Upload 2 files

app.py

@@ -0,0 +1,74 @@
+import torch
+import torchvision
+from torchvision.models.detection import FasterRCNN_ResNet50_FPN_Weights
+from PIL import Image
+import numpy as np
+import matplotlib.pyplot as plt
+import gradio as gr
+
+# Load the pre-trained model once
+model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT)
+model.eval()
+
+# COCO class names
+COCO_INSTANCE_CATEGORY_NAMES = [
+    '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
+    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign',
+    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+    'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A',
+    'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
+    'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
+    'bottle', 'N/A', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
+    'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
+    'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table',
+    'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A', 'book',
+    'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
+]
+
+# Gradio-compatible detection function
+def detect_objects(image, threshold=0.5):
+    transform = FasterRCNN_ResNet50_FPN_Weights.DEFAULT.transforms()
+    image_tensor = transform(image).unsqueeze(0)
+
+    with torch.no_grad():
+        prediction = model(image_tensor)[0]
+
+    boxes = prediction['boxes'].cpu().numpy()
+    labels = prediction['labels'].cpu().numpy()
+    scores = prediction['scores'].cpu().numpy()
+
+    image_np = np.array(image)
+    plt.figure(figsize=(10, 10))
+    plt.imshow(image_np)
+    ax = plt.gca()
+
+    for box, label, score in zip(boxes, labels, scores):
+        if score >= threshold:
+            x1, y1, x2, y2 = box
+            ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1,
+                         fill=False, color='red', linewidth=2))
+            class_name = COCO_INSTANCE_CATEGORY_NAMES[label]
+            ax.text(x1, y1, f'{class_name}: {score:.2f}', bbox=dict(facecolor='yellow', alpha=0.5),
+                    fontsize=12, color='black')
+
+    plt.axis('off')
+    plt.tight_layout()
+    
+    # Save the figure to return
+    plt.savefig("output.png")
+    plt.close()
+    return "output.png"
+
+# Create Gradio interface
+gr.Interface(
+    fn=detect_objects,
+    inputs=[
+        gr.Image(type="pil"),
+        gr.Slider(0, 1, value=0.5, label="Confidence Threshold")
+    ],
+    outputs=gr.Image(type="filepath"),
+    title="Faster R-CNN Object Detection",
+    description="Upload an image to detect objects using a pretrained Faster R-CNN model."
+).launch()
+

requirement.txt

@@ -0,0 +1,5 @@
+torch
+torchvision
+gradio
+matplotlib
+pillow