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4d8bdee 1a6e37b 4d8bdee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | #!/usr/bin/env python3
"""
Test script to verify CSV format matches SCHEMA_EXAMPLE.csv
"""
import csv
from io import StringIO
# Exemple de détections comme generées par le backend
frame_detections = [
{
"frame": 1,
"timestamp_sec": 0.033,
"scene_name": "abc123",
"group_id": "abc123",
"video_name": "traffic_clip1.mp4",
"track_id": "",
"class_name": "Vehicle",
"confidence": 0.912,
"bbox_x1": 340,
"bbox_y1": 210,
"bbox_x2": 520,
"bbox_y2": 310,
"cx": 430,
"cy": 260,
"frame_width": 1920,
"frame_height": 1080,
"crossed_line": "false",
"direction": "",
"speed_px_s": 0.0
},
{
"frame": 2,
"timestamp_sec": 0.067,
"scene_name": "abc123",
"group_id": "abc123",
"video_name": "traffic_clip1.mp4",
"track_id": "",
"class_name": "Vehicle",
"confidence": 0.908,
"bbox_x1": 345,
"bbox_y1": 213,
"bbox_x2": 525,
"bbox_y2": 313,
"cx": 435,
"cy": 263,
"frame_width": 1920,
"frame_height": 1080,
"crossed_line": "false",
"direction": "",
"speed_px_s": 14.2
},
{
"frame": 48,
"timestamp_sec": 1.600,
"scene_name": "abc123",
"group_id": "abc123",
"video_name": "traffic_clip1.mp4",
"track_id": "",
"class_name": "Vehicle",
"confidence": 0.887,
"bbox_x1": 560,
"bbox_y1": 290,
"bbox_x2": 740,
"bbox_y2": 390,
"cx": 650,
"cy": 340,
"frame_width": 1920,
"frame_height": 1080,
"crossed_line": "true",
"direction": "down",
"speed_px_s": 15.1
},
]
# Generate CSV
output = StringIO()
writer = csv.writer(output, lineterminator="\n")
# Header
writer.writerow([
"frame", "timestamp_sec", "scene_name", "group_id",
"video_name", "track_id", "class_name", "confidence",
"bbox_x1", "bbox_y1", "bbox_x2", "bbox_y2",
"cx", "cy", "frame_width", "frame_height",
"crossed_line", "direction", "speed_px_s"
])
# Data rows
for det in frame_detections:
writer.writerow([
det["frame"],
f"{det['timestamp_sec']:.3f}",
det["scene_name"],
det["group_id"],
det["video_name"],
det["track_id"],
det["class_name"],
f"{det['confidence']:.3f}",
det["bbox_x1"],
det["bbox_y1"],
det["bbox_x2"],
det["bbox_y2"],
det["cx"],
det["cy"],
det["frame_width"],
det["frame_height"],
det["crossed_line"],
det["direction"],
f"{det['speed_px_s']:.1f}"
])
# Print result
csv_output = output.getvalue()
print("Generated CSV:")
print("=" * 120)
print(csv_output)
print("=" * 120)
# Compare with expected format
expected_header = "frame,timestamp_sec,scene_name,group_id,video_name,track_id,class_name,confidence,bbox_x1,bbox_y1,bbox_x2,bbox_y2,cx,cy,frame_width,frame_height,crossed_line,direction,speed_px_s"
actual_header = csv_output.split('\n')[0]
print("\nExpected header:")
print(expected_header)
print("\nActual header:")
print(actual_header)
print("\nHeaders match:", expected_header == actual_header)
# Verify number of columns
lines = csv_output.strip().split('\n')
for i, line in enumerate(lines, 1):
cols = line.split(',')
expected_cols = 19
if len(cols) != expected_cols:
print(f"Row {i}: WARNING - Expected {expected_cols} columns, got {len(cols)}")
else:
print(f"Row {i}: OK ({len(cols)} columns)")
print("\n✅ CSV Format validation complete!")
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