AI实战2-face-detect

人脸检测 环境安装源设置conda 环境安装依赖库 概述数据集wider_face转yolo环境依赖标注信息格式转换图片处理生成 train.txt 文件 数据集展示数据集加载和处理 参考文章

环境 安装源设置

conda config --add channels mirrors.tuna.tsinghua.edu /anaconda/pkgs/free/ conda config --add channels mirrors.tuna.tsinghua.edu /anaconda/pkgs/main/ conda config --add channels mirrors.tuna.tsinghua.edu /anaconda/cloud/pytorch/ conda config --set show_channel_urls yes pip config set global.index-url pypi.tuna.tsinghua.edu /simple

conda 环境安装依赖库

conda create -n facePay python=3.7 conda activate facePay conda install pytorch-cpu -c pytorch #使用conda install pytorch-cpu会快很多 pip3 install torchvision -i pypi.tuna.tsinghua.edu /simple pip install opencv-python -i pypi.tuna.tsinghua.edu /simple pip install bcolz pip install scikit-learn pip install tqdm pip install easydict

概述

人脸检测属于目标检测领域，目标检测领域分两大类：通用目标检测（n+1分类），特定类别目标检测（2分类） 人脸检测算法：Faster-RCNN系列，YOLO系列，级联CNN系列 评价指标：召回率，误检率，检测速度

数据集

yolo 通过txt文件标注，标注内容：0 0.15 0.33 0.14 0.22 对应：类别 归一化后中心点坐标 [x,y,w,h]

wider_face转yolo 环境依赖 # PIL 安装 pip install -U Pillow -i pypi.tuna.tsinghua.edu /simple conda install Pillow # pip 安装会报错，conda 安装正常 标注信息格式转换 import os from PIL import Image parent_path = "/home/ai/wider_face_split/" def convert_to_yolo_format(input_file, output_dir, image_dir): with open(input_file, 'r') as f: lines = f.readlines() i = 0 while i < len(lines): image_path = lines[i].strip() # Get the relative path of image num_boxes = int(lines[i + 1].strip()) # Get the number of boxes # Path of the label file label_path = os.path.join(output_dir, os.path.basename(image_path).replace('.jpg', '.txt')) os.makedirs(os.path.dirname(label_path), exist_ok=True) # Get the Absolute Path of the image image_abs_path = os.path.join(image_dir, image_path) # Open the image to get the real size of it with Image.open(image_abs_path) as img: img_width, img_height = img.size # Create the file and write data in with open(label_path, 'w') as label_file: for j in range(num_boxes): # Fetch the box data (x_min, y_min, width, height) box_data = list(map(int, lines[i + 2 + j].strip().split())) x_min, y_min, width, height = box_data[:4] # Calculate the center coordinate (x_center, y_center) x_center = (x_min + width / 2) y_center = (y_min + height / 2) # Convert to the relative coordinates x_center /= img_width y_center /= img_height width /= img_width height /= img_height # The class is defaulted by 0 label_file.write(f"0 {x_center} {y_center} {width} {height}\n") # Update the index and jump to the next image i += 2 + (1 if num_boxes == 0 else num_boxes) if __name__ == "__main__": # Modify the additional section by your own path input_path = parent_path+"wider_face_split/" output_path = parent_path+"wider_for_yolo/" input_file_pre = "wider_face_" input_file_sub = "_bbx_gt.txt" if not os.path.exists(output_path): os.makedirs(output_path) # Train and Validation datasetfile = ["train", "val"] for category in datasetfile: convert_to_yolo_format(input_path + input_file_pre + category + input_file_sub, output_path + category + "/labels", parent_path+f"WIDER_{category}/images") 图片处理

wider_face对不同情景的图片做了分类，YOLO要求训练图片在一个文件夹，因此训练前需要将数据集所有图片copy到一个文件夹下

import os import shutil def copy_images(src_dir, dest_dir): # 确保目标目录存在 if not os.path.exists(dest_dir): os.makedirs(dest_dir) # 递归查找所有图片 for root, _, files in os.walk(src_dir): for file in files: if file.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp')): src_path = os.path.join(root, file) dest_path = os.path.join(dest_dir, file) # 如果目标文件已存在，可以选择覆盖或跳过 if not os.path.exists(dest_path): shutil.copy2(src_path, dest_path) # 保留原文件元数据 print(f"Copied: {src_path} -> {dest_path}") else: print(f"Skipped (already exists): {dest_path}") # 配置源文件夹和目标文件夹路径 train_source_folder = r"/home/a/wider_face_split/WIDER_train/images" train_destination_folder = r"/home/a/wider_face_split/WIDER_train/data" val_source_folder = r"/home/a/wider_face_split/WIDER_val/images" val_destination_folder = r"/home/a/wider_face_split/WIDER_val/data" # 执行复制 copy_images(train_source_folder, train_destination_folder) copy_images(val_source_folder, val_destination_folder) 生成 train.txt 文件 ls -al images/ | awk '{print $NF}' > ../train.txt 数据集展示 import cv2 import os import numpy as np if __name__ == "__main__": # 第一步：指定文件路径 root_path ='/home/neucore/develop/code/pre_research/dl/face_ai/study/yoloDataset/train/images/' path = '/home/neucore/develop/code/pre_research/dl/face_ai/study/yoloDataset/train.txt' path_voc_names = '/home/neucore/develop/code/pre_research/dl/face_ai/study/yoloDataset/face.names' # 第二步：获取目标类别 with open(path_voc_names ,'r') as f: lable_map = f.readlines() for i in range(len(lable_map)): lable_map[i] = lable_map[i].strip() print(i, lable_map[i]) # 第三步：获取图像数据和标注信息 with open(path ,'r') as file: img_files = file.readlines() # img_files = os.path.join(root_path, img_files[i][0:]) for i in range(len(img_files)): img_files[i] = img_files[i].strip() # 图像的绝对路径, [0:]表示去掉多少个字节，[2:]表示去掉前两个字符 img_files[i] = os.path.join(root_path, img_files[i][0:]) # print(i, img_files[i]) label_files = [x.replace('images','labels').replace ('.jpg','.txt') for x in img_files] # print(label_files) #第四步：将标注信息给制在图像上 #读取图像并对标注信息进行绘 # for i in range(len(img_files)): for i in range (3): print (img_files[i]) # 图像读取，获取宽高 img =cv2.imread(img_files[i]) if img is None: print("Error: Image not found or path is incorrect.") w = img.shape[1] h = img.shape[0] # 标签文件的绝对路径 print(i, label_files[i]) if os.path.isfile(label_files[i]): # 获取每一行的标注信息 with open(label_files[i], 'r') as file: lines = file.read().splitlines() # 获取每一行的标准信息（class,x,y,w,h） x = np.array([x.split() for x in lines], dtype=np.float32) for k in range(len(x)): anno = x[k] label = int(anno[0]) # 获取框的坐标值，左上角坐标和右下角坐标 x1 = int((float(anno[1]) - float(anno[3])/2) * w) y1 = int((float(anno[2]) - float(anno[4])/2) * h) x2 = int((float(anno[1]) + float(anno[3])/2) * w) y2 = int((float(anno[2]) + float(anno[4])/2) * h) # 将标注框绘制在图像上 cv2.rectangle(img, (x1,y1), (x2,y2), (255,30,30), 2) # 将标注类别绘制在图像上 cv2.putText(img, ("%s"%(str(lable_map[label]))), (x1,y1),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 1) cv2.imshow('img', img) cv2.waitKey() # if cv2.waitKey(1) == 27: # break cv2.destroyAllWindows() 数据集加载和处理 参考文章

WIDER FACE数据集转YOLO格式