# Image Capture

The Deep Learning tools are capable of handling image and lighting variability, but the tools must be taught what that variability might entail. If the lighting may be brighter or darker from image to image, capture that variability in the images, and teach the tools using that variability in lighting by adding those images to your training image set.

When configuring your lighting and imaging options you can use typical machine vision lighting and optics techniques. However, with Deep Learning, you want to ensure that the lighting and optics are consistent between training and production. If, for example, you train your images based on a certain lighting and optic setup, and you then alter that configuration during production, the tool will be basing its performance on that initial setup, thus failing during production.

If possible, use controlled lighting to avoid effects caused by ambient lighting or visual changes that can be caused differences in the lighting setup. When setting up the cameras, make sure that the camera setup in the lab is the same as what will be used during production. Also attempt to minimize perspective distortion, changes in lens focus, depth of field and field of view.