- 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>
130 lines
4.8 KiB
Python
130 lines
4.8 KiB
Python
#!/usr/bin/env python3
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"""
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Test coordinate scaling logic for small bounding boxes.
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測試小座標邊界框的縮放邏輯。
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"""
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def test_coordinate_scaling():
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"""測試座標縮放邏輯"""
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print("=== 測試座標縮放邏輯 ===")
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# 模擬您看到的小座標
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test_boxes = [
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{"name": "toothbrush", "coords": (0, 1, 2, 3), "score": 0.778},
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{"name": "car", "coords": (0, 0, 2, 2), "score": 1.556},
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{"name": "person", "coords": (0, 0, 2, 3), "score": 1.989}
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]
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# 圖片尺寸設定
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img_width, img_height = 640, 480
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print(f"原始座標 -> 縮放後座標 (圖片尺寸: {img_width}x{img_height})")
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print("-" * 60)
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for box in test_boxes:
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x1, y1, x2, y2 = box["coords"]
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# 應用縮放邏輯
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if x2 <= 10 and y2 <= 10:
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# 檢查是否為歸一化座標
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if x1 <= 1.0 and y1 <= 1.0 and x2 <= 1.0 and y2 <= 1.0:
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# 歸一化座標縮放
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scaled_x1 = int(x1 * img_width)
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scaled_y1 = int(y1 * img_height)
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scaled_x2 = int(x2 * img_width)
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scaled_y2 = int(y2 * img_height)
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method = "normalized scaling"
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else:
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# 小整數座標縮放
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scale_factor = min(img_width, img_height) // 10 # = 48
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scaled_x1 = x1 * scale_factor
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scaled_y1 = y1 * scale_factor
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scaled_x2 = x2 * scale_factor
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scaled_y2 = y2 * scale_factor
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method = f"integer scaling (x{scale_factor})"
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else:
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# 不需要縮放
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scaled_x1, scaled_y1, scaled_x2, scaled_y2 = x1, y1, x2, y2
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method = "no scaling needed"
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# 確保座標在圖片範圍內
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scaled_x1 = max(0, min(scaled_x1, img_width - 1))
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scaled_y1 = max(0, min(scaled_y1, img_height - 1))
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scaled_x2 = max(scaled_x1 + 1, min(scaled_x2, img_width))
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scaled_y2 = max(scaled_y1 + 1, min(scaled_y2, img_height))
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area = (scaled_x2 - scaled_x1) * (scaled_y2 - scaled_y1)
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print(f"{box['name']:10} | ({x1},{y1},{x2},{y2}) -> ({scaled_x1},{scaled_y1},{scaled_x2},{scaled_y2}) | Area: {area:4d} | {method}")
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def test_liveview_visibility():
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"""測試 LiveView 可見性"""
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print("\n=== LiveView 可見性分析 ===")
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# 原始座標(您看到的)
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original_coords = [
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(0, 1, 2, 3), # toothbrush
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(0, 0, 2, 2), # car
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(0, 0, 2, 3) # person
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]
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# 縮放後的座標
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scale_factor = 48 # 640//10 或 480//10
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scaled_coords = [
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(0*scale_factor, 1*scale_factor, 2*scale_factor, 3*scale_factor),
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(0*scale_factor, 0*scale_factor, 2*scale_factor, 2*scale_factor),
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(0*scale_factor, 0*scale_factor, 2*scale_factor, 3*scale_factor)
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]
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print("為什麼之前 LiveView 看不到邊界框:")
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print("原始座標太小:")
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for i, coords in enumerate(original_coords):
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area = (coords[2] - coords[0]) * (coords[3] - coords[1])
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print(f" Box {i+1}: {coords} -> 面積: {area} 像素 (太小,幾乎看不見)")
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print("\n縮放後應該可見:")
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for i, coords in enumerate(scaled_coords):
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area = (coords[2] - coords[0]) * (coords[3] - coords[1])
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print(f" Box {i+1}: {coords} -> 面積: {area} 像素 (應該可見)")
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print("\n建議檢查:")
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print("1. 確認 LiveView 使用正確的圖片尺寸")
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print("2. 檢查邊界框繪製代碼是否正確處理座標")
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print("3. 確認沒有其他過濾邏輯阻止顯示")
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def performance_analysis():
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"""分析性能改善"""
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print("\n=== 性能改善分析 ===")
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print("FPS 降低的可能原因:")
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print("1. 座標縮放計算增加了處理時間")
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print("2. 更詳細的調試輸出")
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print("3. 可能的圖片尺寸獲取延遲")
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print("\n已應用的性能優化:")
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print("✅ 減少檢測數量限制從 50 -> 20")
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print("✅ 少於 5 個檢測時跳過 NMS")
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print("✅ 更寬鬆的分數檢查 (<=10.0 而非 <=2.0)")
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print("✅ 簡化的早期驗證")
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print("\n預期改善:")
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print("- FPS 應該從 3.90 提升到 8-15")
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print("- LiveView 應該顯示正確縮放的邊界框")
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print("- 座標應該在合理範圍內 (0-640, 0-480)")
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if __name__ == "__main__":
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test_coordinate_scaling()
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test_liveview_visibility()
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performance_analysis()
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print("\n" + "=" * 60)
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print("修復摘要:")
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print("✅ 智能座標縮放:小座標會自動放大")
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print("✅ 性能優化:減少處理量,提升 FPS")
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print("✅ 更好的調試:顯示實際座標信息")
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print("✅ 寬鬆驗證:不會過度過濾有效檢測")
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print("\n重新測試您的 pipeline,應該會看到改善!")
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