From RGB to HSV — and Back Again

A basic idea in Pc Imaginative and prescient is knowing how photos are saved and represented. On disk, picture information are encoded in varied methods, from lossy, compressed JPEG information to lossless PNG information. When you load a picture right into a program and decode it from the respective file format, it’s going to almost definitely have an array-like construction that represents the pixels within the picture.

RGB

Every pixel accommodates some coloration data about that particular level within the picture. Now the most typical approach to signify this coloration is within the RGB area, the place every pixel has three values: crimson, inexperienced and blue. These values describe how a lot of every coloration is current and they are going to be combined additively. So for instance, a picture with all values set to zero will probably be black. If all three values are set to 100%, the ensuing picture will probably be white.

Typically the order of those coloration channels may be swapped. One other frequent order is BGR, so the order is reversed. That is generally utilized in OpenCV and the default when studying or displaying photos.

Alpha Channel

Photos may comprise details about transparency. In that case, an extra alpha channel is current (RGBA). The alpha worth signifies the opacity of every pixel: an alpha of zero means the pixel is absolutely clear and a worth of 100% represents a totally opaque pixel.

HSV

Now RGB(A) shouldn’t be the one approach to signify colours. The truth is there are lots of completely different coloration fashions that signify coloration. Some of the helpful fashions is the HSV mannequin. On this mannequin, every coloration is represented by a hue, saturation and worth property. The hue describes the tone of coloration, regardless of brightness and saturation. Typically that is represented on a circle with values between 0 and 360 or 0 to 180, or just between 0 and 100%. Importantly, it’s cyclical, that means the values wrap round. The second property, the saturation describes how intense a coloration tone is, so a saturation of 0 leads to grey colours. Lastly the worth property describes the brightness of the colour, so a brightness of 0% is at all times black.

Now this coloration mannequin is extraordinarily useful in picture processing, because it permits us to decouple the colour tone from the saturation and brightness, which is unattainable to do instantly in RGB. For instance, in order for you a transition between two colours and preserve the identical brightness throughout the full transition, this will probably be very complicated to attain utilizing the RGB coloration mannequin, whereas within the HSV mannequin that is easy by simply interpolating the hue.

Sensible Examples

We are going to have a look at three examples of methods to work with these coloration areas in Python utilizing OpenCV. Within the first instance, we extract components of a picture which might be of a sure coloration. Within the second half, we create a utility operate to transform colours between the colour areas. Lastly, within the third software, we create a steady animation between two colours with fixed brightness and saturation.

1 – Shade Masks

The objective of this half is to discover a masks that isolates colours primarily based on their hue in a picture. Within the following image, there are completely different coloured paper items that we wish to separate.

Utilizing OpenCV, we will load the picture and convert it to the HSV coloration area. By default photos are learn in BGR format, therefore we want the flag cv2.COLOR_BGR2HSV within the conversion:

Python">import cv2

img_bgr = cv2.imread("photos/notes.png")
img_hsv = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)

Now on the HSV picture we will apply a coloration filter utilizing the cv2.inRange operate to specify a decrease and higher certain for every property (hue, saturation, worth). With some experimentation I arrived on the following values for the filter:

masks = cv2.inRange(
    src=img_hsv,
    lowerb=np.array([90, 60, 150]),
    upperb=np.array([110, 100, 200]),
)

The hue filter right here is constrained between 90 and 110, which corresponds to the sunshine blue paper on the backside of the picture. We additionally set a variety of the saturation and the brightness worth to get a fairly correct masks.

To point out the outcomes, we first have to convert the single-channel masks again to a BGR picture form with 3 channels. Moreover, we will additionally apply the masks to the unique picture and visualize the consequence.

mask_bgr = cv2.cvtColor(masks, cv2.COLOR_GRAY2BGR)
img_bgr_masked = cv2.bitwise_and(img_bgr, img_bgr, masks=masks)

composite = cv2.hconcat([img_bgr, mask_bgr, img_bgr_masked])
cv2.imshow("Composite", composite)

By altering the hue vary, we will additionally isolate different items. For instance for the purple paper, we will specify the next vary:

2 – Shade Conversion

Whereas OpenCV supplies a helpful operate to transform full photos between coloration areas, it doesn’t present an out-of-the-box resolution to transform single colours between coloration areas. We are able to write a easy wrapper that creates a small 1×1 pixel picture with an enter coloration, makes use of the built-in OpenCV operate to transform to a different coloration area and extract the colour of this single pixel once more.

def convert_color_space(enter: tuple[int, int, int], mode: int) -> tuple[int, int, int]:
    """
    Converts between coloration areas

    Args:
        enter: A tuple representing the colour in any coloration area (e.g., RGB or HSV).
        mode: The conversion mode (e.g., cv2.COLOR_RGB2HSV or cv2.COLOR_HSV2RGB).

    Returns:
        A tuple representing the colour within the goal coloration area.
    """
    px_img_hsv = np.array([[input]], dtype=np.uint8)
    px_img_bgr = cv2.cvtColor(px_img_hsv, mode)
    b, g, r = px_img_bgr[0][0]
    return int(b), int(g), int(r)

Now we will take a look at the operate with any coloration. We are able to confirm that if we convert from RGB -> HSV -> RGB again to the unique format, we get the identical values.

red_rgb = (200, 120, 0)

red_hsv = convert_color_space(red_rgb, cv2.COLOR_RGB2HSV)
red_bgr = convert_color_space(red_rgb, cv2.COLOR_RGB2BGR)
red_rgb_back = convert_color_space(red_hsv, cv2.COLOR_HSV2RGB)

print(f"{red_rgb=}") # (200, 120, 0)
print(f"{red_hsv=}") # (18, 255, 200)
print(f"{red_bgr=}") # (0, 120, 200)
print(f"{red_rgb_back=}") # (200, 120, 0)

3 – Steady Shade Transition

On this third instance, we’ll create a transition between two colours with a relentless brightness and saturation interpolation. This will probably be in comparison with a direct interpolation between the preliminary and last RGB values.

def interpolate_color_rgb(
    start_rgb: tuple[int, int, int], end_rgb: tuple[int, int, int], t: float
) -> tuple[int, int, int]:
    """
    Interpolates between two colours in RGB coloration area.
    Args:
        start_rgb: The beginning coloration in RGB format.
        end_rgb: The ending coloration in RGB format.
        t: A float between 0 and 1 representing the interpolation issue.
    Returns:
        The interpolated coloration in RGB format.
    """
    return (
        int(start_rgb[0] + (end_rgb[0] - start_rgb[0]) * t),
        int(start_rgb[1] + (end_rgb[1] - start_rgb[1]) * t),
        int(start_rgb[2] + (end_rgb[2] - start_rgb[2]) * t),
    )


def interpolate_color_hsv(
    start_rgb: tuple[int, int, int], end_rgb: tuple[int, int, int], t: float
) -> tuple[int, int, int]:
    """
    Interpolates between two colours in HSV coloration area.
    Args:
        start_rgb: The beginning coloration in RGB format.
        end_rgb: The ending coloration in RGB format.
        t: A float between 0 and 1 representing the interpolation issue.
    Returns:
        The interpolated coloration in RGB format.
    """
    start_hsv = convert_color_space(start_rgb, cv2.COLOR_RGB2HSV)
    end_hsv = convert_color_space(end_rgb, cv2.COLOR_RGB2HSV)

    hue = int(start_hsv[0] + (end_hsv[0] - start_hsv[0]) * t)
    saturation = int(start_hsv[1] + (end_hsv[1] - start_hsv[1]) * t)
    worth = int(start_hsv[2] + (end_hsv[2] - start_hsv[2]) * t)

    return convert_color_space((hue, saturation, worth), cv2.COLOR_HSV2RGB)

Now we will write a loop to match these two interpolation strategies. To create the picture, we use the np.full technique to fill all pixels of the picture array with a specified coloration. Utilizing cv2.hconcat we will mix the 2 photos horizontally into one picture. Earlier than we show them, we have to convert to the OpenCV format BGR.

def run_transition_loop(
    color_start_rgb: tuple[int, int, int],
    color_end_rgb: tuple[int, int, int],
    fps: int,
    time_duration_secs: float,
    image_size: tuple[int, int],
) -> None:
    """
    Runs the colour transition loop.

    Args:
        color_start_rgb: The beginning coloration in RGB format.
        color_end_rgb: The ending coloration in RGB format.
        time_steps: The variety of time steps for the transition.
        time_duration_secs: The length of the transition in seconds.
        image_size: The scale of the photographs to be generated.
    """

    img_shape = (image_size[1], image_size[0], 3)
    num_steps = int(fps * time_duration_secs)

    for t in np.linspace(0, 1, num_steps):
        color_rgb_trans = interpolate_color_rgb(color_start_rgb, color_end_rgb, t)
        color_hue_trans = interpolate_color_hsv(color_start_rgb, color_end_rgb, t)

        img_rgb = np.full(form=img_shape, fill_value=color_rgb_trans, dtype=np.uint8)
        img_hsv = np.full(form=img_shape, fill_value=color_hue_trans, dtype=np.uint8)

        composite = cv2.hconcat((img_rgb, img_hsv))
        composite_bgr = cv2.cvtColor(composite, cv2.COLOR_RGB2BGR)

        cv2.imshow("Shade Transition", composite_bgr)

        key = cv2.waitKey(1000 // fps) & 0xFF
        if key == ord("q"):
            break

    cv2.destroyAllWindows()

Now we will merely name this operate with two colours for which we wish to visualize the transition. Beneath I visualize the transition from blue to yellow.

run_transition_loop(
    color_start_rgb=(0, 0, 255),  # Blue
    color_end_rgb=(255, 255, 0),  # Yellow
    fps=25,
    time_duration_secs=5,
    image_size=(512, 256),
)

The distinction is sort of drastic. Whereas the saturation and brightness stay fixed in the fitting animation, they alter significantly for the transition that interpolates instantly within the RGB area.

For extra implementation particulars, try the complete supply code within the GitHub repository:

https://github.com/trflorian/auto-color-filter

All visualizations on this publish have been created by the creator.