
MainWindow11.show()
faulty_cnt_100 = 0
print("Sari Led Aktif")
Arduino_Tools.sari_led_ac()
sleep(1./5)
Arduino_Tools.setBrightness(str(brightnessValue))
sleep(1./5)
Arduino_Tools.setBrightness(str(brightnessValue))
if Tools.Trigg_Port_Button==True:
try:
src, speed = Arduino_Tools.Feedback_src()
src: int = int(int(src) / 1000)
speed: int = int(speed)
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
ui2.statusbar.showMessage(" "*1 + "Seri Port Hatası Metre bilgileri 0 Olarak Ayarlandı", 1500)
src=0
if str(df.iloc[:]['name'][detect]) not in ['delik', 'leke']:
if len(helper.last_images)>=5:
del helper.last_images[0]
helper.last_images.append(crop)
else:
helper.last_images.append(crop)
detect_faulty_fabric["Other Errors"] += 1
imwrite(Save_image, copy_model_image)
imwrite(Save_crop_image, crop)
Hata_Koordinant = [x1, x2, y1, y2]
Hata_Koordinant = ", ".join(str(coord) for coord in Hata_Koordinant)
faulty_meter = int(round(width_converter.convert(cx) / 10, 2))
try:
helper.append_db(df, detect, Save_image, str(src), str(x), str(y), str(xy), Dok_no, Kalite_no, Hata_Koordinant, cloth_width, faulty_meter)
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
theta_1_center = max(0, cx - threshold)
theta_2_center = min(model_image.shape[1], cx + threshold)
y_detect_2, single_frame, faulty_meter = y_detect(cy, theta_1_center, theta_2_center, cx)
helper.append_db(df, detect, Save_image, str(src), str(x), str(y), str(xy), Dok_no, Kalite_no, Hata_Koordinant, cloth_width, faulty_meter)
ui9.text_Detect_Delik.setText(str(detect_faulty_fabric["Flawed Hole"]))
ui9.text_Detect_Leke.setText(str(detect_faulty_fabric["Flawed Spot"]))
ui9.text_Detect_Diger.setText(str(detect_faulty_fabric["Other Errors"]))
ui9.text_Detect_Kirik.setText(str(detect_faulty_fabric["Flawed Crack"]))
if Arduino_Tools.warning_status == False:
ui9.Ikaz_Durum.setText("KAPALI")
else:
ui9.Ikaz_Durum.setText("AÇIK")
################################################################################################
current_time_faulty_cnt = time.time()
current_time_faulty_cnt_100 = time.time()
current_time_crack_cnt = time.time()
if current_time_faulty_cnt - start_time_faulty_cnt >= limited_time_faulty:
faulty_cnt = 0
start_time_faulty_cnt = current_time_faulty_cnt
if current_time_faulty_cnt_100 - start_time_faulty_cnt_100 >= limited_time_faulty_100:
faulty_cnt_100 = 0
start_time_faulty_cnt_100 = current_time_faulty_cnt_100
if current_time_crack_cnt - start_time_crack_cnt >= limited_time_crack:
crack_ready = True
start_time_crack_cnt = current_time_crack_cnt
################################################################################################
single_frame = resize(out, width=1400)
if model_name == Tools.Camera_Serial[0]:
qImg=QImage(single_frame,width_s,height_s,step_s,QImage.Format_RGB888)
ui2.Camera_1.setPixmap(QPixmap.fromImage(qImg))
elif model_name == Tools.Camera_Serial[1]:
qImg=QImage(single_frame,width_s,height_s,step_s,QImage.Format_RGB888)
ui2.Camera_2.setPixmap(QPixmap.fromImage(qImg))
elif model_name == Tools.Camera_Serial[2]:
qImg=QImage(single_frame,width_s,height_s,step_s,QImage.Format_RGB888)
ui2.Camera_2.setPixmap(QPixmap.fromImage(qImg))
elif model_name == Tools.Camera_Serial[3]:
qImg=QImage(single_frame,width_s,height_s,step_s,QImage.Format_RGB888)
ui2.Camera_2.setPixmap(QPixmap.fromImage(qImg))
ui2.label_8.setText(str(fps))
if choise == "Record":
for detect in range(len(df.iloc[:]['name'])):
ui2.Kayit_pushButton_2.setDisabled(True)
record_now_day = helper.Db_path_time(choice="Now-Day")
save_image_record_path = "./Database"+"/"+helper.Db_path_time(choice="Now-Day")+"/"+"Cam"+"/"+"images"+"/"+ "ss"+ "/" + df.iloc[:]['name'][detect]+"-"+helper.Db_path_time(choice="Now-Time")+"-"+".jpg"
imwrite(save_image_record_path, copy_model_image)
imwrite(Save_crop_image, crop)
waitKey(2)
if len(frames) == 2:
# Kameraların serial ve görüntü alınması
frame, frame2 = frames[0][0], frames[1][0]
frame_model, frame2_model = frames[0][1], frames[1][1]
height, width, channel = frame.shape
height2, width2, channel2 = frame2.shape
# Zoom
if ui2.radioButton_Camera_I.isChecked()==True and frame_model == Tools.Camera_Serial[0]:
configs[1]['value5'] = Tools.zoom_value_5()
configs[1]['value3'] = Tools.zoom_value_3()
configs[1]['value1'] = Tools.zoom_value_1()
if ui2.radioButton_Camera_II.isChecked()==True and frame2_model == Tools.Camera_Serial[1]:
configs[2]['value5'] = Tools.zoom_value_5()
configs[2]['value3'] = Tools.zoom_value_3()
configs[2]['value1'] = Tools.zoom_value_1()
frame = frame[configs[1]['value5'][1]+ configs[1]['value3']: -configs[1]['value5'][1]+height + configs[1]['value3'], configs[1]['value5'][0]+configs[1]['value1'] : width - (configs[1]['value5'][0])+ configs[1]['value1']]
frame2 = frame2[configs[2]['value5'][1]+ configs[2]['value3']: -configs[2]['value5'][1]+height2 + configs[2]['value3'], configs[2]['value5'][0]+configs[2]['value1'] : width2 - (configs[2]['value5'][0])+ configs[2]['value1']]
# Görüntünün yeniden boyutlandırılması
frame_1 = resize(frame, width=1400)
frame_2 = resize(frame2, width=1400)
# Görüntünün cuda ile tensore dönüşümü
tensor1 = tensor(frame_1, device="cuda")
tensor2 = tensor(frame_2, device="cuda")
# Fps ölçümü
new_frame_time = time.time()
fps = 1/(new_frame_time-prev_frame_time)
prev_frame_time = new_frame_time
fps = int(fps)
fps = str(fps)
myTime+=1
# Görüntülerin Birleştirilmesi
tensor_temp = cat([tensor1, tensor2], dim=0)
# Tensorlerin CPU'ya atanması
results = model(tensor_temp.cpu().numpy(), size=2048)
results.display(render=True)
h = results.ims[0].shape[0]
out1= cvtColor(results.ims[0][0:int(h/2), :],COLOR_BGR2RGB)
out2= cvtColor(results.ims[0][int(h/2):, :] ,COLOR_BGR2RGB)
# Kırpma işlemi için kullanılacak görüntünün kopyası
results_2 = tensor_temp.cpu().numpy()
height, width, channel=out1.shape
height2, width2, channel2=out2.shape
step=channel*width
# Benzerlik için y sınırlarının belirlenmesi
outh1 = int((64*height)/256)
outh2 = int((192*height)/256)
outh3 = int((64*height2)/256)
outh4 = int((192*height2)/256)
# GUIde gözüken görüntünün sınırların çizilmesi
out1[outh1,:] = 0
out1[outh2,:] = 0
out2[outh3,:] = 0
out2[outh4,:] = 0
# Görüntü dizisinin oluşturulması
outs = [[out1, frame_model], [out2, frame2_model]]
# Hataların koordinatları
df=results.pandas().xyxy[0]
df=DataFrame(df)
if len(df)!=0: # Hata tespit edildiğinde
for detect in range(len(df.iloc[:]['name'])):
# Resimler için kayıt yolunun belirlenmesi
Save_image="./Database"+"/"+helper.Db_path_time(choice="Now-Day")+"/"+"Cam"+"/"+"images"+"/"+df.iloc[:]['name'][detect]+"-"+helper.Db_path_time(choice="Now-Time")+"-"+".jpg"
Save_crop_image = "./Database"+"/"+helper.Db_path_time(choice="Now-Day")+"/"+"Cam"+"/"+"images"+"/"+"cropped"+"/"+df.iloc[:]['name'][detect]+"-"+helper.Db_path_time(choice="Now-Time")+"-"+".jpg"
# Hataların koordinatları
x1=int(df.iloc[:]['xmin'][detect])
x2=int(df.iloc[:]['xmax'][detect])
y1=int(df.iloc[:]['ymin'][detect])
y2=int(df.iloc[:]['ymax'][detect])
# Sınırlardaki hataların önlenmesi
if(x1<=0):x1=0
if(y1<=0):y1=0
if (x1<=6):x1=6
if(y1<=6):y1=6
yc = (y1+y2)/2 # Hata koordinatlarının merkezi
if (yc>outh1 and yc<outh2) or (yc>outh3+175 and yc<outh4+175) : # Hata merkezinin istenilen aralıkta olması
crop=results_2[y1-5:y2+5,x1-5:x2+5] # Hata görüntüsünün kırpılması
if Tools.Trigg_Port_Button==True: # Serial Portun açılması durumunda
# Arduino_Tools.kirmizi_led_ac()
try:
src, speed = Arduino_Tools.Feedback_src()
src: int = int(int(src) / 1000)
speed: int = int(speed)
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
ui2.statusbar.showMessage(" "*1 + "Seri Port Hatası Metre bilgileri 0 Olarak Ayarlandı", 1500)
src=0
if Tools.Trigg_Port_Button==False:
src=0
#Hata alanının bulunması
x=abs(x2-x1)
y=abs(y2-y1)
x = round(width_converter.convert(x), 2)
y = round(width_converter.convert(y), 2)
xy = round(x * y, 2)
if str(df.iloc[:]['name'][detect])=='delik' or str(df.iloc[:]['name'][detect])=='leke': # Hatanın Delik veya Leke olması durumunda
cnt=cnt+1
if cnt>=1:
cnt=0
if not helper.check_similarity(crop):
MainWindow6.show()
crop=resize_cv2(crop, (320,320),interpolation=INTER_CUBIC)
image = QtGui.QImage(crop.data, crop.shape[1], crop.shape[0], QtGui.QImage.Format_RGB888).rgbSwapped()
ui6.Goster_Label.setPixmap(QtGui.QPixmap.fromImage(image))
ui6.Metre_Label.setText(str(src))
ui6.Sinif_Label.setText(str(df.iloc[:]['name'][detect]))
ui6.Eni_Label.setText(str(x))
ui6.boyu_Label.setText(str(y))
ui6.Alan_Label.setText(str(xy))
postOut = cvtColor(crop, COLOR_BGR2RGB)
img = Image.fromarray(postOut, "RGB")
img_byte_arr = BytesIO()
img.save(img_byte_arr, format='PNG')
img_byte_arr = img_byte_arr.getvalue()
url= "https://menderes-mobile-app.herokuapp.com/errors/add"
headers = {'accept': 'application/json',
"Authorization":'Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI2MmZiMzVjYjY5YThiZDk5MTUwNjhiYTMiLCJpYXQiOjE2NjA4MzM3NTczNTIsImV4cCI6MTY2MDgzMzg0Mzc1Mn0.tfKjNRPlo7IvDN6Cp2K81Z0wfreNQkJtDsYZAxl4w7T3j4m4fcgVoephYxaUljoGKxli6OsnBvnf7BVoV994Mm_b-nvfp9srm-rqQdCOfsB_GI65GyHwpsnVbLo9uODhcbtcKXWE_x_rBBGphHcU12XXxsHrRTGUkhG5btn7f3JBt9uRrkXiuu_0G9cdFRrha8RwYNs6ZvJo3AuUit1iWGVyWSw7mI92wTBZJWt629ozc1Dd7fMR7j6z_twxLjT9mEKFAd7k4wJUTl4s3upKVZNfTOQP_DBJ9ci_FgpJYwxZqMwQbNF8ltTtyC3TFTZTqczL1dIuFaV44t7eu8FM6OcbklY3dQ-1aYtMMDBWmxUA3zDr7Z50f-ZC5n3YZJlE9hN8d7mcAqN47nbTBzkuofp2kSmhTPWwKce3LJWx9B8ZqWssTSKZegFh_Ldn-xrD8mB7IIDM48D-JgHvLTelIxnGkUDKbg8vL26VR-aJmceL89EYA2K-Kal2FBF18qN7I2icGcMp9k3CDZEeBaTqmVGkqmWGLLKCnxeaN2HVDSdD5bGoPFBVCL6TDgf53HYNRU7WafpL6Ln8MnIdr2n9gm6hKEtTUGam_MrEH54yHKTLi4XcxQbYOV4uavOXA0ICck_WfHbIRo6jLBf-eVmoQP5uHzK4mwhRz3C7NjfZOws'}
data = {
"class": str(df.iloc[:]['name'][detect]),
"meterInFabric": str(src),
"width": str(x),
"height": str(y),
}
multiple_files = [
('photo', ('arda.jpg', img_byte_arr, 'image /jpg')),
]
post_reader.append_post_thread(headers, files=multiple_files, data=data, url=url)
if len(helper.last_images)>=5:
del helper.last_images[0]
helper.last_images.append(crop)
else:
helper.last_images.append(crop)
imwrite(Save_image, results_2)
imwrite(Save_crop_image, crop)
faulty_meter = int(round(width_converter.convert(cx) / 10, 2))
helper.append_db(df, detect, Save_image, str(src), str(x), str(y), str(xy), Dok_no, Kalite_no, Hata_Koordinant, cloth_width, faulty_meter)
if Tools.Trigg_Port_Button==True:
# Arduino_Tools.kirmizi_led_ac()
try:
src, speed = Arduino_Tools.Feedback_src()
src: int = int(int(src) / 1000)
speed: int = int(speed)
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
ui2.statusbar.showMessage(" "*1 + "Seri Port Hatası Metre bilgileri 0 Olarak Ayarlandı", 1500)
src=0
if not str(df.iloc[:]['name'][detect])=='delik' or not str(df.iloc[:]['name'][detect])=='leke':
if not helper.check_similarity(crop):
if len(helper.last_images)>=5:
del helper.last_images[0]
helper.last_images.append(crop)
else:
helper.last_images.append(crop)
imwrite(Save_image, results_2)
imwrite(Save_crop_image, crop)
faulty_meter = int(round(width_converter.convert(cx) / 10, 2))
helper.append_db(df, detect, Save_image, str(src), str(x), str(y), str(xy), Dok_no, Kalite_no, Hata_Koordinant, cloth_width, faulty_meter)
for out in outs:
if out[1] == Tools.Camera_Serial[0]:
out_1= cvtColor(out[0],COLOR_BGR2RGB)
qImg=QImage(out_1,width,height,step,QImage.Format_RGB888)
ui2.Camera_1.setPixmap(QPixmap.fromImage(qImg))
if out[1] == Tools.Camera_Serial[1]:
qImg=QImage(out[0],width,height,step,QImage.Format_RGB888)
ui2.Camera_2.setPixmap(QPixmap.fromImage(qImg))
if out[1] == Tools.Camera_Serial[2]:
qImg=QImage(out[0],width,height,step,QImage.Format_RGB888)
ui2.Camera_3.setPixmap(QPixmap.fromImage(qImg))
if out[1] == Tools.Camera_Serial[3]:
qImg=QImage(out[0],width,height,step,QImage.Format_RGB888)
ui2.Camera_4.setPixmap(QPixmap.fromImage(qImg))
waitKey(2)
if len(frames) == 3:
Dok_no= ui2.lineEdit_Dok_No.text()
Kalite_no= ui2.comboBox_Kalite_No.currentText()
frame, frame2, frame3 = frames[0][0], frames[1][0], frames[2][0]
frame = frame[:-1, :-1, :]
frame2 = frame2[:-1, :-1, :]
frame3 = frame3[:-1, :-1, :]
ImgPr1 = ImageProcessor(image=frame, trim_size=115, choise="left")
ImgPr2 = ImageProcessor(image=frame2, trim_size=115)
ImgPr3 = ImageProcessor(image=frame3, trim_size=115, choise="right")
frame, cloth_width_1 = ImgPr1.trim_image()
frame3, cloth_width_3 = ImgPr3.trim_image()
try:
cloth_width_1 = round(width_converter.convert(cloth_width_1), 2)
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
cloth_width_1 = 0
try:
cloth_width_2 = round(width_converter.convert(resolution_pixels_width), 2)
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
cloth_width_2 = 0
try:
cloth_width_3 = round(width_converter.convert(cloth_width_3), 2)
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
cloth_width_3 = 0
cloth_width = int((cloth_width_1 + cloth_width_2 + cloth_width_3) / 10)
frame_model, frame2_model, frame3_model = frames[0][1], frames[1][1], frames[2][1]
height, width, channel = frame.shape
height2, width2, channel2 = frame2.shape
height3, width3, channel3 = frame3.shape
if ui2.radioButton_Camera_I.isChecked()==True and frame_model == Tools.Camera_Serial[0]:
configs[1]['value5'] = Tools.zoom_value_5()
configs[1]['value3'] = Tools.zoom_value_3()
configs[1]['value1'] = Tools.zoom_value_1()
if ui2.radioButton_Camera_II.isChecked()==True and frame2_model == Tools.Camera_Serial[1]:
configs[2]['value5'] = Tools.zoom_value_5()
configs[2]['value3'] = Tools.zoom_value_3()
configs[2]['value1'] = Tools.zoom_value_1()
if ui2.radioButton_Camera_III.isChecked()==True and frame3_model == Tools.Camera_Serial[2]:
configs[3]['value5'] = Tools.zoom_value_5()
configs[3]['value3'] = Tools.zoom_value_3()
configs[3]['value1'] = Tools.zoom_value_1()
frame = frame[configs[1]['value5'][1]+ configs[1]['value3']: -configs[1]['value5'][1]+height + configs[1]['value3'], configs[1]['value5'][0]+configs[1]['value1'] : width - (configs[1]['value5'][0])+ configs[1]['value1']]
frame2 = frame2[configs[2]['value5'][1]+ configs[2]['value3']: -configs[2]['value5'][1]+height2 + configs[2]['value3'], configs[2]['value5'][0]+configs[2]['value1'] : width2 - (configs[2]['value5'][0])+ configs[2]['value1']]
frame3 = frame3[configs[3]['value5'][1]+ configs[3]['value3']: -configs[3]['value5'][1]+height3 + configs[3]['value3'], configs[3]['value5'][0]+configs[3]['value1'] : width3 - (configs[3]['value5'][0])+ configs[3]['value1']]
tensor1 = tensor(frame, device="cuda")
tensor2 = tensor(frame2, device="cuda")
tensor3 = tensor(frame3, device="cuda")
new_frame_time = time.time()
fps = 1/(new_frame_time-prev_frame_time)
prev_frame_time = new_frame_time
fps = int(fps)
fps = str(fps)
myTime+=1
tensor_temp = cat([tensor1, tensor2, tensor3], dim=0)
results = model(tensor_temp.cpu().numpy(), size=2048)
df=results.pandas().xyxy[0]
df=DataFrame(df)
obj_df = []
for obj_class_ai, threshold_ai in class_thresholds.items():
obj_df = df[(df['confidence'] >= threshold_ai) & (df['name'] == obj_class_ai)]
if not obj_df.empty:
results.render()
h = results.ims[0].shape[0]
out1= cvtColor(results.ims[0][0:int(h/3), :],COLOR_BGR2RGB)
out2= cvtColor(results.ims[0][int(h/3):int(2*h/3), :] ,COLOR_BGR2RGB)
out3= cvtColor(results.ims[0][int(2*h/3):int(h), :] ,COLOR_BGR2RGB)
waitKey(2)
# Kırpma işlemi için kullanılacak görüntünün kopyası
results_2 = tensor_temp.cpu().numpy()
height, width, channel=out1.shape
height2, width2, channel2=out2.shape
height3, width3, channel3=out3.shape
step=channel*width
outs = [[out1, frame_model], [out2, frame2_model], [out3, frame3_model]]
# Hataların koordinatları
myTime+=1
def y_detect(cy, theta_1_center, theta_2_center, cx):
if 0 <= cy <= frame.shape[0] * 1:
detect_cam = 1
cv2.line(out1, (int(theta_1_center), (detect_cam-1) * height), (int(theta_1_center), height*detect_cam), (0, 255, 0), thickness=2)
cv2.line(out1, (int(theta_2_center), (detect_cam-1) * height), (int(theta_2_center), height*detect_cam), (0, 255, 0), thickness=2)
faulty_meter = round(width_converter.convert(cx) / 10, 2)
return detect_cam, out1, faulty_meter
elif frame.shape[0] * 1 < cy <= frame.shape[0] * 2:
detect_cam = 2
cv2.line(out2, (int(theta_1_center), (detect_cam-1) * height), (int(theta_1_center), height*detect_cam), (0, 255, 0), thickness=2)
cv2.line(out2, (int(theta_2_center), (detect_cam-1) * height), (int(theta_2_center), height*detect_cam), (0, 255, 0), thickness=2)
faulty_meter = round(width_converter.convert(cx + width) / 10, 2)
return detect_cam, out2, faulty_meter
elif frame.shape[0] * 2 < cy <= frame.shape[0] * 3:
detect_cam = 3
cv2.line(out3, (int(theta_1_center), (detect_cam-1) * height), (int(theta_1_center), height*detect_cam), (0, 255, 0), thickness=2)
cv2.line(out3, (int(theta_2_center), (detect_cam-1) * height), (int(theta_2_center), height*detect_cam), (0, 255, 0), thickness=2)
faulty_meter = round(width_converter.convert(cx + width * 2) / 10, 2)
return detect_cam, out3, faulty_meter
def fault_meter_detect(cy, cx):
if 0 <= cy <= frame.shape[0] * 1:
faulty_meter = round(width_converter.convert(cx) / 10, 2)
return faulty_meter
elif frame.shape[0] * 1 < cy <= frame.shape[0] * 2:
faulty_meter = round(width_converter.convert(cx + width) / 10, 2)
return faulty_meter
elif frame.shape[0] * 2 < cy <= frame.shape[0] * 3:
faulty_meter = round(width_converter.convert(cx + width * 2) / 10, 2)
return faulty_meter
for obj_class_ai, threshold_ai in class_thresholds.items():
obj_df = df[(df['confidence'] >= threshold_ai) & (df['name'] == obj_class_ai)]
if not obj_df.empty:
if len(df)!=0 and int(speed) >= type_speed:
for detect in range(len(df.iloc[:]['name'])):
Save_image="./Database"+"/"+helper.Db_path_time(choice="Now-Day")+"/"+"Cam"+"/"+"images"+"/"+df.iloc[:]['name'][detect]+"-"+helper.Db_path_time(choice="Now-Time")+"-"+".jpg"
Save_crop_image = "./Database"+"/"+helper.Db_path_time(choice="Now-Day")+"/"+"Cam"+"/"+"images"+"/"+"cropped"+"/"+df.iloc[:]['name'][detect]+"-"+helper.Db_path_time(choice="Now-Time")+"-"+".jpg"
x1=int(df.iloc[:]['xmin'][detect])
x2=int(df.iloc[:]['xmax'][detect])
y1=int(df.iloc[:]['ymin'][detect])
y2=int(df.iloc[:]['ymax'][detect])
classId = df.iloc[:]['class'][detect]
cx = (x1 + x2)/ 2
cy = (y1 + y2)/ 2
new_x1, new_x2, new_y1, new_y2 = Tools.expand(results_2.shape, x1, x2, y1, y2, [5.5, 1.1])
try:
crop = results_2[new_y1:new_y2,new_x1:new_x2]
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
crop = results_2[y1:y2, x1:x2]
if Tools.Trigg_Port_Button == True:
try:
src, speed = Arduino_Tools.Feedback_src()
src: int = int(int(src) / 1000)
speed: int = int(speed)
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
ui2.statusbar.showMessage(" "*1 + "Seri Port Hatası Metre bilgileri 0 Olarak Ayarlandı", 1500)
src=0
if Tools.Trigg_Port_Button==False:
src=0
x = abs(x2-x1)
y = abs(y2-y1)
x = round(width_converter.convert(x), 2)
y = round(width_converter.convert(y), 2)
xy = round(x * y, 2)
if (str(df.at[detect, 'name']) in ['dikis']):
start_time_crack_cnt = time.time()
crack_ready = False
if (str(df.at[detect, 'name']) in ['delik', 'leke', 'kirik']):
if faulty_cnt == 0:
start_time_faulty_cnt = time.time()
theta_1_center = max(0, cx - threshold)
theta_2_center = min(width, cx + threshold)
y_detect_1, single_frame, faulty_meter = y_detect(cy, theta_1_center, theta_2_center, cx)
# print(f"yakalanan kamera: {y_detect_1}")
y_detect_2, single_frame, faulty_meter = y_detect(cy, theta_1_center, theta_2_center, cx)
if faulty_cnt_100 == 0:
start_time_faulty_cnt_100 = time.time()
faulty_meter = int(round(width_converter.convert(cx) / 10, 2))
faulty_meter = int(fault_meter_detect(cy, cx))
crop = resize_cv2(crop, (320,320), interpolation = INTER_CUBIC)
single_frame = resize(single_frame, width=1400)
image2 = QtGui.QImage(crop.data, crop.shape[1], crop.shape[0], QtGui.QImage.Format_RGB888).rgbSwapped()
image3 = QtGui.QImage(single_frame.data, single_frame.shape[1], single_frame.shape[0], QtGui.QImage.Format_RGB888).rgbSwapped()
ui6.Goster_Label.setPixmap(QtGui.QPixmap.fromImage(image2))
detect_images.insert(0, image2)
detect_Faulty_Windows.insert(0, image3)
show_images.insert(0, single_frame)
detect_Hata_Eni.insert(0, str(x))
detect_Hata_Boyu.insert(0, str(y))
detect_Hata_Alan.insert(0, str(faulty_meter))
detect_Hata_Metre.insert(0, str(src))
detect_Hata_Sinif.insert(0, str(df.iloc[:]['name'][detect]))
# detect_images için değişiklik
if len(detect_images) > max_data_count:
del detect_images[max_data_count:]
for index in range(len(detect_images)):
index2 = index % 6
index3 = page_num[index2]
try:
Hata_Goster_labels[index2].setPixmap(QtGui.QPixmap.fromImage(detect_images[index3]))
Hata_Eni_labels[index2].setText(detect_Hata_Eni[index3])
Hata_Boyu_labels[index2].setText(detect_Hata_Boyu[index3])
Hata_Alan_labels[index2].setText(detect_Hata_Alan[index3])
Hata_Metre_Labels[index2].setText(detect_Hata_Metre[index3])
Hata_Sinif_labels[index2].setText(detect_Hata_Sinif[index3])
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
pass
# detect_Faulty_Windows için değişiklik
if len(detect_Faulty_Windows) > max_data_count:
del detect_Faulty_Windows[max_data_count:]
for index in range(len(detect_Faulty_Windows)):
index2 = index % 4
try:
if (str(df.at[detect, 'name']) in error_thresholds):
Hata_Faultys_Window_labels[index2].setPixmap(QtGui.QPixmap.fromImage(detect_Faulty_Windows[index2]))
except Exception as e:
logging.error(f'Hata meydana geldi: {str(e)}')
logging.error(f'Hatanın meydana geldiği kod satırı: {traceback.format_exc()}')
pass
if str(df.iloc[:]['name'][detect])=='delik':
detect_faulty_fabric["Flawed Hole"] += 1
elif str(df.iloc[:]['name'][detect])=='leke':
detect_faulty_fabric["Flawed Spot"] += 1
elif str(df.iloc[:]['name'][detect])=='kirik':
detect_faulty_fabric["Flawed Crack"] += 1
waitKey(2)
postOut = cvtColor(crop, COLOR_BGR2RGB)
img = Image.fromarray(postOut, "RGB")
img_byte_arr = BytesIO()
img.save(img_byte_arr, format='PNG')