Update readme.md

readme.md

@@ -124,17 +124,15 @@ Finally, replace the library path in ./renderer/mesh_renderer.py (Line 26) with
 
 2. Download the Expression Basis provided by [Guo et al.](https://github.com/Juyong/3DFace) You can find a link named "CoarseData" in the first row of Introduction part in their repository. Download and unzip the Coarse_Dataset.zip. Put "Exp_Pca.bin" into ./BFM subfolder. The expression basis are constructed using [Facewarehouse](http://kunzhou.net/zjugaps/facewarehouse/) data and transferred to BFM topology.
 
-3. Set up the renderer: Put the compiled rasterize_triangles_kernel.so into ./renderer subfolder. 
+3. Download the pre-trained [reconstruction network](https://drive.google.com/file/d/176LCdUDxAj7T2awQ5knPMPawq5Q2RUWM/view?usp=sharing), unzip it and put "FaceReconModel.pb" into ./network subfolder.
 
-4. Download the pre-trained [reconstruction network](https://drive.google.com/file/d/176LCdUDxAj7T2awQ5knPMPawq5Q2RUWM/view?usp=sharing), unzip it and put "FaceReconModel.pb" into ./network subfolder.
-
-5. Run the demo code.
+4. Run the demo code.
 
 ```
 python demo.py
 ```
 
-7. ./input subfolder contains several test images and ./output subfolder stores their reconstruction results. For each input test image, two output files can be obtained after running the demo code:
+5. ./input subfolder contains several test images and ./output subfolder stores their reconstruction results. For each input test image, two output files can be obtained after running the demo code:
 	- "xxx.mat" : 
 		- cropped_img: an RGB image after alignment, which is the input to the R-Net
 		- recon_img: an RGBA reconstruction image aligned with the input image (only on Linux).