refine psgan code according tho the reviewer's comment

tools/ps_demo.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import sys
+import argparse
+from pathlib import Path
+
+from PIL import Image
+from fire import Fire
+import numpy as np
+
+cur_path = os.path.abspath(os.path.dirname(__file__))
+root_path = os.path.split(cur_path)[0]
+sys.path.append(root_path)
+
+import paddle
+import ppgan.faceutils as futils
+#from psgan import PostProcess
+from ppgan.utils.options import parse_args
+from ppgan.utils.config import get_config
+from ppgan.utils.setup import setup
+from ppgan.utils.filesystem import load
+from ppgan.engine.trainer import Trainer
+from ppgan.models.builder import build_model
+from ppgan.utils.preprocess import *
+import paddle.vision.transforms as T
+
+
+def toImage(net_output):
+    img = net_output.squeeze(0).transpose(
+        (1, 2, 0)).numpy()  # [1,c,h,w]->[h,w,c]
+    img = (img * 255.0).clip(0, 255)
+    img = np.uint8(img)
+    img = Image.fromarray(img, mode='RGB')
+    return img
+
+
+def mask2image(mask: np.array, format="HWC"):
+    H, W = mask.shape
+
+    canvas = np.zeros((H, W, 3), dtype=np.uint8)
+    for i in range(int(mask.max())):
+        color = np.random.rand(1, 1, 3) * 255
+        canvas += (mask == i)[:, :, None] * color.astype(np.uint8)
+    return canvas
+
+
+class PreProcess:
+    def __init__(self, config, need_parser=True):
+        self.img_size = 256
+        self.transform = transform = T.Compose([
+            T.Resize(size=256),
+            T.Permute(to_rgb=False),
+        ])
+        self.norm = T.Normalize([127.5, 127.5, 127.5], [127.5, 127.5, 127.5])
+        if need_parser:
+            self.face_parser = futils.mask.FaceParser()
+        self.up_ratio = 0.6 / 0.85
+        self.down_ratio = 0.2 / 0.85
+        self.width_ratio = 0.2 / 0.85
+
+    def __call__(self, image):
+        face = futils.dlib.detect(image)
+
+        if not face:
+            return
+        face_on_image = face[0]
+        image, face, crop_face = futils.dlib.crop(image, face_on_image,
+                                                  self.up_ratio,
+                                                  self.down_ratio,
+                                                  self.width_ratio)
+        np_image = np.array(image)
+        mask = self.face_parser.parse(
+            np.float32(cv2.resize(np_image, (512, 512))))
+        mask = cv2.resize(mask.numpy(), (self.img_size, self.img_size),
+                          interpolation=cv2.INTER_NEAREST)
+        mask = mask.astype(np.uint8)
+        mask_color = mask2image(mask)
+        cv2.imwrite('mask_temp.png', mask_color)
+        mask_tensor = paddle.to_tensor(mask)
+
+        lms = futils.dlib.landmarks(image, face) * self.img_size / image.width
+        lms = lms.round()
+
+        P_np = generate_P_from_lmks(lms, self.img_size, self.img_size,
+                                    self.img_size)
+
+        mask_aug = generate_mask_aug(mask, lms)
+
+        image = self.transform(np_image)
+
+        return [
+            self.norm(image),
+            np.float32(mask_aug),
+            np.float32(P_np),
+            np.float32(mask)
+        ], face_on_image, crop_face
+
+
+class PostProcess:
+    def __init__(self, config):
+        self.denoise = True
+        self.img_size = 256
+
+    def __call__(self, source: Image, result: Image):
+        # TODO: Refract -> name, resize
+        source = np.array(source)
+        result = np.array(result)
+
+        height, width = source.shape[:2]
+        small_source = cv2.resize(source, (self.img_size, self.img_size))
+        laplacian_diff = source.astype(np.float) - cv2.resize(
+            small_source, (width, height)).astype(np.float)
+        result = (cv2.resize(result,
+                             (width, height)) + laplacian_diff).round().clip(
+                                 0, 255).astype(np.uint8)
+        if self.denoise:
+            result = cv2.fastNlMeansDenoisingColored(result)
+        result = Image.fromarray(result).convert('RGB')
+        return result
+
+
+class Inference:
+    def __init__(self, config, model_path=''):
+        self.model = build_model(config)
+        self.preprocess = PreProcess(config)
+        self.model_path = model_path
+
+    def transfer(self, source, reference, with_face=False):
+        source_input, face, crop_face = self.preprocess(source)
+        reference_input, face, crop_face = self.preprocess(reference)
+
+        consis_mask = np.float32(
+            calculate_consis_mask(source_input[1], reference_input[1]))
+        consis_mask = paddle.to_tensor(np.expand_dims(consis_mask, 0))
+
+        if not (source_input and reference_input):
+            if with_face:
+                return None, None
+            return
+        for i in range(len(source_input) - 1):
+            source_input[i] = paddle.to_tensor(
+                np.expand_dims(source_input[i], 0))
+
+        for i in range(len(reference_input) - 1):
+            reference_input[i] = paddle.to_tensor(
+                np.expand_dims(reference_input[i], 0))
+
+        input_data = {
+            'image_A': source_input[0],
+            'image_B': reference_input[0],
+            'mask_A_aug': source_input[1],
+            'mask_B_aug': reference_input[1],
+            'P_A': source_input[2],
+            'P_B': reference_input[2],
+            'consis_mask': consis_mask
+        }
+        state_dicts = load(self.model_path)
+        net = getattr(self.model, 'netG')
+        net.set_dict(state_dicts['netG'])
+        result, _ = self.model.test(input_data)
+        print('result shape: ', result.shape)
+        min_, max_ = result.min(), result.max()
+        result += -min_
+        result = paddle.divide(result, max_ - min_ + 1e-5)
+        img = toImage(result)
+
+        if with_face:
+            return img, crop_face
+        img.save('before.png')
+
+        return img
+
+
+def main(args, cfg, save_path='transferred_image.png'):
+
+    setup(args, cfg)
+
+    inference = Inference(cfg, args.model_path)
+    postprocess = PostProcess(cfg)
+
+    source = Image.open(args.source_path).convert("RGB")
+    reference_paths = list(Path(args.reference_dir).glob("*"))
+    np.random.shuffle(reference_paths)
+    for reference_path in reference_paths:
+        if not reference_path.is_file():
+            print(reference_path, "is not a valid file.")
+            continue
+
+        reference = Image.open(reference_path).convert("RGB")
+
+        # Transfer the psgan from reference to source.
+        image, face = inference.transfer(source, reference, with_face=True)
+        image.save('before.png')
+        source_crop = source.crop(
+            (face.left(), face.top(), face.right(), face.bottom()))
+        image = postprocess(source_crop, image)
+        image.save(save_path)