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Running on Zero
Running on Zero
| import numpy as np | |
| from cellquant.metrics import count_errors, evaluate_cells, evaluate_nostrils, match_by_centroid, match_by_iou, micro_average | |
| def _two_squares(): | |
| inst = np.zeros((50, 50), np.int32) | |
| inst[5:15, 5:15] = 1 | |
| inst[30:45, 30:45] = 2 | |
| return inst | |
| def test_perfect_prediction_scores_one(): | |
| inst = _two_squares() | |
| labels = ["", "Cell, Antibody +", "Cell, Antibody -"] | |
| r = evaluate_cells(inst, labels, inst.copy(), list(labels)) | |
| assert r["detection"]["f1"] == 1.0 and r["attribute_accuracy"]["both"] == 1.0 | |
| assert r["per_class"]["Cell, Antibody +"]["f1"] == 1.0 | |
| def test_wrong_label_counts_as_fp_and_fn_for_classes_but_detected(): | |
| inst = _two_squares() | |
| gt = ["", "Cell, Antibody +", "Cell, Antibody -"] | |
| pred = ["", "Cell, Antibody +", "Cell, Antibody +"] | |
| r = evaluate_cells(inst, gt, inst.copy(), pred) | |
| assert r["detection"]["tp"] == 2 | |
| assert r["per_class"]["Cell, Antibody -"]["fn"] == 1 and r["per_class"]["Cell, Antibody +"]["fp"] == 1 | |
| assert r["attribute_accuracy"]["antibody"] == 0.5 | |
| def test_shifted_prediction_fails_iou_but_passes_centroid(): | |
| gt = _two_squares() | |
| pred = np.zeros_like(gt) | |
| pred[9:19, 9:19] = 1 # IoU with square 1 = 36/164 < 0.5, centroid (14,14) is inside GT square 1 | |
| pred[30:45, 30:45] = 2 | |
| assert len(match_by_iou(gt, pred, 0.5)) == 1 | |
| r = evaluate_cells(gt, ["", "Cell, Antibody +", "Cell, Antibody +"], pred, ["", "Cell, Antibody +", "Cell, Antibody +"]) | |
| assert r["detection"]["tp"] == 1 and r["detection"]["centroid"]["tp"] == 2 | |
| def test_nostril_matching_by_distance(): | |
| gt = np.zeros((40, 40), np.int32) | |
| gt[10:13, 10:13] = 1 | |
| gt[30:33, 30:33] = 2 | |
| pred = np.zeros_like(gt) | |
| pred[12:15, 12:15] = 1 # 2 px away -> match | |
| pred[5:8, 30:33] = 2 # 25 px away -> no match | |
| m = match_by_centroid(gt, pred, 6.0) | |
| assert len(m) == 1 and m[0][:2] == (1, 1) | |
| r = evaluate_nostrils(gt, ["", "Nostril in Nucleus", "Nostril External of Nucleus"], pred, ["", "Nostril in Nucleus", "Nostril in Nucleus"]) | |
| assert r["detection"]["tp"] == 1 and r["detection"]["fp"] == 1 and r["detection"]["fn"] == 1 | |
| assert r["type_accuracy"] == 1.0 | |
| def test_count_errors_and_micro_average(): | |
| ce = count_errors({"Cell, Antibody +": 10}, {"Cell, Antibody +": 8}) | |
| assert ce["Cell, Antibody +"]["abs_err"] == 2 and abs(ce["Cell, Antibody +"]["rel_err"] - 0.2) < 1e-9 | |
| per = [{"A": {"tp": 1, "fp": 1, "fn": 0, "gt_count": 1, "pred_count": 2}}, {"A": {"tp": 3, "fp": 0, "fn": 1, "gt_count": 4, "pred_count": 3}}] | |
| m = micro_average(per, ["A"]) | |
| assert m["A"]["tp"] == 4 and m["A"]["fp"] == 1 and m["A"]["fn"] == 1 and abs(m["A"]["f1"] - 0.8) < 1e-9 | |