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