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341438f 3366cca 341438f 3366cca 4e76f1b 3366cca 4e76f1b 3366cca 341438f 4e76f1b 3366cca 4e76f1b 3366cca 341438f 3366cca 341438f af05866 3366cca 341438f 4e76f1b 3366cca 341438f 4e76f1b 341438f 4e76f1b 341438f 3366cca | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 | from main import extract_frames, run_eval #run
from PIL import Image
import numpy as np
from skimage.metrics import structural_similarity as ssim
from skimage.metrics import peak_signal_noise_ratio as psnr
import torch
import torchvision.transforms as transforms
import lpips
from pytorch_fid.fid_score import calculate_fid_given_paths
import os
import json
from huggingface_hub import snapshot_download
# Convert PIL to numpy
def pil_to_np(img):
return np.array(img).astype(np.float32) / 255.0
# SSIM
def compute_ssim(img1, img2):
img1_np = pil_to_np(img1)
img2_np = pil_to_np(img2)
h, w = img1_np.shape[:2]
min_dim = min(h, w)
win_size = min(7, min_dim if min_dim % 2 == 1 else min_dim - 1) # ensure odd
return ssim(img1_np, img2_np, win_size=win_size, channel_axis=-1, data_range=1.0)
# PSNR
def compute_psnr(img1, img2):
img1_np = pil_to_np(img1)
img2_np = pil_to_np(img2)
return psnr(img1_np, img2_np, data_range=1.0)
# LPIPS
lpips_model = lpips.LPIPS(net='alex')
lpips_transform = transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5]*3, [0.5]*3)
])
def compute_lpips(img1, img2):
img1_tensor = lpips_transform(img1).unsqueeze(0)
img2_tensor = lpips_transform(img2).unsqueeze(0)
return lpips_model(img1_tensor, img2_tensor).item()
# FID: Save images to temp folders for FID calculation
def compute_fid(img1, img2):
os.makedirs('temp/img1', exist_ok=True)
os.makedirs('temp/img2', exist_ok=True)
img1.save('temp/img1/0.png')
img2.save('temp/img2/0.png')
fid = calculate_fid_given_paths(['temp/img1', 'temp/img2'], batch_size=1, device='cpu', dims=2048)
return fid
with open('metrics.json', 'r') as file:
metrics = json.load(file)
def get_score(item, image_paths, video_path, train_steps=100, inference_steps=10, fps=12, bg_remove=False):
images = []
for path in image_paths:
img = Image.open(path)
images.append([img])
gt_frames = extract_frames(video_path, fps)
os.makedirs('out/'+item, exist_ok=True)
for i, frame in enumerate(gt_frames):
frame.save("out/"+item+"/frame_"+str(i)+".png")
#results = run(images, video_path, train_steps=100, inference_steps=10, fps=12, bg_remove=False, finetune=True)
results, results_base = run_eval(images, video_path, train_steps=100, inference_steps=10, fps=12, modelId="fine_tuned_pcdms", img_width=1920, img_height=1080, bg_remove=False, resize_inputs=False)
print(results)
for i, result in enumerate(results):
result.save("out/"+item+"/result_"+str(i)+".png")
for i, result in enumerate(results_base):
result.save("out/"+item+"/base_"+str(i)+".png")
"""
img1=gt_frames[0]
img2=Image.open("out/base_0.png")
print("SSIM:", compute_ssim(img1, img2))
print("PSNR:", compute_psnr(img1, img2))
print("LPIPS:", compute_lpips(img1, img2))
print("FID:", compute_fid(img1, img2))
"""
ssim = []
psnr = []
lpips = []
fid = []
ssim2 = []
psnr2 = []
lpips2 = []
fid2 = []
for gt, result, base in zip(gt_frames, results, results_base):
ssim.append(float(compute_ssim(gt, result)))
psnr.append(float(compute_psnr(gt, result)))
lpips.append(float(compute_lpips(gt, result)))
fid.append(float(compute_fid(gt, result)))
ssim2.append(float(compute_ssim(gt, base)))
psnr2.append(float(compute_psnr(gt, base)))
lpips2.append(float(compute_lpips(gt, base)))
fid2.append(float(compute_fid(gt, base)))
print("SSIM:", sum(ssim)/len(ssim))
print("PSNR:", sum(psnr)/len(psnr))
print("LPIPS:", sum(lpips)/len(lpips))
print("FID:", sum(fid)/len(fid))
print('baseline:')
print("SSIM:", sum(ssim2)/len(ssim2))
print("PSNR:", sum(psnr2)/len(psnr2))
print("LPIPS:", sum(lpips2)/len(lpips2))
print("FID:", sum(fid2)/len(fid2))
metrics[item] = {'ft': {}, 'base': {}}
metrics[item]['ft']['ssim'] = {'avg': sum(ssim)/len(ssim), 'vals': ssim}
metrics[item]['ft']['psnr'] = {'avg': sum(psnr)/len(psnr), 'vals': psnr}
metrics[item]['ft']['lpips'] = {'avg': sum(lpips)/len(lpips), 'vals': lpips}
metrics[item]['ft']['fid'] = {'avg': sum(fid)/len(fid), 'vals': fid}
metrics[item]['base']['ssim'] = {'avg': sum(ssim2)/len(ssim2), 'vals': ssim2}
metrics[item]['base']['psnr'] = {'avg': sum(psnr2)/len(psnr2), 'vals': psnr2}
metrics[item]['base']['lpips'] = {'avg': sum(lpips2)/len(lpips2), 'vals': lpips2}
metrics[item]['base']['fid'] = {'avg': sum(fid2)/len(fid2), 'vals': fid2}
with open('metrics.json', "w", encoding="utf-8") as json_file:
json.dump(metrics, json_file, ensure_ascii=False, indent=4)
def get_files(directory_path):
"""
Returns a list of all files in the specified directory.
"""
files = []
for entry in os.listdir(directory_path):
full_path = os.path.join(directory_path, entry)
if os.path.isfile(full_path):
files.append(entry)
return files
def run_evaluate():
snapshot_download(repo_id="acmyu/KeyframesAI-eval", local_dir="test", repo_type="dataset")
items = os.listdir('test')
items = [it for it in items if not it[0]=='.' and not os.path.isfile('test/'+it)]
print(items)
items = ['sidewalk'] #['sidewalk', 'aaa', 'azri', 'dead', 'frankgirl', 'kobold', 'ramona', 'renee', 'walk', 'woody']
for item in items:
if item in metrics:
continue
print(item)
try:
files = get_files('test/'+item)
images = list(filter(lambda x: not x.endswith('.mp4'), files))
images = ['test/'+item+'/'+img for img in images]
videos = [x for x in files if x.endswith('.mp4')]
print(images, videos)
if len(videos) == 1:
get_score(item, images, 'test/'+item+'/'+videos[0])
#get_score(item, ['test/'+item+'/1.jpg', 'test/'+item+'/2.jpg', 'test/'+item+'/3.jpg'], 'test/'+item+'/v.mp4')
else:
print('Error: mp4 not found')
except:
print("Error", item)
ssim = []
psnr = []
lpips = []
fid = []
ssim2 = []
psnr2 = []
lpips2 = []
fid2 = []
for item in metrics.keys():
ssim.append(metrics[item]['ft']['ssim']['avg'])
psnr.append(metrics[item]['ft']['psnr']['avg'])
lpips.append(metrics[item]['ft']['lpips']['avg'])
fid.append(metrics[item]['ft']['fid']['avg'])
ssim2.append(metrics[item]['base']['ssim']['avg'])
psnr2.append(metrics[item]['base']['psnr']['avg'])
lpips2.append(metrics[item]['base']['lpips']['avg'])
fid2.append(metrics[item]['base']['fid']['avg'])
print(item)
print("SSIM:", metrics[item]['ft']['ssim']['avg'], metrics[item]['base']['ssim']['avg'])
print("PSNR:", metrics[item]['ft']['psnr']['avg'], metrics[item]['base']['psnr']['avg'])
print("LPIPS:", metrics[item]['ft']['lpips']['avg'], metrics[item]['base']['lpips']['avg'])
print("FID:", metrics[item]['ft']['fid']['avg'], metrics[item]['base']['fid']['avg'])
print('Results:')
print("SSIM:", sum(ssim)/len(ssim))
print("PSNR:", sum(psnr)/len(psnr))
print("LPIPS:", sum(lpips)/len(lpips))
print("FID:", sum(fid)/len(fid))
print('baseline:')
print("SSIM:", sum(ssim2)/len(ssim2))
print("PSNR:", sum(psnr2)/len(psnr2))
print("LPIPS:", sum(lpips2)/len(lpips2))
print("FID:", sum(fid2)/len(fid2))
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