test_csv_format.py

#!/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!")