Due to the limitations of the traditional non-parametric transform measures, a stereo matching algorithm based on non-parametric transform measure with local texture weighted item and semi-global matching method to aggregate cost is proposed. According to the directivity of the image texture metric, a contrast value of local texture is added to calculate the grayscale mean of all of the pixels in the window. The mean and the local texture contrast value are weighted sum as new matching primitive. The matching cost is determined by using semi-global matching from 8 directions. It is subsequently optimized by minimum cost to gain initial disparity. Finally, the parallax histogram of each divided region is obtained through image segmentation based on mean-shift. Peak is selected as the final disparity of each region to obtain the dense disparity map. Experimental results show that the algorithm gets more accurate results than lots of the local algorithms. It is a good solution to the distortion problem and be well adapted to the measurement of the real scene.