Update the calibration document.

apps/calibration/Readme.md

@@ -27,6 +27,11 @@ First, you should record a video with your chessboard for each camera separately
     └── xx.mp4
 ```
 
+In this tutorial, we use our sample datasets as an example. In that dataset, the intri data is just like the picture below.
+
+![sample intri data](assets/intri_sample.png)
+
+
 For the extrinsic parameters, you should place the chessboard pattern where it will be visible to all the cameras (on the floor for example) and then take a picture or a short video on all of the cameras.
 
 ```bash
@@ -38,10 +43,14 @@ For the extrinsic parameters, you should place the chessboard pattern where it w
     └── xx.mp4
 ```
 
+The sample extri data is like the picture below.
+
+![sample extri data](assets/extri_sample.png)
+
 ## 2. Detect the chessboard
 For both intrinsic parameters and extrinsic parameters, we need detect the corners of the chessboard. So in this step, we first extract images from videos and second detect and write the corners.
 ```bash
-# extrac 2d
+# extract 2d
 python3 scripts/preprocess/extract_video.py ${data} --no2d
 # detect chessboard
 python3 apps/calibration/detect_chessboard.py ${data} --out ${data}/output/calibration --pattern 9,6 --grid 0.1
@@ -52,31 +61,110 @@ To specify your chessboard, add the option `--pattern`, `--grid`.
 
 Repeat this step for `<intri_data>` and `<extri_data>`.
 
+After this step, you should get the results like the pictures below.
+
+
+![visualization result](assets/extri_chessboard.jpg)
+
+![vis intri](assets/intri_chessboard.jpg)
+
+## 2.5 Finetune the Chessboard Detection Result
+
+It is vital for calibration to detect the keypoints of chessboard correctly. **Thus we highly recommend you to carefully inspect the visualization result in ${data}/output.** If you find some detection results are wrong, we provide you a tool to make some modifications to them.
+
+```bash
+python apps/annotation/annot_calib.py $data --mode chessboard --pattern 9,6 --annot chessboard
+```
+
+After running the script above, a OpenCV GUI prompt will show, like below:
+
+![](assets/ft1.png)
+
+> This tool is component of our awesome annotation toolkits, so some key mapping is similar. To learn more about our annotation tools, please check [the document](../annotation/Readme.md)
+
+At the same time, you can see that the CLI presents some auxilary information.
+
+![](assets/ft2.png)
+
+You can learn from the CLI prompt to know the information and which point you are labeling.
+
+In the GUI, the current edited point is highlighted by a circle. If you want to make some modification, use mouse to click the correct place, then a white anchor point is presented to show the potential position. 
+
+![](assets/ft3.png)
+
+If you find this position is the correct position, press `Z` to confirm. Then the point position will be changed. 
+
+![](assets/ft4.png)
+
+After finish modifing this point, press `Space` to move to next point.
+
+![](assets/ft5.png)
+
+> Currently we only support move to next point. If you want to move to previous point, please `Space` for many times until it back to start.
+
+If you're satisfied to this frame, you can press `D` move on to next frame.
+
+![](assets/ft6.png)
+
+If you press `A`, you can move back to previous frame.
+
+After finish annotating every frames, press `q` to quit.
+
+![](assets/ft7.png)
+
+Then you can choose whether to save this annotation.
+
+> If your data is on remote server, then the OpenCV GUI may be too slow to operate if you directly run the script via ssh X forwarding. We recommend you use `sshfs` to mount the remote data directory and locally run this script.
+
+
 ## 3. Intrinsic Parameter Calibration
 
+After extracting chessboard, it is available to calib the intri parameter.
+
 ```bash
 python3 apps/calibration/calib_intri.py ${data} --step 5
 ```
 
+After the script finishes, you'll get `intri.yml` under `${data}/output`.
+
+> This step may takes a long time, please be patient. :-)
+
 ## 4. Extrinsic Parameter Calibration
+
+
+Then you can calib the extri parameter.
+
 ```
 python3 apps/calibration/calib_extri.py ${extri} --intri ${intri}/output/intri.yml
 ```
 
+After the script finished, you'll get `extri.yml` under `${intri}/output`.
+
 ## 5. (Optional)Bundle Adjustment
 
 Coming soon
 
 ## 6. Check the calibration
 
+To check whether your camera parameter is correct, we provide several approaches to make verification.
+
 1. Check the calibration results with chessboard:
 ```bash
 python3 apps/calibration/check_calib.py ${extri} --out ${intri}/output --vis --show
 ```
 
+A window will be shown for checking.
+
+![](assets/vis_check.png)
+
+
 Check the results with a cube.
 ```bash
 python3 apps/calibration/check_calib.py ${extri} --out ${extri}/output --cube
 ```
 
+You'll get results in `$data/output/cube`. 
+
+![](assets/cube.jpg)
+
 2. (TODO) Check the calibration results with people.