add esrgan predict api

paddlers/models/ppgan/apps/__init__.py

@@ -16,4 +16,5 @@ from .realsr_predictor import RealSRPredictor
 from .mpr_predictor import MPRPredictor
 from .drn_predictor import DRNPredictor
 from .pan_predictor import PANPredictor
-from .lesrcnn_predictor import LESRCNNPredictor
+from .lesrcnn_predictor import LESRCNNPredictor
+from .esrgan_predictor import ESRGANPredictor

paddlers/models/ppgan/apps/esrgan_predictor.py

@@ -0,0 +1,86 @@
+#  Copyright (c) 2022 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 cv2
+import glob
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+
+import paddle
+from ..models.generators import RRDBNet
+from ..utils.download import get_path_from_url
+from ..utils.logger import get_logger
+
+from .base_predictor import BasePredictor
+
+SR_WEIGHT_URL = 'https://paddlegan.bj.bcebos.com/models/esrgan_x4.pdparams'
+
+class ESRGANPredictor(BasePredictor):
+    def __init__(self, output='output', weight_path=None):
+        self.input = input
+        self.output = os.path.join(output, 'ESRGAN')
+        self.model = RRDBNet(3, 3, 64, 23)
+        if weight_path is None:
+            weight_path = get_path_from_url(SR_WEIGHT_URL)
+
+        state_dict = paddle.load(weight_path)
+        state_dict = state_dict['generator']
+        self.model.load_dict(state_dict)
+        self.model.eval()
+
+    def norm(self, img):
+        img = np.array(img).transpose([2, 0, 1]).astype('float32') / 255.0
+        return img.astype('float32')
+
+    def denorm(self, img):
+        img = img.transpose((1, 2, 0))
+        return (img * 255).clip(0, 255).astype('uint8')
+
+    def run_image(self, img):
+        if isinstance(img, str):
+            ori_img = Image.open(img).convert('RGB')
+        elif isinstance(img, np.ndarray):
+            ori_img = Image.fromarray(img).convert('RGB')
+        elif isinstance(img, Image.Image):
+            ori_img = img
+
+        img = self.norm(ori_img)
+        x = paddle.to_tensor(img[np.newaxis, ...])
+        with paddle.no_grad():
+            out = self.model(x)
+
+        pred_img = self.denorm(out.numpy()[0])
+        pred_img = Image.fromarray(pred_img)
+        return pred_img
+
+    def run(self, input):
+        if not os.path.exists(self.output):
+            os.makedirs(self.output)
+
+        pred_img = self.run_image(input)
+
+        out_path = None
+        if self.output:
+            try:
+                base_name = os.path.splitext(os.path.basename(input))[0]
+            except:
+                base_name = 'result'
+            out_path = os.path.join(self.output, base_name + '.png')
+            pred_img.save(out_path)
+            logger = get_logger()
+            logger.info('Image saved to {}'.format(out_path))
+
+        return pred_img, out_path