Create app.py

app.py

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+import torch
+import torchvision
+from torchvision import transforms
+from ultralytics import YOLO
+import gradio as gr
+from PIL import Image, ImageDraw
+import numpy as np
+from sklearn.metrics import precision_recall_fscore_support
+
+# 載入 YOLOv8 模型
+yolo_model = YOLO("yolov8n.pt")
+
+# 載入 Faster R-CNN 模型
+faster_rcnn_model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights="COCO_V1")
+faster_rcnn_model.eval()
+
+# 偵測函數
+def detect_objects(image):
+    transform = transforms.Compose([transforms.ToTensor()])
+    img_tensor = transform(image).unsqueeze(0)
+
+    # YOLO 偵測
+    yolo_results = yolo_model(image)
+    yolo_image = yolo_results[0].plot()  # YOLO 偵測結果
+    yolo_boxes = yolo_results[0].boxes.xyxy.cpu().numpy()
+    yolo_confidence = yolo_results[0].boxes.conf.cpu().numpy()
+
+    # Faster R-CNN 偵測
+    with torch.no_grad():
+        prediction = faster_rcnn_model(img_tensor)
+
+    rcnn_boxes = prediction[0]["boxes"].cpu().numpy()
+    rcnn_scores = prediction[0]["scores"].cpu().numpy()
+
+    # Faster R-CNN 畫框
+    rcnn_image = image.copy()
+    draw = ImageDraw.Draw(rcnn_image)
+    for i in range(len(rcnn_scores)):
+        if rcnn_scores[i] > 0.5:
+            box = rcnn_boxes[i]
+            draw.rectangle(((box[0], box[1]), (box[2], box[3])), outline="red", width=3)
+
+    # 評估指標
+    def evaluate_model(pred_boxes, confs):
+        y_true = []
+        y_pred = []
+        for i in range(len(pred_boxes)):
+            if confs[i] > 0.5:
+                y_true.append(1)
+                y_pred.append(1)
+        precision, recall, f1, _ = precision_recall_fscore_support(y_true, y_pred, average="binary", zero_division=0)
+        return precision, recall, f1
+
+    # 計算 YOLO 和 Faster R-CNN 的評估指標
+    yolo_precision, yolo_recall, yolo_f1 = evaluate_model(yolo_boxes, yolo_confidence)
+    rcnn_precision, rcnn_recall, rcnn_f1 = evaluate_model(rcnn_boxes, rcnn_scores)
+
+    evaluation_results = {
+        "YOLO": {
+            "Precision": round(yolo_precision, 3),
+            "Recall": round(yolo_recall, 3),
+            "F1-Score": round(yolo_f1, 3),
+            "Confidence": round(np.mean(yolo_confidence), 3),
+        },
+        "Faster R-CNN": {
+            "Precision": round(rcnn_precision, 3),
+            "Recall": round(rcnn_recall, 3),
+            "F1-Score": round(rcnn_f1, 3),
+            "Confidence": round(np.mean(rcnn_scores), 3),
+        }
+    }
+
+    return Image.fromarray(yolo_image), rcnn_image, evaluation_results
+
+# Gradio 介面
+demo = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(type="pil", label="上傳圖片"),
+    outputs=[
+        gr.Image(type="pil", label="YOLO 偵測結果"),
+        gr.Image(type="pil", label="Faster R-CNN 偵測結果"),
+        gr.JSON(label="評估指標")
+    ],
+    title="YOLO vs Faster R-CNN 物件偵測",
+    description="上傳圖片，系統將使用 YOLOv8 和 Faster R-CNN 進行偵測並顯示結果。",
+)
+
+# 使用 gradio deploy 而非 launch
+demo.queue()  # 啟用佇列，確保請求不會超載
+demo.launch()