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feat: enhance score calculation by adjusting ground truth sample poin… #28

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Merged
merged 1 commit into from
Sep 4, 2025
Merged

feat: enhance score calculation by adjusting ground truth sample poin… #28

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24 changes: 13 additions & 11 deletions cvat/apps/quality_control/views.py
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Original file line number Diff line number Diff line change
Expand Up @@ -446,10 +446,18 @@ def immediate_reports(self, request, *args, **kwargs):
# handle label matching task
else:
penalty_factor = 0.1
def calculate_score(gt_samples, ds_samples):
# Group annotations by label
def calculate_score(gt_samples, ds_samples, start_time=0):
gt_samples_adjusted = []
for ann in gt_samples:
adjusted_ann = ann.copy()
adjusted_points = adjusted_ann["points"].copy()
adjusted_points[0] = round(adjusted_points[0] - start_time, 10) # Round to 10 decimal places
adjusted_points[3] = round(adjusted_points[3] - start_time, 10)
adjusted_ann["points"] = adjusted_points
gt_samples_adjusted.append(adjusted_ann)

gt_by_label = {}
for idx, ann in enumerate(gt_samples):
for idx, ann in enumerate(gt_samples_adjusted):
label = ann.get("label_id", "")
if label not in gt_by_label:
gt_by_label[label] = []
Expand All @@ -466,27 +474,22 @@ def calculate_score(gt_samples, ds_samples):
total_gt_count = 0
unused_predictions = []

# Process each label separately
for label in set(list(gt_by_label.keys()) + list(ds_by_label.keys())):
gt_list = gt_by_label.get(label, [])
ds_list = ds_by_label.get(label, [])
total_gt_count += len(gt_list)

# Track best coverage for each GT
gt_coverage = [0.0] * len(gt_list)
used_predictions = [False] * len(ds_list)

# Check each GT against all predictions
for gt_idx, (orig_gt_idx, gt_ann) in enumerate(gt_list):
gt_start, gt_end = gt_ann["points"][0], gt_ann["points"][3]
gt_length = gt_end - gt_start

# Find all overlapping predictions
overlapping_predictions = []
for ds_idx, ds_ann in enumerate(ds_list):
ds_start, ds_end = ds_ann["points"][0], ds_ann["points"][3]

# Calculate overlap
overlap_start = max(gt_start, ds_start)
overlap_end = min(gt_end, ds_end)
overlap = max(0, overlap_end - overlap_start)
Expand All @@ -495,7 +498,6 @@ def calculate_score(gt_samples, ds_samples):
overlapping_predictions.append((ds_idx, overlap))
used_predictions[ds_idx] = True

# Merge overlaps to calculate total coverage
overlapping_predictions.sort(key=lambda x: x[1], reverse=True)
covered_intervals = []

Expand All @@ -520,7 +522,7 @@ def calculate_score(gt_samples, ds_samples):

# Calculate total coverage
total_covered = sum(end - start for start, end in covered_intervals)
coverage_ratio = min(1.0, total_covered / gt_length)
coverage_ratio = min(1.0, total_covered / gt_length) if gt_length > 0 else 0
gt_coverage[gt_idx] = coverage_ratio

# Add unused predictions for this label to global list
Expand All @@ -547,7 +549,7 @@ def calculate_score(gt_samples, ds_samples):

return final_score

score = calculate_score(gt_samples_filtered, ds_samples_filtered)
score = calculate_score(gt_samples_filtered, ds_samples_filtered, start_time)

response_data = {
"score": score,
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