[Docs] Update Whole Picture and Prune Outdated Scripts (#63)

README.md

@@ -22,7 +22,7 @@
 PaddleRS是百度飞桨、遥感科研院所及相关高校共同开发的基于飞桨的遥感影像智能化处理套件，支持**图像分割、目标检测、场景分类、变化检测、图像复原**等常见遥感任务。PaddleRS致力于帮助遥感领域科研从业者快速完成算法的研发、验证和调优。同时，PaddleRS也期望助力投身于产业实践的开发者，便捷地实现从数据预处理到模型部署的**全流程遥感深度学习应用**。
 
 <div align="center">
-<img src="https://user-images.githubusercontent.com/21275753/197486676-8534ddf7-fd76-418d-bbe1-5d9c031ce84b.png"  width = "2000" />  
+<img src="https://user-images.githubusercontent.com/21275753/199727309-796b0b14-1af7-4cea-9a22-c5439766446b.gif"  width = "2000" />  
 </div>
 
 ## <img src="./docs/images/feature.png" width="30"/> 特性

paddlers/models/ppgan/apps/__init__.py

@@ -14,7 +14,4 @@
 
 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 .esrgan_predictor import ESRGANPredictor

paddlers/models/ppgan/apps/drn_predictor.py

@@ -1,90 +0,0 @@
-#  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 numpy as np
-from PIL import Image
-
-import paddle
-from ppgan.models.generators import DRNGenerator
-from ppgan.utils.download import get_path_from_url
-from ppgan.utils.logger import get_logger
-
-from .base_predictor import BasePredictor
-
-REALSR_WEIGHT_URL = 'https://paddlegan.bj.bcebos.com/models/DRNSx4.pdparams'
-
-
-class DRNPredictor(BasePredictor):
-    def __init__(self, output='output', weight_path=None):
-        self.input = input
-        self.output = os.path.join(output,
-                                   'DRN')  #定义超分的结果保存的路径，为output路径+模型名所在文件夹
-        self.model = DRNGenerator((2, 4))  # 实例化模型
-        if weight_path is None:
-            weight_path = get_path_from_url(REALSR_WEIGHT_URL)
-        state_dict = paddle.load(weight_path)  #加载权重
-        state_dict = state_dict['generator']
-        self.model.load_dict(state_dict)
-        self.model.eval()
-
-    # 标准化
-    def norm(self, img):
-        img = np.array(img).transpose([2, 0, 1]).astype('float32') / 1.0
-        return img.astype('float32')
-
-    # 去标准化
-    def denorm(self, img):
-        img = img.transpose((1, 2, 0))
-        return (img * 1).clip(0, 255).astype('uint8')
-
-    # 对图片输入进行预测，输入可以是图像路径，也可以是cv2读取的矩阵，或者PIL读取的图像文件
-    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)  #图像标准化
-        x = paddle.to_tensor(img[np.newaxis, ...])  #转成tensor
-        with paddle.no_grad():
-            out = self.model(
-                x
-            )[2]  # 执行预测，DRN模型会输出三个tensor，第一个是原始低分辨率影像，第二个是放大两倍，第三个才是我们所需要的最后的结果
-
-        pred_img = self.denorm(out.numpy()[0])  #tensor转成numpy的array并去标准化
-        pred_img = Image.fromarray(pred_img)  # array转图像
-        return pred_img
-
-    #输入图像文件路径
-    def run(self, input):
-        # 如果输出路径不存在则新建一个
-        if not os.path.exists(self.output):
-            os.makedirs(self.output)
-
-        pred_img = self.run_image(input)  #对输入的图片进行预测
-        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')  #保存路径
-            pred_img.save(out_path)  #保存输出图片
-            logger = get_logger()
-            logger.info('Image saved to {}'.format(out_path))
-
-        return pred_img, out_path

paddlers/models/ppgan/apps/lesrcnn_predictor.py

@@ -1,88 +0,0 @@
-#  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 numpy as np
-from PIL import Image
-
-import paddle
-from ppgan.models.generators import LESRCNNGenerator
-from ppgan.utils.download import get_path_from_url
-from ppgan.utils.logger import get_logger
-
-from .base_predictor import BasePredictor
-
-REALSR_WEIGHT_URL = 'https://paddlegan.bj.bcebos.com/models/lesrcnn_x4.pdparams'
-
-
-class LESRCNNPredictor(BasePredictor):
-    def __init__(self, output='output', weight_path=None):
-        self.input = input
-        self.output = os.path.join(output,
-                                   'LESRCNN')  #定义超分的结果保存的路径，为output路径+模型名所在文件夹
-        self.model = LESRCNNGenerator()  # 实例化模型
-        if weight_path is None:
-            weight_path = get_path_from_url(REALSR_WEIGHT_URL)
-        state_dict = paddle.load(weight_path)  #加载权重
-        state_dict = state_dict['generator']
-        self.model.load_dict(state_dict)
-        self.model.eval()
-
-    # 标准化
-    def norm(self, img):
-        img = np.array(img).transpose([2, 0, 1]).astype('float32') / 255.0
-        return img.astype('float32')
-
-    # 去标准化
-    def denorm(self, img):
-        img = img.transpose((1, 2, 0))
-        return (img * 255).clip(0, 255).astype('uint8')
-
-    # 对图片输入进行预测，输入可以是图像路径，也可以是cv2读取的矩阵，或者PIL读取的图像文件
-    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)  #图像标准化
-        x = paddle.to_tensor(img[np.newaxis, ...])  #转成tensor
-        with paddle.no_grad():
-            out = self.model(x)
-
-        pred_img = self.denorm(out.numpy()[0])  #tensor转成numpy的array并去标准化
-        pred_img = Image.fromarray(pred_img)  # array转图像
-        return pred_img
-
-    #输入图像文件路径
-    def run(self, input):
-        # 如果输出路径不存在则新建一个
-        if not os.path.exists(self.output):
-            os.makedirs(self.output)
-
-        pred_img = self.run_image(input)  #对输入的图片进行预测
-        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')  #保存路径
-            pred_img.save(out_path)  #保存输出图片
-            logger = get_logger()
-            logger.info('Image saved to {}'.format(out_path))
-
-        return pred_img, out_path

paddlers/models/ppgan/apps/pan_predictor.py

@@ -1,88 +0,0 @@
-#  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 numpy as np
-from PIL import Image
-
-import paddle
-from ppgan.models.generators import PAN
-from ppgan.utils.download import get_path_from_url
-from ppgan.utils.logger import get_logger
-
-from .base_predictor import BasePredictor
-
-REALSR_WEIGHT_URL = 'https://paddlegan.bj.bcebos.com/models/pan_x4.pdparams'
-
-
-class PANPredictor(BasePredictor):
-    def __init__(self, output='output', weight_path=None):
-        self.input = input
-        self.output = os.path.join(output,
-                                   'PAN')  #定义超分的结果保存的路径，为output路径+模型名所在文件夹
-        self.model = PAN(3, 3, 40, 24, 16)  # 实例化模型
-        if weight_path is None:
-            weight_path = get_path_from_url(REALSR_WEIGHT_URL)
-        state_dict = paddle.load(weight_path)  #加载权重
-        state_dict = state_dict['generator']
-        self.model.load_dict(state_dict)
-        self.model.eval()
-
-    # 标准化
-    def norm(self, img):
-        img = np.array(img).transpose([2, 0, 1]).astype('float32') / 255.0
-        return img.astype('float32')
-
-    # 去标准化
-    def denorm(self, img):
-        img = img.transpose((1, 2, 0))
-        return (img * 255).clip(0, 255).astype('uint8')
-
-    # 对图片输入进行预测，输入可以是图像路径，也可以是cv2读取的矩阵，或者PIL读取的图像文件
-    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)  #图像标准化
-        x = paddle.to_tensor(img[np.newaxis, ...])  #转成tensor
-        with paddle.no_grad():
-            out = self.model(x)
-
-        pred_img = self.denorm(out.numpy()[0])  #tensor转成numpy的array并去标准化
-        pred_img = Image.fromarray(pred_img)  # array转图像
-        return pred_img
-
-    #输入图像文件路径
-    def run(self, input):
-        # 如果输出路径不存在则新建一个
-        if not os.path.exists(self.output):
-            os.makedirs(self.output)
-
-        pred_img = self.run_image(input)  #对输入的图片进行预测
-        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')  #保存路径
-            pred_img.save(out_path)  #保存输出图片
-            logger = get_logger()
-            logger.info('Image saved to {}'.format(out_path))
-
-        return pred_img, out_path