The existing machine-vision surface roughness measurement technique extracts relevant evaluation indices from grayscale images without using the strong sensitivity of color information. In addition, most of these measurements use a micro-vision imaging method to measure a small area and cannot make an overall assessment of the workpiece's surface. To address these issues, a method of measuring surface roughness that uses an ordinary light source and a macro-vision perspective to generate a red and green color index for each pixel is proposed in the present study. A comparison test is conducted on a set of test samples before and after surface contamination using the color index and gray-level algebraic averaging, the square of the main component of the Fourier transform in the frequency domain, and the entropy. A strong correlation between the color index and the surface roughness is established; this correlation is not only higher than that of other indices but also present despite contamination and very robust. Verification using a regression model based on a support vector machine proves that the proposed method not only has a simple apparatus and makes measurement easy but also provides high precision and is suitable over a wide measurement range. The impact of the red and green color blocks, the lighting, and the direction of the surface texture on the correlation between the color index and the roughness are also assessed and discussed in this paper.