做网站暴利赚钱,wordpress可以仿任何站,公司部门解散 转岗不同意会怎么样,wordpress简约博客目录 前言一、#xff13;D 关键点可视化二、使用步骤1.300W-LP转为YOLO数据格式2.修改数据入口3.开始训练 总结 前言
300WLP数据集提供来丰富的人脸线索#xff0c;包括#xff12;D或#xff13;D的关键点信息#xff0c;Head Angle和#xff13;DMM的参数等#xff… 目录 前言一、D 关键点可视化二、使用步骤1.300W-LP转为YOLO数据格式2.修改数据入口3.开始训练 总结 前言
300WLP数据集提供来丰富的人脸线索包括D或D的关键点信息Head Angle和DMM的参数等它可以用于D的人脸关键点检测以及头部姿态检测等任务这里我们尝试去实现端到段的D人脸关键点检测 一、D 关键点可视化 如左图所示其特征点其实是在相机平面上的二维投影并非真正的含有深度信息的
二、使用步骤
1.300W-LP转为YOLO数据格式 代码如下示例 import os, cv2
import hdf5storage
import numpy as np
import sysimport scipy.io as siodef process_300WLP(root_folder, folder_name, image_name, label_name, target_size):#modify by WQT, referring from PIPNetimage_path os.path.join(root_folder, folder_name, image_name)label_path os.path.join(root_folder, landmarks, folder_name, label_name)with open(label_path, r) as ff:C sio.loadmat(label_path)anno C[pts_3d] # anno ff.readlines()[3:-1]# anno [x.strip().split() for x in anno]# # anno [[int(float(x[0])), int(float(x[1]))] for x in anno]# anno [[float(x[0]), float(x[1])] for x in anno]anno_x [x[0] for x in anno]anno_y [x[1] for x in anno]# anno_x anno[0, :]# anno_y anno[-1, :]bbox_xmin min(anno_x)bbox_ymin min(anno_y)bbox_xmax max(anno_x)bbox_ymax max(anno_y)bbox_width bbox_xmax - bbox_xmin 1bbox_height bbox_ymax - bbox_ymin 1image cv2.imread(image_path)image_height, image_width, _ image.shapebbox_xcenter bbox_xmin bbox_width/2bbox_ycenter bbox_ymin bbox_height/2padding 2 # to enlarge the face boxisCrowdAndXYWH [0, bbox_xcenter/image_width, bbox_ycenter/image_height, (bbox_widthpadding)/image_width, (bbox_heightpadding)/image_height]anno [[x/image_width, y/image_height, 2] for x,y in anno]return image, isCrowdAndXYWH, annodef gen_meanface(root_folder, data_name):with open(os.path.join(root_folder, data_name, train2yolo.txt), r) as f:annos f.readlines()annos [x.strip().split()[1:] for x in annos]annos [[float(x) for x in anno] for anno in annos]annos np.array(annos)meanface np.mean(annos, axis0)meanface meanface.tolist()meanface [str(x) for x in meanface]with open(os.path.join(root_folder, data_name, meanface.txt), w) as f:f.write( .join(meanface))def convert_wflw(root_folder, data_name):with open(os.path.join(../data/WFLW/test.txt), r) as f:annos f.readlines()annos [x.strip().split() for x in annos]annos_new []for anno in annos:annos_new.append([])# nameannos_new[-1].append(anno[0])anno anno[1:]# jawfor i in range(17):annos_new[-1].append(anno[i*2*2])annos_new[-1].append(anno[i*2*21])# left eyebrowannos_new[-1].append(anno[33*2])annos_new[-1].append(anno[33*21])annos_new[-1].append(anno[34*2])annos_new[-1].append(str((float(anno[34*21])float(anno[41*21]))/2))annos_new[-1].append(anno[35*2])annos_new[-1].append(str((float(anno[35*21])float(anno[40*21]))/2))annos_new[-1].append(anno[36*2])annos_new[-1].append(str((float(anno[36*21])float(anno[39*21]))/2))annos_new[-1].append(anno[37*2])annos_new[-1].append(str((float(anno[37*21])float(anno[38*21]))/2))# right eyebrowannos_new[-1].append(anno[42*2])annos_new[-1].append(str((float(anno[42*21])float(anno[50*21]))/2))annos_new[-1].append(anno[43*2])annos_new[-1].append(str((float(anno[43*21])float(anno[49*21]))/2))annos_new[-1].append(anno[44*2])annos_new[-1].append(str((float(anno[44*21])float(anno[48*21]))/2))annos_new[-1].append(anno[45*2])annos_new[-1].append(str((float(anno[45*21])float(anno[47*21]))/2))annos_new[-1].append(anno[46*2])annos_new[-1].append(anno[46*21])# nosefor i in range(51, 60):annos_new[-1].append(anno[i*2])annos_new[-1].append(anno[i*21])# left eyeannos_new[-1].append(anno[60*2])annos_new[-1].append(anno[60*21])annos_new[-1].append(str(0.666*float(anno[61*2])0.333*float(anno[62*2])))annos_new[-1].append(str(0.666*float(anno[61*21])0.333*float(anno[62*21])))annos_new[-1].append(str(0.666*float(anno[63*2])0.333*float(anno[62*2])))annos_new[-1].append(str(0.666*float(anno[63*21])0.333*float(anno[62*21])))annos_new[-1].append(anno[64*2])annos_new[-1].append(anno[64*21])annos_new[-1].append(str(0.666*float(anno[65*2])0.333*float(anno[66*2])))annos_new[-1].append(str(0.666*float(anno[65*21])0.333*float(anno[66*21])))annos_new[-1].append(str(0.666*float(anno[67*2])0.333*float(anno[66*2])))annos_new[-1].append(str(0.666*float(anno[67*21])0.333*float(anno[66*21])))# right eyeannos_new[-1].append(anno[68*2])annos_new[-1].append(anno[68*21])annos_new[-1].append(str(0.666*float(anno[69*2])0.333*float(anno[70*2])))annos_new[-1].append(str(0.666*float(anno[69*21])0.333*float(anno[70*21])))annos_new[-1].append(str(0.666*float(anno[71*2])0.333*float(anno[70*2])))annos_new[-1].append(str(0.666*float(anno[71*21])0.333*float(anno[70*21])))annos_new[-1].append(anno[72*2])annos_new[-1].append(anno[72*21])annos_new[-1].append(str(0.666*float(anno[73*2])0.333*float(anno[74*2])))annos_new[-1].append(str(0.666*float(anno[73*21])0.333*float(anno[74*21])))annos_new[-1].append(str(0.666*float(anno[75*2])0.333*float(anno[74*2])))annos_new[-1].append(str(0.666*float(anno[75*21])0.333*float(anno[74*21])))# mouthfor i in range(76, 96):annos_new[-1].append(anno[i*2])annos_new[-1].append(anno[i*21])with open(os.path.join(root_folder, data_name, test.txt), w) as f:for anno in annos_new:f.write( .join(anno)\n)def gen_data(root_folder, data_name, target_size):if not os.path.exists(os.path.join(root_folder, data_name, images_train2yolo)):os.mkdir(os.path.join(root_folder, data_name, images_train2yolo))if not os.path.exists(os.path.join(root_folder, data_name, images_test2yolo)):os.mkdir(os.path.join(root_folder, data_name, images_test2yolo)) #这是为了把cropped的人脸存入新文件夹由于我们不需要此步即可省掉也行################################################################################################################if data_name 300W_LP:# folders_train [AFW, HELEN, IBUG, LFPW]folders_train [HELEN, LFPW]annos_train {}for folder_train in folders_train:all_files sorted(os.listdir(os.path.join(root_folder, data_name, folder_train)))image_files [x for x in all_files if .mat not in x]# label_files [x for x in all_files if .mat in x]label_files [x.split(.)[0]_pts.mat for x in all_files if .mat in x]assert len(image_files) len(label_files)for image_name, label_name in zip(image_files, label_files):image_crop, isCrowdAndXYWH, anno process_300WLP(os.path.join(root_folder, 300W_LP), folder_train, image_name, label_name, target_size) image_crop_name image_namecv2.imwrite(os.path.join(root_folder, data_name, images, train, image_crop_name), image_crop)annos_train[image_crop_name] isCrowdAndXYWH, annowith open(os.path.join(root_folder, data_name, train2yolo.txt), w) as f:for image_crop_name, anno in annos_train.items():f.write(./images/train/ image_crop_name) #./images/val2017/000000345356.jpg# f.write(image_crop_name )# for x,y in anno:# f.write(str(x) str(y) )f.write(\n)base_txt os.path.basename(image_crop_name.split(.)[0]) .txtsave_txt_path os.path.join(root_folder, data_name,labels, train, base_txt)with open(save_txt_path, w) as f_txt:for xywh in anno[0]:f_txt.write(str(xywh) )for x, y, z in anno[1]:f_txt.write(str(x) str(y) str(z) )f_txt.write(\n) folders_test [AFW, IBUG]annos_test {}for folder_test in folders_test:all_files sorted(os.listdir(os.path.join(root_folder, data_name, folder_test)))image_files [x for x in all_files if .mat not in x]# label_files [x for x in all_files if .mat in x]label_files [x.split(.)[0]_pts.mat for x in all_files if .mat in x]assert len(image_files) len(label_files)for image_name, label_name in zip(image_files, label_files):image_crop, isCrowdAndXYWH, anno process_300WLP(os.path.join(root_folder, 300W_LP), folder_test, image_name, label_name, target_size) image_crop_name image_namecv2.imwrite(os.path.join(root_folder, data_name, images, test, image_crop_name), image_crop)annos_test[image_crop_name] isCrowdAndXYWH, annowith open(os.path.join(root_folder, data_name, test2yolo.txt), w) as f:for image_crop_name, anno in annos_test.items():f.write(./images/test/ image_crop_name) #./images/val2017/000000345356.jpg# f.write(image_crop_name )# for x,y in anno:# f.write(str(x) str(y) )f.write(\n)base_txt os.path.basename(image_crop_name.split(.)[0]) .txtsave_txt_path os.path.join(root_folder, data_name,labels, test, base_txt)with open(save_txt_path, w) as f_txt:for xywh in anno[0]:f_txt.write(str(xywh) )for x, y, z in anno[1]:f_txt.write(str(x) str(y) str(z) )f_txt.write(\n) gen_meanface(root_folder, data_name)elif data_name LaPa:pass# TODOelse:print(Wrong data!)if __name__ __main__:if len(sys.argv) 2:print(please input the data name.)print(1. 300W_LP)print(0. data_300W)print(2. COFW)print(3. WFLW)print(4. AFLW)print(5. LaPa)exit(0)else:data_name sys.argv[1]gen_data(../, data_name, 256)2.修改数据入口
修改ultrlytics/datasets/coco8-pose.yaml中的path 参考如下示例
# Ultralytics YOLO , AGPL-3.0 license
# COCO8-pose dataset (first 8 images from COCO train2017) by Ultralytics
# Example usage: yolo train datacoco8-pose.yaml
# parent
# ├── ultralytics
# └── datasets
# └── coco8-pose ← downloads here (1 MB)path: /home/wqt/Datasets/300W-LP/300W_LP# ../datasets/coco8-pose # dataset root dir
train: train2yolo.txt# data/video_68out/026_noglasses_mix_tired1/train2yolo.txt# data/video_68out/WIN_20230417_15_51_51_Pro/train2yolo.txt# images/train # train images (relative to path) 4 images
val: test2yolo.txt# images/val # val images (relative to path) 4 images
test: # test images (optional)# Keypoints
kpt_shape: [68, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
flip_idx: [16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 27, 28, 29, 30, 35, 34, 33, 32, 31, 45, 44, 43, 42, 47, 46, 39, 38, 37, 36, 41, 40, 54, 53, 52, 51, 50, 49, 48, 59, 58, 57, 56, 55, 64, 63, 62, 61, 60, 67, 66, 65]
# Classes
names:# 0: person #ori0: face #wqt# Download script/URL (optional)
download: https://ultralytics.com/assets/coco8-pose.zip3.开始训练
超参数设置
yolo/engine/trainer: taskpose, modetrain, model/home/wqt/NewProjects/ultralyticsWholeBody/runs/pose/train10/weights/best.pt, datacoco8-pose.yaml, epochs100, patience50, batch16, imgsz640, saveTrue, save_period20, cacheFalse, device, workers8, projectNone, name/home/wqt/NewProjects/ultralyticsFaceMark/runs/pose/train, exist_okFalse, pretrainedFalse, optimizerSGD, verboseTrue, seed0, deterministicTrue, single_clsFalse, rectFalse, cos_lrFalse, close_mosaic0, resumeFalse, ampTrue, fraction1.0, profileFalse, overlap_maskTrue, mask_ratio4, dropout0.0, valTrue, splitval, save_jsonFalse, save_hybridFalse, confNone, iou0.7, max_det300, halfFalse, dnnFalse, plotsTrue, sourceNone, showFalse, save_txtFalse, save_confFalse, save_cropFalse, show_labelsTrue, show_confTrue, vid_stride1, line_widthNone, visualizeFalse, augmentFalse, agnostic_nmsFalse, classesNone, retina_masksFalse, boxesTrue, formattorchscript, kerasFalse, optimizeFalse, int8False, dynamicFalse, simplifyFalse, opsetNone, workspace4, nmsFalse, lr00.01, lrf0.01, momentum0.937, weight_decay0.0005, warmup_epochs3.0, warmup_momentum0.8, warmup_bias_lr0.1, box7.5, cls0.5, dfl1.5, pose12.0, kobj1.0, label_smoothing0.0, nbs64, hsv_h0.015, hsv_s0.7, hsv_v0.4, degrees0.0, translate0.1, scale0.5, shear0.0, perspective0.0, flipud0.0, fliplr0.5, mosaic1.0, mixup0.0, copy_paste0.0, cfgNone, v5loaderFalse, trackerbotsort.yaml, save_dir/home/wqt/NewProjects/ultralyticsFaceMark/runs/pose/train2
Overriding model.yaml kpt_shape[133, 3] with kpt_shape[68, 3]网络结构参考yolo8 from n params module arguments 0 -1 1 928 ultralytics.nn.modules.conv.Conv [3, 32, 3, 2] 1 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] 2 -1 1 29056 ultralytics.nn.modules.block.C2f [64, 64, 1, True] 3 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 4 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] 5 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 6 -1 2 788480 ultralytics.nn.modules.block.C2f [256, 256, 2, True] 7 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2] 8 -1 1 1838080 ultralytics.nn.modules.block.C2f [512, 512, 1, True] 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5] 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, nearest] 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 12 -1 1 591360 ultralytics.nn.modules.block.C2f [768, 256, 1] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, nearest] 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 15 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] 16 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] 18 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] 19 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 -1 1 1969152 ultralytics.nn.modules.block.C2f [768, 512, 1] 22 [15, 18, 21] 1 5013031 ultralytics.nn.modules.head.Pose [1, [68, 3], [128, 256, 512]]
YOLOv8s-pose summary: 250 layers, 14032583 parameters, 14032567 gradients训练集与测试集
Transferred 361/397 items from pretrained weights
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
optimizer: SGD(lr0.01) with parameter groups 63 weight(decay0.0), 73 weight(decay0.0005), 72 bias
train: Scanning /home/wqt/Datasets/300W-LP/300W_LP/labels/train... 54232 images, 0 backgrounds, 0 corrupt: 100%|██████████| 54232/54232 [00:4500:00, 1190.60it/s]
train: New cache created: /home/wqt/Datasets/300W-LP/300W_LP/labels/train.cache
val: Scanning /home/wqt/Datasets/300W-LP/300W_LP/labels/test... 6993 images, 0 backgrounds, 0 corrupt: 100%|██████████| 6993/6993 [00:0600:00, 1127.92it/s]
val: New cache created: /home/wqt/Datasets/300W-LP/300W_LP/labels/test.cache
Plotting labels to /home/wqt/NewProjects/ultralyticsFaceMark/runs/pose/train2/labels.jpg...
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to /home/wqt/NewProjects/ultralyticsFaceMark/runs/pose/train2
Starting training for 100 epochs...Epoch GPU_mem box_loss pose_loss kobj_loss cls_loss dfl_loss Instances Size1/100 4.58G 0.9736 7 0.6731 0.9241 1.464 34 640: 29%|██▉ | 992/3390 [04:2610:48, 3.70it/s]训练过程 总结
期待好的结果
