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"""
Internal code snippets were obtained from https://github.com/SystemErrorWang/White-box-Cartoonization/
For it to work tensorflow version 2.x changes were obtained from https://github.com/steubk/White-box-Cartoonization
"""
import os
import uuid
import time
import subprocess
import sys
import cv2
import numpy as np
import skvideo.io
try:
import tensorflow.compat.v1 as tf
except ImportError:
import tensorflow as tf
import network
import guided_filter
class WB_Cartoonize:
def __init__(self, weights_dir, gpu):
if not os.path.exists(weights_dir):
raise FileNotFoundError("Weights Directory not found, check path")
self.load_model(weights_dir, gpu)
print("Weights successfully loaded")
def resize_crop(self, image):
h, w, c = np.shape(image)
if min(h, w) > 720:
if h > w:
h, w = int(720*h/w), 720
else:
h, w = 720, int(720*w/h)
image = cv2.resize(image, (w, h),
interpolation=cv2.INTER_AREA)
h, w = (h//8)*8, (w//8)*8
image = image[:h, :w, :]
return image
def load_model(self, weights_dir, gpu):
try:
tf.disable_eager_execution()
except:
None
tf.reset_default_graph()
self.input_photo = tf.placeholder(tf.float32, [1, None, None, 3], name='input_image')
network_out = network.unet_generator(self.input_photo)
self.final_out = guided_filter.guided_filter(self.input_photo, network_out, r=1, eps=5e-3)
all_vars = tf.trainable_variables()
gene_vars = [var for var in all_vars if 'generator' in var.name]
saver = tf.train.Saver(var_list=gene_vars)
if gpu:
gpu_options = tf.GPUOptions(allow_growth=True)
device_count = {'GPU':1}
else:
gpu_options = None
device_count = {'GPU':0}
config = tf.ConfigProto(gpu_options=gpu_options, device_count=device_count)
self.sess = tf.Session(config=config)
self.sess.run(tf.global_variables_initializer())
saver.restore(self.sess, tf.train.latest_checkpoint(weights_dir))
def infer(self, image):
image = self.resize_crop(image)
batch_image = image.astype(np.float32)/127.5 - 1
batch_image = np.expand_dims(batch_image, axis=0)
## Session Run
output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
## Post Process
output = (np.squeeze(output)+1)*127.5
output = np.clip(output, 0, 255).astype(np.uint8)
return output
def process_video(self, fname, frame_rate):
## Capture video using opencv
cap = cv2.VideoCapture(fname)
target_size = (int(cap.get(3)),int(cap.get(4)))
output_fname = os.path.abspath('{}/{}-{}.mp4'.format(fname.replace(os.path.basename(fname), ''),str(uuid.uuid4())[:7],os.path.basename(fname).split('.')[0]))
out = skvideo.io.FFmpegWriter(output_fname, inputdict={'-r':frame_rate}, outputdict={'-r':frame_rate})
while True:
ret, frame = cap.read()
if ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = self.infer(frame)
frame = cv2.resize(frame, target_size)
out.writeFrame(frame)
else:
break
cap.release()
out.close()
final_name = '{}final_{}'.format(fname.replace(os.path.basename(fname), ''), os.path.basename(output_fname))
p = subprocess.Popen(['ffmpeg','-i','{}'.format(output_fname), "-pix_fmt", "yuv420p", final_name])
p.communicate()
p.wait()
os.system("rm "+output_fname)
return final_name
if __name__ == '__main__':
gpu = len(sys.argv) < 2 or sys.argv[1] != '--cpu'
wbc = WB_Cartoonize(os.path.abspath('white_box_cartoonizer/saved_models'), gpu)
img = cv2.imread('white_box_cartoonizer/test.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
cartoon_image = wbc.infer(img)
import matplotlib.pyplot as plt
plt.imshow(cartoon_image)
plt.show()
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