### Installing ControlNet++ Dependencies (Shell)

Source: https://github.com/xinsir6/controlnetplus/blob/main/README.md

This snippet provides shell commands to set up a virtual environment for ControlNet++. It creates a new Conda environment named 'controlplus' with Python 3.8, activates it, and then installs all required Python packages from the 'requirements.txt' file using pip.

```Shell
conda create -n controlplus python=3.8
conda activate controlplus
pip install -r requirements.txt
```

--------------------------------

### Running Single Condition Inference with ControlNet in Shell

Source: https://github.com/xinsir6/controlnetplus/blob/main/README.md

This shell command executes the Python script for single-condition inference using ControlNet. Users must provide a prompt and a control image, adjusting the corresponding lines within the `controlnet_union_test_openpose.py` file to specify inputs.

```Shell
python controlnet_union_test_openpose.py
```

--------------------------------

### Running Multi-Condition Inference with ControlNet in Shell

Source: https://github.com/xinsir6/controlnetplus/blob/main/README.md

This shell command initiates multi-condition inference using the `controlnet_union_test_multi_control.py` script. It requires the input `image_list` and `control_type` to be compatible with the desired control conditions (e.g., OpenPose and depth). While default condition scales are 1.0, users can adjust them in the Condition Transformer Module for increased effect, though this may lead to unpredictable results.

```Shell
python controlnet_union_test_multi_control.py
```

--------------------------------

### Customizing OpenPose draw_bodypose function in Python

Source: https://github.com/xinsir6/controlnetplus/blob/main/README.md

This Python function, intended to replace the original `draw_bodypose` in `controlnet_aux/util.py`, draws body keypoints and limbs on a canvas. It scales drawing elements based on image dimensions and uses `cv2` for rendering. This modification is crucial for achieving optimal OpenPose-control performance.

```Python
def draw_bodypose(canvas: np.ndarray, keypoints: List[Keypoint]) -> np.ndarray:
    """
    Draw keypoints and limbs representing body pose on a given canvas.

    Args:
        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the body pose.
        keypoints (List[Keypoint]): A list of Keypoint objects representing the body keypoints to be drawn.

    Returns:
        np.ndarray: A 3D numpy array representing the modified canvas with the drawn body pose.

    Note:
        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
    """
    H, W, C = canvas.shape

    
    if max(W, H) < 500:
        ratio = 1.0
    elif max(W, H) >= 500 and max(W, H) < 1000:
        ratio = 2.0
    elif max(W, H) >= 1000 and max(W, H) < 2000:
        ratio = 3.0
    elif max(W, H) >= 2000 and max(W, H) < 3000:
        ratio = 4.0
    elif max(W, H) >= 3000 and max(W, H) < 4000:
        ratio = 5.0
    elif max(W, H) >= 4000 and max(W, H) < 5000:
        ratio = 6.0
    else:
        ratio = 7.0

    stickwidth = 4

    limbSeq = [
        [2, 3], [2, 6], [3, 4], [4, 5], 
        [6, 7], [7, 8], [2, 9], [9, 10], 
        [10, 11], [2, 12], [12, 13], [13, 14], 
        [2, 1], [1, 15], [15, 17], [1, 16], 
        [16, 18],
    ]

    colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
              [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
              [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]

    for (k1_index, k2_index), color in zip(limbSeq, colors):
        keypoint1 = keypoints[k1_index - 1]
        keypoint2 = keypoints[k2_index - 1]

        if keypoint1 is None or keypoint2 is None:
            continue

        Y = np.array([keypoint1.x, keypoint2.x]) * float(W)
        X = np.array([keypoint1.y, keypoint2.y]) * float(H)
        mX = np.mean(X)
        mY = np.mean(Y)
        length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
        angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
        polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), int(stickwidth * ratio)), int(angle), 0, 360, 1)
        cv2.fillConvexPoly(canvas, polygon, [int(float(c) * 0.6) for c in color])

    for keypoint, color in zip(keypoints, colors):
        if keypoint is None:
            continue

        x, y = keypoint.x, keypoint.y
        x = int(x * W)
        y = int(y * H)
        cv2.circle(canvas, (int(x), int(y)), int(4 * ratio), color, thickness=-1)

    return canvas
```

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