ssarcandy / hdr-imaging Goto Github PK

In the resuting .hdr image are cyan or magenta colored pixels. Are there parameters wrong or is there an issue with the calculations? I tried diffrent hdr viewer and tonemapper, all witrh the same result.

Greetings!

Thanks for this implementation, I find that it successfully achieved what the OpenCV version currently does not, specifically with respect to proper weighting of saturated pixels.

To support, after some research, I have found an improvement.

Basically, for the current hdr_debvec( ) function, I kept on getting nasty response functions like this:

I realized that what is technically happening is downsampling of the pixel values, such that the pixels are averaged / interpolated across each other. This is very problematic in the case where the user has many, many saturated pixels: averaging these values will make them unsaturated, and create a false color in between.

I figured a more robust approach would be to instead just uniformly sample individual pixels across a grid of the image.

Here is how that changed my camera response function (3600 samples):

Here is the new implementation (not my best code but it works):

def hdr_debvec(img_list, exposure_times, number_of_samples_per_dimension=60):
    B = [math.log(e,2) for e in exposure_times]
    l = constant.L
    w = [z if z <= 0.5*255 else 255-z for z in range(256)]

    samples = []
    width = img_list[0].shape[0]
    height = img_list[0].shape[1]
    width_iteration = width / number_of_samples_per_dimension
    height_iteration = height / number_of_samples_per_dimension

    w_iter = 0
    h_iter = 0

    Z = np.zeros((len(img_list), number_of_samples_per_dimension*number_of_samples_per_dimension))
    for img_index, img in enumerate(img_list):
        h_iter = 0
        for i in range(number_of_samples_per_dimension):
            w_iter = 0
            for j in range(number_of_samples_per_dimension):
                if math.floor(w_iter) < width and math.floor(h_iter) < height:
                    pixel = img[math.floor(w_iter), math.floor(h_iter)]
                    Z[img_index, i*j] = pixel
                w_iter += width_iteration
            h_iter += height_iteration
    
    return response_curve_solver(Z, B, l, w)

Thanks again and great work.

Hello. Thanks for your effort and work. May I ask where can I download the 24 TMO.exe? Appreciate!

Is there a bug in Z[img_index, ij] = pixel ? Since there will be several cases where different combination of i and j leads to same position of ij.