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animeganv2.py
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animeganv2.py
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import sys
import time
import numpy as np
import cv2
import ailia
# import original modules
sys.path.append('../../util')
from arg_utils import get_base_parser, update_parser, get_savepath # noqa: E402
from model_utils import check_and_download_models # noqa: E402
from detector_utils import load_image # noqa: E402C
from webcamera_utils import get_capture, get_writer # noqa: E402
# logger
from logging import getLogger # noqa: E402
logger = getLogger(__name__)
# ======================
# Parameters
# ======================
WEIGHT_PAPRIKA_PATH = 'generator_Paprika.onnx'
MODEL_PAPRIKA_PATH = 'generator_Paprika.onnx.prototxt'
WEIGHT_HAYAO_PATH = 'generator_Hayao.onnx'
MODEL_HAYAO_PATH = 'generator_Hayao.onnx.prototxt'
WEIGHT_SHINKAI_PATH = 'generator_Shinkai.onnx'
MODEL_SHINKAI_PATH = 'generator_Shinkai.onnx.prototxt'
WEIGHT_CELEBA_PATH = 'celeba_distill.onnx'
MODEL_CELEBA_PATH = 'celeba_distill.onnx.prototxt'
WEIGHT_FACE_PAINT_PATH = 'face_paint_512_v2.onnx'
MODEL_FACE_PAINT_PATH = 'face_paint_512_v2.onnx.prototxt'
REMOTE_PATH = \
'https://storage.googleapis.com/ailia-models/animeganv2/'
IMAGE_PATH = 'sample.jpg'
SAVE_IMAGE_PATH = 'output.png'
IMAGE_HEIGHT = 512
IMAGE_WIDTH = 512
# ======================
# Arguemnt Parser Config
# ======================
parser = get_base_parser(
'Anime GAN v2', IMAGE_PATH, SAVE_IMAGE_PATH,
)
parser.add_argument(
'-m', '--model_name', default='paprika',
choices=('paprika', 'hayao', 'shinkai', 'celeba', 'face_paint'),
help='model name'
)
parser.add_argument(
'-k', '--keep',
action='store_true',
help='keep aspect when resizing.'
)
parser.add_argument(
'--x32',
action="store_true",
help='resize image to multiple of 32s.'
)
parser.add_argument(
'--onnx',
action='store_true',
help='execute onnxruntime version.'
)
args = update_parser(parser)
# ======================
# Main functions
# ======================
def preprocess(img):
x32 = args.x32
keep = args.keep
h, w = (IMAGE_HEIGHT, IMAGE_WIDTH)
im_h, im_w, _ = img.shape
if x32:
# resize image to multiple of 32s
def to_32s(x):
return 256 if x < 256 else x - x % 32
oh, ow = to_32s(im_h), to_32s(im_w)
ph = pw = 0
img = cv2.resize(img, (ow, oh))
elif keep:
# adaptive_resize
r = min(h / im_h, w / im_w)
oh, ow = int(im_h * r), int(im_w * r)
resized = cv2.resize(img, (ow, oh))
img = np.zeros((h, w, 3), dtype=np.uint8)
ph, pw = (h - oh) // 2, (w - ow) // 2
img[ph: ph + oh, pw: pw + ow] = resized
else:
oh, ow = h, w
ph = pw = 0
img = cv2.resize(img, (ow, oh))
img = img / 127.5 - 1
img = img.transpose(2, 0, 1) # HWC -> CHW
img = np.expand_dims(img, axis=0)
img = img.astype(np.float32)
return img, (ph, pw), (oh, ow)
def post_processing(img, im_hw, pad_hw, resized_hw):
img = img.transpose(1, 2, 0)
pad_x = pad_hw[1]
pad_y = pad_hw[0]
resized_x = resized_hw[1]
resized_y = resized_hw[0]
img = img[pad_y:pad_y + resized_y, pad_x:pad_x + resized_x, ...]
img = cv2.resize(img, (im_hw[1], im_hw[0]))
img = np.clip(img, -1, 1)
img = img * 127.5 + 127.5
return img
def predict(net, img):
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
im_h, im_w = img.shape[:2]
img, pad_hw, resized_hw = preprocess(img)
# feedforward
if not args.onnx:
output = net.predict([img])
else:
output = net.run(None, {'input_image': img})
output = output[0]
img = post_processing(output[0], (im_h, im_w), pad_hw, resized_hw)
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
img = img.astype(np.uint8)
return img
def recognize_from_image(net):
# input image loop
for image_path in args.input:
logger.info(image_path)
# prepare input data
img = load_image(image_path)
img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
# inference
logger.info('Start inference...')
if args.benchmark:
logger.info('BENCHMARK mode')
total_time_estimation = 0
for i in range(args.benchmark_count):
start = int(round(time.time() * 1000))
out_img = predict(net, img)
end = int(round(time.time() * 1000))
estimation_time = (end - start)
# Loggin
logger.info(f'\tailia processing estimation time {estimation_time} ms')
if i != 0:
total_time_estimation = total_time_estimation + estimation_time
logger.info(f'\taverage time estimation {total_time_estimation / (args.benchmark_count - 1)} ms')
else:
out_img = predict(net, img)
# plot result
savepath = get_savepath(args.savepath, image_path, ext='.png')
logger.info(f'saved at : {savepath}')
cv2.imwrite(savepath, out_img)
logger.info('Script finished successfully.')
def recognize_from_video(net):
video_file = args.video if args.video else args.input[0]
capture = get_capture(video_file)
assert capture.isOpened(), 'Cannot capture source'
# create video writer if savepath is specified as video format
f_h = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
f_w = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
if args.savepath != SAVE_IMAGE_PATH:
logger.warning(
'currently, video results cannot be output correctly...'
)
writer = get_writer(args.savepath, f_h, f_w)
else:
writer = None
frame_shown = False
while True:
ret, frame = capture.read()
if (cv2.waitKey(1) & 0xFF == ord('q')) or not ret:
break
if frame_shown and cv2.getWindowProperty('frame', cv2.WND_PROP_VISIBLE) == 0:
break
# inference
out_img = predict(net, frame)
# show
cv2.imshow('frame', out_img)
frame_shown = True
# save results
if writer is not None:
writer.write(out_img)
capture.release()
cv2.destroyAllWindows()
if writer is not None:
writer.release()
logger.info('Script finished successfully.')
def main():
dic_model = {
'paprika': (WEIGHT_PAPRIKA_PATH, MODEL_PAPRIKA_PATH),
'hayao': (WEIGHT_HAYAO_PATH, MODEL_HAYAO_PATH),
'shinkai': (WEIGHT_SHINKAI_PATH, MODEL_SHINKAI_PATH),
'celeba': (WEIGHT_CELEBA_PATH, MODEL_CELEBA_PATH),
'face_paint': (WEIGHT_FACE_PAINT_PATH, MODEL_FACE_PAINT_PATH),
}
weight_path, model_path = dic_model[args.model_name]
check_and_download_models(weight_path, model_path, REMOTE_PATH)
env_id = args.env_id
# initialize
if not args.onnx:
memory_mode = ailia.get_memory_mode(
reduce_constant=True, ignore_input_with_initializer=True,
reduce_interstage=False, reuse_interstage=True)
net = ailia.Net(model_path, weight_path, env_id=env_id, memory_mode=memory_mode)
else:
import onnxruntime
net = onnxruntime.InferenceSession(weight_path)
if args.video is not None:
recognize_from_video(net)
else:
recognize_from_image(net)
if __name__ == '__main__':
main()