add dataset

.gitignore

@@ -0,0 +1 @@
+__pycache__

app.py

@@ -1,7 +1,101 @@
+from PIL import Image
 import gradio as gr
+import numpy as np
+from datasets import load_dataset
 
-def greet(name):
-    return "Hello " + name + "!!"
+dataset = load_dataset("erceguder/histocan-test", token=True)
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+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()

requirements.txt

@@ -1 +1,2 @@
-gradio==3.45.2
+gradio==3.45.2
+datasets==2.14.6