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 LegAcupointsDetector: def __init__(self): """ 初始化 LegAcupointsDetector 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.LEG_MODEL_PATH) self.device = "cpu" # 默认使用 CPU # 穴位名称列表 self.name = ["承扶左", "委中左", "昆仑左", "承扶右", "委中右", "昆仑右", "殷门左", "殷门右", "上委中左", "上委中右", "承山左", "承筋左", "合阳左","承山右","承筋右","合阳右"] # 定义约束 self.unique_list = [ "委中左", "委中右", "殷门左", "殷门右", "上委中左", "上委中右", "承山左", "承筋左", "合阳左","承山右","承筋右","合阳右" ] # 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 # 异常处理 def error_process(self, coordinate): """ 检查坐标数据是否有效。 :param coordinate: 包含穴位点坐标的字典 :return: 如果坐标有效返回 True,否则返回 None """ if not coordinate: # 判断 coordinates 是否为空 print("Coordinates are empty...") return None # 遍历 unique_list 中的每个点 for point in self.unique_list: # 检查点是否存在于 coordinate 中 if point in coordinate: # 检查点的值是否为空或 (0, 0) if not coordinate[point] or coordinate[point] == (0, 0): print(f"Point '{point}' is empty or (0, 0), skipping the calculation for this iteration...") return None else: print(f"Point '{point}' is missing in the coordinates, skipping the calculation for this iteration...") return None # 如果所有检查都通过，返回 True print("All points are valid, proceeding with the calculation...") return True 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 == "上委中左": x_offset, y_offset = -40 * scale_factor, -5 * scale_factor elif "左" in name: x_offset, y_offset = -30 * scale_factor, -5 * scale_factor else: x_offset, y_offset = 5 * scale_factor, -5 * 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) result = self.error_process(acupoints) if result is None: print("Pass this image due to error processing:Yolov8穴位产生为None或(0,0),人躺太上或者是躺太下了.") return None # 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("leg.png") output_image_path = Config.get_output_path("leg_yolo.png") # input_image_path = "aucpuncture2point/configs/using_img/leg.png" # 替换为你的图片路径 # output_coordinate_image_path = "aucpuncture2point/configs/using_img/leg_16points.png" # 替换为输出图片路径 # 初始化检测器 detector = LegAcupointsDetector() # 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 # """ # # 批量处理 # leg_point = LegAcupointsDetector() # # # 输入和输出文件夹路径 # # input_folder = r"/home/kira/codes/datas/colors-0872E1" # # output_folder = r"/home/kira/codes/datas/results-0872E11" # # input_folder = r"/home/kira/codes/IoT_img_process/output_classified/leg" # # output_folder = r"/home/kira/codes/datas/results-3-31-leg" # #异常处理的 # input_folder = r"/home/kira/codes/datas/colors_leg_yichang" # output_folder = r"/home/kira/codes/datas/results-leg_yichang" # # 确保输出文件夹存在 # if not os.path.exists(output_folder): # os.makedirs(output_folder) # # 遍历输入文件夹中的所有文件 # for filename in os.listdir(input_folder): # # 只处理图片文件（支持常见格式） # if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.tiff')): # # 输入图片路径 # input_image_path = os.path.join(input_folder, filename) # # 输出图片路径 # output_image_name = f"processed_{filename}" # output_coordinate_image_path = os.path.join(output_folder, output_image_name) # # 处理单张图片 # try: # leg_acupoints_list = leg_point.process_image(input_image_path, output_coordinate_image_path) # print(f"处理完成：{filename} -> {output_image_name}") # print(f"穴位点坐标：{leg_acupoints_list}") # except Exception as e: # print(f"处理失败：{filename}，错误信息：{e}")