from ultralytics import YOLO import cv2 import os import numpy as np from PIL import ImageFont, ImageDraw, Image try: from .config import Config except: from config import Config class AbdominalAcupointsDetector: def __init__(self): """ 初始化 AbdominalAcupointsDetector 类，加载模型和字体。 这个类使用 YOLO 模型自动检测腹部穴位点。 """ # 初始化模型 self.model = YOLO(Config.ABDOMEN_MODEL_PATH) self.device = "cpu" # 默认使用 CPU # 穴位名称列表 self.name = ["神阙", "天枢右", "天枢左", "气海", "石门", "关元", "水分", "外陵右", "滑肉右", "外陵左", "滑肉左", "大横左", "大横右"] # 获取字体路径 self.font_path = Config.get_font_path() if self.font_path is None: print("警告：未找到合适的字体文件，将使用系统默认字体") def detect_keypoints(self, image_path): """ 加载模型并推理关键点 :param image_path: 输入图片路径 :return: 推理结果（包含关键点信息） """ # 进行推理 results = self.model.predict( source=image_path, conf=0.5, # 置信度阈值 device=self.device, # 使用 CPU 或 GPU ) result = results[0] if result.keypoints is not None: print("检测到的关键点坐标：", result.keypoints.data.cpu().numpy()) else: print("未检测到关键点") return result def plot_acupoints_on_image(self, image_path, acupoints, output_path): """ 在指定图片上绘制穴位点，并标注名称，保存结果。 参数: image_path (str): 图片文件路径。 acupoints (dict): 包含穴位坐标的字典 {"穴位名称": (x, y), ...}。 output_path (str): 保存结果图片的路径。 """ # 读取图片 image = cv2.imread(image_path) if image is None: raise FileNotFoundError(f"无法加载背景图：{image_path}") # 将图像分辨率增大到原来的三倍 scale_factor = 2 image = cv2.resize(image, None, fx=scale_factor, fy=scale_factor, interpolation=cv2.INTER_LINEAR) # 调整穴位点坐标 acupoints = {name: (x * scale_factor, y * scale_factor) for name, (x, y) in acupoints.items()} # (252, 229, 179) # 定义蓝紫色渐变颜色BGR purple_colors = [ (223, 87, 98), # 浅蓝紫色 (223, 87, 98), # 中等蓝紫色 (223, 87, 98), ] # 绘制穴位点 for name, (x, y) in acupoints.items(): if x == 0 and y == 0: continue # 绘制蓝紫色带光圈的穴位点 radius = 4 # 穴位点半径 for r in range(radius, 0, -1): alpha = r / radius # alpha 从 1（中心）到 0（边缘） # 计算渐变颜色 color_index = int(alpha * (len(purple_colors) - 1)) color = purple_colors[color_index] # 绘制渐变圆 cv2.circle(image, (x, y), r, color, -1, cv2.LINE_AA) # 添加高光效果 highlight_radius = int(radius * 0.6) # highlight_color = (200, 200, 200) # 白色高光 highlight_color = (0, 255, 185) # 白色高光 highlight_pos = (x, y) cv2.circle(image, highlight_pos, highlight_radius, highlight_color, -1, cv2.LINE_AA) # 使用 PIL 绘制文本 image_pil = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) draw = ImageDraw.Draw(image_pil) font = ImageFont.truetype(self.font_path, 8 * scale_factor) # 字体大小也放大三倍 for name, (x, y) in acupoints.items(): if x == 0 and y == 0: continue if name in ["滑肉左", "大横左", "天枢左"]: x_offset, y_offset = -15 * scale_factor, -15 * scale_factor elif name in ["滑肉右", "大横右","天枢右"]: x_offset, y_offset = -15 * scale_factor, -15 * scale_factor elif name == "外陵左": x_offset, y_offset = -35 * scale_factor, -5 * scale_factor elif name == "关元": x_offset, y_offset = -20 * scale_factor, -5 * scale_factor elif name in ["石门","外陵右"]: x_offset, y_offset = 5 * scale_factor, -5 * scale_factor else: x_offset, y_offset = -10 * scale_factor, -15 * scale_factor # 绘制带白边的黑字 text_pos = (x + x_offset, y + y_offset) for offset in [(-1,-1), (-1,1), (1,-1), (1,1)]: draw.text((text_pos[0]+offset[0], text_pos[1]+offset[1]), name, font=font, fill=(255,255,255)) # 白边 draw.text(text_pos, name, font=font, fill=(0,0,0)) # 黑字 # 将图像转换回 OpenCV 格式 image = cv2.cvtColor(np.array(image_pil), cv2.COLOR_RGB2BGR) # 保存结果 cv2.imwrite(output_path, image) print(f"结果已保存到：{output_path}") def process_image(self, image_path, output_path): """ 处理单张图片：推理关键点并绘制结果 参数: image_path (str): 输入图片路径 output_path (str): 输出图片路径 返回: dict: 包含穴位坐标的字典 {"穴位名称": (x, y), ...} """ # 检查输入图片是否存在 if not os.path.exists(image_path): print(f"输入图片不存在：{image_path}") return {} # 创建输出文件夹（如果不存在） output_dir = os.path.dirname(output_path) if not os.path.exists(output_dir): os.makedirs(output_dir) # 推理关键点 result = self.detect_keypoints(image_path) # 获取关键点坐标 keypoints = result.keypoints.data.cpu().numpy() # 关键点坐标 num_keypoints = keypoints.shape[1] # 关键点数量 # 构建穴位点字典 acupoints = {} for i in range(num_keypoints): x, y = int(keypoints[0, i, 0]), int(keypoints[0, i, 1]) point_name = self.name[i] if i < len(self.name) else f"未知穴位_{i + 1}" acupoints[point_name] = (x, y) # 绘制穴位点并保存结果 self.plot_acupoints_on_image(image_path, acupoints, output_path) # 返回穴位点字典 return acupoints if __name__ == "__main__": """ single_picture """ input_image_path = Config.get_image_path("color_mz.png") output_image_path = Config.get_output_path("color_mz_abdomen.png") # 初始化检测器 detector = AbdominalAcupointsDetector() # 处理单张图片，并获取穴位点字典 acupoints = detector.process_image(input_image_path, output_image_path) print(acupoints) """ batch_picture """ # # 输入输出文件夹路径 # input_folder = os.path.join(Config.IMAGE_DIR, "train", "images") # output_folder = os.path.join(Config.IMAGE_DIR, "output_all") # Config.ensure_dir(output_folder) # # 遍历输入文件夹中的所有图片文件 # for filename in os.listdir(input_folder): # if filename.lower().endswith(('.png', '.jpg', '.jpeg')): # input_image_path = os.path.join(input_folder, filename) # output_image_path = os.path.join(output_folder, filename) # acupoints = detector.process_image(input_image_path, output_image_path) # print(f"处理图片: {filename}") # print("检测到的穴位点：") # for name, coords in acupoints.items(): # print(f"{name}: {coords}") # print("-" * 40)