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init
Browse files- app.py +1 -4
- p2c/test.py +10 -19
app.py
CHANGED
@@ -19,7 +19,7 @@ import cv2
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from io import BytesIO
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sys.path.insert(0, 'p2c')
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from
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ORIGINAL_REPO_URL = 'https://github.com/minivision-ai/photo2cartoon'
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@@ -47,9 +47,6 @@ def parse_args() -> argparse.Namespace:
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parser.add_argument('--allow-screenshot', action='store_true')
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return parser.parse_args()
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def run(
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image,
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p2c : Photo2Cartoon,
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from io import BytesIO
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sys.path.insert(0, 'p2c')
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from test import Photo2Cartoon
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ORIGINAL_REPO_URL = 'https://github.com/minivision-ai/photo2cartoon'
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parser.add_argument('--allow-screenshot', action='store_true')
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return parser.parse_args()
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def run(
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image,
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p2c : Photo2Cartoon,
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p2c/test.py
CHANGED
@@ -7,25 +7,22 @@ import argparse
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from utils import Preprocess
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parser = argparse.ArgumentParser()
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parser.add_argument('--photo_path', type=str, help='input photo path')
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parser.add_argument('--save_path', type=str, help='cartoon save path')
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args = parser.parse_args()
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os.makedirs(os.path.dirname(args.save_path), exist_ok=True)
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class Photo2Cartoon:
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def __init__(self):
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self.pre = Preprocess()
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self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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self.net = ResnetGenerator(ngf=32, img_size=256, light=True).to(self.device)
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self.net.load_state_dict(params['genA2B'])
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print('[Step1: load weights] success!')
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def inference(self,
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# face alignment and segmentation
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face_rgba = self.pre.process(img)
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if face_rgba is None:
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@@ -49,15 +46,9 @@ class Photo2Cartoon:
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cartoon = np.transpose(cartoon.cpu().numpy(), (1, 2, 0))
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cartoon = (cartoon + 1) * 127.5
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cartoon = (cartoon * mask + 255 * (1 - mask)).astype(np.uint8)
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cartoon = cv2.cvtColor(cartoon, cv2.COLOR_RGB2BGR)
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print('[Step3: photo to cartoon] success!')
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return cartoon
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img = cv2.cvtColor(cv2.imread(args.photo_path), cv2.COLOR_BGR2RGB)
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c2p = Photo2Cartoon()
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cartoon = c2p.inference(img)
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if cartoon is not None:
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cv2.imwrite(args.save_path, cartoon)
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print('Cartoon portrait has been saved successfully!')
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from utils import Preprocess
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class Photo2Cartoon:
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def __init__(self):
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self.pre = Preprocess()
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self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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self.net = ResnetGenerator(ngf=32, img_size=256, light=True).to(self.device)
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curPath = os.path.abspath(os.path.dirname(__file__))
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#assert os.path.exists('./models/photo2cartoon_weights.pt'), "[Step1: load weights] Can not find 'photo2cartoon_weights.pt' in folder 'models!!!'"
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params = torch.load(os.path.join(curPath, 'models/photo2cartoon_weights.pt'), map_location=self.device)
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self.net.load_state_dict(params['genA2B'])
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print('[Step1: load weights] success!')
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def inference(self, in_path):
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img = cv2.cvtColor(cv2.imread(in_path), cv2.COLOR_BGR2RGB)
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# face alignment and segmentation
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face_rgba = self.pre.process(img)
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if face_rgba is None:
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cartoon = np.transpose(cartoon.cpu().numpy(), (1, 2, 0))
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cartoon = (cartoon + 1) * 127.5
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cartoon = (cartoon * mask + 255 * (1 - mask)).astype(np.uint8)
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#cartoon = cv2.cvtColor(cartoon, cv2.COLOR_RGB2BGR)
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print('[Step3: photo to cartoon] success!')
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return cartoon
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