Difference between revisions of "PSD Sensor"
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| − | == | + | ==Introduction== |
| − | == | + | This page will present the development of the Camera Turbidity and PSD Sensor. |
| − | === | + | More introductions to come... |
| − | This is for | + | |
| + | ==Development Log== | ||
| + | ===04 July 2022 (The 1st Experiment)=== | ||
| + | '''Camera:''' [https://www.arducam.com/ov2640/ OmniVision OV2640 Camera], 640*480, JPG output, default camera settings (automatic exposure, gain, and white balance), lens not changed. The camera is connected to a [https://www.arducam.com/product/b0051b0011-arducam-f-shield-v2-camera-module-shield-with-ov2640-for-arduino-uno-mega2560-due/ ArduCAM Arduino Shield] which is compatible with the Arduino Mega board. The board is set up using ArduCAM's [https://github.com/ArduCAM/Arduino/tree/master/ArduCAM Arduino Library]. When taking images, the Arduino Mega is connected directly to the PC, and the images are sent and saved on the PC directly, for now. | ||
| + | |||
| + | '''Turbidity Solution:''' The tap water is mixed with natural silts to create the synthetic solution. The turbidity value ranges from 0 to 639 NTU in this experiment. | ||
| + | |||
| + | '''Methods:''' First, put the synthetic turbidity solution into the 3d-printed black container, after measuring the actual turbidity (in the unit of NTU) using the Thermo Fisher turbidity meter. Then, light up the LED (from the top hole of the container lid) and take images (from the side window) of the solution at a 90-degree angle. This light scattering method is a common method used for turbidity measurement. Generally, when the water is more turbid, the increased particles in the water scatter more light beams in directions other than the straight direction, hence the light intensity received from the side directions (such as 90 degrees) will increase. The RGB (red, green, and blue) values of each pixel of the images are obtained using python script and converted into a single value - grayscale (0 - 255, completely black to completely white) using the Luma formula (Y = 0.299R + 0.587G + 0.114B). The average grayscale value of all pixels of each photo is calculated. Finally, plot the relationship between the average grayscale value of each image and its corresponding solution turbidity. | ||
| + | |||
| + | '''Results:''' | ||
Revision as of 05:07, 18 August 2022
Introduction
This page will present the development of the Camera Turbidity and PSD Sensor. More introductions to come...
Development Log
04 July 2022 (The 1st Experiment)
Camera: OmniVision OV2640 Camera, 640*480, JPG output, default camera settings (automatic exposure, gain, and white balance), lens not changed. The camera is connected to a ArduCAM Arduino Shield which is compatible with the Arduino Mega board. The board is set up using ArduCAM's Arduino Library. When taking images, the Arduino Mega is connected directly to the PC, and the images are sent and saved on the PC directly, for now.
Turbidity Solution: The tap water is mixed with natural silts to create the synthetic solution. The turbidity value ranges from 0 to 639 NTU in this experiment.
Methods: First, put the synthetic turbidity solution into the 3d-printed black container, after measuring the actual turbidity (in the unit of NTU) using the Thermo Fisher turbidity meter. Then, light up the LED (from the top hole of the container lid) and take images (from the side window) of the solution at a 90-degree angle. This light scattering method is a common method used for turbidity measurement. Generally, when the water is more turbid, the increased particles in the water scatter more light beams in directions other than the straight direction, hence the light intensity received from the side directions (such as 90 degrees) will increase. The RGB (red, green, and blue) values of each pixel of the images are obtained using python script and converted into a single value - grayscale (0 - 255, completely black to completely white) using the Luma formula (Y = 0.299R + 0.587G + 0.114B). The average grayscale value of all pixels of each photo is calculated. Finally, plot the relationship between the average grayscale value of each image and its corresponding solution turbidity.
Results:
