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segmenter.py
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segmenter.py
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import torch
import torchvision.transforms as transforms
from PIL import Image
import numpy as np
import os
from utils import rfutil, imutil, pidfile
from tqdm import tqdm
import cv2
from collections import Counter, defaultdict, namedtuple
import matplotlib.pyplot as plt
import argparse
import oyaml as yaml
import sys
sys.path.append('resources/face_parsing_pytorch/')
from model import BiSeNet
import random
# grouped cluster assignments
cluster_assn = {
0: 'background',
1: 'skin',
2: 'brows',
3: 'brows',
4: 'eye',
5: 'eye',
6: 'eye',
7: 'ear',
8: 'ear',
9: 'ear',
10: 'nose',
11: 'mouth',
12: 'mouth',
13: 'mouth',
14: 'neck',
15: 'neck',
16: 'clothes',
17: 'hair',
18: 'hat',
}
def cluster(args, outpath):
# network setup
n_classes = 19
net = BiSeNet(n_classes=n_classes).cuda()
save_pth = 'resources/face_parsing_pytorch/res/cp/79999_iter.pth'
net.load_state_dict(torch.load(save_pth))
net.eval()
to_tensor = transforms.Compose([
transforms.Resize((512, 512), Image.BILINEAR),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
subsets = ['reals_easiest', 'fakes_easiest']
PatchInfo = namedtuple('PatchInfo', ['patch', 'pos', 'file', 'value'])
for subset in subsets:
print(subset)
path = args.path
cluster_dir = os.path.join(outpath, subset + '_clusters')
os.makedirs(cluster_dir, exist_ok =True)
with open(os.path.join(path, subset+'_files.txt')) as f:
files = [line.strip() for line in f]
patches = np.load(os.path.join(path, subset + '.npz'))
which_model_netD = patches['which_model_netD']
fineSize = patches['finesize']
rfs = rfutil.find_rf_patches(which_model_netD, fineSize)
clusters = defaultdict(list)
clusters_baseline = Counter() # a counter for segmentations of random patches
# assign each patch to a cluster based on segmentation
for index, (patch, pos, value, file) in tqdm(enumerate(
zip(patches['patch'], patches['pos'], patches['value'], files)), total=len(files)):
image = Image.open(file).convert('RGB')
with torch.no_grad():
tensor = to_tensor(image)[None].cuda()
out = net(tensor)[0]
parsing = out[0].cpu().numpy().argmax(0)
interp = cv2.resize(parsing, (fineSize, fineSize), interpolation=cv2.INTER_NEAREST)[None]
seg_patch = rfutil.get_patch_from_img(interp, rfs[(pos[0], pos[1])], patches['rf'], pad_value=-1)
# how many pixels of each segmentation class in the patch
counter_patch = Counter(seg_patch[0].ravel())
# how many pixels of each segmentation class in the full img
counter_full = Counter(interp[0].ravel())
# normalize: omit padding value from normalization
counter_norm = {k: counter_patch[k] / counter_full[k] for k in counter_patch.keys() if k != -1}
cluster_id = max(counter_norm, key=counter_norm.get)
cluster_label = cluster_assn[cluster_id]
clusters[cluster_label].append(PatchInfo(patch, pos, file, value))
# pick a random patch for baseline
random_patch = random.choice(list(rfs.values()))
seg_patch = rfutil.get_patch_from_img(interp, random_patch, patches['rf'], pad_value=-1)
counter_patch = Counter(seg_patch[0].ravel())
counter_full = Counter(interp[0].ravel())
# omit padding value from normalization
counter_norm = {k: counter_patch[k] / counter_full[k] for k in counter_patch.keys() if k != -1}
cluster_id = max(counter_norm, key=counter_norm.get)
cluster_label = cluster_assn[cluster_id]
clusters_baseline[cluster_label] += 1
# plot each cluster in a grid
counts, labels = [], []
infos = []
for index, (k,v) in enumerate(sorted(clusters.items(), key=lambda item: len(item[1]))[::-1]):
line = '%d: %s, %d patches' % (index, k, len(v))
print(line)
infos.append(line)
counts.append(len(v))
labels.append(k)
cluster = np.asarray([patchinfo.patch for patchinfo in v])
files = [patchinfo.file for patchinfo in clusters[k]]
normalized = (cluster[-225:] * 0.5) + 0.5 # at most 15x15 grid
grid = imutil.imgrid(np.uint8(normalized * 255), pad=0, cols=int(np.ceil(np.sqrt(normalized.shape[0]))))
grid_im = Image.fromarray(grid)
grid_im.save(os.path.join(cluster_dir, 'cluster_%d.png' % index))
np.savez(os.path.join(cluster_dir, 'cluster_%d.npz' % index),
patch=cluster, rf=patches['rf'],
finesize=patches['finesize'],
outsize=patches['outsize'],
which_model_netD=patches['which_model_netD'],
pos=np.array([patchinfo.pos for patchinfo in v]),
value=np.array([patchinfo.value for patchinfo in v]))
with open(os.path.join(cluster_dir, 'cluster_%d.txt' % index), 'w') as f:
[f.write('%s\n' % file) for file in files]
# histogram
f, ax = plt.subplots(1, 1, figsize=(6, 4))
ax.bar(range(1, len(labels) + 1), counts)
ax.set_xticks(range(1, len(labels) + 1))
ax.set_xticklabels(labels, rotation='vertical')
ax.set_ylabel('count')
f.savefig(os.path.join(cluster_dir, 'histogram.pdf'),
bbox_inches='tight')
# write counts to file
with open(os.path.join(cluster_dir, 'counts.txt'), 'w') as f:
[f.write('%s\n' % line) for line in infos]
# write random patch baseline to file
infos = []
for index, (k,v) in enumerate(sorted(clusters_baseline.items(), key=lambda item: item[1])[::-1]):
infos.append('%d: %s, %d patches' % (index, k, v))
with open(os.path.join(cluster_dir, 'baseline.txt'), 'w') as f:
[f.write('%s\n' % line) for line in infos]
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Cluster patches using face segmentation.')
parser.add_argument('path', type=str, help='path to precomputed top clusters')
args = parser.parse_args()
outpath = os.path.join(args.path, 'clusters')
os.makedirs(outpath, exist_ok =True)
pidfile.exit_if_job_done(outpath,redo=True)
cluster(args, outpath)
pidfile.mark_job_done(outpath)