Add app.py

app.py

@@ -1,9 +1,11 @@
 import gradio as gr
 import numpy as np
+import pandas as pd
+from PIL import Image, ImageDraw, ImageFont
 from ultralytics import YOLO
 
 # Load YOLO model
-model = YOLO('TrashDetection/trash_detection.pt')  # Ganti dengan path model YOLO Anda
+model = YOLO('/Users/fajar/Kuliah/Semester 5/Computer-Vision/Project/src/app/TrashDetection/trash_detection.pt')
 
 def predict(image):
     """
@@ -11,29 +13,67 @@ def predict(image):
     Args:
         image (PIL.Image): Input image.
     Returns:
-        List[List]: Predictions with labels, confidence, and bounding boxes.
+        Tuple: Processed image with bounding boxes and a list of predictions with labels, confidence, and bounding boxes.
     """
     # Convert PIL image to numpy array
     img = np.array(image)
 
-    # Get predictions from the model
-    results = model(img)
+    results = model(img)  # Run the model on the image
+    if isinstance(results, list):
+        results = results[0]  # Take the first result if it's a list
 
-    # Extract predictions
-    predictions = results.pandas().xyxy[0]  # Pandas DataFrame of predictions
-    # Select necessary columns and convert to list
-    output = predictions[['name', 'confidence', 'xmin', 'ymin', 'xmax', 'ymax']].values.tolist()
+    # Access the bounding boxes and other relevant information
+    boxes = results[0].boxes  # Get the boxes from the first result
 
-    return output
+    # Convert the boxes to a Pandas DataFrame
+    df = pd.DataFrame(boxes.xyxy.cpu().numpy(), columns=['x1', 'y1', 'x2', 'y2'])
+    df['confidence'] = boxes.conf.cpu().numpy()
+    df['class'] = boxes.cls.cpu().numpy()
+
+    # Get the class names
+    class_names = results.names  # Class names dictionary
+    df['label'] = df['class'].apply(lambda x: class_names[int(x)])
+
+    # Select the necessary columns for the output
+    df_output = df[['label', 'confidence', 'x1', 'y1', 'x2', 'y2']]
+
+    # Convert DataFrame to list of lists
+    output = df_output.values.tolist()
+
+    # Draw bounding boxes on the image
+    pil_img = Image.fromarray(img)  # Convert numpy array back to PIL image
+    draw = ImageDraw.Draw(pil_img)
+
+    # Load a font with a larger size
+    try:
+        font = ImageFont.truetype("/Library/Fonts/Arial.ttf", 24)  # Adjust the font size as needed
+    except IOError:
+        font = ImageFont.load_default()  # Use default font if the specified one is not found
+
+    for _, row in df.iterrows():
+        x1, y1, x2, y2 = row['x1'], row['y1'], row['x2'], row['y2']
+        label = row['label']
+        confidence = row['confidence']
+
+        # Draw bounding box
+        draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
+
+        # Draw label and confidence with larger font
+        text = f"{label} ({confidence:.2f})"
+        draw.text((x1, y1 - 30), text, font=font, fill="red")
+
+    # Return the image with bounding boxes and the prediction results
+    return pil_img, output
 
 # Create Gradio interface
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil"),  # Input image as PIL
-    outputs=gr.Dataframe(
-        headers=["Label", "Confidence", "Xmin", "Ymin", "Xmax", "Ymax"],
-        label="Predictions"
-    ),
+    outputs=[gr.Image(type="pil"),  # Output image with bounding boxes
+             gr.Dataframe(
+                 headers=["Label", "Confidence", "Xmin", "Ymin", "Xmax", "Ymax"],
+                 label="Predictions"
+             )],
     title="YOLO Object Detection",
     description="Upload an image to detect objects using YOLO."
 )