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O
Oil_Level_Recognition_System
Commit a965fd26 by yhb
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1

parent 9f93245a
Showing with 6967 additions and 230 deletions
.cursor/rules/detectionlogic.mdc 0 → 100644
---
alwaysApply: true
---
database/config/channel_config.yaml
......@@ -18,13 +18,13 @@ channels:
channel2:
general:
task_id: '1'
task_name: 海信现场测试
save_liquid_data_path: D:\restructure\liquid_level_line_detection_system\database\mission_result\1_海信现场测试
task_name: test
save_liquid_data_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\1_test
channel3:
general:
task_id: '1'
task_name: 海信现场测试
save_liquid_data_path: D:\restructure\liquid_level_line_detection_system\database\mission_result\1_海信现场测试
task_name: test
save_liquid_data_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\1_test
channel4:
general:
task_id: '1'
......@@ -33,7 +33,7 @@ channel4:
channel1:
general:
task_id: '1'
task_name: 历史回放功能展示
task_name: 曲线
area_count: 0
safe_low: 2.0mm
safe_high: 10.0mm
......@@ -41,7 +41,7 @@ channel1:
video_format: AVI
push_address: ''
video_path: ''
save_liquid_data_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\1_历史回放功能展示
save_liquid_data_path: D:\restructure\liquid_level_line_detection_system\database\mission_result\1_曲线
areas:
area_1: 通道1_区域1
area_heights:
......
database/config/mission/1_曲线.yaml 0 → 100644
task_id: '1'
task_id: '1'
task_name: 曲线
status: 未启动
selected_channels:
- 通道1
created_time: '2025-12-02 11:19:50'
mission_result_folder_path: D:\restructure\liquid_level_line_detection_system\database\mission_result\1_曲线
database/mission_result/1_曲线/1_曲线.yaml 0 → 100644
task_id: '1'
task_id: '1'
task_name: 曲线
status: 未启动
selected_channels:
- 通道1
created_time: '2025-12-02 11:19:50'
mission_result_folder_path: D:\restructure\liquid_level_line_detection_system\database\mission_result\1_曲线
database/mission_result/1_曲线/通道1_区域1.csv 0 → 100644
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2025-12-02-12:21:25.747 11.7
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2025-12-02-12:21:41.237 14.5
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2025-12-02-12:22:05.732 18.7
2025-12-02-12:22:05.916 16.9
2025-12-02-12:22:06.230 18.7
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2025-12-02-12:22:06.539 18.4
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2025-12-02-12:22:29.535 16.4
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2025-12-02-12:22:29.988 0.0
2025-12-02-12:22:30.114 0.0
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2025-12-02-12:22:33.494 20.0
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2025-12-02-12:22:36.440 15.6
2025-12-02-12:22:36.730 15.6
2025-12-02-12:22:36.876 15.6
2025-12-02-12:22:37.070 15.1
2025-12-02-12:22:37.435 15.1
2025-12-02-12:22:37.586 15.1
2025-12-02-12:22:37.785 0.0
2025-12-02-12:22:38.003 0.0
2025-12-02-12:22:38.216 0.0
2025-12-02-12:22:38.468 15.8
2025-12-02-12:22:38.656 15.6
2025-12-02-12:22:38.815 15.8
2025-12-02-12:22:39.121 15.3
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2025-12-02-12:22:40.699 15.3
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2025-12-02-12:23:23.239 15.6
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2025-12-02-12:23:23.650 15.6
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2025-12-02-12:23:24.187 15.6
2025-12-02-12:23:24.404 15.6
2025-12-02-12:23:24.669 15.6
2025-12-02-12:23:24.851 15.6
2025-12-02-12:23:25.390 15.3
2025-12-02-12:23:25.543 15.6
2025-12-02-12:23:25.818 15.3
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2025-12-02-12:23:26.402 15.3
2025-12-02-12:23:26.603 15.3
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2025-12-02-12:23:28.244 15.6
2025-12-02-12:23:28.462 15.3
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2025-12-02-12:23:29.330 15.6
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2025-12-02-12:23:30.277 15.6
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2025-12-02-12:23:30.920 15.6
2025-12-02-12:23:31.252 15.6
2025-12-02-12:23:31.534 15.6
2025-12-02-12:23:31.834 15.3
2025-12-02-12:23:32.072 15.3
2025-12-02-12:23:32.339 15.3
2025-12-02-12:23:32.620 15.3
2025-12-02-12:23:32.853 15.3
2025-12-02-12:23:33.238 15.3
2025-12-02-12:23:33.546 15.3
2025-12-02-12:23:33.894 15.6
2025-12-02-12:23:34.326 15.6
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2025-12-02-12:23:35.068 15.3
2025-12-02-12:23:35.351 15.3
2025-12-02-12:23:35.619 15.3
2025-12-02-12:23:35.971 15.3
2025-12-02-12:23:36.263 15.3
2025-12-02-12:23:36.537 15.3
2025-12-02-12:23:36.826 15.3
2025-12-02-12:23:37.154 15.3
2025-12-02-12:23:37.480 15.6
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2025-12-02-12:24:30.944 15.6
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2025-12-02-12:24:31.610 15.6
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2025-12-02-12:24:32.424 15.6
2025-12-02-12:24:32.703 15.6
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2025-12-02-12:25:31.394 15.3
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2025-12-02-12:31:07.353 15.3
2025-12-02-12:31:07.751 15.3
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2025-12-02-12:31:15.219 15.6
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2025-12-02-12:31:15.801 15.3
2025-12-02-12:31:16.131 15.3
2025-12-02-12:31:16.416 0.0
2025-12-02-12:31:16.670 0.0
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2025-12-02-12:31:17.276 15.3
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2025-12-02-12:31:48.451 0.0
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2025-12-02-12:32:31.362 0.0
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2025-12-02-12:32:32.250 0.0
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2025-12-02-12:33:32.554 15.6
handlers/videopage/curvepanel_handler.py
......@@ -364,37 +364,17 @@ class CurvePanelHandler:
print(f" - 实际添加数据点数: {added_count}")
print(f" - 更新后数据点数: {after_add_count}")
# 🔥 同步布局模式：根据时间轴范围过滤数据
is_sync_layout = hasattr(self, '_video_layout_mode') and self._video_layout_mode == 1
if is_sync_layout and self.curve_panel:
start_time, end_time = self.curve_panel.getTimeAxisRange()
if start_time is not None and end_time is not None:
print(f" - 同步布局模式，过滤时间范围: {start_time:.3f} 到 {end_time:.3f}")
# 过滤数据点，只保留在时间范围内的点
filtered_time = []
filtered_value = []
for t, v in zip(channel['time'], channel['value']):
if start_time <= t <= end_time:
filtered_time.append(t)
filtered_value.append(v)
before_filter = len(channel['time'])
channel['time'] = filtered_time
channel['value'] = filtered_value
after_filter = len(channel['time'])
print(f" - 时间过滤: {before_filter} -> {after_filter} 个数据点")
# 🔥 限制数据点数量
# - 'realtime' 同步布局模式：限制为3000个点（滚动窗口）
# - 'history' 历史回放模式：显示完整数据（不采样）
if self.curve_load_mode == 'realtime':
# 同步布局模式：保留最新3000个点
if len(channel['time']) > self.max_points:
before_limit = len(channel['time'])
channel['time'] = channel['time'][-self.max_points:]
channel['value'] = channel['value'][-self.max_points:]
print(f" - 限制数据点: {before_limit} -> {len(channel['time'])}")
# 🔥 数据点数量限制（已禁用）
# - 原逻辑：'realtime' 同步布局模式限制为3000个点（滚动窗口）
# - 已禁用：不再限制数据点数量，所有模式都显示完整数据
# if self.curve_load_mode == 'realtime':
# # 同步布局模式：保留最新3000个点
# if len(channel['time']) > self.max_points:
# before_limit = len(channel['time'])
# channel['time'] = channel['time'][-self.max_points:]
# channel['value'] = channel['value'][-self.max_points:]
# print(f" - 限制数据点: {before_limit} -> {len(channel['time'])}")
# 🔥 处理时间间隔断点：超过2分钟的数据点之间插入NaN断开连接
processed_time, processed_value = self._processTimeGaps(
......@@ -404,9 +384,43 @@ class CurvePanelHandler:
)
print(f" - 处理后数据点数: {len(processed_time)}")
# 🔥 数据验证：确保有数据才更新UI
if not processed_time or not processed_value:
print(f" - ⚠️ 处理后数据为空，跳过UI更新")
return
if len(processed_time) != len(processed_value):
print(f" - ⚠️ 处理后数据长度不匹配: time={len(processed_time)}, value={len(processed_value)}")
return
# 🔥 检查是否有有效数据点（至少有一个非NaN值）
has_valid_data = False
for i in range(len(processed_value)):
if np.isfinite(processed_value[i]) and np.isfinite(processed_time[i]):
has_valid_data = True
break
if not has_valid_data:
print(f" - ⚠️ 没有有效数据点（全是NaN/Inf），跳过UI更新")
return
# 更新UI显示（只更新一次）
if self.curve_panel:
print(f" - 开始更新UI显示...")
# 🔥 安全地计算数据范围（避免NaN值导致错误）
try:
valid_values = [v for v in processed_value if np.isfinite(v)]
valid_times = [t for t in processed_time if np.isfinite(t)]
if valid_values and valid_times:
value_range_str = f"[{min(valid_values):.2f}, {max(valid_values):.2f}]"
time_range_str = f"[{min(valid_times):.2f}, {max(valid_times):.2f}]"
else:
value_range_str = "无有效值"
time_range_str = "无有效值"
print(f" - 数据详情: time范围={time_range_str}, value范围={value_range_str}")
except Exception as e:
print(f" - 数据详情: 计算范围时出错: {e}")
self.curve_panel.updateCurveDisplay(
channel_id,
processed_time,
......@@ -414,13 +428,26 @@ class CurvePanelHandler:
)
print(f" - UI显示更新完成")
# 更新X轴范围
# 🔥 根据加载模式决定X轴范围设置
if channel['time']:
min_time = min(channel['time'])
max_time = max(channel['time'])
self.curve_panel.setXRangeAuto(max_time)
if self.curve_load_mode == 'history':
# 历史模式：显示全部数据范围
if channel['value']:
max_value = max(channel['value'])
else:
max_value = 23
self.curve_panel.setViewAll(min_time, max_time, max_value)
print(f" - 历史模式：显示全部数据范围 [{min_time:.2f}, {max_time:.2f}]")
else:
# 实时模式：只显示最后几分钟（自动跟随）
self.curve_panel.setXRangeAuto(max_time)
print(f" - 实时模式：显示最后几分钟数据")
# 更新Y轴范围
if channel['value']:
# 更新Y轴范围（仅在实时模式下）
if self.curve_load_mode == 'realtime' and channel['value']:
max_value = max(channel['value'])
self.curve_panel.setYRangeAuto(max_value)
else:
......@@ -439,6 +466,14 @@ class CurvePanelHandler:
Returns:
tuple: (处理后的时间列表, 处理后的数值列表)
"""
# 🔥 处理空数据情况
if not time_data or not value_data:
return [], []
if len(time_data) != len(value_data):
print(f"⚠️ [时间间隔处理] 数据长度不匹配: time={len(time_data)}, value={len(value_data)}")
return time_data, value_data
if len(time_data) <= 1:
return time_data, value_data
......@@ -707,7 +742,15 @@ class CurvePanelHandler:
tuple: (starttime, endtime) - 时间轴起始时刻和结束时刻（Unix时间戳）
"""
if self.curve_panel:
return self.curve_panel.getTimeAxisRange()
result = self.curve_panel.getTimeAxisRange()
# 🔥 调试信息：输出handler获取的结果
import datetime
start_time, end_time = result
start_str = datetime.datetime.fromtimestamp(start_time).strftime('%Y-%m-%d %H:%M:%S') if start_time else 'None'
end_str = datetime.datetime.fromtimestamp(end_time).strftime('%Y-%m-%d %H:%M:%S') if end_time else 'None'
print(f"🎯 [Handler获取时间轴范围] starttime={start_time} ({start_str}) -> endtime={end_time} ({end_str})")
return result
print(f"⚠️ [Handler获取时间轴范围] curve_panel不存在，返回(None, None)")
return (None, None)
# ========== 数据管理方法 ==========
......@@ -1027,6 +1070,33 @@ class CurvePanelHandler:
print(f"✅ [曲线数据加载] 成功加载 {count} 个文件")
# 🔥 设置历史数据已加载标志
self._history_data_loaded = True
# 🔥 历史数据加载完成后，确保显示全部数据范围
if self.curve_load_mode == 'history' and self.curve_panel:
# 找出所有通道的最小和最大时间值、最大数值
min_time = None
max_time = None
max_value = 0
for channel_id, channel in self.channel_data.items():
if len(channel['time']) > 0:
channel_min_time = min(channel['time'])
channel_max_time = max(channel['time'])
if min_time is None or channel_min_time < min_time:
min_time = channel_min_time
if max_time is None or channel_max_time > max_time:
max_time = channel_max_time
if len(channel['value']) > 0:
channel_max_value = max(channel['value'])
max_value = max(max_value, channel_max_value)
# 如果有数据，设置视图显示全部范围
if min_time is not None and max_time is not None:
print(f"🔄 [历史数据加载完成] 设置视图显示全部数据范围")
print(f" - 时间范围: [{min_time:.2f}, {max_time:.2f}]")
print(f" - 数值范围: [0, {max_value:.2f}]")
self.curve_panel.setViewAll(min_time, max_time, max_value)
else:
print(f"⚠️ [曲线数据加载] 加载失败")
......
handlers/videopage/detection.py
......@@ -11,6 +11,13 @@ from pathlib import Path
# 导入动态路径获取函数
from database.config import get_temp_models_dir
# 导入检测逻辑模块
from handlers.videopage.detection_logic import (
parse_yolo_result,
calculate_liquid_height,
process_yolo_result_to_liquid_height
)
# ==================== 辅助函数 ====================
......@@ -25,84 +32,6 @@ def get_class_color(class_name):
return color_map.get(class_name, (128, 128, 128)) # 默认灰色
def calculate_foam_boundary_lines(mask):
"""计算foam mask的顶部和底部边界线"""
if np.sum(mask) == 0:
return None, None
y_coords = np.where(mask)[0]
if len(y_coords) == 0:
return None, None
top_y = np.min(y_coords)
bottom_y = np.max(y_coords)
# 计算顶部边界线的平均位置
top_region_height = max(1, int((bottom_y - top_y) * 0.1))
top_region_mask = mask[top_y:top_y + top_region_height, :]
if np.sum(top_region_mask) > 0:
top_y_coords = np.where(top_region_mask)[0] + top_y
top_line_y = np.mean(top_y_coords)
else:
top_line_y = top_y
# 计算底部边界线的平均位置
bottom_region_height = max(1, int((bottom_y - top_y) * 0.1))
bottom_region_mask = mask[bottom_y - bottom_region_height:bottom_y + 1, :]
if np.sum(bottom_region_mask) > 0:
bottom_y_coords = np.where(bottom_region_mask)[0] + (bottom_y - bottom_region_height)
bottom_line_y = np.mean(bottom_y_coords)
else:
bottom_line_y = bottom_y
return top_line_y, bottom_line_y
def analyze_multiple_foams(foam_masks, container_pixel_height):
"""分析多个foam，找到可能的液位边界"""
if len(foam_masks) < 2:
return None
foam_boundaries = []
# 计算每个foam的边界信息
for i, mask in enumerate(foam_masks):
top_y, bottom_y = calculate_foam_boundary_lines(mask)
if top_y is not None and bottom_y is not None:
center_y = (top_y + bottom_y) / 2
foam_boundaries.append({
'index': i,
'top_y': top_y,
'bottom_y': bottom_y,
'center_y': center_y
})
if len(foam_boundaries) < 2:
return None
# 按垂直位置排序
foam_boundaries.sort(key=lambda x: x['center_y'])
error_threshold_px = container_pixel_height * 0.1
# 检查相邻foam之间的边界
for i in range(len(foam_boundaries) - 1):
upper_foam = foam_boundaries[i]
lower_foam = foam_boundaries[i + 1]
upper_bottom = upper_foam['bottom_y']
lower_top = lower_foam['top_y']
boundary_distance = abs(upper_bottom - lower_top)
if boundary_distance <= error_threshold_px:
liquid_level_y = (upper_bottom + lower_top) / 2
return liquid_level_y
return None
def stable_median(data, max_std=1.0):
"""稳健地计算中位数"""
if len(data) == 0:
......@@ -606,26 +535,13 @@ class LiquidDetectionEngine:
liquid_height = None
# 处理检测结果
if mission_result.masks is not None:
masks = mission_result.masks.data.cpu().numpy() > 0.5
classes = mission_result.boxes.cls.cpu().numpy().astype(int)
confidences = mission_result.boxes.conf.cpu().numpy()
else:
return None
# 收集所有mask信息
all_masks_info = []
for i in range(len(masks)):
class_name = self.model.names[classes[i]]
conf = confidences[i]
if confidences[i] >= 0.5:
resized_mask = cv2.resize(
masks[i].astype(np.uint8),
(cropped.shape[1], cropped.shape[0])
) > 0.5
all_masks_info.append((resized_mask, class_name, confidences[i]))
# 解析YOLO推理结果
all_masks_info = parse_yolo_result(
mission_result=mission_result,
model_names=self.model.names,
image_shape=cropped.shape[:2],
confidence_threshold=0.5
)
if len(all_masks_info) == 0:
return None
......@@ -642,12 +558,12 @@ class LiquidDetectionEngine:
# print(f" - 裁剪图像中容器高度: {container_bottom_in_crop - container_top_in_crop}px（应等于{container_pixel_height}px）")
# 分析mask获取液位高度（使用裁剪图像坐标）
liquid_height = self._enhanced_liquid_detection(
all_masks_info,
container_bottom_in_crop, # 使用裁剪图像坐标
container_pixel_height,
container_height_mm,
idx
liquid_height = calculate_liquid_height(
all_masks_info=all_masks_info,
container_bottom=container_bottom_in_crop, # 使用裁剪图像坐标
container_pixel_height=container_pixel_height,
container_height_mm=container_height_mm,
no_liquid_count=self.no_liquid_count[idx] if idx < len(self.no_liquid_count) else 0
)
return liquid_height
......@@ -656,85 +572,6 @@ class LiquidDetectionEngine:
print(f"[检测-目标{idx}] ❌ 检测异常: {e}")
return None
def _enhanced_liquid_detection(self, all_masks_info, container_bottom,
container_pixel_height, container_height_mm, idx):
"""
增强的液位检测，结合连续帧逻辑和foam分析
Args:
all_masks_info: mask信息列表 [(mask, class_name, confidence), ...]
container_bottom: 容器底部y坐标
container_pixel_height: 容器像素高度
container_height_mm: 容器实际高度（毫米）
idx: 目标索引
Returns:
float: 液位高度（毫米），失败返回 None
"""
pixel_per_mm = container_pixel_height / container_height_mm
# 分离不同类别的mask
liquid_masks = []
foam_masks = []
air_masks = []
for mask, class_name, confidence in all_masks_info:
if class_name == 'liquid':
liquid_masks.append(mask)
elif class_name == 'foam':
foam_masks.append(mask)
elif class_name == 'air':
air_masks.append(mask)
# 方法1：直接liquid检测（优先）
if liquid_masks:
# 找到最上层的液体mask
topmost_y = float('inf')
for i, mask in enumerate(liquid_masks):
y_indices = np.where(mask)[0]
if len(y_indices) > 0:
mask_top_y = np.min(y_indices)
# print(f" - liquid mask {i+1}: 顶部y={mask_top_y}px")
if mask_top_y < topmost_y:
topmost_y = mask_top_y
if topmost_y != float('inf'):
liquid_height_px = container_bottom - topmost_y
liquid_height_mm = liquid_height_px / pixel_per_mm
return max(0, min(liquid_height_mm, container_height_mm))
# 方法2：foam边界分析（备选）- 连续3帧未检测到liquid时启用
if self.no_liquid_count[idx] >= 3:
if len(foam_masks) >= 2:
# 多个foam，寻找液位边界
liquid_y = analyze_multiple_foams(foam_masks, container_pixel_height)
if liquid_y is not None:
liquid_height_px = container_bottom - liquid_y
liquid_height_mm = liquid_height_px / pixel_per_mm
return max(0, min(liquid_height_mm, container_height_mm))
elif len(foam_masks) == 1:
# 单个foam，使用下边界
foam_mask = foam_masks[0]
top_y, bottom_y = calculate_foam_boundary_lines(foam_mask)
if bottom_y is not None:
liquid_height_px = container_bottom - bottom_y
liquid_height_mm = liquid_height_px / pixel_per_mm
return max(0, min(liquid_height_mm, container_height_mm))
elif len(air_masks) == 1:
# 单个air，使用下边界
air_mask = air_masks[0]
y_coords = np.where(air_mask)[0]
if len(y_coords) > 0:
bottom_y = np.max(y_coords)
liquid_height_px = container_bottom - bottom_y
liquid_height_mm = liquid_height_px / pixel_per_mm
return max(0, min(liquid_height_mm, container_height_mm))
return None
def _apply_kalman_filter(self, observation, idx, container_height_mm):
"""
......
handlers/videopage/detection_logic.py 0 → 100644
# -*- coding: utf-8 -*-
"""
液位检测逻辑模块
从YOLO模型推理结果到输出液位高度的核心逻辑
使用示例:
# 方式1: 使用完整流程函数（推荐）
from handlers.videopage.detection_logic import process_yolo_result_to_liquid_height
liquid_height_mm = process_yolo_result_to_liquid_height(
mission_result=yolo_result,
model_names=model.names,
image_shape=(height, width),
container_bottom=container_bottom_y,
container_pixel_height=container_height_px,
container_height_mm=container_height_mm,
no_liquid_count=0
)
# 方式2: 分步处理
from handlers.videopage.detection_logic import parse_yolo_result, calculate_liquid_height
# 步骤1: 解析YOLO结果
all_masks_info = parse_yolo_result(
mission_result=yolo_result,
model_names=model.names,
image_shape=(height, width),
confidence_threshold=0.5
)
# 步骤2: 计算液位高度
liquid_height_mm = calculate_liquid_height(
all_masks_info=all_masks_info,
container_bottom=container_bottom_y,
container_pixel_height=container_height_px,
container_height_mm=container_height_mm,
no_liquid_count=0
)
"""
import cv2
import numpy as np
from typing import List, Tuple, Optional, Dict, Any
# ==================== 辅助函数 ====================
def calculate_foam_boundary_lines(mask: np.ndarray) -> Tuple[Optional[float], Optional[float]]:
"""
计算foam mask的顶部和底部边界线
Args:
mask: 二值mask数组 (H, W)
Returns:
(top_line_y, bottom_line_y): 顶部和底部边界线的y坐标，失败返回 (None, None)
"""
if np.sum(mask) == 0:
return None, None
y_coords = np.where(mask)[0]
if len(y_coords) == 0:
return None, None
top_y = np.min(y_coords)
bottom_y = np.max(y_coords)
# 计算顶部边界线的平均位置
top_region_height = max(1, int((bottom_y - top_y) * 0.1))
top_region_mask = mask[top_y:top_y + top_region_height, :]
if np.sum(top_region_mask) > 0:
top_y_coords = np.where(top_region_mask)[0] + top_y
top_line_y = np.mean(top_y_coords)
else:
top_line_y = top_y
# 计算底部边界线的平均位置
bottom_region_height = max(1, int((bottom_y - top_y) * 0.1))
bottom_region_mask = mask[bottom_y - bottom_region_height:bottom_y + 1, :]
if np.sum(bottom_region_mask) > 0:
bottom_y_coords = np.where(bottom_region_mask)[0] + (bottom_y - bottom_region_height)
bottom_line_y = np.mean(bottom_y_coords)
else:
bottom_line_y = bottom_y
return top_line_y, bottom_line_y
def analyze_multiple_foams(foam_masks: List[np.ndarray], container_pixel_height: float) -> Optional[float]:
"""
分析多个foam，找到可能的液位边界
Args:
foam_masks: foam mask列表
container_pixel_height: 容器像素高度
Returns:
float: 液位y坐标，失败返回 None
"""
if len(foam_masks) < 2:
return None
foam_boundaries = []
# 计算每个foam的边界信息
for i, mask in enumerate(foam_masks):
top_y, bottom_y = calculate_foam_boundary_lines(mask)
if top_y is not None and bottom_y is not None:
center_y = (top_y + bottom_y) / 2
foam_boundaries.append({
'index': i,
'top_y': top_y,
'bottom_y': bottom_y,
'center_y': center_y
})
if len(foam_boundaries) < 2:
return None
# 按垂直位置排序
foam_boundaries.sort(key=lambda x: x['center_y'])
error_threshold_px = container_pixel_height * 0.1
# 检查相邻foam之间的边界
for i in range(len(foam_boundaries) - 1):
upper_foam = foam_boundaries[i]
lower_foam = foam_boundaries[i + 1]
upper_bottom = upper_foam['bottom_y']
lower_top = lower_foam['top_y']
boundary_distance = abs(upper_bottom - lower_top)
if boundary_distance <= error_threshold_px:
liquid_level_y = (upper_bottom + lower_top) / 2
return liquid_level_y
return None
# ==================== YOLO结果解析 ====================
def parse_yolo_result(
mission_result: Any,
model_names: Dict[int, str],
image_shape: Tuple[int, int],
confidence_threshold: float = 0.5
) -> List[Tuple[np.ndarray, str, float]]:
"""
解析YOLO推理结果，提取mask信息
Args:
mission_result: YOLO推理结果对象（ultralytics Results对象）
model_names: 类别名称映射字典 {class_id: class_name}
image_shape: 图像尺寸 (height, width)
confidence_threshold: 置信度阈值，默认0.5
Returns:
List[Tuple[mask, class_name, confidence]]: mask信息列表
- mask: 二值mask数组 (H, W, bool)
- class_name: 类别名称 ('liquid', 'foam', 'air')
- confidence: 置信度
"""
all_masks_info = []
# 检查是否有检测结果
if mission_result.masks is None:
return all_masks_info
# 提取数据
masks = mission_result.masks.data.cpu().numpy() > 0.5
classes = mission_result.boxes.cls.cpu().numpy().astype(int)
confidences = mission_result.boxes.conf.cpu().numpy()
# 处理每个检测结果
for i in range(len(masks)):
class_id = classes[i]
confidence = confidences[i]
# 过滤低置信度结果
if confidence < confidence_threshold:
continue
# 获取类别名称
class_name = model_names.get(class_id, 'unknown')
# 调整mask尺寸到图像尺寸
resized_mask = cv2.resize(
masks[i].astype(np.uint8),
(image_shape[1], image_shape[0])
) > 0.5
all_masks_info.append((resized_mask, class_name, float(confidence)))
return all_masks_info
# ==================== 液位高度计算 ====================
def calculate_liquid_height(
all_masks_info: List[Tuple[np.ndarray, str, float]],
container_bottom: float,
container_pixel_height: float,
container_height_mm: float,
no_liquid_count: int = 0
) -> Optional[float]:
"""
从mask信息计算液位高度（核心逻辑）
Args:
all_masks_info: mask信息列表 [(mask, class_name, confidence), ...]
container_bottom: 容器底部y坐标（裁剪图像坐标系）
container_pixel_height: 容器像素高度
container_height_mm: 容器实际高度（毫米）
no_liquid_count: 连续未检测到liquid的帧数，默认0
Returns:
float: 液位高度（毫米），失败返回 None
"""
pixel_per_mm = container_pixel_height / container_height_mm
# 分离不同类别的mask
liquid_masks = []
foam_masks = []
air_masks = []
for mask, class_name, confidence in all_masks_info:
if class_name == 'liquid':
liquid_masks.append(mask)
elif class_name == 'foam':
foam_masks.append(mask)
elif class_name == 'air':
air_masks.append(mask)
# 方法1：直接liquid检测（优先）
if liquid_masks:
# 找到最上层的液体mask
topmost_y = float('inf')
for mask in liquid_masks:
y_indices = np.where(mask)[0]
if len(y_indices) > 0:
mask_top_y = np.min(y_indices)
if mask_top_y < topmost_y:
topmost_y = mask_top_y
if topmost_y != float('inf'):
liquid_height_px = container_bottom - topmost_y
liquid_height_mm = liquid_height_px / pixel_per_mm
return max(0, min(liquid_height_mm, container_height_mm))
# 方法2：foam边界分析（备选）- 连续3帧未检测到liquid时启用
if no_liquid_count >= 3:
if len(foam_masks) >= 2:
# 多个foam，寻找液位边界
liquid_y = analyze_multiple_foams(foam_masks, container_pixel_height)
if liquid_y is not None:
liquid_height_px = container_bottom - liquid_y
liquid_height_mm = liquid_height_px / pixel_per_mm
return max(0, min(liquid_height_mm, container_height_mm))
elif len(foam_masks) == 1:
# 单个foam，使用下边界
foam_mask = foam_masks[0]
top_y, bottom_y = calculate_foam_boundary_lines(foam_mask)
if bottom_y is not None:
liquid_height_px = container_bottom - bottom_y
liquid_height_mm = liquid_height_px / pixel_per_mm
return max(0, min(liquid_height_mm, container_height_mm))
elif len(air_masks) == 1:
# 单个air，使用下边界
air_mask = air_masks[0]
y_coords = np.where(air_mask)[0]
if len(y_coords) > 0:
bottom_y = np.max(y_coords)
liquid_height_px = container_bottom - bottom_y
liquid_height_mm = liquid_height_px / pixel_per_mm
return max(0, min(liquid_height_mm, container_height_mm))
return None
# ==================== 完整流程函数 ====================
def process_yolo_result_to_liquid_height(
mission_result: Any,
model_names: Dict[int, str],
image_shape: Tuple[int, int],
container_bottom: float,
container_pixel_height: float,
container_height_mm: float,
no_liquid_count: int = 0,
confidence_threshold: float = 0.5
) -> Optional[float]:
"""
完整的处理流程：从YOLO推理结果到液位高度
Args:
mission_result: YOLO推理结果对象
model_names: 类别名称映射字典 {class_id: class_name}
image_shape: 图像尺寸 (height, width)
container_bottom: 容器底部y坐标（裁剪图像坐标系）
container_pixel_height: 容器像素高度
container_height_mm: 容器实际高度（毫米）
no_liquid_count: 连续未检测到liquid的帧数，默认0
confidence_threshold: 置信度阈值，默认0.5
Returns:
float: 液位高度（毫米），失败返回 None
"""
# 步骤1: 解析YOLO结果
all_masks_info = parse_yolo_result(
mission_result=mission_result,
model_names=model_names,
image_shape=image_shape,
confidence_threshold=confidence_threshold
)
# 如果没有检测到任何mask，返回None
if len(all_masks_info) == 0:
return None
# 步骤2: 计算液位高度
liquid_height_mm = calculate_liquid_height(
all_masks_info=all_masks_info,
container_bottom=container_bottom,
container_pixel_height=container_pixel_height,
container_height_mm=container_height_mm,
no_liquid_count=no_liquid_count
)
return liquid_height_mm
rules/曲线绘制逻辑.md
时间轴变量starttime，endtime
时间轴变量starttime，endtime
......@@ -2,4 +2,4 @@
采样逻辑，根据数据点个数设置不同采样率
@curvepanel_handler.py#L371-378 修改self._video_layout_mode = 1同步布局，绘制曲线的数据范围
实验最长时间三天，strattime到endtime，缩放限制
曲线面板所有都历史模式下，
\ No newline at end of file
曲线面板历史模式下，，
\ No newline at end of file
rules/检测数据流向说明.md 0 → 100644
# 液位检测引擎数据流向说明
# 液位检测引擎数据流向说明
## 总体架构
```
输入数据 → 初始化/配置 → 检测处理 → 输出结果
```
---
## 一、初始化阶段 (Initialization)
### 1.1 引擎创建
```python
engine = LiquidDetectionEngine(model_path, device, batch_size)
```
**数据流：**
```
输入参数:
├── model_path: 模型路径 (.pt 或 .dat)
├── device: 计算设备 ('cuda', 'cpu')
└── batch_size: 批处理大小 (默认4)
初始化状态:
├── self.model = None (延迟加载)
├── self.targets = [] (标注区域)
├── self.fixed_container_bottoms = [] (容器底部坐标)
├── self.fixed_container_tops = [] (容器顶部坐标)
├── self.actual_heights = [] (实际高度，单位：mm)
└── 各种滤波器和状态列表
```
### 1.2 模型加载
```python
engine.load_model(model_path)
```
**数据流：**
```
模型路径 → 验证文件存在
↓
如果是 .dat 文件 → _decode_dat_model()
├── 读取加密文件
├── XOR解密
└── 保存临时 .pt 文件
↓
验证模型文件完整性 (_validate_model_file)
├── 检查文件大小
├── 检查文件扩展名
└── 验证PyTorch魔数
↓
加载YOLO模型
├── from ultralytics import YOLO
├── self.model = YOLO(model_path)
└── self.model.to(device) # 移动到GPU/CPU
```
---
## 二、配置阶段 (Configuration)
### 2.1 标注数据配置
```python
engine.configure(boxes, fixed_bottoms, fixed_tops, actual_heights)
```
**数据流：**
```
输入参数:
├── boxes: [[x1,y1,x2,y2], ...] 或 [[cx,cy,size], ...]
├── fixed_bottoms: [y1, y2, ...] 容器底部y坐标
├── fixed_tops: [y1, y2, ...] 容器顶部y坐标
└── actual_heights: [h1, h2, ...] 实际高度(毫米)
处理流程:
├── _parse_targets(boxes)
│ └── 转换为统一格式: [(cx, cy, size), ...]
├── 存储到实例变量
├── 初始化状态列表 (每个目标一个)
└── _init_kalman_filters()
└── 为每个目标创建卡尔曼滤波器
```
**坐标转换示例：**
```
输入: boxes = [[100, 200, 300, 400], ...]
↓ _parse_targets()
输出: targets = [(200, 300, 200), ...]
其中: cx = (100+300)/2 = 200
cy = (200+400)/2 = 300
size = max(200, 200) = 200
```
---
## 三、检测阶段 (Detection) - 核心流程
### 3.1 主检测入口
```python
result = engine.detect(frame, annotation_config=None)
```
**输入数据：**
```
frame: numpy.ndarray (H, W, 3) - 视频帧图像
annotation_config: 可选，动态配置字典
{
'boxes': [...],
'fixed_bottoms': [...],
'fixed_tops': [...],
'actual_heights': [...]
}
```
**输出数据：**
```python
{
'liquid_line_positions': {
0: {
'y': float, # 液位线y坐标(像素)
'height_mm': float, # 液位高度(毫米) ⭐ 主要输出
'height_px': int, # 液位高度(像素)
'left': int, # 检测区域左边界
'right': int # 检测区域右边界
},
1: {...},
...
},
'success': bool
}
```
### 3.2 检测流程详解
#### 步骤1: 配置解析
```
detect() 开始
↓
检查模型是否加载
↓
解析配置 (动态配置 or 实例配置)
├── targets = _parse_targets(boxes)
├── fixed_bottoms = [...]
├── fixed_tops = [...]
└── actual_heights = [...]
```
#### 步骤2: 遍历每个检测目标
```python
for idx, (center_x, center_y, crop_size) in enumerate(targets):
```
**数据流：**
```
目标信息 (center_x, center_y, crop_size)
↓
计算裁剪区域:
├── top = max(center_y - half_size, 0)
├── bottom = min(center_y + half_size, h)
├── left = max(center_x - half_size, 0)
└── right = min(center_x + half_size, w)
↓
裁剪图像: cropped = frame[top:bottom, left:right]
↓
调用 _detect_single_target()
```
#### 步骤3: 单目标检测 (_detect_single_target)
**输入：**
```
cropped: 裁剪后的图像 (numpy.ndarray)
idx: 目标索引
crop_top_y: 裁剪区域在原图中的top坐标
container_bottom: 容器底部y坐标(原图绝对坐标)
container_top: 容器顶部y坐标(原图绝对坐标)
container_height_mm: 容器实际高度(毫米)
```
**处理流程：**
**3.3.1 计算容器参数**
```
container_bottom_offset = container_bottom (原图绝对坐标)
container_top_offset = container_top (原图绝对坐标)
container_pixel_height = container_bottom - container_top
pixel_per_mm = container_pixel_height / container_height_mm
```
**3.3.2 YOLO推理**
```
cropped图像
↓
self.model.predict()
├── source: cropped
├── imgsz: 640
├── conf: 0.5
├── device: GPU/CPU
├── batch: batch_size
└── half: True (FP16加速)
↓
mission_results = [mission_result]
mission_result 包含:
├── .masks: 分割mask (Tensor)
├── .boxes.cls: 类别ID (Tensor)
└── .boxes.conf: 置信度 (Tensor)
```
**3.3.3 解析YOLO结果**
```
mission_result.masks.data
↓ CPU转换
masks = masks.cpu().numpy() > 0.5 # 布尔mask数组
classes = boxes.cls.cpu().numpy().astype(int)
confidences = boxes.conf.cpu().numpy()
↓
遍历每个检测结果
├── class_name = model.names[classes[i]]
├── confidence = confidences[i]
└── 如果 confidence >= 0.5:
└── resized_mask = cv2.resize(mask, (w, h))
↓
添加到 all_masks_info:
[(mask, 'liquid', 0.95), (mask, 'foam', 0.87), ...]
```
**3.3.4 坐标转换 (关键步骤)**
```
原图绝对坐标 → 裁剪图像相对坐标
container_bottom_in_crop = container_bottom_offset - crop_top_y
container_top_in_crop = container_top_offset - crop_top_y
示例:
原图: container_bottom = 500px, crop_top_y = 300px
裁剪图: container_bottom_in_crop = 500 - 300 = 200px
```
**3.3.5 增强液位检测 (_enhanced_liquid_detection)**
**输入：**
```
all_masks_info: [(mask, class_name, confidence), ...]
container_bottom: 容器底部y坐标(裁剪图像坐标)
container_pixel_height: 容器像素高度
container_height_mm: 容器实际高度(毫米)
idx: 目标索引
```
**处理流程：**
```
all_masks_info
↓
分离不同类别:
├── liquid_masks = [所有'liquid'类别的mask]
├── foam_masks = [所有'foam'类别的mask]
└── air_masks = [所有'air'类别的mask]
↓
方法1: 直接liquid检测 (优先)
├── 遍历所有liquid_masks
├── 找到最上层的液体mask顶部y坐标
├── liquid_height_px = container_bottom - topmost_y
└── liquid_height_mm = liquid_height_px / pixel_per_mm
↓ 成功则返回
↓ 失败则继续
↓
方法2: foam边界分析 (备选，连续3帧未检测到liquid时启用)
├── 多个foam: analyze_multiple_foams()
│ └── 寻找相邻foam之间的边界
├── 单个foam: calculate_foam_boundary_lines()
│ └── 使用下边界
└── 单个air: 使用air的下边界
↓
返回 liquid_height_mm 或 None
```
#### 步骤4: 坐标转换回原图
```
liquid_height_mm (毫米)
↓
计算像素高度:
height_px = liquid_height_mm * pixel_per_mm
↓
转换为原图绝对坐标:
liquid_line_y_absolute = container_bottom_y - height_px
示例:
container_bottom_y = 500px (原图)
height_px = 100px
liquid_line_y_absolute = 500 - 100 = 400px (原图)
```
#### 步骤5: 构建输出结果
```
liquid_line_positions[idx] = {
'y': liquid_line_y_absolute, # 原图y坐标
'height_mm': liquid_height_mm, # 毫米单位 ⭐
'height_px': height_px, # 像素单位
'left': left, # 检测区域
'right': right
}
```
---
## 四、坐标系统说明
### 4.1 三种坐标系
1. **原图绝对坐标系**
- 相对于完整视频帧的坐标
- 范围: [0, frame_width] × [0, frame_height]
- 存储位置: `fixed_container_bottoms`, `fixed_container_tops`
2. **裁剪图像相对坐标系**
- 相对于裁剪区域的坐标
- 范围: [0, crop_width] × [0, crop_height]
- YOLO推理在此坐标系进行
3. **物理坐标系**
- 实际物理高度(毫米)
- 通过 `pixel_per_mm` 转换
### 4.2 坐标转换公式
```
原图坐标 → 裁剪图坐标:
crop_y = original_y - crop_top_y
裁剪图坐标 → 原图坐标:
original_y = crop_y + crop_top_y
像素坐标 → 物理坐标:
height_mm = height_px / pixel_per_mm
物理坐标 → 像素坐标:
height_px = height_mm * pixel_per_mm
```
---
## 五、完整数据流向图
```
┌─────────────────────────────────────────────────────────────┐
│ 初始化阶段 │
├─────────────────────────────────────────────────────────────┤
│ 1. LiquidDetectionEngine.__init__() │
│ └── 初始化状态变量 │
│ │
│ 2. load_model(model_path) │
│ ├── 验证文件 → 解码.dat → 验证完整性 │
│ └── 加载YOLO模型 → 移动到GPU │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 配置阶段 │
├─────────────────────────────────────────────────────────────┤
│ configure(boxes, fixed_bottoms, fixed_tops, actual_heights) │
│ ├── _parse_targets(boxes) → [(cx, cy, size), ...] │
│ ├── 存储标注数据 │
│ └── _init_kalman_filters() → 初始化滤波器 │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 检测阶段 │
├─────────────────────────────────────────────────────────────┤
│ detect(frame, annotation_config) │
│ │ │
│ ├─→ 解析配置 (动态 or 实例) │
│ │ │
│ └─→ for each target: │
│ │ │
│ ├─→ 裁剪图像区域 │
│ │ cropped = frame[top:bottom, left:right] │
│ │ │
│ ├─→ _detect_single_target(cropped, ...) │
│ │ │ │
│ │ ├─→ 计算容器参数 │
│ │ │ pixel_per_mm = height_px / height_mm │
│ │ │ │
│ │ ├─→ YOLO推理 │
│ │ │ model.predict(cropped) │
│ │ │ └─→ masks, classes, confidences │
│ │ │ │
│ │ ├─→ 解析检测结果 │
│ │ │ all_masks_info = [(mask, class, conf), ...] │
│ │ │ │
│ │ ├─→ 坐标转换 │
│ │ │ 原图坐标 → 裁剪图坐标 │
│ │ │ │
│ │ └─→ _enhanced_liquid_detection() │
│ │ │ │
│ │ ├─→ 分离mask类别 │
│ │ │ liquid_masks, foam_masks, air_masks │
│ │ │ │
│ │ ├─→ 方法1: 直接liquid检测 │
│ │ │ 找最上层liquid → 计算高度 │
│ │ │ │
│ │ └─→ 方法2: foam/air边界分析 │
│ │ (连续3帧未检测到liquid时) │
│ │ │
│ ├─→ 转换回原图坐标 │
│ │ liquid_line_y = container_bottom - height_px │
│ │ │
│ └─→ 构建输出结果 │
│ { │
│ 'y': liquid_line_y, │
│ 'height_mm': liquid_height_mm, ⭐ │
│ 'height_px': height_px, │
│ 'left': left, │
│ 'right': right │
│ } │
│ │
│ └─→ 返回最终结果 │
│ { │
│ 'liquid_line_positions': {...}, │
│ 'success': bool │
│ } │
└─────────────────────────────────────────────────────────────┘
```
---
## 六、关键数据转换点
### 6.1 图像数据流
```
完整帧 (H×W×3)
↓ 裁剪
裁剪区域 (crop_h×crop_w×3)
↓ YOLO推理
分割mask (crop_h×crop_w, bool)
↓ 坐标转换
原图坐标位置
```
### 6.2 高度数据流
```
像素高度 (height_px)
↓ ÷ pixel_per_mm
物理高度 (height_mm) ⭐ 主要输出
↓ 限制范围
最终高度 [0, container_height_mm]
```
### 6.3 坐标数据流
```
原图绝对坐标 (fixed_bottoms, fixed_tops)
↓ - crop_top_y
裁剪图相对坐标 (container_bottom_in_crop)
↓ + crop_top_y
原图绝对坐标 (liquid_line_y_absolute)
```
---
## 七、注意事项
1. **单位系统**
- 像素坐标：整数 (px)
- 物理高度：浮点数 (mm) - 主要输出单位
- 坐标转换：`pixel_per_mm = 像素高度 / 物理高度`
2. **坐标系统**
- YOLO推理在裁剪图像坐标系
- 最终输出在原图绝对坐标系
- 需要正确转换两个坐标系
3. **检测策略**
- 优先使用 `liquid` 类别检测
- 备选使用 `foam`/`air` 边界分析
- 需要连续3帧未检测到liquid才启用备选方法
4. **多目标支持**
- 每个目标独立检测
- 每个目标有独立的滤波器
- 输出字典以索引为键
5. **动态配置**
- 支持运行时传入 `annotation_config`
- 用于多通道共享模型场景
- 优先级高于实例配置
widgets/videopage/curvepanel.py
......@@ -624,11 +624,45 @@ class CurvePanel(QtWidgets.QWidget):
注意：此方法只负责UI更新，数据处理由handler完成
"""
if channel_id not in self.curve_items:
print(f"⚠️ [UI更新] 通道不存在: {channel_id}")
return
# 🔥 数据验证和调试
if not time_data or not value_data:
print(f"⚠️ [UI更新] 数据为空: channel_id={channel_id}, time_data长度={len(time_data) if time_data else 0}, value_data长度={len(value_data) if value_data else 0}")
# 即使数据为空，也调用setData清空显示
curve = self.curve_items[channel_id]
curve.setData([], [])
return
if len(time_data) != len(value_data):
print(f"⚠️ [UI更新] 数据长度不匹配: channel_id={channel_id}, time_data长度={len(time_data)}, value_data长度={len(value_data)}")
return
# 🔥 转换为numpy数组（PyQtGraph需要numpy数组）
try:
time_array = np.array(time_data, dtype=np.float64)
value_array = np.array(value_data, dtype=np.float64)
except Exception as e:
print(f"⚠️ [UI更新] 数据转换失败: channel_id={channel_id}, 错误={e}")
return
# 🔥 检查有效数据点数量（排除NaN和Inf）
valid_mask = np.isfinite(time_array) & np.isfinite(value_array)
valid_count = np.sum(valid_mask)
if valid_count == 0:
print(f"⚠️ [UI更新] 没有有效数据点: channel_id={channel_id}, 总数据点={len(time_array)}, 有效数据点=0")
# 即使没有有效数据，也调用setData清空显示
curve = self.curve_items[channel_id]
curve.setData([], [])
return
print(f"✅ [UI更新] 更新曲线: channel_id={channel_id}, 总数据点={len(time_array)}, 有效数据点={valid_count}")
# 只更新UI显示
curve = self.curve_items[channel_id]
curve.setData(time_data, value_data)
curve.setData(time_array, value_array)
# 动态调整X轴范围
self._updateXRange()
......@@ -729,6 +763,12 @@ class CurvePanel(QtWidgets.QWidget):
Returns:
tuple: (starttime, endtime) - 时间轴起始时刻和结束时刻（Unix时间戳）
"""
# 🔥 调试信息：输出获取的时间轴范围
import datetime
start_str = datetime.datetime.fromtimestamp(self.starttime).strftime('%Y-%m-%d %H:%M:%S') if self.starttime else 'None'
end_str = datetime.datetime.fromtimestamp(self.endtime).strftime('%Y-%m-%d %H:%M:%S') if self.endtime else 'None'
print(f"📊 [获取时间轴范围] starttime={self.starttime} ({start_str}) -> endtime={self.endtime} ({end_str})")
return (self.starttime, self.endtime)
......@@ -793,12 +833,35 @@ class CurvePanel(QtWidgets.QWidget):
Args:
max_time: 最大时间戳
view_width: 显示宽度（秒），默认60秒
view_width: 显示宽度（秒），默认120秒（2分钟）
"""
if self.chk_auto_follow.isChecked():
min_time = max_time - view_width
self.plot_widget.setXRange(min_time, max_time, padding=0)
def setViewAll(self, min_time, max_time, max_value):
"""
显示全部数据范围（用于历史数据加载）
Args:
min_time: 最小时间戳
max_time: 最大时间戳
max_value: 最大数值
"""
# 设置X轴显示全部时间范围（留出5%的边距）
time_span = max_time - min_time
if time_span > 0:
padding = time_span * 0.05 # 5%边距
self.plot_widget.setXRange(min_time - padding, max_time + padding, padding=0)
# 设置Y轴范围（留出10%的边距）
if max_value > 0:
y_max = min(23, max_value * 1.1) # 不超过23
self.plot_widget.setYRange(0, y_max, padding=0)
# 固定X轴位置
self._fixXAxisPosition()
def setYRangeAuto(self, max_value):
"""
自动设置Y轴范围（由handler调用）
......@@ -829,6 +892,26 @@ class CurvePanel(QtWidgets.QWidget):
self.starttime = x_min # 时间轴起始时刻（Unix时间戳）
self.endtime = x_max # 时间轴结束时刻（Unix时间戳）
# 🔥 调试信息：输出时间轴范围变化（安全处理时间戳转换）
import datetime
try:
if x_min and np.isfinite(x_min):
start_str = datetime.datetime.fromtimestamp(x_min).strftime('%Y-%m-%d %H:%M:%S')
else:
start_str = 'None'
except (OSError, ValueError, OverflowError):
start_str = f'Invalid({x_min})'
try:
if x_max and np.isfinite(x_max):
end_str = datetime.datetime.fromtimestamp(x_max).strftime('%Y-%m-%d %H:%M:%S')
else:
end_str = 'None'
except (OSError, ValueError, OverflowError):
end_str = f'Invalid({x_max})'
print(f"🔄 [时间轴范围] starttime={x_min:.3f} ({start_str}) -> endtime={x_max:.3f} ({end_str})")
# 确保Y轴范围在0-23之间
if y_min < 0 or y_max > 23:
# 调整到合法范围
......
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