199 lines
8.6 KiB
Python
199 lines
8.6 KiB
Python
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import cv2
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import numpy as np
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import os
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from tqdm import tqdm
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# -------------------------- 1. 核心配置（请根据需求修改） --------------------------
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# 输入目录（图片和标签需一一对应，文件名相同，仅后缀不同）
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INPUT_IMAGE_DIR = r"D:\DataPreHandler\data\train\images" # 原始图片目录
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INPUT_LABEL_DIR = r"D:\DataPreHandler\data\train\labels" # 原始YOLO标签目录
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# 输出目录（标注后的图片会保存在这里）
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OUTPUT_IMAGE_DIR = r"D:\DataPreHandler\data\test\da\output2"
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# 关键配置：类别ID与类别名称的映射（必须与你的YOLO训练类别顺序一致！）
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CLASS_CONFIG = [
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(0, "Abdomen", (0, 255, 0)),
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(1, "Hips", (0, 255, 0)),
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(2, "Chest", (0, 255, 0)),
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(3, "vulva", (0, 255, 0)),
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(4, "back", (0, 255, 0)),
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(5, "penis", (0, 255, 0)),
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(6, "Horror", (0, 255, 0))
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]
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# 绘制参数（可按需调整）
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BOX_THICKNESS = 2 # 边界框线条厚度（像素）
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FONT_FACE = cv2.FONT_HERSHEY_SIMPLEX # 字体类型
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FONT_SCALE = 0.6 # 字体大小（根据图片尺寸调整）
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FONT_THICKNESS = 1 # 字体线条厚度
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TEXT_PADDING = 5 # 文字与边界框的间距（像素）
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TEXT_BG_OPACITY = 0.7 # 文字背景的透明度（0-1，0为完全透明）
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# 支持的图片格式（无需修改）
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SUPPORTED_IMAGE_FORMATS = ('.jpg', '.jpeg', '.png', '.bmp', '.tiff')
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# -------------------------- 2. 工具函数 --------------------------
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def yolo2pixel(yolo_coords, img_w, img_h):
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"""
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将YOLO相对坐标转换为图片像素坐标（边界框：x1, y1, x2, y2）
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:param yolo_coords: YOLO坐标列表 [xc, yc, w, h]（相对值，0-1）
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:param img_w: 图片宽度（像素）
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:param img_h: 图片高度（像素）
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:return: 像素坐标元组 (x1, y1, x2, y2)
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"""
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xc, yc, w, h = yolo_coords
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# 计算边界框左上角和右下角坐标
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x1 = int((xc - w / 2) * img_w)
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y1 = int((yc - h / 2) * img_h)
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x2 = int((xc + w / 2) * img_w)
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y2 = int((yc + h / 2) * img_h)
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# 确保坐标不超出图片范围
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x1 = max(0, x1)
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y1 = max(0, y1)
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x2 = min(img_w, x2)
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y2 = min(img_h, y2)
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return x1, y1, x2, y2
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def draw_annotation(img, bbox, class_name, color):
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"""
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在图片上绘制边界框和类别名称
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:param img: 原始图片（OpenCV格式，BGR通道）
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:param bbox: 像素坐标边界框 (x1, y1, x2, y2)
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:param class_name: 类别名称（字符串）
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:param color: 边界框和文字颜色（BGR元组，如 (0,255,0) 代表绿色）
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:return: 标注后的图片
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"""
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img_h, img_w = img.shape[:2]
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x1, y1, x2, y2 = bbox
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# 1. 绘制边界框
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cv2.rectangle(img, (x1, y1), (x2, y2), color, BOX_THICKNESS)
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# 2. 计算文字尺寸（用于创建文字背景）
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text_size, _ = cv2.getTextSize(class_name, FONT_FACE, FONT_SCALE, FONT_THICKNESS)
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text_w, text_h = text_size
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# 3. 确定文字位置（避免超出图片范围）
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# 文字默认放在边界框左上角，若左上角空间不足则放在右上角
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text_x = x1 + TEXT_PADDING
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text_y = y1 - TEXT_PADDING - text_h # 文字基线在y轴上方
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if text_y < 0: # 左上角超出图片顶部，调整到右上角
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text_x = x2 - TEXT_PADDING - text_w
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text_y = y1 + TEXT_PADDING + text_h
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# 4. 绘制文字背景（半透明矩形，避免遮挡图片内容）
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bg_x1 = text_x - TEXT_PADDING
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bg_y1 = text_y - text_h - TEXT_PADDING
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bg_x2 = text_x + text_w + TEXT_PADDING
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bg_y2 = text_y + TEXT_PADDING
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# 确保背景不超出图片范围
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bg_x1 = max(0, bg_x1)
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bg_y1 = max(0, bg_y1)
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bg_x2 = min(img_w, bg_x2)
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bg_y2 = min(img_h, bg_y2)
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# 半透明背景：先创建背景层，再与原图混合
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bg = img[bg_y1:bg_y2, bg_x1:bg_x2].copy()
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bg = cv2.rectangle(bg, (0, 0), (bg_x2 - bg_x1, bg_y2 - bg_y1), color, -1) # 实心矩形
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img[bg_y1:bg_y2, bg_x1:bg_x2] = cv2.addWeighted(bg, TEXT_BG_OPACITY, img[bg_y1:bg_y2, bg_x1:bg_x2], 1 - TEXT_BG_OPACITY, 0)
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# 5. 绘制类别名称
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cv2.putText(img, class_name, (text_x, text_y), FONT_FACE, FONT_SCALE, (0, 0, 0), FONT_THICKNESS) # 白色文字
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return img
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# -------------------------- 3. 主函数 --------------------------
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def main():
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# 1. 创建输出目录（若不存在）
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os.makedirs(OUTPUT_IMAGE_DIR, exist_ok=True)
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print(f"标注后的图片将保存到：{OUTPUT_IMAGE_DIR}\n")
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# 2. 构建类别ID到（名称+颜色）的映射字典
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class_map = {cls_id: (cls_name, cls_color) for cls_id, cls_name, cls_color in CLASS_CONFIG}
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print("类别配置：")
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for cls_id, cls_name, cls_color in CLASS_CONFIG:
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print(f" ID {cls_id} → 名称：{cls_name}，颜色：{cls_color}")
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print()
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# 3. 获取所有图片文件（仅处理支持的格式）
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image_files = [f for f in os.listdir(INPUT_IMAGE_DIR) if f.lower().endswith(SUPPORTED_IMAGE_FORMATS)]
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if not image_files:
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raise FileNotFoundError(f"在 {INPUT_IMAGE_DIR} 中未找到任何支持的图片文件（{SUPPORTED_IMAGE_FORMATS}）")
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print(f"找到 {len(image_files)} 张图片，开始标注...\n")
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# 4. 遍历图片并标注
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for img_filename in tqdm(image_files, desc="处理进度"):
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# 4.1 构建图片和标签的路径
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img_name, img_ext = os.path.splitext(img_filename)
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img_path = os.path.join(INPUT_IMAGE_DIR, img_filename)
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label_path = os.path.join(INPUT_LABEL_DIR, f"{img_name}.txt") # 标签文件与图片同名，后缀为txt
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# 4.2 读取图片（OpenCV默认读取为BGR通道）
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img = cv2.imread(img_path)
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if img is None:
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tqdm.write(f"⚠️ 跳过：无法读取图片 {img_filename}（可能损坏或格式不支持）")
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continue
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img_h, img_w = img.shape[:2]
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# 4.3 读取标签文件（若不存在则跳过标注，直接保存原图）
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if not os.path.exists(label_path):
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tqdm.write(f"⚠️ 警告：图片 {img_filename} 无对应标签文件 {os.path.basename(label_path)}，直接保存原图")
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annotated_img = img.copy()
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else:
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# 复制原图用于标注（避免修改原始图片）
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annotated_img = img.copy()
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# 读取标签内容
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with open(label_path, 'r', encoding='utf-8') as f:
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label_lines = [line.strip() for line in f.readlines() if line.strip()] # 过滤空行
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# 4.4 解析每个标签并绘制
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for line_idx, line in enumerate(label_lines):
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try:
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# YOLO标签格式：class_id xc yc w h（空格分隔）
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parts = line.split()
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if len(parts) != 5:
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raise ValueError(f"格式错误（需5个字段，实际{len(parts)}个）")
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# 解析类别ID和坐标
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cls_id = int(float(parts[0]))
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yolo_coords = [float(p) for p in parts[1:]]
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# 检查YOLO坐标有效性（必须在0-1范围内）
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if not all(0 <= coord <= 1 for coord in yolo_coords):
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raise ValueError(f"YOLO坐标超出0-1范围：{yolo_coords}")
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# 4.5 转换坐标并绘制
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# 检查类别ID是否在配置中
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if cls_id not in class_map:
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tqdm.write(f"⚠️ 跳过：图片 {img_filename} 标签第{line_idx+1}行，未知类别ID {cls_id}（未在CLASS_CONFIG中配置）")
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continue
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# 获取类别名称和颜色
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cls_name, cls_color = class_map[cls_id]
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# 转换YOLO坐标为像素坐标
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bbox = yolo2pixel(yolo_coords, img_w, img_h)
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# 绘制标注
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annotated_img = draw_annotation(annotated_img, bbox, cls_name, cls_color)
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except Exception as e:
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tqdm.write(f"⚠️ 跳过：图片 {img_filename} 标签第{line_idx+1}行解析失败 → {str(e)}")
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continue
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# 4.6 保存标注后的图片
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output_img_path = os.path.join(OUTPUT_IMAGE_DIR, f"{img_name}_annotated{img_ext}")
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# 保存为JPG格式（若原始是PNG，也可改为img_ext保持原格式）
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# 注：JPG不支持透明通道，若原始是PNG且有透明，建议保留img_ext
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cv2.imwrite(output_img_path, annotated_img)
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# 5. 完成提示
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print(f"\n✅ 标注完成！共处理 {len(image_files)} 张图片，标注后的图片已保存到：")
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print(f" {OUTPUT_IMAGE_DIR}")
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if __name__ == "__main__":
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try:
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main()
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except Exception as e:
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print(f"\n❌ 程序异常终止：{str(e)}")
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