refine duplicate frames and fix some bugs (#81)

* refine duplicate frames and fix some bugs

docs/en_US/tutorials/psgan.md

@@ -33,8 +33,8 @@ python tools/psgan_infer.py \
 2. Downloading the landmarks [data](https://paddlegan.bj.bcebos.com/landmarks.tar), and uncompress it
 3. Runnint the following command to substitute files:
 ```
-mv landmarks/makeup MT-Dataset/landmarks/makeup
-mv landmarks/non-makeup MT-Dataset/landmarks/non-makeup
+rm -rf MT-Dataset/landmarks/makeup && mv landmarks/makeup MT-Dataset/landmarks/
+rm -rf MT-Dataset/landmarks/non-makeup && mv landmarks/non-makeup MT-Dataset/landmarks/
 cp landmarks/train_makeup.txt MT-Dataset/train_makeup.txt
 cp landmarks/train_non-makeup.txt MT-Dataset/train_non-makeup.txt
 ```

docs/zh_CN/tutorials/psgan.md

@@ -33,8 +33,8 @@ python tools/psgan_infer.py \
 2. 下载landmarks数据[lmks](https://paddlegan.bj.bcebos.com/landmarks.tar)，并解压
 3. 运行如下命令进行文件夹及文件替换:
 ```
-mv landmarks/makeup MT-Dataset/landmarks/makeup
-mv landmarks/non-makeup MT-Dataset/landmarks/non-makeup
+rm -rf MT-Dataset/landmarks/makeup && mv landmarks/makeup MT-Dataset/landmarks/
+rm -rf MT-Dataset/landmarks/non-makeup && mv landmarks/non-makeup MT-Dataset/landmarks/
 cp landmarks/train_makeup.txt MT-Dataset/train_makeup.txt
 cp landmarks/train_non-makeup.txt MT-Dataset/train_non-makeup.txt
 ```

ppgan/apps/dain_predictor.py

@@ -269,6 +269,7 @@ class DAINPredictor(BasePredictor):
             return sum([2**i for (i, v) in enumerate(diff.flatten()) if v])
 
         hashes = {}
+        max_interp = 9
         image_paths = sorted(glob.glob(os.path.join(paths, '*.png')))
         for image_path in image_paths:
             image = cv2.imread(image_path)
@@ -283,7 +284,16 @@ class DAINPredictor(BasePredictor):
                 last_index = int(
                     hashed_paths[-1].split('/')[-1].split('.')[-2]) + 1
                 gap = 2 * (last_index - first_index) - 1
-                if gap > 9:
+                if gap > 2 * max_interp:
+                    cut1 = len(hashed_paths) // 3
+                    cut2 = cut1 * 2
+                    for p in hashed_paths[1:cut1 - 1]:
+                        os.remove(p)
+                    for p in hashed_paths[cut1 + 1:cut2]:
+                        os.remove(p)
+                    for p in hashed_paths[cut2 + 1:]:
+                        os.remove(p)
+                if gap > max_interp:
                     mid = len(hashed_paths) // 2
                     for p in hashed_paths[1:mid - 1]:
                         os.remove(p)

ppgan/apps/psgan_predictor.py

@@ -43,16 +43,6 @@ def toImage(net_output):
     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
-
-
 PS_WEIGHT_URL = "https://paddlegan.bj.bcebos.com/models/psgan_weight.pdparams"
 
 
@@ -81,6 +71,7 @@ class PreProcess:
                                                   self.down_ratio,
                                                   self.width_ratio)
         np_image = np.array(image)
+        image_trans = self.transform(np_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),
@@ -88,7 +79,8 @@ class PreProcess:
         mask = mask.astype(np.uint8)
         mask_tensor = paddle.to_tensor(mask)
 
-        lms = futils.dlib.landmarks(image, face) * self.img_size / image.width
+        lms = futils.dlib.landmarks(
+            image, face) / image_trans.shape[:2] * self.img_size
         lms = lms.round()
 
         P_np = generate_P_from_lmks(lms, self.img_size, self.img_size,
@@ -96,10 +88,8 @@ class PreProcess:
 
         mask_aug = generate_mask_aug(mask, lms)
 
-        image = self.transform(np_image)
-
         return [
-            self.norm(image).unsqueeze(0),
+            self.norm(image_trans).unsqueeze(0),
             np.float32(mask_aug),
             np.float32(P_np),
             np.float32(mask)
@@ -212,6 +202,9 @@ class PSGANPredictor(BasePredictor):
             image = postprocess(source_crop, image)
 
             ref_img_name = os.path.split(reference_path)[1]
+            if not os.path.exists(self.output_path):
+                os.makedirs(sefl.output_path)
+
             save_path = os.path.join(self.output_path,
                                      'transfered_ref_' + ref_img_name)
             image.save(save_path)

ppgan/models/makeup_model.py

@@ -11,6 +11,7 @@
 # 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 numpy as np
 
 import paddle

tools/psgan_infer.py

@@ -12,7 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
+import sys
 import argparse
+
+cur_path = os.path.abspath(os.path.dirname(__file__))
+root_path = os.path.split(cur_path)[0]
+sys.path.append(root_path)
+
 from ppgan.utils.options import parse_args
 from ppgan.utils.config import get_config
 from ppgan.apps.psgan_predictor import PSGANPredictor