import os import requests import base64 import urllib.parse import json import cv2 import math from PIL import Image, ImageDraw, ImageFont try: from .config import Config except: from config import Config import time import numpy as np import sys parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) sys.path.append(parent_dir) from tools.log import CustomLogger class LegAcupointsDetector: def __init__(self): """ 初始化 LegAcupointsDetector 类 """ self.api_key = "D00xknhpZJg7oz0QN6lraJQc" self.secret_key = "2z9nicevtdBEs4I1NXbMVhzGuvUz6yAT" self.font_path = Config.get_font_path() self.logger = CustomLogger(log_name="LegAcupointsDetector",propagate=True) def get_access_token(self): """ 获取百度 AI 的 access_token :return: access_token """ url = "https://aip.baidubce.com/oauth/2.0/token" params = { "grant_type": "client_credentials", "client_id": self.api_key, "client_secret": self.secret_key } retries = 3 for i in range(retries): try: response = requests.post(url, params=params) response.raise_for_status() # 如果响应状态码不是 200，会抛出异常 return response.json().get("access_token") except requests.exceptions.RequestException as e: self.logger.log_error(f"请求失败，重试 {i+1}/{retries} 次: {e}") if i < retries - 1: time.sleep(3) # 等待 3 秒后重试 else: self.logger.log_error("获取 access_token 失败，超过最大重试次数") return None def get_file_content_as_base64(self, path, urlencoded=False): """ 获取文件的 base64 编码 :param path: 文件路径 :param urlencoded: 是否对结果进行 URL 编码 :return: base64 编码的字符串 """ with open(path, "rb") as f: content = base64.b64encode(f.read()).decode("utf8") if urlencoded: content = urllib.parse.quote_plus(content) return content def get_inital_6points_from_api(self, image_path): """ 处理单张图片并返回腿部六个关键点坐标 :param image_path: 输入图片路径 :return: 腿部六个关键点坐标的字典 """ self.logger.log_info(f"开始处理图片：{image_path}") access_token = self.get_access_token() if access_token is None: self.logger.log_error(f"没有获取到token") return None url = f"https://aip.baidubce.com/rest/2.0/image-classify/v1/body_analysis?access_token={access_token}" payload = 'image=' + self.get_file_content_as_base64(image_path, True) headers = { 'Content-Type': 'application/x-www-form-urlencoded', 'Accept': 'application/json' } # 发送请求 self.logger.log_info("正在发送 API 请求...") response = requests.post(url, headers=headers, data=payload.encode("utf-8")) # 解析 API 返回的结果 try: output = json.loads(response.text) if not isinstance(output, dict) or 'person_info' not in output or not isinstance(output['person_info'], list): self.logger.log_error("API 返回结果格式无效") return None # 获取第一个人的信息 person_info = output['person_info'][0] # 提取腿部六个关键点坐标 leg_points = { "承扶左": (int(person_info['body_parts']['left_hip']['x']), int(person_info['body_parts']['left_hip']['y'])), "委中左": (int(person_info['body_parts']['left_knee']['x']), int(person_info['body_parts']['left_knee']['y'])), "昆仑左": (int(person_info['body_parts']['left_ankle']['x']), int(person_info['body_parts']['left_ankle']['y'])), "承扶右": (int(person_info['body_parts']['right_hip']['x']), int(person_info['body_parts']['right_hip']['y'])), "委中右": (int(person_info['body_parts']['right_knee']['x']), int(person_info['body_parts']['right_knee']['y'])), "昆仑右": (int(person_info['body_parts']['right_ankle']['x']), int(person_info['body_parts']['right_ankle']['y'])) } self.logger.log_info("成功解析 API 返回的关键点坐标") self.logger.log_info(f"关键点坐标：{leg_points}") return leg_points except KeyError as e: self.logger.log_error(f"API 返回结果缺少关键字段：{e}") return None except json.JSONDecodeError as e: self.logger.log_error(f"API 返回结果解析失败：{e}") return None def error_process(self, leg_points, threshold=20): """ 检查左右承扶部、膝盖、脚踝之间的距离是否过近，并确保左右两边的点没有反 :param leg_points: 腿部关键点坐标字典 :param threshold: 距离阈值（默认 10 像素） :return: 如果检查通过返回 True，否则返回 None """ self.logger.log_info("开始检查关键点距离和左右顺序...") # 定义左右关键点名称 left_keys = ["承扶左", "委中左", "昆仑左"] right_keys = ["承扶右", "委中右", "昆仑右"] # 检查左右两边的点是否反了，如果反了则交换 for left_key, right_key in zip(left_keys, right_keys): left_point = leg_points[left_key] right_point = leg_points[right_key] # 如果左边的 x 坐标大于右边的 x 坐标，则交换 if left_point[0] > right_point[0]: leg_points[left_key], leg_points[right_key] = right_point, left_point self.logger.log_warning(f"左右点反了，已交换：{left_key} 和 {right_key}") # 计算左右承扶部距离 left_hip = leg_points["承扶左"] right_hip = leg_points["承扶右"] hip_distance = math.sqrt((left_hip[0] - right_hip[0]) ** 2 + (left_hip[1] - right_hip[1]) ** 2) # 计算左右委中盖距离 left_knee = leg_points["委中左"] right_knee = leg_points["委中右"] knee_distance = math.sqrt((left_knee[0] - right_knee[0]) ** 2 + (left_knee[1] - right_knee[1]) ** 2) # 计算左右脚踝距离 left_ankle = leg_points["昆仑左"] right_ankle = leg_points["昆仑右"] ankle_distance = math.sqrt((left_ankle[0] - right_ankle[0]) ** 2 + (left_ankle[1] - right_ankle[1]) ** 2) # 检查距离是否小于阈值 if hip_distance < threshold: self.logger.log_error(f"左右承扶部距离过近：{hip_distance} < {threshold}") return None if knee_distance < threshold: self.logger.log_error(f"左右委中盖距离过近：{knee_distance} < {threshold}") return None if ankle_distance < threshold: self.logger.log_error(f"左右脚踝距离过近：{ankle_distance} < {threshold}") return None self.logger.log_info("关键点距离检查通过") return True def calculate_new_point(self, point1, point2, ratio): """ 基于两个点，沿着两个点的连线移动一定比例的距离，计算新的点 :param point1: 第一个点 (x1, y1) :param point2: 第二个点 (x2, y2) :param ratio: 移动的比例（例如 2/5） :return: 新的点 (x, y) """ x1, y1 = point1 x2, y2 = point2 # 计算两点之间的向量 dx = x2 - x1 dy = y2 - y1 # 计算两点之间的距离 current_distance = math.sqrt(dx ** 2 + dy ** 2) # 计算新点的坐标 if current_distance == 0: raise ValueError("两点重合，无法计算新点") scale = ratio # 比例直接作为缩放因子 new_x = x1 + dx * scale new_y = y1 + dy * scale return int(new_x), int(new_y) def calculate_points_between_kunlun_and_weizhong(self, kunlun, weizhong): """ 基于昆仑和委中两个点，沿着委中方向移动 1/5、2/5、4/5 的距离，计算三个新点 :param kunlun: 昆仑点 (x, y) :param weizhong: 委中点 (x, y) :return: 三个新点的列表 [(x1, y1), (x2, y2), (x3, y3)] """ # 计算昆仑和委中之间的距离 dx = weizhong[0] - kunlun[0] dy = weizhong[1] - kunlun[1] distance = math.sqrt(dx ** 2 + dy ** 2) # 计算 1/5、2/5、4/5 的距离 # distances = [distance * 0.15, distance * 0.35, distance * 0.65] distances = [distance * 0.4, distance * 0.65, distance * 0.85] # 计算三个新点 new_points = [] for dist in distances: scale = dist / distance new_x = kunlun[0] + dx * scale new_y = kunlun[1] + dy * scale new_points.append((int(new_x), int(new_y))) return new_points 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, input_image_path, output_coordinate_image_path): """ 处理单张图片并返回腿部关键点坐标 :param input_image_path: 输入图片路径 :param output_coordinate_image_path: 输出图片保存路径 :return: 腿部关键点坐标字典 """ self.logger.log_info(f"开始处理图片：{input_image_path}") # 通过 API 获取初始 6 个关键点 leg_points = self.get_inital_6points_from_api(input_image_path) if leg_points is None: self.logger.log_error("无法获取初始关键点坐标") return None # 检查关键点距离是否过近 error = self.error_process(leg_points) if error is None: self.logger.log_error("关键点距离检查未通过") return None # 计算大腿部分的左右两个穴位 leg_points["殷门左"] = self.calculate_new_point(leg_points["承扶左"], leg_points["委中左"], ratio=2/5) leg_points["殷门右"] = self.calculate_new_point(leg_points["承扶右"], leg_points["委中右"], ratio=2/5) leg_points["上委中左"] = self.calculate_new_point(leg_points["承扶左"], leg_points["委中左"], ratio=5.5/7) leg_points["上委中右"] = self.calculate_new_point(leg_points["承扶右"], leg_points["委中右"], ratio=5.5/7) # 计算小腿部分的左右两边 6 个穴位点 leg_points["承山左"], leg_points["承筋左"], leg_points["合阳左"] = self.calculate_points_between_kunlun_and_weizhong(leg_points["昆仑左"], leg_points["委中左"]) leg_points["承山右"], leg_points["承筋右"], leg_points["合阳右"] = self.calculate_points_between_kunlun_and_weizhong(leg_points["昆仑右"], leg_points["委中右"]) # 绘制腿部关键点并保存图片 self.plot_acupoints_on_image(input_image_path, leg_points, output_coordinate_image_path) self.logger.log_info(f"图片处理完成，关键点坐标：{leg_points}") return leg_points if __name__ == '__main__': leg_point = LegAcupointsDetector() input_image_path = Config.get_image_path("leg.png") output_image_path = Config.get_output_path("leg_16points.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 leg_acupoints_list = leg_point.process_image(input_image_path, output_image_path) print(leg_acupoints_list) # # 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" # # 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}")