def test():
args = cmdd.args()
args.images_dir_test = args.images_dir_train
args.pairs_file_test = 'data/market-re-id-pairs.csv'
pairs_for_each = 2
train_file_name = 'train.txt'
store_train_images = True
generated_as_separate = False
df_keypoints = pd.read_csv(args.annotations_file_train, sep=':')
df = filter_not_valid(df_keypoints)
print('Compute pair for train re-id...')
pairs_df_train = make_pairs(df, pairs_for_each)
print('Number of pairs: %s' % len(pairs_df_train))
pairs_df_train.to_csv('data/market-re-id-pairs.csv', index=False)
dataset = PoseHMDataset(test_phase=True, **vars(args))
generator = make_generator(args.image_size, args.use_input_pose,
args.warp_skip, args.disc_type, args.warp_agg,
args.use_bg, args.pose_rep_type)
assert (args.generator_checkpoint is not None)
generator.load_weights(args.generator_checkpoint)
print("Generate images...")
generate_images(dataset,
generator,
args.use_input_pose,
args.generated_images_dir,
store_train_images=store_train_images)
print("Creating train file...")
create_train_file(args.generated_images_dir, train_file_name,
generated_as_separate)
def test():
args = cmdd.args()
if args.load_generated_images:
print ("Loading images...")
input_images, target_images, generated_images, names = load_generated_images(args.generated_images_dir)
else:
print ("Generate images...")
from keras import backend as K
if args.use_dropout_test:
K.set_learning_phase(1)
dataset = PoseHMDataset(test_phase=True, **vars(args))
generator = make_generator(args.image_size, args.use_input_pose, args.warp_skip, args.disc_type, args.warp_agg,
args.use_bg, args.pose_rep_type)
assert (args.generator_checkpoint is not None)
generator.load_weights(args.generator_checkpoint)
input_images, target_images, generated_images, names = generate_images(dataset, generator, args.use_input_pose)
print ("Save images to %s..." % (args.generated_images_dir, ))
save_images(input_images, target_images, generated_images, names,
args.generated_images_dir)
def main():
args = cmdd.args()
generator = make_generator(args.image_size, args.use_input_pose,
args.warp_skip, args.disc_type, args.warp_agg,
args.use_bg, args.pose_rep_type)
if args.generator_checkpoint is not None:
generator.load_weights(args.generator_checkpoint)
discriminator = make_discriminator(args.image_size, args.use_input_pose,
args.warp_skip, args.disc_type,
args.warp_agg, args.use_bg,
args.pose_rep_type)
if args.discriminator_checkpoint is not None:
discriminator.load_weights(args.discriminator_checkpoint)
dataset = PoseHMDataset(test_phase=False, **vars(args))
gan = CGAN(generator, discriminator, **vars(args))
trainer = Trainer(dataset, gan, **vars(args))
trainer.train()
import pandas as pd
from cmdd import args
import pose_transform
import pose_utils
from itertools import permutations
args = args()
def filter_not_valid(df_keypoints):
def check_valid(x):
kp_array = pose_utils.load_pose_cords_from_strings(
x['keypoints_y'], x['keypoints_x'])
distractor = x['name'].startswith('-1') or x['name'].startswith('0000')
return pose_transform.check_valid(kp_array) and not distractor
return df_keypoints[df_keypoints.apply(check_valid, axis=1)].copy()
def make_pairs(df):
persons = df.apply(lambda x: '_'.join(x['name'].split('_')[0:1]), axis=1)
df['person'] = persons
fr, to = [], []
for person in pd.unique(persons):
pairs = zip(*list(permutations(df[df['person'] == person]['name'], 2)))
if len(pairs) != 0:
fr += list(pairs[0])
to += list(pairs[1])
pair_df = pd.DataFrame(index=range(len(fr)))
pair_df['from'] = fr
pair_df['to'] = to
from create_pairs_dataset import filter_not_valid
import cmdd
import os
from shutil import copy, rmtree
import pandas as pd
from pose_dataset import PoseHMDataset
from conditional_gan import make_generator
from tqdm import tqdm
from test import generate_images, save_images
from keras.models import load_model
from skimage.io import imread, imsave
from skimage.transform import resize
import numpy as np
if __name__ == "__main__":
args = cmdd.args()
dataset = 'tmp'
target_images_folder = 'data/target-images'
source_image = 'data/source-image.jpg'
bg_image = 'data/bg-image.jpg'
#For target images use kp from previous frame if not detected
interpolate = True
args.images_dir_test = 'data/' + dataset + '-dataset/test'
args.annotations_file_test = 'data/' + dataset + '-annotation-test.csv'
args.pairs_file_test = 'data/' + dataset + '-pairs-test.csv'
args.bg_images_dir_test = 'data/' + dataset + '-dataset/test-bg'