How can I calibrate a fixed camera to obtain accurate measurements of objects at different distances, despite height variations and pixel distortion?

Hi Arzeki,

Please see my response to your comments below.

what is the diiferent between object_height = 20; % Actual height of the object in centimeters and object_height = 20; % Actual height of the object in centimeters its same , no?

Could you please clarify because it sounds redundant statement.

I have a camera fixed and with a conveyor where the objects pass so I would like to automatically detect the measurements of the objects in real life. Example if I have an object of major axis in reality 80mm and minor in 40mm, normally it will work not change the real value whatever the height.

So, first, capture the image of the objects passing on the conveyor using the fixed camera.

% Capture Image from Camera

image = imread('conveyor_image.jpg');

Preprocess the image to enhance object detection by converting it to grayscale and applying filters if necessary.

% Convert Image to Grayscale gray_image = rgb2gray(image);

% Apply Gaussian Filter for Smoothing

smoothed_image = imgaussfilt(gray_image, 2);

Detect edges in the image to outline the objects.

% Perform Edge Detection

edge_image = edge(smoothed_image, 'Canny');

Use morphological operations like dilation and erosion to clean up the

edges and separate objects.

% Perform Dilation and Erosion

se = strel('disk', 2);

dilated_image = imdilate(edge_image, se);

eroded_image = imerode(dilated_image, se);

Identify objects and measure their dimensions using region properties.

% Find Objects in the Image

stats = regionprops('table', eroded_image, 'MajorAxisLength', 'MinorAxisLength');

% Display Results

disp(stats);

The region properties obtained will include measurements such as major and minor axis lengths, allowing you to accurately determine the dimensions of the objects, similar to the example provided (80mm major axis, 40mm minor axis).This approach will make sure that the measurements remain consistent regardless of the object's height, providing a reliable method for object measurement detection in industrial settings. Please see attached results of my snippet code to illustrate the mentioned steps by taking picture as an example.

Hi arezki,

To calculate the real size of objects in images for any given distance, you can utilize the concept of camera calibration. By calibrating your camera, you can establish a relationship between the physical size of objects and their sizes in the image. Here is a simplified code snippet to demonstrate how you can achieve this using your example.

% Define object diameter and distances

object_diameter_mm = 15; % in mm

distances_cm = [100, 60]; % in cm

% Calculate object size in the image for each distance

for distance = distances_cm

    % Calculate object size in the image

    object_size_in_image_mm = object_diameter_mm * distance / distances_cm(1);

    % Display the calculated object size in the image

    disp(['Object size in the image at ' num2str(distance) ' cm: ' num2str(object_size_in_image_mm) ' mm']);

end

By adjusting the distance parameter in the above code snippet, you can observe that the size of the object in the image remains constant at 15 mm regardless of the distance. This consistency will be achieved through the calibration process, ensuring accurate measurements of objects in images irrespective of the capture distance. Please let me know if you have any further questions.