Year: 2020

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Note. The Median Cut algorithm is typically used for color quantization, which involves reducing the number of colors in an image while preserving its visual quality. It doesn’t directly provide a way to identify the brightest areas in an image. However, if you’re interested in identifying the brightest areas, you might want to look into other methods like thresholding, histogram analysis, or edge detection, through openCV for example.

 

Here is an openCV example:

 

# bottom left coordinates = 0,0
import numpy as np
import cv2

# Load the HDR or EXR image
image = cv2.imread('your_image_path.exr', cv2.IMREAD_UNCHANGED)  # Load as-is without modification

# Calculate the luminance from the HDR channels (assuming RGB format)
luminance = np.dot(image[..., :3], [0.299, 0.587, 0.114])

# Set a threshold value based on estimated EV
threshold_value = 2.4  # Estimated threshold value based on 4.8 EV

# Apply the threshold to identify bright areas
# The luminance array contains the calculated luminance values for each pixel in the image. # The threshold_value is a user-defined value that represents a cutoff point, separating "bright" and "dark" areas in terms of perceived luminance.

thresholded = (luminance > threshold_value) * 255 

# Convert the thresholded image to uint8 for contour detection 
thresholded = thresholded.astype(np.uint8) 

# Find contours of the bright areas 
contours, _ = cv2.findContours(thresholded, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) 

# Create a list to store the bounding boxes of bright areas 
bright_areas = [] 

# Iterate through contours and extract bounding boxes for contour in contours: 
x, y, w, h = cv2.boundingRect(contour) 

# Adjust y-coordinate based on bottom-left origin 
y_bottom_left_origin = image.shape[0] - (y + h) bright_areas.append((x, y_bottom_left_origin, x + w, y_bottom_left_origin + h)) 

# Store as (x1, y1, x2, y2) 
# Print the identified bright areas 
print("Bright Areas (x1, y1, x2, y2):") for area in bright_areas: print(area)

 

More details

 

Luminance and Exposure in an EXR Image:

• An EXR (Extended Dynamic Range) image format is often used to store high dynamic range (HDR) images that contain a wide range of luminance values, capturing both dark and bright areas.
• Luminance refers to the perceived brightness of a pixel in an image. In an RGB image, luminance is often calculated using a weighted sum of the red, green, and blue channels, where different weights are assigned to each channel to account for human perception.
• In an EXR image, the pixel values can represent radiometrically accurate scene values, including actual radiance or irradiance levels. These values are directly related to the amount of light emitted or reflected by objects in the scene.

 

The luminance line is calculating the luminance of each pixel in the image using a weighted sum of the red, green, and blue channels. The three float values [0.299, 0.587, 0.114] are the weights used to perform this calculation.

 

These weights are based on the concept of luminosity, which aims to approximate the perceived brightness of a color by taking into account the human eye’s sensitivity to different colors. The values are often derived from the NTSC (National Television System Committee) standard, which is used in various color image processing operations.

 

Here’s the breakdown of the float values:

• 0.299: Weight for the red channel.
• 0.587: Weight for the green channel.
• 0.114: Weight for the blue channel.

 

The weighted sum of these channels helps create a grayscale image where the pixel values represent the perceived brightness. This technique is often used when converting a color image to grayscale or when calculating luminance for certain operations, as it takes into account the human eye’s sensitivity to different colors.

 

For the threshold, remember that the exact relationship between EV values and pixel values can depend on the tone-mapping or normalization applied to the HDR image, as well as the dynamic range of the image itself.

 

To establish a relationship between exposure and the threshold value, you can consider the relationship between linear and logarithmic scales:

1. Linear and Logarithmic Scales:
   • Exposure values in an EXR image are often represented in logarithmic scales, such as EV (exposure value). Each increment in EV represents a doubling or halving of the amount of light captured.
   • Threshold values for luminance thresholding are usually linear, representing an actual luminance level.
2. Conversion Between Scales:
   • To establish a mathematical relationship, you need to convert between the logarithmic exposure scale and the linear threshold scale.
   • One common method is to use a power function. For instance, you can use a power function to convert EV to a linear intensity value.
   threshold_value = base_value * (2 ** EV)


   Here, EV is the exposure value, base_value is a scaling factor that determines the relationship between EV and threshold_value, and 2 ** EV is used to convert the logarithmic EV to a linear intensity value.

3. Choosing the Base Value:
   • The base_value factor should be determined based on the dynamic range of your EXR image and the specific luminance values you are dealing with.
   • You may need to experiment with different values of base_value to achieve the desired separation of bright areas from the rest of the image.

 

Let’s say you have an EXR image with a dynamic range of 12 EV, which is a common range for many high dynamic range images. In this case, you want to set a threshold value that corresponds to a certain number of EV above the middle gray level (which is often considered to be around 0.18).

Here’s an example of how you might determine a base_value to achieve this:

 

# Define the dynamic range of the image in EV
dynamic_range = 12

# Choose the desired number of EV above middle gray for thresholding
desired_ev_above_middle_gray = 2

# Calculate the threshold value based on the desired EV above middle gray
threshold_value = 0.18 * (2 ** (desired_ev_above_middle_gray / dynamic_range))

print("Threshold Value:", threshold_value)