Cervical cancer is a leading cause of death for women in low resource settings. Visual methods for cervical cancer screening have become more widespread. To improve diagnosis of cervical precancerous lesions, a smartphone-based mobile colposcope was developed that uses auxiliary lens and light source inside a custom-designed case. However, acquiring a sharp image in a clinical setting using the mobile colposcope is tricky. For example, trying to use the phone's auto-focus functionality struggles with the external lens placed in front of the phone's internal lens, because translation of the internal lens has a non-trivial effect the image. Moreover, auto-focus algorithm struggles with the high contrast caused by artifacts as patients' vaginal walls and pubic hair. A more robust algorithm that feeds commands back to the phone's camera module is needed. Previously, a classifier that measures image sharpness was presented. Implementing a method to correct for an out of focus image requires manipulating the smartphone's camera control parameters. This can be done either through the phone's operating system (Camera 2 API) or through the manufacturer's camera interface (Samsung Camera SDK), as called for from the application. This paper reviews how manipulations in a smartphone app affects image quality. In addition to image sharpness, analyses of brightness and color are also presented. Special apps that sweep through camera conditions were developed. Sample images from both anatomical models and calibration targets are given.