HuangMason320
ccd7cdd6b9
- Move test scripts to tests/ directory for better organization - Add improved YOLOv5 postprocessing with reference implementation - Update gitignore to exclude *.mflow files and include main.spec - Add debug capabilities and coordinate scaling improvements - Enhance multi-series support with proper validation - Add AGENTS.md documentation and example utilities 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
226 lines
8.3 KiB
Python
226 lines
8.3 KiB
Python
#!/usr/bin/env python3
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"""
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Test script to verify YOLOv5 postprocessing fixes
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This script tests the improved YOLOv5 postprocessing configuration
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to ensure positive probabilities and proper bounding box detection.
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"""
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import sys
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import os
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import numpy as np
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# Add core functions to path
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current_dir = os.path.dirname(os.path.abspath(__file__))
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parent_dir = os.path.dirname(current_dir)
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sys.path.append(os.path.join(parent_dir, 'core', 'functions'))
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def test_yolov5_postprocessor():
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"""Test the improved YOLOv5 postprocessor with mock data"""
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from Multidongle import PostProcessorOptions, PostProcessType, PostProcessor
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print("=" * 60)
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print("Testing Improved YOLOv5 Postprocessor")
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print("=" * 60)
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# Create YOLOv5 postprocessor options
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options = PostProcessorOptions(
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postprocess_type=PostProcessType.YOLO_V5,
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threshold=0.3,
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class_names=["person", "bicycle", "car", "motorbike", "aeroplane", "bus"],
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nms_threshold=0.5,
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max_detections_per_class=50
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)
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postprocessor = PostProcessor(options)
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print(f"✓ Postprocessor created with type: {options.postprocess_type.value}")
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print(f"✓ Confidence threshold: {options.threshold}")
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print(f"✓ NMS threshold: {options.nms_threshold}")
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print(f"✓ Number of classes: {len(options.class_names)}")
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# Create mock YOLOv5 output data - format: [batch, detections, features]
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# Features: [x_center, y_center, width, height, objectness, class0_prob, class1_prob, ...]
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mock_output = create_mock_yolov5_output()
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# Test processing
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try:
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result = postprocessor.process([mock_output])
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print(f"\n📊 Processing Results:")
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print(f" Result type: {type(result).__name__}")
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print(f" Detected objects: {result.box_count}")
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print(f" Available classes: {result.class_count}")
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if result.box_count > 0:
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print(f"\n📦 Detection Details:")
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for i, box in enumerate(result.box_list):
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print(f" Detection {i+1}:")
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print(f" Class: {box.class_name} (ID: {box.class_num})")
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print(f" Confidence: {box.score:.3f}")
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print(f" Bounding Box: ({box.x1}, {box.y1}) to ({box.x2}, {box.y2})")
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print(f" Box Size: {box.x2 - box.x1} x {box.y2 - box.y1}")
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# Verify positive probabilities
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all_positive = all(box.score > 0 for box in result.box_list)
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print(f"\n✓ All probabilities positive: {all_positive}")
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# Verify reasonable coordinates
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valid_coords = all(
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box.x2 > box.x1 and box.y2 > box.y1
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for box in result.box_list
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)
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print(f"✓ All bounding boxes valid: {valid_coords}")
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return result
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except Exception as e:
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print(f"❌ Postprocessing failed: {e}")
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import traceback
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traceback.print_exc()
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return None
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def create_mock_yolov5_output():
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"""Create mock YOLOv5 output data for testing"""
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# YOLOv5 output format: [batch_size, num_detections, num_features]
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# Features: [x_center, y_center, width, height, objectness, class_probs...]
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batch_size = 1
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num_detections = 25200 # Typical YOLOv5 output size
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num_classes = 80 # COCO classes
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num_features = 5 + num_classes # coords + objectness + class probs
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# Create mock output
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mock_output = np.zeros((batch_size, num_detections, num_features), dtype=np.float32)
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# Add some realistic detections
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detections = [
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# Format: [x_center, y_center, width, height, objectness, class_id, class_prob]
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[320, 240, 100, 150, 0.8, 0, 0.9], # person
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[500, 300, 80, 60, 0.7, 2, 0.85], # car
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[150, 100, 60, 120, 0.6, 1, 0.75], # bicycle
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]
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for i, detection in enumerate(detections):
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x_center, y_center, width, height, objectness, class_id, class_prob = detection
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# Set coordinates and objectness
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mock_output[0, i, 0] = x_center
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mock_output[0, i, 1] = y_center
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mock_output[0, i, 2] = width
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mock_output[0, i, 3] = height
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mock_output[0, i, 4] = objectness
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# Set class probabilities (one-hot style)
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mock_output[0, i, 5 + int(class_id)] = class_prob
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print(f"✓ Created mock YOLOv5 output: {mock_output.shape}")
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print(f" Added {len(detections)} test detections")
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# Wrap in mock output object
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class MockOutput:
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def __init__(self, data):
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self.ndarray = data
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return MockOutput(mock_output)
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def test_result_formatting():
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"""Test the result formatting functions"""
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from Multidongle import ObjectDetectionResult, BoundingBox
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print(f"\n" + "=" * 60)
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print("Testing Result Formatting")
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print("=" * 60)
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# Create mock detection result
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boxes = [
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BoundingBox(x1=100, y1=200, x2=200, y2=350, score=0.85, class_num=0, class_name="person"),
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BoundingBox(x1=300, y1=150, x2=380, y2=210, score=0.75, class_num=2, class_name="car"),
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BoundingBox(x1=50, y1=100, x2=110, y2=220, score=0.65, class_num=1, class_name="bicycle"),
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]
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result = ObjectDetectionResult(
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class_count=80,
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box_count=len(boxes),
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box_list=boxes
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)
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# Test the enhanced result string generation
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from Multidongle import MultiDongle, PostProcessorOptions, PostProcessType
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# Create a minimal MultiDongle instance to access the method
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options = PostProcessorOptions(postprocess_type=PostProcessType.YOLO_V5)
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multidongle = MultiDongle(port_id=[1], postprocess_options=options) # Dummy port
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result_string = multidongle._generate_result_string(result)
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print(f"📝 Generated result string: {result_string}")
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# Test individual object summaries
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print(f"\n📊 Object Summary:")
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object_counts = {}
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for box in boxes:
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if box.class_name in object_counts:
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object_counts[box.class_name] += 1
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else:
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object_counts[box.class_name] = 1
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for class_name, count in sorted(object_counts.items()):
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print(f" {count} {class_name}{'s' if count > 1 else ''}")
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return result
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def show_configuration_usage():
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"""Show how to use the fixed configuration"""
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print(f"\n" + "=" * 60)
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print("Configuration Usage Instructions")
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print("=" * 60)
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print(f"\n🔧 Updated Configuration:")
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print(f" 1. Modified multi_series_example.mflow:")
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print(f" - Set 'enable_postprocessing': true")
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print(f" - Added ExactPostprocessNode with YOLOv5 settings")
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print(f" - Connected Model → Postprocess → Output")
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print(f"\n⚙️ Postprocessing Settings:")
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print(f" - postprocess_type: 'yolo_v5'")
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print(f" - confidence_threshold: 0.3")
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print(f" - nms_threshold: 0.5")
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print(f" - class_names: Full COCO 80 classes")
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print(f"\n🎯 Expected Improvements:")
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print(f" ✓ Positive probability values (0.0 to 1.0)")
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print(f" ✓ Proper object detection with bounding boxes")
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print(f" ✓ Correct class names (person, car, bicycle, etc.)")
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print(f" ✓ Enhanced live view with corner markers")
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print(f" ✓ Detailed terminal output with object counts")
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print(f" ✓ Non-Maximum Suppression to reduce duplicates")
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print(f"\n📁 Files Modified:")
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print(f" - core/functions/Multidongle.py (improved YOLO processing)")
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print(f" - multi_series_example.mflow (added postprocess node)")
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print(f" - Enhanced live view display and terminal output")
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if __name__ == "__main__":
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print("YOLOv5 Postprocessing Fix Verification")
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print("=" * 60)
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try:
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# Test the postprocessor
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result = test_yolov5_postprocessor()
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if result:
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# Test result formatting
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test_result_formatting()
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# Show usage instructions
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show_configuration_usage()
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print(f"\n🎉 All tests passed! YOLOv5 postprocessing should now work correctly.")
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print(f" Use the updated multi_series_example.mflow configuration.")
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else:
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print(f"\n❌ Tests failed. Please check the error messages above.")
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except Exception as e:
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print(f"\n❌ Test suite failed with error: {e}")
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import traceback
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traceback.print_exc()
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sys.exit(1)
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