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| from PIL import Image | |
| import gradio as gr | |
| import numpy as np | |
| from datasets import load_dataset | |
| dataset = load_dataset("erceguder/histocan-test", token=True) | |
| COLOR_PALETTE = { | |
| 'others': (0, 0, 0), | |
| 't-g1': (0, 192, 0), | |
| 't-g2': (255, 224, 32), | |
| 't-g3': (255, 0, 0), | |
| 'normal-mucosa': (0, 32, 255) | |
| } | |
| def files_uploaded(paths): | |
| if len(paths) != 16: | |
| raise gr.Error("16 segmentation masks are needed.") | |
| def evaluate(paths): | |
| if paths == None: | |
| raise gr.Error("Upload segmentation masks first!") | |
| # Init dicts for accumulating image metrics and calculating per-class scores | |
| metrics = {} | |
| for class_ in COLOR_PALETTE.keys(): | |
| idict = { | |
| "tp": 0.0, | |
| "fp": 0.0, | |
| "tn": 0.0, | |
| "fn": 0.0, | |
| } | |
| metrics[class_] = idict | |
| scores = {} | |
| for class_ in COLOR_PALETTE.keys(): | |
| idict = { | |
| "recall": 0.0, | |
| "precision": 0.0, | |
| "f1": 0.0 | |
| } | |
| scores[class_] = idict | |
| for path in paths: | |
| pred = np.array(Image.open(path.name)) # shape (H, W, 3) | |
| # gt = np.array(Image.open(os.path.basename(file.name))) | |
| # assert gt.ndim == 2 | |
| assert pred.ndim == 3 and pred.shape[-1] == 3 | |
| # assert gt.shape == pred.shape[:-1] | |
| # Get predictions for all classes | |
| out = [(pred == color).all(axis=-1) for color in COLOR_PALETTE.values()] | |
| maps = np.stack(out) | |
| # Calculate confusion matrix and metrics | |
| for i, class_ in enumerate(COLOR_PALETTE.keys()): | |
| class_pred = maps[i] | |
| # class_gt = (gt == i) | |
| # tp = np.sum(class_pred[class_gt==True]) | |
| # fp = np.sum(class_pred[class_gt==False]) | |
| # tn = np.sum(np.logical_not(class_pred)[class_gt==False]) | |
| # fn = np.sum(np.logical_not(class_pred)[class_gt==True]) | |
| # # Accumulate metrics for each class | |
| # metrics[class_]['tp'] += tp | |
| # metrics[class_]['fp'] += fp | |
| # metrics[class_]['tn'] += tn | |
| # metrics[class_]['fn'] += fn | |
| # Init mean recall, precision and F1 score | |
| mRecall = 0.0 | |
| mPrecision = 0.0 | |
| mF1 = 0.0 | |
| # Calculate recall, precision and f1 scores for each class | |
| for i, class_ in enumerate(COLOR_PALETTE.keys()): | |
| scores[class_]['recall'] = metrics[class_]['tp'] / (metrics[class_]['tp'] + metrics[class_]['fn']) if metrics[class_]['tp'] > 0 else 0.0 | |
| scores[class_]['precision'] = metrics[class_]['tp'] / (metrics[class_]['tp'] + metrics[class_]['fp']) if metrics[class_]['tp'] > 0 else 0.0 | |
| scores[class_]['f1'] = 2 * scores[class_]['precision'] * scores[class_]['recall'] / (scores[class_]['precision'] + scores[class_]['recall']) if (scores[class_]['precision'] != 0 and scores[class_]['recall'] != 0) else 0.0 | |
| mRecall += scores[class_]['recall'] | |
| mPrecision += scores[class_]['precision'] | |
| mF1 += scores[class_]['f1'] | |
| # Calculate mean recall, precision and F1 score over all classes | |
| class_count = len(COLOR_PALETTE) | |
| mRecall /= class_count | |
| mPrecision /= class_count | |
| mF1 /= class_count | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# HistoCan Evaluation Page") | |
| files = gr.File(label="Upload the segmentation masks for test set", file_count="multiple", file_types=["image"]) | |
| run = gr.Button(value="Run evaluation") | |
| files.upload(files_uploaded, files, []) | |
| run.click(evaluate, files, []) | |
| demo.launch() |