Hardi Kurnianto
Published

Object Size Measurement on Nvidia Jeston Nano

Currently object measurements are carried out in many ways such as laser, ultrasonic, etc. I will try with an ordinary webcam to measure obj

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Object Size Measurement on Nvidia Jeston Nano

Things used in this project

Hardware components

Webcam, Logitech® HD Pro
Webcam, Logitech® HD Pro
×1
NVIDIA Jetson Nano Developer Kit
NVIDIA Jetson Nano Developer Kit
×1
Aruco Marker
×1

Story

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Code

measure_object_size_camera.py

Python
import cv2
from object_detector import *
import numpy as np

# Load Aruco detector
parameters = cv2.aruco.DetectorParameters_create()
aruco_dict = cv2.aruco.Dictionary_get(cv2.aruco.DICT_5X5_50)


# Load Object Detector
detector = HomogeneousBgDetector()

# Load Cap
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)

while True:
    _, img = cap.read()

    # Get Aruco marker
    corners, _, _ = cv2.aruco.detectMarkers(img, aruco_dict, parameters=parameters)
    if corners:

        # Draw polygon around the marker
        int_corners = np.int0(corners)
        cv2.polylines(img, int_corners, True, (0, 255, 0), 5)

        # Aruco Perimeter
        aruco_perimeter = cv2.arcLength(corners[0], True)

        # Pixel to cm ratio
        pixel_cm_ratio = aruco_perimeter / 33

        contours = detector.detect_objects(img)

        # Draw objects boundaries
        for cnt in contours:
            # Get rect
            rect = cv2.minAreaRect(cnt)
            (x, y), (w, h), angle = rect

            # Get Width and Height of the Objects by applying the Ratio pixel to cm
            object_width = w / pixel_cm_ratio
            object_height = h / pixel_cm_ratio

            # Display rectangle
            box = cv2.boxPoints(rect)
            box = np.int0(box)

            cv2.circle(img, (int(x), int(y)), 5, (0, 0, 255), -1)
            cv2.polylines(img, [box], True, (255, 0, 0), 2)
            cv2.putText(img, "Dowo {} cm".format(round(object_width, 1)), (int(x - 100), int(y - 20)), cv2.FONT_HERSHEY_PLAIN, 2, (100, 200, 0), 2)
            cv2.putText(img, "Ombo {} cm".format(round(object_height, 1)), (int(x - 100), int(y + 15)), cv2.FONT_HERSHEY_PLAIN, 2, (100, 200, 0), 2)
            print(object_width,object_height)



    cv2.imshow("Fish Size Measurement", img)
    key = cv2.waitKey(1)
    if key == 27:
        break

cap.release()
cv2.destroyAllWindows()

object_detector

Python
import cv2


class HomogeneousBgDetector():
    def __init__(self):
        pass

    def detect_objects(self, frame):
        # Convert Image to grayscale
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        # Create a Mask with adaptive threshold
        mask = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 19, 5)

        # Find contours
        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        #cv2.imshow("mask", mask)
        objects_contours = []

        for cnt in contours:
            area = cv2.contourArea(cnt)
            if area > 2000:
                #cnt = cv2.approxPolyDP(cnt, 0.03*cv2.arcLength(cnt, True), True)
                objects_contours.append(cnt)

        return objects_contours

    # def get_objects_rect(self):
    #     box = cv2.boxPoints(rect)  # cv2.boxPoints(rect) for OpenCV 3.x
    #     box = np.int0(box)

Credits

Hardi Kurnianto
17 projects • 17 followers
Master student at Intelligent Control and Systems Engineering Department

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  • Debugging
  • Debugging
  • NVIDIA