Update rasp.py

rasp.py

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+import tensorflow as tf
+import cv2
+import numpy as np
+import gradio as gr
+
+# Load the pre-trained MobileNet SSD model
+model = tf.saved_model.load("http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_fpnlite_320x320/saved_model")
+
+# Define the label map for the MobileNet SSD model
+category_index = {
+    1: {'id': 1, 'name': 'person'},
+    2: {'id': 2, 'name': 'bicycle'},
+    3: {'id': 3, 'name': 'car'},
+    # Add more label mappings as needed
+}
+
+# Function to detect objects in the image
+def detect_objects(image):
+    # Preprocess the image
+    input_tensor = tf.convert_to_tensor(image)
+    input_tensor = input_tensor[tf.newaxis,...]
+
+    # Run the model and get detections
+    detections = model(input_tensor)
+
+    # Process detections and draw bounding boxes
+    num_detections = int(detections.pop('num_detections'))
+    detections = {key: value[0, :num_detections].numpy() for key, value in detections.items()}
+    detection_classes = detections['detection_classes'].astype(np.int64)
+    detection_boxes = detections['detection_boxes']
+    detection_scores = detections['detection_scores']
+
+    # Draw boxes on the image
+    for i in range(num_detections):
+        if detection_scores[i] > 0.5:  # Only consider confident detections
+            class_name = category_index.get(detection_classes[i], {'name': 'N/A'})['name']
+            box = detection_boxes[i]
+            height, width, _ = image.shape
+            ymin, xmin, ymax, xmax = box
+            (startX, startY, endX, endY) = (int(xmin * width), int(ymin * height), int(xmax * width), int(ymax * height))
+
+            # Draw bounding box and label
+            cv2.rectangle(image, (startX, startY), (endX, endY), (0, 255, 0), 2)
+            label = f"{class_name}: {detection_scores[i]:.2f}"
+            cv2.putText(image, label, (startX, startY - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
+    return image
+
+# Function to handle the image from file upload or path
+def gradio_interface(image):
+    if isinstance(image, str):  # Check if it's a path string
+        image = cv2.imread(image)
+    else:
+        # Convert PIL image (Gradio) to OpenCV format (numpy array)
+        image = np.array(image)
+
+    # Convert to RGB format
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    # Detect objects in the image
+    detected_image = detect_objects(image_rgb)
+
+    # Convert back to BGR for display in Gradio (OpenCV uses BGR)
+    detected_image_bgr = cv2.cvtColor(detected_image, cv2.COLOR_RGB2BGR)
+    
+    return detected_image_bgr
+
+# Create Gradio app with image input (supports path or upload)
+iface = gr.Interface(fn=gradio_interface, 
+                     inputs=gr.inputs.Image(type="filepath"),  # Use "filepath" to allow local path or upload
+                     outputs="image", 
+                     title="Object Detection with Bounding Boxes", 
+                     description="Upload an image or provide a file path to detect objects.")
+
+# Launch the Gradio app (for local or Hugging Face)
+iface.launch()