Upload 6 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+image-logo.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,47 @@
+import gradio as gr
+from ultralytics import YOLO
+import cv2
+import numpy as np
+from PIL import Image
+
+# Load YOLOv8 model
+model = YOLO("best-glaucoma-od.pt")
+
+# Function to perform prediction
+def predict_image(input_image):
+    # Convert Gradio input image (PIL Image) to numpy array
+    image_np = np.array(input_image)
+
+    # Ensure the image is in the correct format
+    if len(image_np.shape) == 2:  # grayscale to RGB
+        image_np = cv2.cvtColor(image_np, cv2.COLOR_GRAY2RGB)
+    elif image_np.shape[2] == 4:  # RGBA to RGB
+        image_np = cv2.cvtColor(image_np, cv2.COLOR_RGBA2RGB)
+
+    # Perform prediction
+    results = model(image_np)
+
+    # Draw bounding boxes on the image
+    image_with_boxes = image_np.copy()
+    raw_predictions = []
+    for result in results[0].boxes:
+        label = "Positive" if result.cls.item() == 1 else "Negative"  # Convert tensor to standard Python type
+        confidence = result.conf.item()  # Convert tensor to standard Python type
+        xmin, ymin, xmax, ymax = map(int, result.xyxy[0])
+        cv2.rectangle(image_with_boxes, (xmin, ymin), (xmax, ymax), (255, 0, 0), 2)
+        cv2.putText(image_with_boxes, f'{label} {confidence:.2f}', (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
+        raw_predictions.append(f"Label: {label}, Confidence: {confidence:.2f}, Box: [{xmin}, {ymin}, {xmax}, {ymax}]")
+
+    raw_predictions_str = "\n".join(raw_predictions)
+
+    return image_with_boxes, raw_predictions_str
+
+# Create Gradio interface
+inputs = gr.Image(type="pil")
+outputs = [gr.Image(type="numpy", label="Predicted Image"), gr.Textbox(label="Raw Result")]
+title = "YOLOv8 Glaucoma Detection"
+description = "Insert an image and click submit to detect glaucoma."
+iface = gr.Interface(fn=predict_image, inputs=inputs, outputs=outputs, title=title, description=description)
+
+# Launch the interface
+iface.launch()

best-glaucoma-seg.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:022416d3f286d1449c3a316c63be5434df1cf5aebee202b56ef80bd79206e994
+size 6809443

last.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:559b294684f9a5cef7c3e05296bd334e6b304a639cabf172451462215227bd75
+size 6802157

requirements(1).txt

@@ -0,0 +1,6 @@
+gradio
+opencv-python-headless
+ultralytics
+numpy
+Pillow
+scikit-image