Latest development news:
In the very next version, the ViDiLabs calc, in addition to the existing two sub-applications (Visual and Digital), will be enhanced with an accurate lux-meter, as a third sub-application. Stay tuned!
We have designed a unique tool for the CCTV industry - the ViDiLabs calculator. Although it is designed primarily for cameras used in surveillance, it can also be used by cinematographers, photographers, and anybody using digital cameras of any kind.
Inside the app we have a database of all commercially available sensors, from CIF size (352x288), through HD and 4k, up to 100MP (11608x8708) sensors.
The ViDiLabs calc calculates all variables associated with finding the best camera and lens combination for your coverage.
It advises the pixel density for Face Inspection, Identification and Recognition as per various standards.
For the first time ever in the industry, it also calculates the blurriness that moving objects produce in a video and advises the best setting to minimise this effect, which is very important for Face and Licence Plate Recognition systems.
We have also included a storage calculator and image quality indicator, based on our SD/HD test chart.
This App is available for iOS smart devices via the iTunes App store, check here: iTunes.
For Android smart devices please check the Google Play Store here: Google Play Store.
Very soon this App will be available for Android devices via Google Play, and we’ll put a link here.
Download the Manual here, which is also included in the App itself.
ViDiLabs calculator - Visual part and Motion Blur:
ViDiLabs calculator - Digital part:
ViDiLabs calculator - Wide angle lens distortions:
The screen-shot on the left shows the appearance of the Visual calculator of the ViDiLabs App.
At the bottom, the blue entry scrolling windows allow you to chose the sensor and resolution of your camera. Once you have done this, the ViDiLabs calculator finds out the focal length for the given pixel density.
The example at left shows a 1/3” sensor with 4.15mm lens (iPhone 7) and by clicking the Face Identification button on the right (20 pix/m), the system comes up with a suggestion that at 13.94m distance, the camera will show a person with sufficient details to identify his/her face, as per IEC 62676-4.
The screen-shot at left shows the appearance of the Digital calculator of the ViDiLabs App.
The user choses any values he requires (in the blue scrolling windows) to calculate the required hard disk capacity in order to record the required days.
In the example on the left, 16 cameras are selected, streaming with 4Mb/s, in continuous motion (100%) and required to record 7 days.
The calculator produces the Storage capacity required to be 4.615 TB.
If the drives used in this setup are of 2TB capacity, then the calculator shows that 4 drives are required for RAID-5, and 5 required if RAID-6 HDD arrangement is to be used.
Furthermore, the test chart at left is designed to indicate the image quality of the test chart observed by the cameras in question, using the chosen Video compression (in this example 4Mb/s).
By simply double-tapping on the test chart image, at the chosen compression, you can zoom-in and see the detail quality of it. This simulation is designed to suggest to users what it means if a particular compression is chosen.
The sensor blur calculation is a first in the industry, derived from our own research and testing.
By double-clicking on the Sensor blur buttons, you are able to calculate the apparent blurrines of moving objects, in pixels. In the example on the left, an object moving with 20km/h at 100m distance, while using an 8mm lens, on a camera with 1/3” sensor, will produce an apparent motion blur of around 7.1 pixels. This may appear quite blurry on the screen, and will prevent the operator to determine a person, or see a number-plate of a vehicle at such a distance.
By changing the electronic Exposure from 1/25s (which is a standard “live” exposure) to 1/250s we obtain a motion blur of only 0.7 pixels, which in reality means there is no visible blurriness of the moving object.
The image of the object at 100m distance, moving with 20km/h, will be be quite sharp and clear.
Other examples can be shown, like for example, casinos can determine a roulette camera exposure for optimal and sharp image of the winning number while the roulette wheel is still revolving.
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