metric_scripts/compute_all.py

import argparse
from pathlib import Path
from tqdm.auto import tqdm
import numpy as np
import scipy.misc
import scipy.io
from os.path import dirname
from os.path import join
import scipy
from PIL import Image
import numpy as np
import scipy.ndimage
import numpy as np
import scipy.special
import math
import cv2
import json
from brisque import BRISQUE
import torch

from kiui.cli.clip_sim_text import CLIP

import ipdb
st=ipdb.set_trace


gamma_range = np.arange(0.2, 10, 0.001)
a = scipy.special.gamma(2.0/gamma_range)
a *= a
b = scipy.special.gamma(1.0/gamma_range)
c = scipy.special.gamma(3.0/gamma_range)
prec_gammas = a/(b*c)

def aggd_features(imdata):
    #flatten imdata
    imdata.shape = (len(imdata.flat),)
    imdata2 = imdata*imdata
    left_data = imdata2[imdata<0]
    right_data = imdata2[imdata>=0]
    left_mean_sqrt = 0
    right_mean_sqrt = 0
    if len(left_data) > 0:
        left_mean_sqrt = np.sqrt(np.average(left_data))
    if len(right_data) > 0:
        right_mean_sqrt = np.sqrt(np.average(right_data))

    if right_mean_sqrt != 0:
      gamma_hat = left_mean_sqrt/right_mean_sqrt
    else:
      gamma_hat = np.inf
    #solve r-hat norm

    imdata2_mean = np.mean(imdata2)
    if imdata2_mean != 0:
      r_hat = (np.average(np.abs(imdata))**2) / (np.average(imdata2))
    else:
      r_hat = np.inf
    rhat_norm = r_hat * (((math.pow(gamma_hat, 3) + 1)*(gamma_hat + 1)) / math.pow(math.pow(gamma_hat, 2) + 1, 2))

    #solve alpha by guessing values that minimize ro
    pos = np.argmin((prec_gammas - rhat_norm)**2);
    alpha = gamma_range[pos]

    gam1 = scipy.special.gamma(1.0/alpha)
    gam2 = scipy.special.gamma(2.0/alpha)
    gam3 = scipy.special.gamma(3.0/alpha)

    aggdratio = np.sqrt(gam1) / np.sqrt(gam3)
    bl = aggdratio * left_mean_sqrt
    br = aggdratio * right_mean_sqrt

    #mean parameter
    N = (br - bl)*(gam2 / gam1)#*aggdratio
    return (alpha, N, bl, br, left_mean_sqrt, right_mean_sqrt)

def ggd_features(imdata):
    nr_gam = 1/prec_gammas
    sigma_sq = np.var(imdata)
    E = np.mean(np.abs(imdata))
    rho = sigma_sq/E**2
    pos = np.argmin(np.abs(nr_gam - rho));
    return gamma_range[pos], sigma_sq

def paired_product(new_im):
    shift1 = np.roll(new_im.copy(), 1, axis=1)
    shift2 = np.roll(new_im.copy(), 1, axis=0)
    shift3 = np.roll(np.roll(new_im.copy(), 1, axis=0), 1, axis=1)
    shift4 = np.roll(np.roll(new_im.copy(), 1, axis=0), -1, axis=1)

    H_img = shift1 * new_im
    V_img = shift2 * new_im
    D1_img = shift3 * new_im
    D2_img = shift4 * new_im

    return (H_img, V_img, D1_img, D2_img)


def gen_gauss_window(lw, sigma):
    sd = np.float32(sigma)
    lw = int(lw)
    weights = [0.0] * (2 * lw + 1)
    weights[lw] = 1.0
    sum = 1.0
    sd *= sd
    for ii in range(1, lw + 1):
        tmp = np.exp(-0.5 * np.float32(ii * ii) / sd)
        weights[lw + ii] = tmp
        weights[lw - ii] = tmp
        sum += 2.0 * tmp
    for ii in range(2 * lw + 1):
        weights[ii] /= sum
    return weights

def compute_image_mscn_transform(image, C=1, avg_window=None, extend_mode='constant'):
    if avg_window is None:
      avg_window = gen_gauss_window(3, 7.0/6.0)
    assert len(np.shape(image)) == 2
    h, w = np.shape(image)
    mu_image = np.zeros((h, w), dtype=np.float32)
    var_image = np.zeros((h, w), dtype=np.float32)
    image = np.array(image).astype('float32')
    scipy.ndimage.correlate1d(image, avg_window, 0, mu_image, mode=extend_mode)
    scipy.ndimage.correlate1d(mu_image, avg_window, 1, mu_image, mode=extend_mode)
    scipy.ndimage.correlate1d(image**2, avg_window, 0, var_image, mode=extend_mode)
    scipy.ndimage.correlate1d(var_image, avg_window, 1, var_image, mode=extend_mode)
    var_image = np.sqrt(np.abs(var_image - mu_image**2))
    return (image - mu_image)/(var_image + C), var_image, mu_image


def _niqe_extract_subband_feats(mscncoefs):
    # alpha_m,  = extract_ggd_features(mscncoefs)
    alpha_m, N, bl, br, lsq, rsq = aggd_features(mscncoefs.copy())
    pps1, pps2, pps3, pps4 = paired_product(mscncoefs)
    alpha1, N1, bl1, br1, lsq1, rsq1 = aggd_features(pps1)
    alpha2, N2, bl2, br2, lsq2, rsq2 = aggd_features(pps2)
    alpha3, N3, bl3, br3, lsq3, rsq3 = aggd_features(pps3)
    alpha4, N4, bl4, br4, lsq4, rsq4 = aggd_features(pps4)
    return np.array([alpha_m, (bl+br)/2.0,
            alpha1, N1, bl1, br1,  # (V)
            alpha2, N2, bl2, br2,  # (H)
            alpha3, N3, bl3, bl3,  # (D1)
            alpha4, N4, bl4, bl4,  # (D2)
    ])

def get_patches_train_features(img, patch_size, stride=8):
    return _get_patches_generic(img, patch_size, 1, stride)

def get_patches_test_features(img, patch_size, stride=8):
    return _get_patches_generic(img, patch_size, 0, stride)

def extract_on_patches(img, patch_size):
    h, w = img.shape
    patch_size = np.int_(patch_size)
    patches = []
    for j in range(0, h-patch_size+1, patch_size):
        for i in range(0, w-patch_size+1, patch_size):
            patch = img[j:j+patch_size, i:i+patch_size]
            patches.append(patch)

    patches = np.array(patches)
    
    patch_features = []
    for p in patches:
        patch_features.append(_niqe_extract_subband_feats(p))
    patch_features = np.array(patch_features)

    return patch_features

def _get_patches_generic(img, patch_size, is_train, stride):
    h, w = np.shape(img)
    if h < patch_size or w < patch_size:
        print("Input image is too small")
        exit(0)

    # ensure that the patch divides evenly into img
    hoffset = (h % patch_size)
    woffset = (w % patch_size)

    if hoffset > 0: 
        img = img[:-hoffset, :]
    if woffset > 0:
        img = img[:, :-woffset]


    img = img.astype(np.float32)
    img2 = cv2.resize(img, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC)

    mscn1, var, mu = compute_image_mscn_transform(img)
    mscn1 = mscn1.astype(np.float32)

    mscn2, _, _ = compute_image_mscn_transform(img2)
    mscn2 = mscn2.astype(np.float32)


    feats_lvl1 = extract_on_patches(mscn1, patch_size)
    feats_lvl2 = extract_on_patches(mscn2, patch_size/2)

    feats = np.hstack((feats_lvl1, feats_lvl2))# feats_lvl3))

    return feats

def niqe(inputImgData):

    patch_size = 96
    module_path = dirname(__file__)

    # TODO: memoize
    params = scipy.io.loadmat(join(module_path, 'data', 'niqe_image_params.mat'))
    pop_mu = np.ravel(params["pop_mu"])
    pop_cov = params["pop_cov"]


    M, N = inputImgData.shape

    # assert C == 1, "niqe called with videos containing %d channels. Please supply only the luminance channel" % (C,)
    assert M > (patch_size*2+1), "niqe called with small frame size, requires > 192x192 resolution video using current training parameters"
    assert N > (patch_size*2+1), "niqe called with small frame size, requires > 192x192 resolution video using current training parameters"


    feats = get_patches_test_features(inputImgData, patch_size)
    sample_mu = np.mean(feats, axis=0)
    sample_cov = np.cov(feats.T)

    X = sample_mu - pop_mu
    covmat = ((pop_cov+sample_cov)/2.0)
    pinvmat = scipy.linalg.pinv(covmat)
    niqe_score = np.sqrt(np.dot(np.dot(X, pinvmat), X))

    return niqe_score


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--input_dir', type=str, help="input directory")
    args = parser.parse_args()

    obj = BRISQUE(url=False)
    clip = CLIP('cuda', model_name='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k')

    input_dir = Path(args.input_dir)
    if 'gaussiandreamer' in str(input_dir):
        input_dir = input_dir / "gaussiandreamer-sd"
    dir_list = list(input_dir.glob('*'))

    niqe_all_results = []
    BRISQUE_all_results = []
    clip_all_results = []
    for video_dir in tqdm(dir_list):
        if video_dir.is_dir():
            text_prompt = video_dir.name.replace('_', ' ')

            with torch.no_grad():
                ref_features = clip.encode_text(text_prompt)

            if 'gaussiandreamer' in str(video_dir):
                images_dir = video_dir / "save" / "it1200-test"
                method = 'gaussiandreamer'
            elif 'lgm' in str(video_dir):
                images_dir = video_dir / video_dir.name
                method = 'lgm'
            elif 'director3d' in str(video_dir):
                images_dir = video_dir / "0" / video_dir.name
                method = 'director3d'
            elif 'prometheus' in str(video_dir):
                images_dir = video_dir / "0" / video_dir.name
                method = f'prometheus_{input_dir.parent.name}'
            else:
                raise ValueError(f"Unknown video directory: {video_dir}")
            images_list = list(images_dir.glob('*'))

            niqe_results = []
            BRISQUE_results = []
            clip_results = []
            for image_path in tqdm(images_list, desc=f"Processing {video_dir.name}"):
                try:
                    image_pil = Image.open(image_path)
                except:
                    continue
                niqe_metric = niqe(np.array(image_pil.convert('LA'))[:,:,0] )
                BRISQUE_metric = obj.score(np.array(image_pil))

                with torch.no_grad():
                    try:
                        cur_features = clip.encode_image(image_pil)
                    except:
                        continue
                similarity = (ref_features * cur_features).sum(dim=-1).mean().item()

                niqe_results.append(niqe_metric)
                
                
                if np.isnan(BRISQUE_metric):
                    print(f"NaN found in {image_path}")
                    continue
                BRISQUE_results.append(BRISQUE_metric)
                clip_results.append(similarity)

            niqe_all_results.append(np.mean(niqe_results))
            BRISQUE_all_results.append(np.mean(BRISQUE_results))
            clip_all_results.append(np.mean(clip_results))
    
    average_niqe = np.mean(niqe_all_results)
    average_BRISQUE = np.mean(BRISQUE_all_results)
    average_clip = np.mean(clip_all_results)
    
    print(f"{method} Average NIQE: {average_niqe}")
    print(f"{method} Average BRISQUE: {average_BRISQUE}")
    print(f"{method} Average CLIP score: {average_clip}")
    
    output_metrics = {'average_niqe': average_niqe,
                        'average_BRISQUE': average_BRISQUE,
                        'average_CLIP_score': average_clip,
                        'niqe_results': niqe_all_results,
                        'BRISQUE_results': BRISQUE_all_results,
                        'clip_results': clip_all_results}
    with open(input_dir / 'all_metric.json', 'w') as f:
        json.dump(output_metrics, f, indent=4)