From RGB to HSV — and Back Again

A basic idea in Pc Imaginative and prescient is knowing how photographs are saved and represented. On disk, picture information are encoded in numerous 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’ll probably have an array-like construction that represents the pixels within the picture.

RGB

Every pixel comprises some coloration data about that particular level within the picture. Now the commonest strategy to characterize this coloration is within the RGB house, the place every pixel has three values: purple, inexperienced and blue. These values describe how a lot of every coloration is current and they are going to be blended additively. So for instance, a picture with all values set to zero might be black. If all three values are set to 100%, the ensuing picture might be white.

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

Alpha Channel

Photographs can even comprise details about transparency. In that case, a further 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 completely opaque pixel.

HSV

Now RGB(A) shouldn’t be the one strategy to characterize colours. The truth is there are numerous completely different coloration fashions that characterize coloration. One of the vital 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, no matter 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 all the time 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 not possible to do instantly in RGB. For instance, if you would like a transition between two colours and hold the identical brightness through the full transition, this might 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 take a look at three examples of the right way to work with these coloration areas in Python utilizing OpenCV. Within the first instance, we extract components of a picture which are of a sure coloration. Within the second half, we create a utility perform 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 – Coloration Masks

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

Utilizing OpenCV, we will load the picture and convert it to the HSV coloration house. By default photographs are learn in BGR format, therefore we’d like the flag cv2.COLOR_BGR2HSV within the conversion:

Python">import cv2

img_bgr = cv2.imread("photographs/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 perform 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 end result.

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 – Coloration Conversion

Whereas OpenCV supplies a helpful perform to transform full photographs between coloration areas, it doesn’t present an out-of-the-box answer to transform single colours between coloration areas. We will write a easy wrapper that creates a small 1×1 pixel picture with an enter coloration, makes use of the built-in OpenCV perform to transform to a different coloration house 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 house (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 house.
    """
    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 perform with any coloration. We will 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 Coloration Transition

On this third instance, we’ll create a transition between two colours with a continuing brightness and saturation interpolation. This might be in comparison with a direct interpolation between the preliminary and ultimate 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 house.
    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 house.
    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 check these two interpolation strategies. To create the picture, we use the np.full methodology to fill all pixels of the picture array with a specified coloration. Utilizing cv2.hconcat we will mix the 2 photographs 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("Coloration Transition", composite_bgr)

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

    cv2.destroyAllWindows()

Now we will merely name this perform with two colours for which we need 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 kind of drastic. Whereas the saturation and brightness stay fixed in the appropriate animation, they modify significantly for the transition that interpolates instantly within the RGB house.

For extra implementation particulars, take a look at the total supply code within the GitHub repository:

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

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