4.[Using a Different Camera Brand](#using-a-different-camera-brand)
...
...
@@ -16,48 +18,45 @@ Note: We are not aiming to have the best calibration toolbox, but the simplest o
## Installing the OpenPose 3-D Reconstruction Module
## Installing the Calibration Module
Check [doc/installation.md#calibration-module](./installation.md#calibration-module) for installation steps.
## Quick Start
## Running Calibration
Note: In order to maximize calibration quality, **do not reuse the same video sequence for both intrinsic and extrinsic parameter estimation**. The intrinsic parameter calibration should be run camera by camera, where each recorded video sequence should be focused in covering all regions of the camera view and repeated from several distances. In the extrinsic sequence, this video sequence should be focused in making sure that the checkboard is visible from at least 2 cameras at the time. So for 3-camera calibration, you would need 1 video sequence per camera as well as a final sequence for the extrinsic parameter calibration.
1. Distortion and intrinsic parameter calibration:
1. Run OpenPose and save images for your desired camera. Use a grid (chessboard) pattern and move around all the image area.
1. Quality tips:
1. Keep the same orientation of the chessboard, i.e., do not rotate it circularly more than ~15-30 degress. Our algorithm assumes that the origin is the corner at the top left, so rotating the chessboard circularly will change this origin across frames, resulting in many frames being rejected for the final calibration, i.e., lower calibration accuracy.
2. You should get at least 100-200 images for a good calibration (and no more than 500).
3. Cover several distances, and within each distance, cover all parts of the image view (all corners and center).
4. Save the images in PNG format (default behavior) in order to improve calibration quality.
5. The calibration of a camera takes about 3 minutes with about 100 images, about 1.5h with 200 images, and about 9.5h with 450 images. Required RAM memory also grows exponentially.
2. Flir camera calibration: Add the flags `--flir_camera --flir_camera_index 0` (or the desired flir camera index) to the webcam command.
3. Calibration from video sequence: Add the flag `--video {video_path}` to the webcam command.
4. Any other camera brand: Simply save your images in {intrinsic_images_folder_path}, file names are not relevant.
2. Get familiar with the calibration parameters used in point 3 (i.e., `grid_square_size_mm`, `grid_number_inner_corners`, etc.) by running:
### Step 1 - Distortion and Intrinsic Parameter Calibration
1. Run OpenPose and save images for your desired camera. Use a grid (chessboard) pattern and move around all the image area.
1. Quality tips:
1. Keep the same orientation of the chessboard, i.e., do not rotate it circularly more than ~15-30 degress. Our algorithm assumes that the origin is the corner at the top left, so rotating the chessboard circularly will change this origin across frames, resulting in many frames being rejected for the final calibration, i.e., lower calibration accuracy.
2. You should get at least 100-200 images for a good calibration (and no more than 500).
3. Cover several distances, and within each distance, cover all parts of the image view (all corners and center).
4. Save the images in PNG format (default behavior) in order to improve calibration quality.
5. The calibration of a camera takes about 3 minutes with about 100 images, about 1.5h with 200 images, and about 9.5h with 450 images. Required RAM memory also grows exponentially.
4. In this case, the intrinsic parameters would have been generated as {intrinsic_images_folder_path}/18079958.xml.
5. Run steps 1-4 for each one of your cameras.
6. After you calibrate the camera intrinsics, when you run OpenPose with those cameras, you should see the lines in real-life to be (almost) perfect lines in the image. Otherwise, the calibration was not good. Try checking straight patterns such us wall corners or ceilings:
4. In this case, the intrinsic parameters would have been generated as {intrinsic_images_folder_path}/18079958.xml.
5. Run steps 1-4 for each one of your cameras.
6. After you calibrate the camera intrinsics, when you run OpenPose with those cameras, you should see the lines in real-life to be (almost) perfect lines in the image. Otherwise, the calibration was not good. Try checking straight patterns such us wall corners or ceilings:
```
# With distortion (lines might seem with a more circular shape)
