净空检测与报告
This commit is contained in:
何德超
parent
1cd2676814
commit
bd3b95395a
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import cv2
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import numpy as np
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from ultralytics import YOLO
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import os
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import json
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import argparse
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class Target2D:
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"""二维坐标下的叶尖跟踪器"""
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def __init__(self, positions=None, hub=None):
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if positions is None:
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positions = np.array([[-100, -100], [-200, -200], [-300, -300]])
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if hub is None:
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hub = np.array([400, 300])
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self.positions = np.array(positions, dtype=float) # 叶尖位置
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self.hub = np.array(hub, dtype=float) # 轮毂位置
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def update(self, new_positions, new_hub=None):
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"""更新叶尖位置，使用最近点匹配算法"""
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if new_positions is None or len(new_positions) == 0:
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return self.positions, []
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# 确保新位置是二维数组
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new_positions = np.array(new_positions, dtype=float)
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if new_positions.ndim == 1:
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new_positions = new_positions.reshape(1, -1)
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# 更新轮毂位置
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if new_hub is not None:
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self.hub = np.array(new_hub, dtype=float)
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# 如果还没有初始化位置，使用新位置
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if self.positions.size == 0:
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self.positions = new_positions
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return self.positions
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# 创建位置列表副本
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new_pos_list = new_positions.tolist()
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update_positons = self.positions.copy()
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used = set()
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# 对于每个新位置，找到最近的点
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for i, new_pos in enumerate(new_pos_list):
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# 计算所有距离
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distances = np.linalg.norm(self.positions - new_pos, axis=1)
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available_indices = [j for j in range(len(distances)) if j not in used]
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# 找到最近点的索引
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closest_idx = min(available_indices, key=lambda j: distances[j])
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# 记录ID并更新位置
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update_positons[closest_idx] = new_pos # new_pos
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# 更新位置
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self.positions = np.array(update_positons)
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return self.positions
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class ClearanceCalculator:
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def __init__(self, model_path, scale_factor=0.02,width=544, height=960):
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self.size = (width, height)
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# 初始化模型配置
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self.model = YOLO(model_path)
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self.cap = None
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self.video_path = ""
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self.is_playing = False
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self.scale_factor = scale_factor
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self.reference_pixel = 0.0
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self.reference_actual = 0.0
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self.current_clearance = 0.0
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self.use_camera = False
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self.hub_x_range = 100 # 轮毂x坐标的左右区间（像素）
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self.hub_y_range = 50
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self.inside_region = False # 轨迹是否在轮毂区域内
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self.entered_from_left = False # 轨迹是否从左侧进入
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self.last_pixel_clearance = 0.0 # 保存上一次的像素净空
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self.last_actual_clearance = 0.0 # 保存上一次的实际净空
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# 轨迹相关变量
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self.trajectories = []
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# self.lowest_points = []
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self.calculate_points = []
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self.hub_position = None
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self.last_tip_positions = []
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self.frame_count = 0
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self.nearest_tip_trajectory = [] # 只存储与轮毂x坐标最近的叶尖轨迹
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self.traj_cnt = 0
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self.tip_clearances = {
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0: None, # tip1
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1: None, # tip2
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2: None # tip3
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}
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# 净空历史记录
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self.clearance_history = {0: [], 1: [], 2: []}
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# 初始化轨迹颜色
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self.tip_colors = [
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(255, 0, 0), # 红色
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(0, 255, 0), # 绿色
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(0, 0, 255) # 蓝色
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]
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# 初始化2D跟踪器
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self.tracker = Target2D()
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def set_hub_x_range(self, x_value, y_value=None):
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"""设置轮毂x和y坐标的区间"""
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self.hub_x_range = x_value
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if y_value is not None:
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self.hub_y_range = y_value
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def load_video(self, path):
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"""加载视频文件"""
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self.use_camera = False
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self.video_path = path
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self.cap = cv2.VideoCapture(path)
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return self.cap.isOpened()
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def process_frame(self, frame):
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"""处理视频帧并检测叶尖和轮毂"""
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frame = cv2.resize(frame, self.size)
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# 使用模型进行检测
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results = self.model(frame, verbose=False)
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# 初始化检测结果字典
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detected = {'tip': [], 'hub': []}
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# 解析检测结果
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for result in results:
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boxes = result.boxes
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classes = boxes.cls
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class_names = result.names
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for i in range(len(boxes)):
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class_id = int(classes[i])
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box = boxes.xyxy[i]
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x1, y1, x2, y2 = box.tolist()
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class_name = class_names[class_id]
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# 只关注叶尖(tip)和轮毂(hub)
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if class_name in detected:
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# 使用底部中心点作为位置
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if class_name == 'tip':
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tip_x = (x1 + x2) / 2
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tip_y = y2 # 使用底部作为叶尖位置
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detected[class_name].append([tip_x, tip_y])
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else: # hub
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hub_x = (x1 + x2) / 2
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hub_y = (y1 + y2) / 2
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detected[class_name].append([hub_x, hub_y])
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# 限制检测数量
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if len(detected['tip']) > 3:
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detected['tip'] = detected['tip'][:3]
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if len(detected['hub']) > 1:
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detected['hub'] = detected['hub'][:1]
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return frame, detected['tip'], detected['hub']
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def check_trajectory_state(self, tip, hub, thresh=20):
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"""检查轨迹是否进入或离开轮毂区域"""
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hub_x = hub[0] if hub else None
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if hub_x is None or tip is None:
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return None, None
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# 检查轨迹是否在轮毂区域内
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inside = (hub_x - self.hub_x_range) < tip[0] < (hub_x + self.hub_x_range)
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# 如果轨迹进入区域，并且是从左侧进入
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if inside and not self.inside_region:
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self.inside_region = True
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self.entered_from_left = tip[0] >= (hub_x - self.hub_x_range)
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if inside and self.inside_region and self.entered_from_left and (hub_x - thresh) < tip[0] < (hub_x + thresh):
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distance, actual_distance = self.calculate_clearance()
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return distance, actual_distance
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# 如果轨迹离开区域，并且是从左侧进入且从右侧离开
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if not inside and self.inside_region and self.entered_from_left and tip[0] > (hub_x + self.hub_x_range):
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self.inside_region = False
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self.traj_cnt += 1
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return None, None
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def update_trajectories(self, tips, hub):
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"""更新叶尖轨迹，只关注轮毂x和y坐标附近的叶尖"""
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# 使用2D跟踪器更新位置
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self.tracker.update(tips, hub)
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sorted_tips = self.tracker.positions
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# 如果没有轮毂位置，无法判断附近
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if hub is None or len(hub) == 0:
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return None, None
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hub_x = hub[0] # 轮毂的x坐标
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hub_y = hub[1] # 轮毂的y坐标
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# 初始化轨迹
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if not self.trajectories:
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for _ in range(len(sorted_tips)):
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self.trajectories.append([])
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# 找到与轮毂x和y坐标都最近的叶尖
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nearest_tip = None
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min_distance = float('inf')
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for tip in sorted_tips:
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tip_x, tip_y = tip
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# 检查是否在轮毂x坐标左右区间内
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if abs(tip_x - hub_x) > self.hub_x_range + 20:
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continue
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# 检查是否大于轮毂y坐标加y区间
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if tip_y < hub_y + self.hub_y_range:
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continue
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# 计算与轮毂的综合距离（考虑x）
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distance = abs(tip_x - hub_x) # + (tip_y - hub_y) ** 2
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if distance < min_distance:
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min_distance = distance
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nearest_tip = tip
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# 更新最近的叶尖轨迹
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if nearest_tip is not None:
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if not self.nearest_tip_trajectory:
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self.nearest_tip_trajectory = [nearest_tip]
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else:
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self.nearest_tip_trajectory.append(nearest_tip)
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# 限制轨迹长度
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if len(self.nearest_tip_trajectory) > 200:
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self.nearest_tip_trajectory.pop(0)
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return self.check_trajectory_state(nearest_tip, hub)
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def find_closest_tip_x(self):
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"""找到与轮毂x坐标最接近的叶尖x坐标的叶尖"""
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if not self.nearest_tip_trajectory:
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return None
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hub_x = self.hub_position[0] if self.hub_position else None
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if hub_x is None:
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return None
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trajectories = np.array(self.nearest_tip_trajectory[:][0])
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distances = np.abs(trajectories - hub_x)
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idx = np.argmin(distances)
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tip = self.nearest_tip_trajectory[idx]
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self.calculate_points = [tip] # 只保留一个最低点
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return tip
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def calculate_clearance(self):
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"""计算叶尖到轮毂的净空"""
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if not self.nearest_tip_trajectory or self.hub_position is None:
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return None, None
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hub_position = self.hub_position
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tip_x, tip_y = self.find_closest_tip_x()
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self.calculate_points = [[tip_x, tip_y]] # 只保留一个最低点
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hub_x, hub_y = hub_position
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distance = np.sqrt((tip_x - hub_x) ** 2 + (tip_y - hub_y) ** 2)
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# 更新UI显示净空
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if self.scale_factor:
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actual_distance = distance * self.scale_factor
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tip_idx = self.traj_cnt % 3 # 根据实际匹配逻辑
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self.tip_clearances[tip_idx] = actual_distance
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# 记录到历史
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self.clearance_history[tip_idx].append(actual_distance)
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return distance, actual_distance
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def draw_annotations(self, frame):
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"""在帧上绘制轨迹和标记"""
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# 绘制轮毂位置
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if self.hub_position:
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hub_x, hub_y = self.hub_position
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# 绘制轮毂区域标记
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cv2.rectangle(frame,
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(int(hub_x - self.hub_x_range), int(hub_y - 10)),
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(int(hub_x + self.hub_x_range), int(hub_y + 10)),
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(0, 255, 255), 2)
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cv2.circle(frame, (int(hub_x), int(hub_y)), 8, (0, 255, 255), -1)
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cv2.putText(frame, "Hub", (int(hub_x) + 15, int(hub_y)),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
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# 绘制最近的叶尖轨迹
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if len(self.nearest_tip_trajectory) >= 2:
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# 绘制轨迹线（黄色）
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for j in range(1, len(self.nearest_tip_trajectory)):
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start_point = (int(self.nearest_tip_trajectory[j - 1][0]),
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int(self.nearest_tip_trajectory[j - 1][1]))
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end_point = (int(self.nearest_tip_trajectory[j][0]),
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int(self.nearest_tip_trajectory[j][1]))
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cv2.line(frame, start_point, end_point, (0, 255, 255), 1)
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# 绘制三个叶尖的净空值
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y_offset = 30
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for i in range(3):
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clearance = self.tip_clearances.get(i, None)
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if clearance is not None:
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text = f"tip{i + 1}: {clearance:.2f} m"
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else:
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text = f"tip{i + 1}: -- m"
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cv2.putText(frame, text, (10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
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y_offset += 25
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# 绘制其他叶尖（不绘制轨迹）
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if self.tracker.positions.size > 0:
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for i, tip in enumerate(self.tracker.positions):
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if not any(np.array_equal(tip, self.nearest_tip_trajectory[-1]) for p in self.nearest_tip_trajectory if
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len(self.nearest_tip_trajectory) > 0):
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color = self.tip_colors[i % len(self.tip_colors)]
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cv2.circle(frame, (int(tip[0]), int(tip[1])), 6, color, -1)
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cv2.putText(frame, f"Tip {i + 1}", (int(tip[0]) + 15, int(tip[1])),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1)
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return frame
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def process_video(self, output_dir):
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"""处理整个视频并保存结果"""
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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last_frame = None
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frame_count = 0
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while True:
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ret, frame = self.cap.read()
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if not ret:
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break
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# 处理帧
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processed_frame, tips, hubs = self.process_frame(frame)
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# 更新轮毂位置
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if hubs:
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self.hub_position = hubs[0]
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# 更新轨迹
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if tips and self.hub_position is not None:
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pixel_clearance, actual_clearance = self.update_trajectories(tips, self.hub_position)
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# 绘制注释
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annotated_frame = self.draw_annotations(processed_frame)
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last_frame = annotated_frame.copy()
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frame_count += 1
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# 显示处理进度
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if frame_count % 10 == 0:
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print(f"已处理 {frame_count} 帧...")
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# 保存最后一帧
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last_frame_path = os.path.join(output_dir, "last_frame.jpg")
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cv2.imwrite(last_frame_path, last_frame)
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print(f"最后一帧已保存至: {last_frame_path}")
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# 准备结果数据
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result = {
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"clearance_history": {
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"tip0": self.clearance_history[0],
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"tip1": self.clearance_history[1],
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"tip2": self.clearance_history[2]
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},
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"last_frame_clearances": {
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"tip0": self.tip_clearances[0],
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"tip1": self.tip_clearances[1],
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"tip2": self.tip_clearances[2]
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},
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"last_frame_image": last_frame_path
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}
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# 保存JSON结果
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json_path = os.path.join(output_dir, "results.json")
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with open(json_path, 'w') as f:
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json.dump(result, f, indent=4)
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print(f"结果已保存至: {json_path}")
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return result
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def main():
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parser = argparse.ArgumentParser(description='叶片净空计算系统')
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parser.add_argument('--video_path', type=str, required=True, help='输入视频路径')
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parser.add_argument('--model_path', type=str, required=True, help='模型文件路径')
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parser.add_argument('--output_dir', type=str, required=True, help='结果保存目录')
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parser.add_argument('--scale_factor', type=float, default=0.02, help='比例尺因子 (实际距离/像素距离)')
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parser.add_argument('--width', type=int, default=544, help='图像宽度（像素）')
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parser.add_argument('--height', type=int, default=960, help='图像高度（像素）')
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args = parser.parse_args()
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calculator = ClearanceCalculator(
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model_path=args.model_path,
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scale_factor=args.scale_factor,
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width=args.width,
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height=args.height
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)
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if not calculator.load_video(args.video_path):
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print(f"无法加载视频: {args.video_path}")
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return
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print(f"开始处理视频: {args.video_path}")
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print(f"图像尺寸设置为: ({args.width}, {args.height})")
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result = calculator.process_video(args.output_dir)
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if __name__ == "__main__":
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main()
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"""
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python cli_clearance.py \
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--video test.mp4 \
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--model best.pt \
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--output ./results \
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--scale_factor 0.02 \
|
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--width 640 \
|
||||
--height 480
|
||||
"""
|
||||
"""
|
||||
{
|
||||
"clearances": {
|
||||
"0": [5.12, 5.09, 5.11],
|
||||
"1": [4.98, 5.01],
|
||||
"2": []
|
||||
},
|
||||
"last_frame_clearances": {
|
||||
"tip1": 5.11,
|
||||
"tip2": 5.01,
|
||||
"tip3": null
|
||||
}
|
||||
}
|
||||
"""
|
||||
Loading…
Reference in New Issue