| from tqdm import tqdm |
|
|
| import aix |
| from aix import AIX_DATASETS, AIX_EVALS, MATURE, IMMATURE |
| from aix.np_metrics import hard_dice, dice, rel_size_diff |
| import cv2 |
| import pandas as pd |
| import json |
|
|
| all_measures = { |
| "dice": dice, |
| "hard_dice": hard_dice, |
| "rel_size_diff": rel_size_diff |
| } |
|
|
| OOCYTE = "oocyte" |
| REFERENCE = "reference" |
| COMPARED = "compared" |
|
|
|
|
| def load_csv(filename): |
| csv_file_path = AIX_EVALS / filename |
| if csv_file_path.is_file(): |
| print("Loaded") |
| return pd.read_csv(csv_file_path, dtype={OOCYTE: str}) |
| else: |
| print("NOT FOUND!!!") |
| print(csv_file_path) |
| return None |
|
|
|
|
| def compare_masks(reference: str, alternatives: str, measures=all_measures, |
| in_csv_metrics="eval.csv", out_csv_metrics="out.csv"): |
| df_old = load_csv(in_csv_metrics) |
| print(df_old) |
| reference_dataset = aix.Dataset.from_file(AIX_DATASETS / reference) |
| alt_datasets = {} |
| for alt in alternatives: |
| alt_datasets[alt] = aix.Dataset.from_file(AIX_DATASETS / alt) |
| l = [] |
| for item_mask in tqdm(reference_dataset.iterate_mask_items()): |
| ref_mask_img = item_mask.norm_image(cv2.IMREAD_GRAYSCALE) |
| for alt, alt_d in alt_datasets.items(): |
| if item_mask.index in alt_d.oocytes: |
| alt_mask = aix.Item(alt_d, mask=item_mask.mask, stage=item_mask.stage, index=item_mask.index) |
| alt_mask_img = alt_mask.norm_image(cv2.IMREAD_GRAYSCALE) |
| d = {OOCYTE: item_mask.index, |
| MATURE: item_mask.stage, |
| REFERENCE: reference_dataset.name, |
| COMPARED: alt_d.name} |
| for measure_name, measure in measures.items(): |
| m = measure(ref_mask_img, alt_mask_img) |
| d[measure_name] = m |
| l.append(d) |
| else: |
| print("Ignoring oocyte " + str(item_mask.index) + " for dataset " + alt_d.name) |
| |
| df = pd.DataFrame(l) |
| if df_old is not None: |
| index_fields = [OOCYTE, MATURE, REFERENCE, COMPARED] |
| df = df_old.merge(df, how='outer', on=index_fields, suffixes=("_x", None)) |
| print("After merge") |
| print(df[COMPARED].unique()) |
| for measure_name in measures: |
| index_fields.append(measure_name) |
| df[measure_name].fillna(df[measure_name + "_x"], inplace=True) |
| df = df[index_fields] |
| out_csv_file_path = AIX_EVALS / out_csv_metrics |
| out_csv_file_path.parent.mkdir(parents=True, exist_ok=True) |
| print(out_csv_file_path) |
| df.to_csv(out_csv_file_path, index=False) |
|
|
| return df |
|
|
|
|
| def make_dict(df: pd.DataFrame): |
| d = {} |
|
|
| metrics = df.columns |
| for index, row in df.iterrows(): |
| name = "-".join(index) |
| for i, val in enumerate(row): |
| namem = name + "-" + metrics[i] |
| d[namem] = val |
| return d |
|
|
| def summarize_comparison(df:pd.DataFrame, metrics_file="eval.json"): |
| print(df) |
| gb = df.drop([MATURE,OOCYTE], axis=1).groupby([REFERENCE, COMPARED]) |
| print(gb) |
| means = make_dict(gb.mean()) |
| medians = make_dict(gb.median()) |
| d = {"MEANS": means, "MEDIANS": medians} |
| filepath = AIX_EVALS/metrics_file |
| with filepath.open("w") as f: |
| json.dump(d, f) |
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| def evaluate_scale_loss(reference: str, dim, interpolation=cv2.INTER_AREA, extrapolation=cv2.INTER_CUBIC, measures=all_measures): |
| reference_dataset = aix.Dataset.from_file(reference) |
| measure_vals = {} |
| for measure_name in measures: |
| measure_vals[measure_name] = [] |
| for item_mask in tqdm(reference_dataset.iterate_mask_items()): |
| ref_mask_img = item_mask.uint_norm_image() |
| |
| resized = cv2.resize(ref_mask_img, dim, interpolation=interpolation) |
| back_area = cv2.resize(resized, (ref_mask_img.shape[1], ref_mask_img.shape[0]), interpolation=extrapolation) |
| back_area[back_area<0.]=0. |
| back_area[back_area>1.]=1. |
| for measure_name, measure in measures.items(): |
| measure_vals[measure_name].append(measure(ref_mask_img, back_area)) |
| |
| print("\nReference: " + reference + "\n") |
| |
| first_measure_name = next(iter(measures)) |
| n_masks = len(measure_vals[first_measure_name]) |
| print(" N.Images : %4d" % n_masks) |
| for measure_name in measures: |
| print(" " + measure_name + ": %4.2f" % (sum(measure_vals[measure_name]) / n_masks)) |
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