347 lines
12 KiB
Python
347 lines
12 KiB
Python
import math
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import cv2
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import numpy as np
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from PIL import Image, ImageDraw, ImageFont
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import json
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import os
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import sys
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import math
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try:
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from .leg_data import LegData
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from .config import Config
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except:
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from leg_data import LegData
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from config import Config
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parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
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sys.path.append(parent_dir)
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from tools.log import CustomLogger
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class LegAcupointsDetector:
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def __init__(self):
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self.logger = CustomLogger(log_name="LegAcupointsDetector",propagate=True)
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self.font_path = Config.get_font_path()
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self.ratios = {
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"thigh": {
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"殷门": 2/5,
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"上委中": 5.5/7
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},
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"calf": {
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"承山": 0.4,
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"承筋": 0.65,
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"合阳": 0.85
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}
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}
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def load_data(self, json_data):
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"""加载并处理背部数据"""
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self.leg_processor = LegData(json_data)
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self.is_data_loaded = True
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return self
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def distance(self, p1, p2):
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return math.hypot(p1[0] - p2[0], p1[1] - p2[1])
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def validate_anatomy(self, points, min_distance=50):
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# 承扶左 和 委中左
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if self.distance(points["承扶左"], points["委中左"]) < min_distance:
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self.logger.log_warning("承扶和委中很接近，提示：躺得太上了")
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return None
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# 承扶右 和 委中右
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if self.distance(points["承扶右"], points["委中右"]) < min_distance:
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self.logger.log_warning("承扶和委中很接近，提示：躺得太上了")
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return None
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# 委中左 和 昆仑左
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if self.distance(points["委中左"], points["昆仑左"]) < min_distance:
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self.logger.log_warning("委中和昆仑很接近，提示：躺得太下了")
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return None
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# 委中右 和 昆仑右
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if self.distance(points["委中右"], points["昆仑右"]) < min_distance:
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self.logger.log_warning("委中和昆仑很接近，提示：躺得太下了")
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return None
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return True
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def compute_control_points(self, start, mid, end, curvature=0):
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"""计算贝塞尔曲线的控制点"""
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mid_x = (start[0] + end[0]) / 2
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mid_y = (start[1] + end[1]) / 2
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dx = end[0] - start[0]
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dy = end[1] - start[1]
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normal = (-dy, dx)
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norm = math.hypot(*normal)
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if norm < 1e-6:
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return [start, mid, end]
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ctrl_point = (
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mid_x + (normal[0]/norm) * curvature,
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mid_y + (normal[1]/norm) * curvature
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)
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return [start, ctrl_point, end]
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def bezier_interpolation(self, points, t):
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"""基于贝塞尔曲线的插值"""
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if len(points) == 2:
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x = (1 - t) * points[0][0] + t * points[1][0]
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y = (1 - t) * points[0][1] + t * points[1][1]
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else:
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x = (1 - t)**2 * points[0][0] + 2 * (1 - t) * t * points[1][0] + t**2 * points[2][0]
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y = (1 - t)**2 * points[0][1] + 2 * (1 - t) * t * points[1][1] + t**2 * points[2][1]
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return (int(x), int(y))
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def generate_bezier_trajectory(self, points, num_points=100):
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"""生成贝塞尔曲线轨迹"""
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trajectory = []
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for t in np.linspace(0, 1, num_points):
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point = self.bezier_interpolation(points, t)
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trajectory.append(point)
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return trajectory
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def predict_meridians(self, base_points, curvatures=None):
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"""经络预测主算法 - 基于贝塞尔曲线"""
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if curvatures is None:
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curvatures = {
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"left": {
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"thigh": 30,
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"calf": 25
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},
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"right": {
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"thigh": -25,
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"calf": -20
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}
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}
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meridians = base_points.copy()
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for side in ["左", "右"]:
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side_key = "left" if side == "左" else "right"
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thigh_control = self.compute_control_points(
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base_points[f"承扶{side}"],
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base_points[f"委中{side}"],
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base_points[f"委中{side}"],
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curvatures[side_key]["thigh"]
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)
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calf_control = self.compute_control_points(
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base_points[f"昆仑{side}"],
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base_points[f"委中{side}"],
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base_points[f"委中{side}"],
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curvatures[side_key]["calf"]
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)
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for name, ratio in self.ratios["thigh"].items():
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meridians[f"{name}{side}"] = self.bezier_interpolation(
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thigh_control, ratio
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)
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for name, ratio in self.ratios["calf"].items():
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meridians[f"{name}{side}"] = self.bezier_interpolation(
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calf_control, ratio
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)
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meridians[f"thigh_trajectory_{side}"] = self.generate_bezier_trajectory(thigh_control)
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meridians[f"calf_trajectory_{side}"] = self.generate_bezier_trajectory(calf_control)
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return meridians
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def visualize_meridians(self, image_path, points, output_path, scale=2):
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"""可视化引擎 - 展示贝塞尔曲线轨迹"""
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# 读取并缩放图像
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base_img = cv2.imread(image_path)
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if base_img is None:
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raise FileNotFoundError(f"找不到背景图: {image_path}")
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# 缩放图像
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highres_img = cv2.resize(base_img, None, fx=scale, fy=scale,
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interpolation=cv2.INTER_LINEAR)
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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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# 绘制曲线
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for side in ["左", "右"]:
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thigh_traj = points[f"thigh_trajectory_{side}"]
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scaled_thigh_traj = [(int(x * scale), int(y * scale)) for x, y in thigh_traj]
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cv2.polylines(highres_img, [np.array(scaled_thigh_traj)], False, (220, 240, 255), 1, cv2.LINE_AA)
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calf_traj = points[f"calf_trajectory_{side}"]
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scaled_calf_traj = [(int(x * scale), int(y * scale)) for x, y in calf_traj]
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cv2.polylines(highres_img, [np.array(scaled_calf_traj)], False, (220, 240, 255), 1, cv2.LINE_AA)
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# 先绘制所有OpenCV元素（曲线和圆圈）
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for name, point in points.items():
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if "trajectory" in name:
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continue
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x, y = point
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x_scaled, y_scaled = int(x * scale), int(y * scale)
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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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color_index = int(alpha * (len(purple_colors) - 1))
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color = purple_colors[color_index]
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cv2.circle(highres_img, (x_scaled, y_scaled), 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 = (0, 255, 185) # 白色高光
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cv2.circle(highres_img, (x_scaled, y_scaled), highlight_radius, highlight_color, -1, cv2.LINE_AA)
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# 转换为PIL图像用于绘制文本
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canvas = Image.fromarray(cv2.cvtColor(highres_img, cv2.COLOR_BGR2RGB))
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drawer = ImageDraw.Draw(canvas)
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font = ImageFont.truetype(self.font_path, int(8 * scale)) # 调整为参考代码的字体大小
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# 绘制文本
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for name, point in points.items():
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if "trajectory" in name:
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continue
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x, y = point
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x_scaled, y_scaled = int(x * scale), int(y * scale)
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# 确定文本偏移量
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if name == "上委中左":
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x_offset, y_offset = -40 * scale, -5 * scale
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elif "左" in name:
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x_offset, y_offset = -30 * scale, -5 * scale
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else:
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x_offset, y_offset = 5 * scale, -5 * scale
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# 绘制带白边的黑字
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text_pos = (x_scaled + x_offset, y_scaled + y_offset)
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for offset in [(-1,-1), (-1,1), (1,-1), (1,1)]:
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drawer.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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drawer.text(text_pos, name, font=font, fill=(0,0,0)) # 黑字
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# 转换回OpenCV格式并保存
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result_img = cv2.cvtColor(np.array(canvas), cv2.COLOR_RGB2BGR)
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cv2.imwrite(output_path, result_img)
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self.logger.log_info(f"可视化结果已保存到: {output_path}")
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def build_point_mapping_and_curvatures(self):
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"""
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构建穴位坐标映射和曲率字典
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Args:
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leg_processor: LegData实例
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Returns:
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tuple: (point_mapping, curvatures)
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"""
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point_mapping = {
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'承扶左': self.leg_processor.get_left_point1(),
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'委中左': self.leg_processor.get_left_point2(),
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'昆仑左': self.leg_processor.get_left_point3(),
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'承扶右': self.leg_processor.get_right_point1(),
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'委中右': self.leg_processor.get_right_point2(),
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'昆仑右': self.leg_processor.get_right_point3()
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}
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curvatures = {
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"left": self.leg_processor.get_left_curvatures(),
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"right": self.leg_processor.get_right_curvatures()
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}
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return point_mapping, curvatures
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def process_image(self, image_path, output_path):
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# 构建映射字典
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if not self.is_data_loaded:
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raise ValueError("请先加载数据")
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# 构建基本数据字典
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point_mapping, curvatures = self.build_point_mapping_and_curvatures()
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error_result = self.validate_anatomy(point_mapping)
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if error_result is None:
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return None
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result = self.predict_meridians(point_mapping, curvatures)
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# 如果预测成功，执行可视化
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if result:
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self.visualize_meridians(image_path,result,output_path)
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acupoints = {k: v for k, v in result.items() if "trajectory" not in k}
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return acupoints
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# 主程序
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if __name__ == "__main__":
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sample_json = {
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"shapes": [
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{
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"label": "C1",
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"points": [
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[
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258,
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2.0
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],
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[
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265,
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130
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],
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[
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281,
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315
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]
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],
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"curvatures": {
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"thigh": 10,
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"calf": -10
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},
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"shape_type": "linestrip"
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},
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{
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"label": "C2",
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"points": [
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[
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350,
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1.55
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],
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[
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362,
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110
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],
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[
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401,
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302
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]
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],
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"curvatures": {
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"thigh": -10,
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"calf": 20
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},
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"shape_type": "linestrip"
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}
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]
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}
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# 指定JSON文件路径
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# json_file_path = "/home/kira/codes/leg_test/leg_Json/d2025331_color199.json" # 请替换为实际路径，例如 "C:/data/keypoints.json"
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# with open(json_file_path, 'r', encoding='utf-8') as f:
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# sample_data = json.load(f)
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# 初始化系统
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detector = LegAcupointsDetector().load_data(sample_json)
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# 计算并获取穴位
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output_path = Config.get_output_path("leg_acupoints.png")
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image_path = Config.get_image_path("color_199.png")
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acupoints = detector.process_image(image_path, output_path)
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print(acupoints)
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