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demo.py
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demo.py
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#!/usr/bin/env python
# -- coding: utf-8 --
"""
Copyright (c) 2019. All rights reserved.
Created by C. L. Wang on 2020/1/2
"""
import os
import cv2
import numpy as np
import tensorflow as tf
from UGATIT import UGATIT
from main import parse_args
from my_utils.mst_utils import show_img_rgb
from my_utils.project_utils import traverse_dir_files, mkdir_if_not_exist
from root_dir import DATA_DIR
from utils import inverse_transform
class ImgPredictor(object):
"""
图像预测类
"""
def __init__(self):
self.gan, self.sess = self.init_model()
def init_model(self):
args = parse_args()
if args is None:
exit()
args.phase = 'test'
args.dataset = 'selfie2anime'
# args.light = 'True'
args.img_size = 256
# open session
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
gan = UGATIT(sess, args)
# build graph
gan.build_model()
# show network architecture
# show_all_variables()
gan.init_model(sess)
return gan, sess
def predict_img(self, img_path):
img_np = self.gan.read_img(img_path)
print('[Info] img_np shape: {}'.format(img_np.shape))
img_fake = self.gan.predict_img(img_np, self.sess)
img_fake = np.squeeze(img_fake, axis=0)
print('[Info] img_fake shape: {}'.format(img_fake.shape))
img_fake = inverse_transform(img_fake)
img_fake = img_fake.astype(np.uint8)
# show_img_rgb(img_fake)
return img_fake
def close_sess(self):
self.sess.close()
def img_predictor_test():
"""
图像预测测试
"""
img_dir = os.path.join(DATA_DIR, 'imgs')
img_out_dir = os.path.join(DATA_DIR, 'outputs')
mkdir_if_not_exist(img_out_dir)
paths_list, names_list = traverse_dir_files(img_dir)
ip = ImgPredictor()
for path, name in zip(paths_list, names_list):
img_fake = ip.predict_img(path)
img_fake = cv2.cvtColor(img_fake, cv2.COLOR_RGB2BGR)
img_out_path = os.path.join(img_out_dir, '{}.out.jpg'.format(name))
cv2.imwrite(img_out_path, img_fake)
def merge_imgs(imgs, cols=6, rows=6):
"""
合并图像
:param imgs: 图像序列
:param cols: 行数
:param rows: 列数
:param sk: 间隔,当sk=2时,即0, 2, 4, 6
:return: 大图
"""
if not imgs:
raise Exception('[Exception] 合并图像的输入为空!')
img_shape = imgs[0].shape
h, w, _ = img_shape
large_imgs = np.zeros((rows * h, cols * w, 3)) # 大图
for j in range(rows):
for i in range(cols):
idx = j * cols + i
if idx > len(imgs) - 1: # 少于帧数,输出透明帧
break
# print('[Info] 帧的idx: {}, i: {}, j:{}'.format(idx, i, j))
large_imgs[(j * h):(j * h + h), (i * w): (i * w + w)] = imgs[idx]
# print(large_imgs.shape)
# show_png(large_imgs)
# show_png(large_imgs)
return large_imgs
def merge_one_img():
img_dir = os.path.join(DATA_DIR, 'imgs')
img_out_dir = os.path.join(DATA_DIR, 'outputs')
img_merge_dir = os.path.join(DATA_DIR, 'merge')
paths_list, names_list = traverse_dir_files(img_dir)
out_paths_list, out_names_list = traverse_dir_files(img_out_dir)
merge_paths_list, merge_names_list = traverse_dir_files(img_merge_dir)
img_size = 256
img_list = []
for path, out_path, merge_path in zip(paths_list, out_paths_list, merge_paths_list):
img = cv2.imread(path)
img = cv2.resize(img, (img_size, img_size))
img_list.append(img)
img_out = cv2.imread(out_path)
img_out = cv2.resize(img_out, (img_size, img_size))
img_list.append(img_out)
img_merge = cv2.imread(merge_path)
img_merge = cv2.resize(img_merge, (img_size, img_size))
img_list.append(img_merge)
large_img = merge_imgs(img_list, cols=3, rows=7)
large_img_path = os.path.join(DATA_DIR, 'large_img.jpg')
cv2.imwrite(large_img_path, large_img)
def main():
img_predictor_test()
# merge_one_img()
if __name__ == '__main__':
main()