def compute_perceptual_similarity()

in evaluation/evaluate_perceptualsim.py [0:0]

• 49 lines of code
• 6 McCabe index (conditional complexity)

def compute_perceptual_similarity(folder, pred_img, tgt_img, take_every_other):

    # Load VGG16 for feature similarity
    vgg16 = PNet().to("cuda")
    vgg16.eval()
    vgg16.cuda()

    values_percsim = []
    values_ssim = []
    values_psnr = []
    folders = os.listdir(folder)
    for i, f in tqdm(enumerate(sorted(folders))):
        pred_imgs = glob.glob(folder + f + "/" + pred_img)
        tgt_imgs = glob.glob(folder + f + "/" + tgt_img)
        assert len(tgt_imgs) == 1

        perc_sim = 10000
        ssim_sim = -10
        psnr_sim = -10
        for p_img in pred_imgs:
            t_img = load_img(tgt_imgs[0])
            p_img = load_img(p_img, size=t_img.size(2))
            t_perc_sim = perceptual_sim(p_img, t_img, vgg16).item()
            perc_sim = min(perc_sim, t_perc_sim)

            ssim_sim = max(ssim_sim, ssim_metric(p_img, t_img).item())
            psnr_sim = max(psnr_sim, psnr(p_img, t_img).item())

        values_percsim += [perc_sim]
        values_ssim += [ssim_sim]
        values_psnr += [psnr_sim]

    if take_every_other:
        n_valuespercsim = []
        n_valuesssim = []
        n_valuespsnr = []
        for i in range(0, len(values_percsim) // 2):
            n_valuespercsim += [
                min(values_percsim[2 * i], values_percsim[2 * i + 1])
            ]
            n_valuespsnr += [max(values_psnr[2 * i], values_psnr[2 * i + 1])]
            n_valuesssim += [max(values_ssim[2 * i], values_ssim[2 * i + 1])]

        values_percsim = n_valuespercsim
        values_ssim = n_valuesssim
        values_psnr = n_valuespsnr

    avg_percsim = np.mean(np.array(values_percsim))
    std_percsim = np.std(np.array(values_percsim))

    avg_psnr = np.mean(np.array(values_psnr))
    std_psnr = np.std(np.array(values_psnr))

    avg_ssim = np.mean(np.array(values_ssim))
    std_ssim = np.std(np.array(values_ssim))

    return {
        "Perceptual similarity": (avg_percsim, std_percsim),
        "PSNR": (avg_psnr, std_psnr),
        "SSIM": (avg_ssim, std_ssim),
    }