A small python library to help you calculate pixel accurate:
- TP (true positives)
- TN (true negatives)
- FP (false positives)
- FN (false negatives)
- Jaccard Similarity Index (IoU)
- Threshold Jaccard Index
- Dice Coefficient
- Sensitivity
- Specificity
- Accuracy
for a binary segmentation task.
import cv2
import segmentation_metrics as sm
mask = cv2.imread("mask.png", cv2.IMREAD_GRAYSCALE)
predicted_mask = cv2.imread("predicted_mask.png", cv2.IMREAD_GRAYSCALE)
metrics = sm.calculate(
masks=[mask],
predicted_masks=[predicted_mask],
jaccard_threshold=0.65
)
print(metrics)
Example Output:
{
'n_images': 300,
'n_true_positives': 3086436,
'n_true_positives_%': 0.9052690301820284,
'n_true_negatives': 11023919,
'n_true_negatives_%': 0.9724538771281737,
'n_false_positives': 312268,
'n_false_negatives': 322977,
'threshold_jaccard_index': 0.7551666249674276,
'jaccard_similarity_index_(iou_score)': 0.8186671808348286,
'dice_coefficient': 0.8902348083199045,
'sensitivity': 0.9189991951163548,
'specificity': 0.9669434672587298,
'accuracy': 0.9569196912977429
}
- Add support for multiclass segmentation metrics