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fix: Address file path and data processing bugs, add real-time viewer

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Masonmason 2025-07-17 09:18:27 +08:00
parent 7e173c42de
commit af9adc8e82
7 changed files with 105 additions and 15 deletions
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8
cluster4npu_ui/TODO.md
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@ -4,9 +4,11 @@
✅ **Pipeline Core**: Multi-stage pipeline with device auto-detection working
✅ **Hardware Integration**: Kneron NPU dongles connecting and initializing successfully
✅ **Auto-resize Preprocessing**: Model input shape detection and automatic preprocessing implemented
❌ **Data Input Sources**: Missing camera and file input implementations
❌ **Result Persistence**: No result saving or output mechanisms
❌ **End-to-End Workflow**: Gaps between UI configuration and core pipeline execution
✅ **Data Input Sources**: Camera and video file inputs implemented
✅ **Result Persistence**: Result saving to file implemented
✅ **End-to-End Workflow**: UI configuration now connects to core pipeline execution
✅ **Bug Fixes**: Addressed file path and data processing issues
✅ **Real-time Viewer**: Implemented a live view for real-time inference visualization
---

3
cluster4npu_ui/core/functions/InferencePipeline.py
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@ -215,7 +215,8 @@ class PipelineStage:
timeout_start = time.time()
while time.time() - timeout_start < 5.0: # 5 second timeout
result = self.multidongle.get_latest_inference_result(timeout=0.1)
if result:
# Check if result is not None and not an empty dict
if result is not None and (not isinstance(result, dict) or result):
inference_result = result
break
time.sleep(0.01)

13
cluster4npu_ui/core/functions/camera_source.py
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@ -13,7 +13,8 @@ class CameraSource:
camera_index: int = 0,
resolution: Optional[tuple[int, int]] = None,
fps: Optional[int] = None,
data_callback: Optional[Callable[[object], None]] = None):
data_callback: Optional[Callable[[object], None]] = None,
frame_callback: Optional[Callable[[object], None]] = None):
"""
Initializes the CameraSource.
@ -21,12 +22,14 @@ class CameraSource:
camera_index (int): The index of the camera to use.
resolution (Optional[tuple[int, int]]): The desired resolution (width, height).
fps (Optional[int]): The desired frames per second.
data_callback (Optional[Callable[[object], None]]): A callback function to send data to.
data_callback (Optional[Callable[[object], None]]): A callback function to send data to the pipeline.
frame_callback (Optional[Callable[[object], None]]): A callback function for raw frame updates.
"""
self.camera_index = camera_index
self.resolution = resolution
self.fps = fps
self.data_callback = data_callback
self.frame_callback = frame_callback
self.cap = None
self.running = False
@ -108,6 +111,12 @@ class CameraSource:
except Exception as e:
print(f"Error in data_callback: {e}")
if self.frame_callback:
try:
self.frame_callback(frame)
except Exception as e:
print(f"Error in frame_callback: {e}")
# Control frame rate if FPS is set
if self.fps:
time.sleep(1.0 / self.fps)

24
cluster4npu_ui/core/functions/result_handler.py
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@ -50,9 +50,18 @@ class FileOutputManager:
format (str): The format to save the result in ('json' or 'csv').
"""
try:
# Sanitize pipeline_name to be a valid directory name
sanitized_pipeline_name = "".join(c for c in pipeline_name if c.isalnum() or c in (' ', '_')).rstrip()
if not sanitized_pipeline_name:
sanitized_pipeline_name = "default_pipeline"
# Ensure base_path is valid
if not self.base_path or not isinstance(self.base_path, str):
self.base_path = "./output"
# Create directory structure
today = time.strftime("%Y-%m-%d")
output_dir = os.path.join(self.base_path, pipeline_name, today)
output_dir = os.path.join(self.base_path, sanitized_pipeline_name, today)
os.makedirs(output_dir, exist_ok=True)
# Create filename
@ -69,10 +78,15 @@ class FileOutputManager:
# For CSV, we expect a list of dicts. If it's a single dict, wrap it.
data_to_save = result_data if isinstance(result_data, list) else [result_data]
if data_to_save:
fieldnames = list(data_to_save[0].keys())
content = self.serializer.to_csv(data_to_save, fieldnames)
with open(file_path, 'w') as f:
f.write(content)
# Ensure all items in the list are dictionaries
if all(isinstance(item, dict) for item in data_to_save):
fieldnames = list(data_to_save[0].keys())
content = self.serializer.to_csv(data_to_save, fieldnames)
with open(file_path, 'w') as f:
f.write(content)
else:
print(f"Error: CSV data must be a list of dictionaries.")
return
else:
print(f"Error: Unsupported format '{format}'")
return

11
cluster4npu_ui/core/functions/video_source.py
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@ -12,17 +12,20 @@ class VideoFileSource:
def __init__(self,
file_path: str,
data_callback: Optional[Callable[[object], None]] = None,
frame_callback: Optional[Callable[[object], None]] = None,
loop: bool = False):
"""
Initializes the VideoFileSource.
Args:
file_path (str): The path to the video file.
data_callback (Optional[Callable[[object], None]]): A callback function to send data to.
data_callback (Optional[Callable[[object], None]]): A callback function to send data to the pipeline.
frame_callback (Optional[Callable[[object], None]]): A callback function for raw frame updates.
loop (bool): Whether to loop the video when it ends.
"""
self.file_path = file_path
self.data_callback = data_callback
self.frame_callback = frame_callback
self.loop = loop
self.cap = None
@ -106,6 +109,12 @@ class VideoFileSource:
except Exception as e:
print(f"Error in data_callback: {e}")
if self.frame_callback:
try:
self.frame_callback(frame)
except Exception as e:
print(f"Error in frame_callback: {e}")
# Control frame rate
time.sleep(1.0 / self.fps)

15
cluster4npu_ui/core/functions/workflow_orchestrator.py
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@ -30,6 +30,7 @@ class WorkflowOrchestrator:
self.result_handler = None
self.running = False
self._stop_event = threading.Event()
self.frame_callback = None
def start(self):
"""
@ -88,6 +89,12 @@ class WorkflowOrchestrator:
print("Workflow orchestrator stopped.")
def set_frame_callback(self, callback):
"""
Sets the callback function for frame updates.
"""
self.frame_callback = callback
def _create_data_source(self) -> Optional[Any]:
"""
Creates the appropriate data source based on the input configuration.
@ -99,13 +106,17 @@ class WorkflowOrchestrator:
return CameraSource(
camera_index=self.input_config.get('device_id', 0),
resolution=self._parse_resolution(self.input_config.get('resolution')),
fps=self.input_config.get('fps', 30)
fps=self.input_config.get('fps', 30),
data_callback=self.pipeline.put_data,
frame_callback=self.frame_callback
)
elif source_type == 'file':
# Assuming 'file' means video file for now
return VideoFileSource(
file_path=self.input_config.get('source_path', ''),
loop=True # Or get from config if available
loop=True, # Or get from config if available
data_callback=self.pipeline.put_data,
frame_callback=self.frame_callback
)
# Add other source types here (e.g., 'rtsp stream', 'image file')
else:

46
cluster4npu_ui/ui/dialogs/deployment.py
View File

@ -31,7 +31,7 @@ from PyQt5.QtWidgets import (
QHeaderView, QMessageBox, QSplitter, QFrame
)
from PyQt5.QtCore import Qt, QThread, pyqtSignal, QTimer
from PyQt5.QtGui import QFont, QColor, QPalette
from PyQt5.QtGui import QFont, QColor, QPalette, QImage, QPixmap
# Import our converter and pipeline system
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'core', 'functions'))
@ -63,6 +63,7 @@ class DeploymentWorker(QThread):
deployment_started = pyqtSignal()
deployment_completed = pyqtSignal(bool, str) # success, message
error_occurred = pyqtSignal(str)
frame_updated = pyqtSignal(object) # For live view
def __init__(self, pipeline_data: Dict[str, Any]):
super().__init__()
@ -128,6 +129,7 @@ class DeploymentWorker(QThread):
# Create and start the orchestrator
self.orchestrator = WorkflowOrchestrator(pipeline, config.input_config, config.output_config)
self.orchestrator.set_frame_callback(self.frame_updated.emit)
self.orchestrator.start()
self.progress_updated.emit(100, "Pipeline deployed successfully!")
@ -189,6 +191,10 @@ class DeploymentDialog(QDialog):
self.deployment_tab = self.create_deployment_tab()
self.tab_widget.addTab(self.deployment_tab, "Deployment")
# Live View tab
self.live_view_tab = self.create_live_view_tab()
self.tab_widget.addTab(self.live_view_tab, "Live View")
layout.addWidget(self.tab_widget)
# Progress bar
@ -322,6 +328,30 @@ class DeploymentDialog(QDialog):
return widget
def create_live_view_tab(self) -> QWidget:
"""Create the live view tab for real-time output."""
widget = QWidget()
layout = QHBoxLayout(widget)
# Video display
video_group = QGroupBox("Live Video Feed")
video_layout = QVBoxLayout(video_group)
self.live_view_label = QLabel("Live view will appear here after deployment.")
self.live_view_label.setAlignment(Qt.AlignCenter)
self.live_view_label.setMinimumSize(640, 480)
video_layout.addWidget(self.live_view_label)
layout.addWidget(video_group, 2)
# Inference results
results_group = QGroupBox("Inference Results")
results_layout = QVBoxLayout(results_group)
self.results_text = QTextEdit()
self.results_text.setReadOnly(True)
results_layout.addWidget(self.results_text)
layout.addWidget(results_group, 1)
return widget
def populate_overview(self):
"""Populate overview tab with pipeline data."""
self.name_label.setText(self.pipeline_data.get('project_name', 'Untitled'))
@ -483,6 +513,7 @@ Stage Configurations:
self.deployment_worker.deployment_started.connect(self.on_deployment_started)
self.deployment_worker.deployment_completed.connect(self.on_deployment_completed)
self.deployment_worker.error_occurred.connect(self.on_deployment_error)
self.deployment_worker.frame_updated.connect(self.update_live_view)
self.deployment_worker.start()
@ -532,6 +563,19 @@ Stage Configurations:
self.close_button.setText("Close")
self.progress_bar.setVisible(False)
def update_live_view(self, frame):
"""Update the live view with a new frame."""
try:
# Convert the OpenCV frame to a QImage
height, width, channel = frame.shape
bytes_per_line = 3 * width
q_image = QImage(frame.data, width, height, bytes_per_line, QImage.Format_RGB888).rgbSwapped()
# Display the QImage in the QLabel
self.live_view_label.setPixmap(QPixmap.fromImage(q_image))
except Exception as e:
print(f"Error updating live view: {e}")
def apply_theme(self):
"""Apply consistent theme to the dialog."""
self.setStyleSheet("""