diff --git a/modules/image/image_processing/enlightengan/README.md b/modules/image/image_processing/enlightengan/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ef46cb940029b07ca2a2d5594c831815962b1be9 --- /dev/null +++ b/modules/image/image_processing/enlightengan/README.md @@ -0,0 +1,137 @@ +# enlightengan + +|模型名称|enlightengan| +| :--- | :---: | +|类别|图像 - 暗光增强| +|网络|EnlightenGAN| +|数据集|-| +|是否支持Fine-tuning|否| +|模型大小|83MB| +|最新更新日期|2021-11-04| +|数据指标|-| + + +## 一、模型基本信息 + +- ### 应用效果展示 + - 样例结果示例： +

+ +
+ 输入图像 +
+ +
+ 输出图像 +
+

+ +- ### 模型介绍 + + - EnlightenGAN使用非成对的数据进行训练，通过设计自特征保留损失函数和自约束注意力机制，训练的网络可以应用到多种场景下的暗光增强中。 + + - 更多详情参考：[EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://arxiv.org/abs/1906.06972) + + + +## 二、安装 + +- ### 1、环境依赖 + - onnxruntime + - x2paddle + - pillow + +- ### 2、安装 + + - ```shell + $ hub install enlightengan + ``` + - 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md) + | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md) + +## 三、模型API预测 + +- ### 1、命令行预测 + + - ```shell + # Read from a file + $ hub run enlightengan --input_path "/PATH/TO/IMAGE" + ``` + - 通过命令行方式实现暗光增强模型的调用，更多请见 [PaddleHub命令行指令](../../../../docs/docs_ch/tutorial/cmd_usage.rst) + +- ### 2、预测代码示例 + + - ```python + import paddlehub as hub + + enlightener = hub.Module(name="enlightengan") + input_path = ["/PATH/TO/IMAGE"] + # Read from a file + enlightener.enlightening(paths=input_path, output_dir='./enlightening_result/', use_gpu=True) + ``` + +- ### 3、API + + - ```python + def enlightening(images=None, paths=None, output_dir='./enlightening_result/', use_gpu=False, visualization=True) + ``` + - 暗光增强API。 + + - **参数** + + - images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]；
+ - paths (list\[str\]): 图片的路径；
+ - output\_dir (str): 结果保存的路径；
+ - use\_gpu (bool): 是否使用 GPU；
+ - visualization(bool): 是否保存结果到本地文件夹 + + +## 四、服务部署 + +- PaddleHub Serving可以部署一个在线图像风格转换服务。 + +- ### 第一步：启动PaddleHub Serving + + - 运行启动命令： + - ```shell + $ hub serving start -m enlightengan + ``` + + - 这样就完成了一个图像风格转换的在线服务API的部署，默认端口号为8866。 + + - **NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA\_VISIBLE\_DEVICES环境变量，否则不用设置。 + +- ### 第二步：发送预测请求 + + - 配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果 + + - ```python + import requests + import json + import cv2 + import base64 + + + def cv2_to_base64(image): + data = cv2.imencode('.jpg', image)[1] + return base64.b64encode(data.tostring()).decode('utf8') + + # 发送HTTP请求 + data = {'images':[cv2_to_base64(cv2.imread("/PATH/TO/IMAGE"))]} + headers = {"Content-type": "application/json"} + url = "http://127.0.0.1:8866/predict/enlightengan" + r = requests.post(url=url, headers=headers, data=json.dumps(data)) + + # 打印预测结果 + print(r.json()["results"]) + ``` + +## 五、更新历史 + +* 1.0.0 + + 初始发布 + + - ```shell + $ hub install enlightengan==1.0.0 + ``` diff --git a/modules/image/image_processing/enlightengan/enlighten_inference/pd_model/x2paddle_code.py b/modules/image/image_processing/enlightengan/enlighten_inference/pd_model/x2paddle_code.py new file mode 100755 index 0000000000000000000000000000000000000000..d211efac274f7d6be42ee8a765726526d9e51888 --- /dev/null +++ b/modules/image/image_processing/enlightengan/enlighten_inference/pd_model/x2paddle_code.py @@ -0,0 +1,201 @@ +import paddle +import math + + +class ONNXModel(paddle.nn.Layer): + def __init__(self): + super(ONNXModel, self).__init__() + self.conv0 = paddle.nn.Conv2D(in_channels=3, out_channels=3, kernel_size=[1, 1], groups=3) + self.pool0 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.pool1 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.conv1 = paddle.nn.Conv2D(in_channels=4, out_channels=32, kernel_size=[3, 3], padding=1) + self.pool2 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.leakyrelu0 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.pool3 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.batchnorm0 = paddle.nn.BatchNorm( + num_channels=32, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv2 = paddle.nn.Conv2D(in_channels=32, out_channels=32, kernel_size=[3, 3], padding=1) + self.leakyrelu1 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm1 = paddle.nn.BatchNorm( + num_channels=32, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.pool4 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.conv3 = paddle.nn.Conv2D(in_channels=32, out_channels=64, kernel_size=[3, 3], padding=1) + self.leakyrelu2 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm2 = paddle.nn.BatchNorm( + num_channels=64, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv4 = paddle.nn.Conv2D(in_channels=64, out_channels=64, kernel_size=[3, 3], padding=1) + self.leakyrelu3 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm3 = paddle.nn.BatchNorm( + num_channels=64, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.pool5 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.conv5 = paddle.nn.Conv2D(in_channels=64, out_channels=128, kernel_size=[3, 3], padding=1) + self.leakyrelu4 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm4 = paddle.nn.BatchNorm( + num_channels=128, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv6 = paddle.nn.Conv2D(in_channels=128, out_channels=128, kernel_size=[3, 3], padding=1) + self.leakyrelu5 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm5 = paddle.nn.BatchNorm( + num_channels=128, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.pool6 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.conv7 = paddle.nn.Conv2D(in_channels=128, out_channels=256, kernel_size=[3, 3], padding=1) + self.leakyrelu6 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm6 = paddle.nn.BatchNorm( + num_channels=256, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv8 = paddle.nn.Conv2D(in_channels=256, out_channels=256, kernel_size=[3, 3], padding=1) + self.leakyrelu7 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm7 = paddle.nn.BatchNorm( + num_channels=256, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.pool7 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.conv9 = paddle.nn.Conv2D(in_channels=256, out_channels=512, kernel_size=[3, 3], padding=1) + self.leakyrelu8 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm8 = paddle.nn.BatchNorm( + num_channels=512, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv10 = paddle.nn.Conv2D(in_channels=512, out_channels=512, kernel_size=[3, 3], padding=1) + self.leakyrelu9 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm9 = paddle.nn.BatchNorm( + num_channels=512, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv11 = paddle.nn.Conv2D(in_channels=512, out_channels=256, kernel_size=[3, 3], padding=1) + self.conv12 = paddle.nn.Conv2D(in_channels=512, out_channels=256, kernel_size=[3, 3], padding=1) + self.leakyrelu10 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm10 = paddle.nn.BatchNorm( + num_channels=256, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv13 = paddle.nn.Conv2D(in_channels=256, out_channels=256, kernel_size=[3, 3], padding=1) + self.leakyrelu11 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm11 = paddle.nn.BatchNorm( + num_channels=256, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv14 = paddle.nn.Conv2D(in_channels=256, out_channels=128, kernel_size=[3, 3], padding=1) + self.conv15 = paddle.nn.Conv2D(in_channels=256, out_channels=128, kernel_size=[3, 3], padding=1) + self.leakyrelu12 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm12 = paddle.nn.BatchNorm( + num_channels=128, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv16 = paddle.nn.Conv2D(in_channels=128, out_channels=128, kernel_size=[3, 3], padding=1) + self.leakyrelu13 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm13 = paddle.nn.BatchNorm( + num_channels=128, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv17 = paddle.nn.Conv2D(in_channels=128, out_channels=64, kernel_size=[3, 3], padding=1) + self.conv18 = paddle.nn.Conv2D(in_channels=128, out_channels=64, kernel_size=[3, 3], padding=1) + self.leakyrelu14 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm14 = paddle.nn.BatchNorm( + num_channels=64, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv19 = paddle.nn.Conv2D(in_channels=64, out_channels=64, kernel_size=[3, 3], padding=1) + self.leakyrelu15 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm15 = paddle.nn.BatchNorm( + num_channels=64, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv20 = paddle.nn.Conv2D(in_channels=64, out_channels=32, kernel_size=[3, 3], padding=1) + self.conv21 = paddle.nn.Conv2D(in_channels=64, out_channels=32, kernel_size=[3, 3], padding=1) + self.leakyrelu16 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm16 = paddle.nn.BatchNorm( + num_channels=32, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv22 = paddle.nn.Conv2D(in_channels=32, out_channels=32, kernel_size=[3, 3], padding=1) + self.leakyrelu17 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.conv23 = paddle.nn.Conv2D(in_channels=32, out_channels=3, kernel_size=[1, 1]) + + def forward(self, x2paddle_input): + x2paddle_137 = paddle.full(dtype='float32', shape=[1], fill_value=1.0) + x2paddle_145 = paddle.full(dtype='float32', shape=[1], fill_value=0.29899999499320984) + x2paddle_147 = paddle.full(dtype='float32', shape=[1], fill_value=0.5870000123977661) + x2paddle_150 = paddle.full(dtype='float32', shape=[1], fill_value=0.11400000005960464) + x2paddle_153 = paddle.full(dtype='float32', shape=[1], fill_value=2.0) + x2paddle_155 = paddle.full(dtype='float32', shape=[1], fill_value=1.0) + x2paddle_256 = paddle.full(dtype='float32', shape=[1], fill_value=1.0) + x2paddle_134 = self.conv0(x2paddle_input) + x2paddle_135, = paddle.split(x=x2paddle_134, num_or_sections=[1]) + x2paddle_257 = paddle.multiply(x=x2paddle_134, y=x2paddle_256) + x2paddle_136 = paddle.squeeze(x=x2paddle_135, axis=[0]) + x2paddle_138 = paddle.add(x=x2paddle_136, y=x2paddle_137) + x2paddle_139_p0, x2paddle_139_p1, x2paddle_139_p2 = paddle.split(x=x2paddle_138, num_or_sections=[1, 1, 1]) + x2paddle_142 = paddle.squeeze(x=x2paddle_139_p0, axis=[0]) + x2paddle_143 = paddle.squeeze(x=x2paddle_139_p1, axis=[0]) + x2paddle_144 = paddle.squeeze(x=x2paddle_139_p2, axis=[0]) + x2paddle_146 = paddle.multiply(x=x2paddle_142, y=x2paddle_145) + x2paddle_148 = paddle.multiply(x=x2paddle_143, y=x2paddle_147) + x2paddle_151 = paddle.multiply(x=x2paddle_144, y=x2paddle_150) + x2paddle_149 = paddle.add(x=x2paddle_146, y=x2paddle_148) + x2paddle_152 = paddle.add(x=x2paddle_149, y=x2paddle_151) + x2paddle_154 = paddle.divide(x=x2paddle_152, y=x2paddle_153) + x2paddle_156 = paddle.subtract(x=x2paddle_155, y=x2paddle_154) + x2paddle_157 = paddle.unsqueeze(x=x2paddle_156, axis=[0]) + x2paddle_158 = paddle.unsqueeze(x=x2paddle_157, axis=[0]) + x2paddle_159 = self.pool0(x2paddle_158) + x2paddle_163 = paddle.concat(x=[x2paddle_134, x2paddle_158], axis=1) + x2paddle_160 = self.pool1(x2paddle_159) + x2paddle_164 = self.conv1(x2paddle_163) + x2paddle_161 = self.pool2(x2paddle_160) + x2paddle_165 = self.leakyrelu0(x2paddle_164) + x2paddle_162 = self.pool3(x2paddle_161) + x2paddle_166 = self.batchnorm0(x2paddle_165) + x2paddle_167 = self.conv2(x2paddle_166) + x2paddle_168 = self.leakyrelu1(x2paddle_167) + x2paddle_169 = self.batchnorm1(x2paddle_168) + x2paddle_170 = self.pool4(x2paddle_169) + x2paddle_246 = paddle.multiply(x=x2paddle_169, y=x2paddle_158) + x2paddle_171 = self.conv3(x2paddle_170) + x2paddle_172 = self.leakyrelu2(x2paddle_171) + x2paddle_173 = self.batchnorm2(x2paddle_172) + x2paddle_174 = self.conv4(x2paddle_173) + x2paddle_175 = self.leakyrelu3(x2paddle_174) + x2paddle_176 = self.batchnorm3(x2paddle_175) + x2paddle_177 = self.pool5(x2paddle_176) + x2paddle_232 = paddle.multiply(x=x2paddle_176, y=x2paddle_159) + x2paddle_178 = self.conv5(x2paddle_177) + x2paddle_179 = self.leakyrelu4(x2paddle_178) + x2paddle_180 = self.batchnorm4(x2paddle_179) + x2paddle_181 = self.conv6(x2paddle_180) + x2paddle_182 = self.leakyrelu5(x2paddle_181) + x2paddle_183 = self.batchnorm5(x2paddle_182) + x2paddle_184 = self.pool6(x2paddle_183) + x2paddle_218 = paddle.multiply(x=x2paddle_183, y=x2paddle_160) + x2paddle_185 = self.conv7(x2paddle_184) + x2paddle_186 = self.leakyrelu6(x2paddle_185) + x2paddle_187 = self.batchnorm6(x2paddle_186) + x2paddle_188 = self.conv8(x2paddle_187) + x2paddle_189 = self.leakyrelu7(x2paddle_188) + x2paddle_190 = self.batchnorm7(x2paddle_189) + x2paddle_191 = self.pool7(x2paddle_190) + x2paddle_204 = paddle.multiply(x=x2paddle_190, y=x2paddle_161) + x2paddle_192 = self.conv9(x2paddle_191) + x2paddle_193 = self.leakyrelu8(x2paddle_192) + x2paddle_194 = self.batchnorm8(x2paddle_193) + x2paddle_195 = paddle.multiply(x=x2paddle_194, y=x2paddle_162) + x2paddle_196 = self.conv10(x2paddle_195) + x2paddle_197 = self.leakyrelu9(x2paddle_196) + x2paddle_198 = self.batchnorm9(x2paddle_197) + x2paddle_203 = paddle.nn.functional.interpolate(x=x2paddle_198, scale_factor=[2.0, 2.0], mode='bilinear') + x2paddle_205 = self.conv11(x2paddle_203) + x2paddle_206 = paddle.concat(x=[x2paddle_205, x2paddle_204], axis=1) + x2paddle_207 = self.conv12(x2paddle_206) + x2paddle_208 = self.leakyrelu10(x2paddle_207) + x2paddle_209 = self.batchnorm10(x2paddle_208) + x2paddle_210 = self.conv13(x2paddle_209) + x2paddle_211 = self.leakyrelu11(x2paddle_210) + x2paddle_212 = self.batchnorm11(x2paddle_211) + x2paddle_217 = paddle.nn.functional.interpolate(x=x2paddle_212, scale_factor=[2.0, 2.0], mode='bilinear') + x2paddle_219 = self.conv14(x2paddle_217) + x2paddle_220 = paddle.concat(x=[x2paddle_219, x2paddle_218], axis=1) + x2paddle_221 = self.conv15(x2paddle_220) + x2paddle_222 = self.leakyrelu12(x2paddle_221) + x2paddle_223 = self.batchnorm12(x2paddle_222) + x2paddle_224 = self.conv16(x2paddle_223) + x2paddle_225 = self.leakyrelu13(x2paddle_224) + x2paddle_226 = self.batchnorm13(x2paddle_225) + x2paddle_231 = paddle.nn.functional.interpolate(x=x2paddle_226, scale_factor=[2.0, 2.0], mode='bilinear') + x2paddle_233 = self.conv17(x2paddle_231) + x2paddle_234 = paddle.concat(x=[x2paddle_233, x2paddle_232], axis=1) + x2paddle_235 = self.conv18(x2paddle_234) + x2paddle_236 = self.leakyrelu14(x2paddle_235) + x2paddle_237 = self.batchnorm14(x2paddle_236) + x2paddle_238 = self.conv19(x2paddle_237) + x2paddle_239 = self.leakyrelu15(x2paddle_238) + x2paddle_240 = self.batchnorm15(x2paddle_239) + x2paddle_245 = paddle.nn.functional.interpolate(x=x2paddle_240, scale_factor=[2.0, 2.0], mode='bilinear') + x2paddle_247 = self.conv20(x2paddle_245) + x2paddle_248 = paddle.concat(x=[x2paddle_247, x2paddle_246], axis=1) + x2paddle_249 = self.conv21(x2paddle_248) + x2paddle_250 = self.leakyrelu16(x2paddle_249) + x2paddle_251 = self.batchnorm16(x2paddle_250) + x2paddle_252 = self.conv22(x2paddle_251) + x2paddle_253 = self.leakyrelu17(x2paddle_252) + x2paddle_254 = self.conv23(x2paddle_253) + x2paddle_255 = paddle.multiply(x=x2paddle_254, y=x2paddle_158) + x2paddle_output = paddle.add(x=x2paddle_255, y=x2paddle_257) + return x2paddle_output, x2paddle_255 diff --git a/modules/image/image_processing/enlightengan/module.py b/modules/image/image_processing/enlightengan/module.py new file mode 100644 index 0000000000000000000000000000000000000000..0c8f441c55c32c364112d3b3121183cb3964596f --- /dev/null +++ b/modules/image/image_processing/enlightengan/module.py @@ -0,0 +1,147 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# 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 argparse +import os + +import cv2 +import numpy as np +import paddle + +import paddlehub as hub +from .enlighten_inference.pd_model.x2paddle_code import ONNXModel +from .util import base64_to_cv2 +from paddlehub.module.module import moduleinfo +from paddlehub.module.module import runnable +from paddlehub.module.module import serving + + +@moduleinfo(name="enlightengan", + type="CV/enlighten", + author="paddlepaddle", + author_email="", + summary="", + version="1.0.0") +class EnlightenGAN: + + def __init__(self): + self.pretrained_model = os.path.join(self.directory, "enlighten_inference/pd_model") + self.model = ONNXModel() + params = paddle.load(os.path.join(self.pretrained_model, 'model.pdparams')) + self.model.set_dict(params, use_structured_name=True) + + def enlightening(self, + images: list = None, + paths: list = None, + output_dir: str = './enlightening_result/', + use_gpu: bool = False, + visualization: bool = True): + ''' + enlighten images in the low-light scene. + + images (list[numpy.ndarray]): data of images, shape of each is [H, W, C], color space must be BGR(read by cv2). + paths (list[str]): paths to images + output_dir (str): the dir to save the results + use_gpu (bool): if True, use gpu to perform the computation, otherwise cpu. + visualization (bool): if True, save results in output_dir. + ''' + results = [] + paddle.disable_static() + place = 'gpu:0' if use_gpu else 'cpu' + place = paddle.set_device(place) + if images == None and paths == None: + print('No image provided. Please input an image or a image path.') + return + self.model.eval() + + if images != None: + for image in images: + image = image[:, :, ::-1] + image = np.expand_dims(np.transpose(image, (2, 0, 1)).astype(np.float32) / 255., 0) + inputtensor = paddle.to_tensor(image) + out, out1 = self.model(inputtensor) + out = out.numpy()[0] + out = (np.transpose(out, (1, 2, 0)) + 1) / 2.0 * 255.0 + out = np.clip(out, 0, 255) + out = out.astype('uint8') + results.append(out) + + if paths != None: + for path in paths: + image = cv2.imread(path)[:, :, ::-1] + image = np.expand_dims(np.transpose(image, (2, 0, 1)).astype(np.float32) / 255., 0) + inputtensor = paddle.to_tensor(image) + out, out1 = self.model(inputtensor) + out = out.numpy()[0] + out = (np.transpose(out, (1, 2, 0)) + 1) / 2.0 * 255.0 + out = np.clip(out, 0, 255) + out = out.astype('uint8') + results.append(out) + + if visualization == True: + if not os.path.exists(output_dir): + os.makedirs(output_dir, exist_ok=True) + for i, out in enumerate(results): + cv2.imwrite(os.path.join(output_dir, 'output_{}.png'.format(i)), out[:, :, ::-1]) + + return results + + @runnable + def run_cmd(self, argvs: list): + """ + Run as a command. + """ + self.parser = argparse.ArgumentParser(description="Run the {} module.".format(self.name), + prog='hub run {}'.format(self.name), + usage='%(prog)s', + add_help=True) + + self.arg_input_group = self.parser.add_argument_group(title="Input options", description="Input data. Required") + self.arg_config_group = self.parser.add_argument_group( + title="Config options", description="Run configuration for controlling module behavior, not required.") + self.add_module_config_arg() + self.add_module_input_arg() + self.args = self.parser.parse_args(argvs) + results = self.enlightening(paths=[self.args.input_path], + output_dir=self.args.output_dir, + use_gpu=self.args.use_gpu, + visualization=self.args.visualization) + return results + + @serving + def serving_method(self, images, **kwargs): + """ + Run as a service. + """ + images_decode = [base64_to_cv2(image) for image in images] + results = self.enlightening(images=images_decode, **kwargs) + tolist = [result.tolist() for result in results] + return tolist + + def add_module_config_arg(self): + """ + Add the command config options. + """ + self.arg_config_group.add_argument('--use_gpu', action='store_true', help="use GPU or not") + + self.arg_config_group.add_argument('--output_dir', + type=str, + default='enlightening_result', + help='output directory for saving result.') + self.arg_config_group.add_argument('--visualization', type=bool, default=False, help='save results or not.') + + def add_module_input_arg(self): + """ + Add the command input options. + """ + self.arg_input_group.add_argument('--input_path', type=str, help="path to input image.") diff --git a/modules/image/image_processing/enlightengan/util.py b/modules/image/image_processing/enlightengan/util.py new file mode 100644 index 0000000000000000000000000000000000000000..531a0ae0d487822a870ba7f09817e658967aff10 --- /dev/null +++ b/modules/image/image_processing/enlightengan/util.py @@ -0,0 +1,11 @@ +import base64 + +import cv2 +import numpy as np + + +def base64_to_cv2(b64str): + data = base64.b64decode(b64str.encode('utf8')) + data = np.fromstring(data, np.uint8) + data = cv2.imdecode(data, cv2.IMREAD_COLOR) + return data