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人脸修复祛马赛克算法CodeFormer——C++与Python模型部署

admin 阅读： 2024-03-20

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一、人脸修复算法

1.算法简介

CodeFormer是一种基于AI技术深度学习的人脸复原模型，由南洋理工大学和商汤科技联合研究中心联合开发，它能够接收模糊或马赛克图像作为输入，并生成更清晰的原始图像。算法源码地址：https://github.com/sczhou/CodeFormer
这种技术在图像修复、图像增强和隐私保护等领域可能会有广泛的应用。算法是由南洋理工大学和商汤科技联合研究中心联合开发的，结合了VQGAN和Transformer。
VQGAN是一个生成模型，通常用于图像生成任务。它使用了向量量化技术，将图像编码成一系列离散的向量，然后通过解码器将这些向量转化为图像。这种方法通常能够生成高质量的图像，尤其在与Transformer等神经网络结合使用时。
Transformer是一种广泛用于自然语言处理和计算机视觉等领域的神经网络架构。它在序列数据处理中表现出色，也可以用于图像生成和处理任务。
在这里插入图片描述

在监控、安全和隐私保护领域，人脸图像通常会受到多种因素的影响，其中包括光照、像素限制、聚焦问题和人体运动等。这些因素可能导致图像模糊、变形或者包含大量的噪声。在这种情况下，尝试恢复清晰的原始人脸图像是一个极具挑战性的任务。
盲人脸复原是一个不适定问题（ill-posed problem），这意味着存在多个可能的解决方案，而且从有限的观察数据中无法唯一确定真实的原始图像。因此，在这个领域中，通常需要依赖先进的计算机视觉和图像处理技术，以及深度学习模型，来尝试改善模糊或受损图像的质量。
一些方法和技术可以用于处理盲人脸复原问题，包括但不限于：
深度学习模型: 使用卷积神经网络（CNN）和生成对抗网络（GAN）等深度学习模型，可以尝试从模糊或变形的人脸图像中恢复原始细节。
超分辨率技术: 超分辨率方法旨在从低分辨率图像中重建高分辨率图像，这也可以用于人脸图像复原。
先验知识: 利用先验知识，如人脸结构、光照模型等，可以帮助提高复原的准确性。
多模态融合: 结合不同传感器和信息源的数据，可以提高复原的鲁棒性。
然而，即使使用这些技术，由于问题的不适定性，完全恢复清晰的原始人脸图像仍然可能是一项极具挑战性的任务，特别是在极端条件下。在实际应用中，可能需要权衡图像质量和可用的信息，以达到最佳的结果。

2.算法效果

在官方公布修复的人脸效果里面，可以看到算法在各种输入的修复效果：
老照片修复
在这里插入图片描述
人脸修复

黑白人脸图像增强修复

人脸恢复

二、模型部署

如果想用C++进行模型推理部署，首先要把模型转换成onnx，转成onnx就可以使用onnxruntime c++库进行部署，或者使用OpenCV的DNN也可以，转成onnx后，还可以再转成ncnn模型使用ncnn进行模型部署。原模型可以从官方开源界面可以下载。
模型推理这块有两种做法，一是不用判断有没有人脸，直接对全图进行超分，但这种方法好像对本来是清晰的图像会出现bug，就是生成一些无法理解的处理。

1. C++使用onnxruntime部署模型

#include "CodeFormer.h" CodeFormer::CodeFormer(std::string model_path) { //OrtStatus* status = OrtSessionOptionsAppendExecutionProvider_CUDA(sessionOptions, 0); ///nvidia-cuda加速 sessionOptions.SetGraphOptimizationLevel(ORT_ENABLE_BASIC); std::wstring widestr = std::wstring(model_path.begin(), model_path.end()); ///如果在windows系统就这么写 ort_session = new Ort::Session(env, widestr.c_str(), sessionOptions); ///如果在windows系统就这么写 ///ort_session = new Session(env, model_path.c_str(), sessionOptions); ///如果在linux系统，就这么写 size_t numInputNodes = ort_session->GetInputCount(); size_t numOutputNodes = ort_session->GetOutputCount(); Ort::AllocatorWithDefaultOptions allocator; for (int i = 0; i < numInputNodes; i++) { input_names.push_back(ort_session->GetInputName(i, allocator)); Ort::TypeInfo input_type_info = ort_session->GetInputTypeInfo(i); auto input_tensor_info = input_type_info.GetTensorTypeAndShapeInfo(); auto input_dims = input_tensor_info.GetShape(); input_node_dims.push_back(input_dims); } for (int i = 0; i < numOutputNodes; i++) { output_names.push_back(ort_session->GetOutputName(i, allocator)); Ort::TypeInfo output_type_info = ort_session->GetOutputTypeInfo(i); auto output_tensor_info = output_type_info.GetTensorTypeAndShapeInfo(); auto output_dims = output_tensor_info.GetShape(); output_node_dims.push_back(output_dims); } this->inpHeight = input_node_dims[0][2]; this->inpWidth = input_node_dims[0][3]; this->outHeight = output_node_dims[0][2]; this->outWidth = output_node_dims[0][3]; input2_tensor.push_back(0.5); } void CodeFormer::preprocess(cv::Mat &srcimg) { cv::Mat dstimg; cv::cvtColor(srcimg, dstimg, cv::COLOR_BGR2RGB); resize(dstimg, dstimg, cv::Size(this->inpWidth, this->inpHeight), cv::INTER_LINEAR); this->input_image_.resize(this->inpWidth * this->inpHeight * dstimg.channels()); int k = 0; for (int c = 0; c < 3; c++) { for (int i = 0; i < this->inpHeight; i++) { for (int j = 0; j < this->inpWidth; j++) { float pix = dstimg.ptr<uchar>(i)[j * 3 + c]; this->input_image_[k] = (pix / 255.0 - 0.5) / 0.5; k++; } } } } cv::Mat CodeFormer::detect(cv::Mat &srcimg) { int im_h = srcimg.rows; int im_w = srcimg.cols; this->preprocess(srcimg); std::array<int64_t, 4> input_shape_{ 1, 3, this->inpHeight, this->inpWidth }; std::vector<int64_t> input2_shape_ = { 1 }; auto allocator_info = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU); std::vector<Ort::Value> ort_inputs; ort_inputs.push_back(Ort::Value::CreateTensor<float>(allocator_info, input_image_.data(), input_image_.size(), input_shape_.data(), input_shape_.size())); ort_inputs.push_back(Ort::Value::CreateTensor<double>(allocator_info, input2_tensor.data(), input2_tensor.size(), input2_shape_.data(), input2_shape_.size())); std::vector<Ort::Value> ort_outputs = ort_session->Run(Ort::RunOptions{ nullptr }, input_names.data(), ort_inputs.data(), ort_inputs.size(), output_names.data(), output_names.size()); post_process float* pred = ort_outputs[0].GetTensorMutableData<float>(); //cv::Mat mask(outHeight, outWidth, CV_32FC3, pred); /经过试验,直接这样赋值,是不行的 const unsigned int channel_step = outHeight * outWidth; std::vector<cv::Mat> channel_mats; cv::Mat rmat(outHeight, outWidth, CV_32FC1, pred); // R cv::Mat gmat(outHeight, outWidth, CV_32FC1, pred + channel_step); // G cv::Mat bmat(outHeight, outWidth, CV_32FC1, pred + 2 * channel_step); // B channel_mats.push_back(rmat); channel_mats.push_back(gmat); channel_mats.push_back(bmat); cv::Mat mask; merge(channel_mats, mask); // CV_32FC3 allocated ///不用for循环遍历cv::Mat里的每个像素值,实现numpy.clip函数 mask.setTo(this->min_max[0], mask < this->min_max[0]); mask.setTo(this->min_max[1], mask > this->min_max[1]); 也可以用threshold函数,阈值类型THRESH_TOZERO_INV mask = (mask - this->min_max[0]) / (this->min_max[1] - this->min_max[0]); mask *= 255.0; mask.convertTo(mask, CV_8UC3); //cvtColor(mask, mask, cv::COLOR_BGR2RGB); return mask; } void CodeFormer::detect_video(const std::string& video_path,const std::string& output_path, unsigned int writer_fps) { cv::VideoCapture video_capture(video_path); if (!video_capture.isOpened()) { std::cout << "Can not open video: " << video_path << "\n"; return; } cv::Size S = cv::Size((int)video_capture.get(cv::CAP_PROP_FRAME_WIDTH), (int)video_capture.get(cv::CAP_PROP_FRAME_HEIGHT)); cv::VideoWriter output_video(output_path, cv::VideoWriter::fourcc('m', 'p', '4', 'v'), 30.0, S); if (!output_video.isOpened()) { std::cout << "Can not open writer: " << output_path << "\n"; return; } cv::Mat cv_mat; while (video_capture.read(cv_mat)) { cv::Mat cv_dst = detect(cv_mat); output_video << cv_dst; } video_capture.release(); output_video.release(); }

先试试官方给的样本的效果：
在这里插入图片描述
薄马赛克的超分效果：

厚马赛克的超分效果不是很好，就是有点贴脸的感觉：

如果是已经是清晰的图像，超分之后不是很理想，基本上是不能用的，onnx这个效果只能优化人脸：

在这里插入图片描述

2.onnx模型python推理

import os import cv2 import argparse import glob import torch import torch.onnx from torchvision.transforms.functional import normalize from basicsr.utils import imwrite, img2tensor, tensor2img from basicsr.utils.download_util import load_file_from_url from basicsr.utils.misc import gpu_is_available, get_device from facelib.utils.face_restoration_helper import FaceRestoreHelper from facelib.utils.misc import is_gray import onnxruntime as ort from basicsr.utils.registry import ARCH_REGISTRY pretrain_model_url = { 'restoration': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth', } if __name__ == '__main__': # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') device = get_device() parser = argparse.ArgumentParser() parser.add_argument('-i', '--input_path', type=str, default='./inputs/whole_imgs', help='Input image, video or folder. Default: inputs/whole_imgs') parser.add_argument('-o', '--output_path', type=str, default=None, help='Output folder. Default: results/_') parser.add_argument('-w', '--fidelity_weight', type=float, default=0.5, help='Balance the quality and fidelity. Default: 0.5') parser.add_argument('-s', '--upscale', type=int, default=2, help='The final upsampling scale of the image. Default: 2') parser.add_argument('--has_aligned', action='store_true', help='Input are cropped and aligned faces. Default: False') parser.add_argument('--only_center_face', action='store_true', help='Only restore the center face. Default: False') parser.add_argument('--draw_box', action='store_true', help='Draw the bounding box for the detected faces. Default: False') # large det_model: 'YOLOv5l', 'retinaface_resnet50' # small det_model: 'YOLOv5n', 'retinaface_mobile0.25' parser.add_argument('--detection_model', type=str, default='retinaface_resnet50', help='Face detector. Optional: retinaface_resnet50, retinaface_mobile0.25, YOLOv5l, YOLOv5n, dlib. \ Default: retinaface_resnet50') parser.add_argument('--bg_upsampler', type=str, default='None', help='Background upsampler. Optional: realesrgan') parser.add_argument('--face_upsample', action='store_true', help='Face upsampler after enhancement. Default: False') parser.add_argument('--bg_tile', type=int, default=400, help='Tile size for background sampler. Default: 400') parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces. Default: None') parser.add_argument('--save_video_fps', type=float, default=None, help='Frame rate for saving video. Default: None') args = parser.parse_args() # ------------------------ input & output ------------------------ w = args.fidelity_weight input_video = False if args.input_path.endswith(('jpg', 'jpeg', 'png', 'JPG', 'JPEG', 'PNG')): # input single img path input_img_list = [args.input_path] result_root = f'results/test_img_{w}' # elif args.input_path.endswith(('mp4', 'mov', 'avi', 'MP4', 'MOV', 'AVI')): # input video path # from basicsr.utils.video_util import VideoReader, VideoWriter # input_img_list = [] # vidreader = VideoReader(args.input_path) # image = vidreader.get_frame() # while image is not None: # input_img_list.append(image) # image = vidreader.get_frame() # audio = vidreader.get_audio() # fps = vidreader.get_fps() if args.save_video_fps is None else args.save_video_fps # video_name = os.path.basename(args.input_path)[:-4] # result_root = f'results/{video_name}_{w}' # input_video = True # vidreader.close() # else: # input img folder # if args.input_path.endswith('/'): # solve when path ends with / # args.input_path = args.input_path[:-1] # # scan all the jpg and png images # input_img_list = sorted(glob.glob(os.path.join(args.input_path, '*.[jpJP][pnPN]*[gG]'))) # result_root = f'results/{os.path.basename(args.input_path)}_{w}' else: raise ValueError("wtf???") if not args.output_path is None: # set output path result_root = args.output_path test_img_num = len(input_img_list) if test_img_num == 0: raise FileNotFoundError('No input image/video is found...\n' '\tNote that --input_path for video should end with .mp4|.mov|.avi') # # ------------------ set up background upsampler ------------------ # if args.bg_upsampler == 'realesrgan': # bg_upsampler = set_realesrgan() # else: # bg_upsampler = None # # ------------------ set up face upsampler ------------------ # if args.face_upsample: # if bg_upsampler is not None: # face_upsampler = bg_upsampler # else: # face_upsampler = set_realesrgan() # else: # face_upsampler = None # ------------------ set up CodeFormer restorer ------------------- net = ARCH_REGISTRY.get('CodeFormer')(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(device) # ckpt_path = 'weights/CodeFormer/codeformer.pth' ckpt_path = load_file_from_url(url=pretrain_model_url['restoration'], model_dir='weights/CodeFormer', progress=True, file_name=None) checkpoint = torch.load(ckpt_path)['params_ema'] net.load_state_dict(checkpoint) net.eval() # # ------------------ set up FaceRestoreHelper ------------------- # # large det_model: 'YOLOv5l', 'retinaface_resnet50' # # small det_model: 'YOLOv5n', 'retinaface_mobile0.25' # if not args.has_aligned: # print(f'Face detection model: {args.detection_model}') # # if bg_upsampler is not None: # # print(f'Background upsampling: True, Face upsampling: {args.face_upsample}') # # else: # # print(f'Background upsampling: False, Face upsampling: {args.face_upsample}') # else: # raise ValueError("wtf???") face_helper = FaceRestoreHelper( args.upscale, face_size=512, crop_ratio=(1, 1), # det_model = args.detection_model, # save_ext='png', # use_parse=True, # device=device ) # -------------------- start to processing --------------------- for i, img_path in enumerate(input_img_list): # # clean all the intermediate results to process the next image # face_helper.clean_all() if isinstance(img_path, str): img_name = os.path.basename(img_path) basename, ext = os.path.splitext(img_name) print(f'[{i+1}/{test_img_num}] Processing: {img_name}') img = cv2.imread(img_path, cv2.IMREAD_COLOR) # else: # for video processing # basename = str(i).zfill(6) # img_name = f'{video_name}_{basename}' if input_video else basename # print(f'[{i+1}/{test_img_num}] Processing: {img_name}') # img = img_path if args.has_aligned: # the input faces are already cropped and aligned img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_LINEAR) # face_helper.is_gray = is_gray(img, threshold=10) # if face_helper.is_gray: # print('Grayscale input: True') face_helper.cropped_faces = [img] # else: # face_helper.read_image(img) # # get face landmarks for each face # num_det_faces = face_helper.get_face_landmarks_5( # only_center_face=args.only_center_face, resize=640, eye_dist_threshold=5) # print(f'\tdetect {num_det_faces} faces') # # align and warp each face # face_helper.align_warp_face() else: raise ValueError("wtf???") # face restoration for each cropped face for idx, cropped_face in enumerate(face_helper.cropped_faces): # prepare data cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True) normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) cropped_face_t = cropped_face_t.unsqueeze(0).to(device) try: with torch.no_grad(): # output = net(cropped_face_t, w=w, adain=True)[0] # output = net(cropped_face_t)[0] output = net(cropped_face_t, w)[0] restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1)) del output # torch.cuda.empty_cache() except Exception as error: print(f'\tFailed inference for CodeFormer: {error}') restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1)) # now, export the "net" codeformer to onnx print("Exporting CodeFormer to ONNX...") torch.onnx.export(net, # (cropped_face_t,), (cropped_face_t,w), "codeformer.onnx", # verbose=True, export_params=True, opset_version=11, do_constant_folding=True, input_names = ['x','w'], output_names = ['y'], ) # now, try to load the onnx model and run it print("Loading CodeFormer ONNX...") ort_session = ort.InferenceSession("codeformer.onnx", providers=['CPUExecutionProvider']) print("Running CodeFormer ONNX...") ort_inputs = { ort_session.get_inputs()[0].name: cropped_face_t.cpu().numpy(), ort_session.get_inputs()[1].name: torch.tensor(w).double().cpu().numpy(), } ort_outs = ort_session.run(None, ort_inputs) restored_face_onnx = tensor2img(torch.from_numpy(ort_outs[0]), rgb2bgr=True, min_max=(-1, 1)) restored_face_onnx = restored_face_onnx.astype('uint8') restored_face = restored_face.astype('uint8') print("Comparing CodeFormer outputs...") # see how similar the outputs are: flatten and then compute all the differences diff = (restored_face_onnx.astype('float32') - restored_face.astype('float32')).flatten() # calculate min, max, mean, and std min_diff = diff.min() max_diff = diff.max() mean_diff = diff.mean() std_diff = diff.std() print(f"Min diff: {min_diff}, Max diff: {max_diff}, Mean diff: {mean_diff}, Std diff: {std_diff}") # face_helper.add_restored_face(restored_face, cropped_face) face_helper.add_restored_face(restored_face_onnx, cropped_face) # # paste_back # if not args.has_aligned: # # upsample the background # if bg_upsampler is not None: # # Now only support RealESRGAN for upsampling background # bg_img = bg_upsampler.enhance(img, outscale=args.upscale)[0] # else: # bg_img = None # face_helper.get_inverse_affine(None) # # paste each restored face to the input image # if args.face_upsample and face_upsampler is not None: # restored_img = face_helper.paste_faces_to_input_image(upsample_img=bg_img, draw_box=args.draw_box, face_upsampler=face_upsampler) # else: # restored_img = face_helper.paste_faces_to_input_image(upsample_img=bg_img, draw_box=args.draw_box) # save faces for idx, (cropped_face, restored_face) in enumerate(zip(face_helper.cropped_faces, face_helper.restored_faces)): # save cropped face if not args.has_aligned: save_crop_path = os.path.join(result_root, 'cropped_faces', f'{basename}_{idx:02d}.png') imwrite(cropped_face, save_crop_path) # save restored face if args.has_aligned: save_face_name = f'{basename}.png' else: save_face_name = f'{basename}_{idx:02d}.png' if args.suffix is not None: save_face_name = f'{save_face_name[:-4]}_{args.suffix}.png' save_restore_path = os.path.join(result_root, 'restored_faces', save_face_name) imwrite(restored_face, save_restore_path) # # save restored img # if not args.has_aligned and restored_img is not None: # if args.suffix is not None: # basename = f'{basename}_{args.suffix}' # save_restore_path = os.path.join(result_root, 'final_results', f'{basename}.png') # imwrite(restored_img, save_restore_path) # # save enhanced video # if input_video: # print('Video Saving...') # # load images # video_frames = [] # img_list = sorted(glob.glob(os.path.join(result_root, 'final_results', '*.[jp][pn]g'))) # for img_path in img_list: # img = cv2.imread(img_path) # video_frames.append(img) # # write images to video # height, width = video_frames[0].shape[:2] # if args.suffix is not None: # video_name = f'{video_name}_{args.suffix}.png' # save_restore_path = os.path.join(result_root, f'{video_name}.mp4') # vidwriter = VideoWriter(save_restore_path, height, width, fps, audio) # for f in video_frames: # vidwriter.write_frame(f) # vidwriter.close() print(f'\nAll results are saved in {result_root}')

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