diff --git a/modules/image/image_processing/seeinthedark/README.md b/modules/image/image_processing/seeinthedark/README.md index 786b40493a7e5ad6639b3d2281883867f0172b2d..9822f8d879331cbac87685547afa890415069ef8 100644 --- a/modules/image/image_processing/seeinthedark/README.md +++ b/modules/image/image_processing/seeinthedark/README.md @@ -13,6 +13,19 @@ ## 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 - 通过大量暗光条件下短曝光和长曝光组成的图像对，以RAW图像为输入，RGB图像为参照进行训练，该模型实现端到端直接将暗光下的RAW图像处理得到可见的RGB图像。 @@ -25,7 +38,6 @@ - ### 1、环境依赖 - rawpy - - pillow - ### 2、安装 @@ -53,26 +65,64 @@ denoiser = hub.Module(name="seeinthedark") input_path = "/PATH/TO/IMAGE" # Read from a raw file - denoiser.denoising(input_path, output_path='./denoising_result.png', use_gpu=True) + denoiser.denoising(paths=[input_path], output_path='./denoising_result.png', use_gpu=True) ``` - ### 3、API - ```python - def denoising(input_path, output_path='./denoising_result.png', use_gpu=False) + def denoising(images=None, paths=None, output_dir='./denoising_result/', use_gpu=False, visualization=True) ``` - 暗光增强API，完成对暗光RAW图像的降噪并处理生成RGB图像。 - **参数** - - - input\_path (str): 暗光图像文件的路径，Sony的RAW格式;
- - output\_path (str): 结果保存的路径, 需要指定输出文件名；
+ - images (list\[numpy.ndarray\]): 输入的图像，单通道的马赛克图像;
+ - paths (list\[str\]): 暗光图像文件的路径，Sony的RAW格式；
+ - 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 seeinthedark + ``` + + - 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 rawpy + 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(rawpy.imread("/PATH/TO/IMAGE").raw_image_visible)]} + headers = {"Content-type": "application/json"} + url = "http://127.0.0.1:8866/predict/seeinthedark/" + r = requests.post(url=url, headers=headers, data=json.dumps(data)) + # 打印预测结果 + print(r.json()["results"]) -## 四、更新历史 +## 五、更新历史 * 1.0.0 diff --git a/modules/image/image_processing/seeinthedark/module.py b/modules/image/image_processing/seeinthedark/module.py index 1ba3c3821b24cafa4a27d6fe3dbee3187af3cdc7..434e40a79b0a8523e7a87eb1d5394aa4eaa0dbac 100644 --- a/modules/image/image_processing/seeinthedark/module.py +++ b/modules/image/image_processing/seeinthedark/module.py @@ -17,15 +17,19 @@ import argparse import paddle import paddlehub as hub -from paddlehub.module.module import moduleinfo, runnable +from paddlehub.module.module import moduleinfo, runnable, serving import numpy as np import rawpy -from PIL import Image +import cv2 + +from .util import base64_to_cv2 def pack_raw(raw): # pack Bayer image to 4 channels - im = raw.raw_image_visible.astype(np.float32) + im = raw + if not isinstance(raw, np.ndarray): + im = raw.raw_image_visible.astype(np.float32) im = np.maximum(im - 512, 0) / (16383 - 512) # subtract the black level im = np.expand_dims(im, axis=2) @@ -42,47 +46,98 @@ def pack_raw(raw): class LearningToSeeInDark: def __init__(self): self.pretrained_model = os.path.join(self.directory, "pd_model/inference_model") + self.cpu_have_loaded = False + self.gpu_have_loaded = False + + def set_device(self, use_gpu=False): + if use_gpu == False: + if not self.cpu_have_loaded: + exe = paddle.static.Executor(paddle.CPUPlace()) + [prog, inputs, outputs] = paddle.static.load_inference_model( + path_prefix=self.pretrained_model, + executor=exe, + model_filename="model.pdmodel", + params_filename="model.pdiparams") + self.cpuexec, self.cpuprog, self.cpuinputs, self.cpuoutputs = exe, prog, inputs, outputs + self.cpu_have_loaded = True + + return self.cpuexec, self.cpuprog, self.cpuinputs, self.cpuoutputs - def denoising(self, input_path, output_path='./denoising_result.png', use_gpu=False): + else: + if not self.gpu_have_loaded: + exe = paddle.static.Executor(paddle.CUDAPlace(0)) + [prog, inputs, outputs] = paddle.static.load_inference_model( + path_prefix=self.pretrained_model, + executor=exe, + model_filename="model.pdmodel", + params_filename="model.pdiparams") + self.gpuexec, self.gpuprog, self.gpuinputs, self.gpuoutputs = exe, prog, inputs, outputs + self.gpu_have_loaded = True + + return self.gpuexec, self.gpuprog, self.gpuinputs, self.gpuoutputs + + def denoising(self, images=None, paths=None, output_dir='./enlightening_result/', use_gpu=False, + visualization=True): ''' Denoise a raw image in the low-light scene. - input_path: the raw image path - output_path: the path to save the results + images (list[numpy.ndarray]): data of images, shape of each is [H, W], must be sing-channel image captured by camera. + paths (list[str]): paths to images + output_dir: the dir to save the results use_gpu: if True, use gpu to perform the computation, otherwise cpu. + visualization: if True, save results in output_dir. ''' + results = [] paddle.enable_static() - if use_gpu == False: - exe = paddle.static.Executor(paddle.CPUPlace()) - else: - exe = paddle.static.Executor(paddle.CUDAPlace(0)) - [prog, inputs, outputs] = paddle.static.load_inference_model( - path_prefix=self.pretrained_model, - executor=exe, - model_filename="model.pdmodel", - params_filename="model.pdiparams") - raw = rawpy.imread(input_path) - input_full = np.expand_dims(pack_raw(raw), axis=0) * 300 - px = input_full.shape[1] // 512 - py = input_full.shape[2] // 512 - rx, ry = px * 512, py * 512 - input_full = input_full[:, :rx, :ry, :] - output = np.random.randn(rx * 2, ry * 2, 3) - input_full = np.minimum(input_full, 1.0) - for i in range(px): - for j in range(py): - input_patch = input_full[:, i * 512:i * 512 + 512, j * 512:j * 512 + 512, :] - result = exe.run(prog, feed={inputs[0]: input_patch}, fetch_list=outputs) - output[i * 512 * 2:i * 512 * 2 + 512 * 2, j * 512 * 2:j * 512 * 2 + 512 * 2, :] = result[0][0] - output = np.minimum(np.maximum(output, 0), 1) - - print('Denoising Over.') - try: - Image.fromarray(np.uint8(output * 255)).save(os.path.join(output_path)) - print('Image saved in {}'.format(output_path)) - except: - print('Save image failed. Please check the output_path, should\ - be image format ext, e.g. png. current output path {}'.format(output_path)) + exe, prog, inputs, outputs = self.set_device(use_gpu) + + if images != None: + for raw in images: + input_full = np.expand_dims(pack_raw(raw), axis=0) * 300 + px = input_full.shape[1] // 512 + py = input_full.shape[2] // 512 + rx, ry = px * 512, py * 512 + input_full = input_full[:, :rx, :ry, :] + output = np.random.randn(rx * 2, ry * 2, 3) + input_full = np.minimum(input_full, 1.0) + for i in range(px): + for j in range(py): + input_patch = input_full[:, i * 512:i * 512 + 512, j * 512:j * 512 + 512, :] + result = exe.run(prog, feed={inputs[0]: input_patch}, fetch_list=outputs) + output[i * 512 * 2:i * 512 * 2 + 512 * 2, j * 512 * 2:j * 512 * 2 + 512 * 2, :] = result[0][0] + output = np.minimum(np.maximum(output, 0), 1) + output = output * 255 + output = np.clip(output, 0, 255) + output = output.astype('uint8') + results.append(output) + if paths != None: + for path in paths: + raw = rawpy.imread(path) + input_full = np.expand_dims(pack_raw(raw), axis=0) * 300 + px = input_full.shape[1] // 512 + py = input_full.shape[2] // 512 + rx, ry = px * 512, py * 512 + input_full = input_full[:, :rx, :ry, :] + output = np.random.randn(rx * 2, ry * 2, 3) + input_full = np.minimum(input_full, 1.0) + for i in range(px): + for j in range(py): + input_patch = input_full[:, i * 512:i * 512 + 512, j * 512:j * 512 + 512, :] + result = exe.run(prog, feed={inputs[0]: input_patch}, fetch_list=outputs) + output[i * 512 * 2:i * 512 * 2 + 512 * 2, j * 512 * 2:j * 512 * 2 + 512 * 2, :] = result[0][0] + output = np.minimum(np.maximum(output, 0), 1) + output = output * 255 + output = np.clip(output, 0, 255) + output = output.astype('uint8') + results.append(output) + + 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): @@ -101,7 +156,21 @@ class LearningToSeeInDark: self.add_module_config_arg() self.add_module_input_arg() self.args = self.parser.parse_args(argvs) - self.denoising(input_path=self.args.input_path, output_path=self.args.output_path, use_gpu=self.args.use_gpu) + self.denoising( + paths=[self.args.input_path], + output_dir=self.args.output_dir, + use_gpu=self.args.use_gpu, + visualization=self.args.visualization) + + @serving + def serving_method(self, images, **kwargs): + """ + Run as a service. + """ + images_decode = [base64_to_cv2(image) for image in images] + results = self.denoising(images=images_decode, **kwargs) + tolist = [result.tolist() for result in results] + return tolist def add_module_config_arg(self): """ @@ -110,7 +179,8 @@ class LearningToSeeInDark: self.arg_config_group.add_argument('--use_gpu', action='store_true', help="use GPU or not") self.arg_config_group.add_argument( - '--output_path', type=str, default='denoising_result.png', help='output path for saving result.') + '--output_dir', type=str, default='denoising_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): """ diff --git a/modules/image/image_processing/seeinthedark/requirements.txt b/modules/image/image_processing/seeinthedark/requirements.txt index b49bea0673ed1c7e3e4d23e998eaa21f3d4494b1..32c8259e1c5451cc0f2bec980fa8476ac1708771 100644 --- a/modules/image/image_processing/seeinthedark/requirements.txt +++ b/modules/image/image_processing/seeinthedark/requirements.txt @@ -1,2 +1 @@ rawpy -pillow