Illegal instruction(core dumped) error in cli and also i shared my python code .please help me

import cv2
import ultralytics
from ultralytics import YOLO
from PIL import Image, ImageFile
import requests
from io import BytesIO
import pandas as pd
import time
import functions
import os
ImageFile.LOAD_TRUNCATED_IMAGES = True
pd.options.display.max_columns = 21

v3: 0: ‘awb’, 1: ‘awb-njv’, 2: ‘weighing-platform-bulky’, 3: ‘weighing-platform-small’

def predicted_lzd_gcs(model3):
awb =
ci_awb =
platform =
ci_platform =
ids =
predicted_value =
reason =
count = 0

# Open the webcam for capturing frames
cap = cv2.VideoCapture(0)  # 0 represents the default webcam

try:
    while True:
        count = count + 1
        print(count)
        functions.stop(count)
        
        # Capture a frame from the webcam
        ret, frame = cap.read()

        # Convert the frame to a PIL Image
        source = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
        results = model3(source)

        # Process the detected objects and update your data accordingly
        for box in results[0].boxes:
            cls = box.cls
            conf = box.conf

            # Your object analysis logic goes here...

            # Example: Check if cls is a certain class and update your data accordingly
            if cls == 0:
                awb.append(1)
                ci_awb.append(conf)
            elif cls in [2, 3]:
                platform.append(1)
                ci_platform.append(conf)

        # Display the processed frame (optional)
        cv2.imshow("Processed Frame", cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))

        # Press 'q' to exit the loop
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

finally:
    # Release the webcam and close any open windows
    cap.release()
    cv2.destroyAllWindows()

# Rest of your code to create the DataFrame and return it...

def recommend_lzd_gcs(pred, ci_awb, platform, ci_platform):
if pred == ‘pass’:
if min(ci_awb) < 0.60:
return 1
elif 1 in platform and min(ci_platform) < 0.956:
return 1
else:
return 0

elif pred == 'fail':
    return 1

#-----------------------------------------------------------------------------------------------------#

LZD - Drive

def predicted_lzd_drive(model):
awb =
ci_awb =
platform =
ci_platform =
ids =
predicted_value =
reason =
orders =

# Open the webcam for capturing frames
cap = cv2.VideoCapture(0)  # 0 represents the default webcam

try:
    while True:
        # Capture a frame from the webcam
        ret, frame = cap.read()

        # Convert the frame to a PIL Image
        source = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
        results = model(source)
        cls4 = []
        ci_cls4 = []

        # Rest of your code to process and analyze the frame goes here...

        # Press 'q' to exit the loop
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

finally:
    # Release the webcam and close any open windows
    cap.release()
    cv2.destroyAllWindows()

# Rest of your code to create the DataFrame and return it...

def recommend_lzd_drive(pred, ci_awb, platform, ci_platform):
if pred == ‘pass’:
if ci_awb < float(0.60):
return 1
elif platform == 1 and ci_platform < float(0.956):
return 1
else:
return 0

elif pred == 'fail':
    return 1

#-----------------------------------------------------------------------------------------------------#

Non - partnership

def predicted_npsp(folder_pth, model):
platform =
orders =
ci_platform =
ids =
predicted_value =
reason =
for i in os.listdir(folder_pth):
id_path = f’{folder_pth}/{i}’
print(id_path)

    for j in os.listdir(id_path):
        name = j.split('.')[0]
        length = len(name.split('_'))
        id = name.split('_')[0]
        order = name.split('_')[length-1]
        source = f'{id_path}/{j}'
        results = model(source)
        cls4 = []
        ci_cls4 = []

        # Predict Fail or Pass
        if str(results[0].boxes.cls) == 'tensor([])':
            reason.append('invisible')
            predicted_value.append('fail')
            

        else:
            for box in results[0].boxes:
                cls = box.cls
                ci = box.conf
            # Do not accept awb and weighing platform of confident interval of awb and weighing
            # platform less than 64% and 70% respectively.
                cls4.append(int(cls))
                ci_cls4.append(float(ci))

            if functions.unique(cls4) == [0]:
                reason.append('strange stuffs')
            elif functions.unique(cls4) == [1]:
                reason.append('strange stuffs')
            else:
                reason.append('')

            if functions.unique(cls4) == [0, 2] or functions.unique(cls4) == [0, 3]:
                predicted_value.append('pass')

            elif functions.unique(cls4) == [2] or functions.unique(cls4) == [3]:
                predicted_value.append('pass')

            else:
                predicted_value.append('fail')

            # Input class and confident interval

        model4_dict = functions.m_dict_5(cls4, ci_cls4)
        model4_dict = functions.transform_dict_5(model4_dict)


        if model4_dict[2] > float(0) or model4_dict[3] > float(0):
            platform.append(1)
            if model4_dict[2] > float(0):
                ci_platform.append(model4_dict[2])
            elif model4_dict[3] > float(0):
                ci_platform.append(model4_dict[3])
        else:
            platform.append(0)
            ci_platform.append(0.0)
        ids.append(id)
        orders.append(order)
        print(reason)

cm = pd.DataFrame(list(zip(ids, orders, predicted_value, reason, platform, ci_platform)),
        columns=['TID', 'Photo_ID', 'Predicted_Value', 'Fail_Reason', 
                'Weighing_Platform', 'Conf_Weighing_Platform'])
return cm

def recommend_npsp(pred, platform, ci_platform):
if pred == ‘pass’:
if platform == 1 and ci_platform < float(0.948):
return 1
else:
return 0

elif pred == 'fail':
    return 1

#------------------------------------------------appended---------------------------------------#

Initialize your object detection model (replace with your model)

model3 = YOLO(“standard_image_v3.pt”)

Define a function to perform object detection and analysis

def perform_object_detection():
# Call the ‘predicted_lzd_gcs’ function with your object detection model
result = predicted_lzd_gcs(model3)
print(result)

Run object detection (e.g., when the script is executed)

if name == “main”:
perform_object_detection()

Hi,

Please check below link:
https://elinux.org/Jetson/L4T/TRT_Customized_Example#.22Illegal_instruction_.28core_dumped.29.22

Thanks.

Thank you

This topic was automatically closed 14 days after the last reply. New replies are no longer allowed.